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Speaker 1: Hello. Welcome to Fast Politics. I'm Molly Jongfest, and we

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Speaker 1: have a great show for you today. Later in the show,

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Speaker 1: we'll talk to the economist Owen Winter about how AI

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Speaker 1: changed the 2026 election. But first, we have the Lincoln

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Speaker 1: Project's own Rick Wilson on the mistake Trump made choosing

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Speaker 1: Stephen Miller as his chief of staff. Rick Wilson.

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Speaker 2: Hello, Molly.

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Speaker 1: Stephen Miller.

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Speaker 2: Hello.

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Speaker 1: In charge of the Trump White House because Trump has

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Speaker 1: learned exactly nothing.

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Speaker 2: Listen, I just want to remind MAGA, when the consequences

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Speaker 2: for this decision start to come home, you chose violence. Right.

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Speaker 2: You chose this violence. You put a madman, and all

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Speaker 2: of you know it, in charge of the White House.

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Speaker 2: And when the consequences start to arrive, and they will arrive,

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Speaker 2: as they say, unloomed, You're going to remember. You guys

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Speaker 2: were all like, it's Miller time, baby. You're going to

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Speaker 2: all remember that because it's going to be fantastic.

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Speaker 1: The nine people who are still MAGA are like, it's

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Speaker 1: Miller time.

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Speaker 2: Benny Johnson tweeting like, the White House is getting its

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Speaker 2: energy back. It's so excited for Miller time. I'm like, uh-huh. Sure, pal.

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Speaker 1: But let's talk about this. So Susie Wiles was sort

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Speaker 1: of the genteel face of Trumpism. She was the Marco

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Speaker 1: Rubio of the executive branch, right?

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Speaker 2: Sure.

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Speaker 1: And though Marco Rubio is the Marco Rubio of the

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Speaker 1: executive branch, but you know what I mean. She was,

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Speaker 1: she put a pretty face on fascism. She did authoritarianism,

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Speaker 1: but with a Palm Beach accent.

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Speaker 2: She was authoritarianism in a Talbot suit.

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Speaker 1: Right. I don't know that Talbot still exists, but yes. Oh, no, no.

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Speaker 2: I deliberately chose that as a dig, but okay.

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Speaker 1: I like it. So, so she was, but she was,

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Speaker 1: you know, very much like, we're not going to be autocrats.

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Speaker 1: We're not going to do this. We're not going to

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Speaker 1: do that.

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Speaker 2: We're not going to arrest journalists.

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Speaker 1: Right. Stephen Miller and DOJ consider arresting journalists over leaks.

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Speaker 1: And Stephen Miller is all aboard the plan discuss.

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Speaker 2: Honestly, I can't wait because this is, this is setting

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Speaker 2: up the predicate for 2028, where the Democrats take over Oklahoma,

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Speaker 2: where the last president, sane vestiges of the Republican Party

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Speaker 2: anywhere at all go, yeah, no, that wasn't me. This

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Speaker 2: is going to end up being like the moment's in

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Speaker 2: Germany in the late 40s and early 50s were like,

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Speaker 2: I wasn't a member of the Nazi Party. I mean,

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Speaker 2: I was a member of the Nazi Party, but I

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Speaker 2: had to be, but I wasn't really a Nazi Nazi.

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Speaker 2: That's what you're going to see from these people very soon.

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Speaker 2: They're going to realize that Stephen Miller and Donald Trump,

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Speaker 2: they will never learn, they will never change, they have

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Speaker 2: no moral compass, no shame, no judgment, and they will

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Speaker 2: do things that will destroy the GOP for a generation because...

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Speaker 2: Because they feel like power is theirs forever.

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Speaker 1: Putting Stephen Miller in this job is the bunker moment.

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Speaker 1: It's the, you know, we're not going to, we don't

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Speaker 1: care about any, you know, we don't care about the

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Speaker 1: possible presidency of J.D. Vance. Lulz. We just want to

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Speaker 1: flip North and South Dakota.

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Speaker 2: Molly, I think that we're going to watch an evolution

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Speaker 2: inside the GOP in the coming weeks where- The White

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Speaker 2: House goes further and further out over the cliff where

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Speaker 2: the handful of semi vaguely normies like John Thune try

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Speaker 2: to wheel it back and they won't. Yeah. And where

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Speaker 2: the investigations start to pile up and Trump's anxiety and

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Speaker 2: attention gets drawn into the fact that they're looking at

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Speaker 2: Peg Seth and RFK Jr. and Todd Blanch and all

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Speaker 2: the rest. And they're going to get madder and madder.

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Speaker 2: And Miller is going to do crazier and crazier stuff.

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Speaker 2: And it will end up being, it will end up

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Speaker 2: blowing back on them because they can't read the room. Yeah.

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Speaker 2: The country just rejected this stuff at a level that should,

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Speaker 2: and with sane people in the process would have said, yeah,

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Speaker 2: you know what? Maybe we should learn a lesson. Maybe

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Speaker 2: this wasn't the way to do this. Yeah. But instead

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Speaker 2: they will say, no, no, we were right all along.

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Speaker 2: We just didn't get, we didn't get Nazi enough. We

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Speaker 2: just weren't crazy enough.

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Speaker 1: What's interesting to me about this moment, besides the fact

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Speaker 1: that I think you're absolutely right, and to watch like

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Speaker 1: Stephen Miller go in there, he's probably the architect of

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Speaker 1: the least popular part of this administration's policy.

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Speaker 3: For sure.

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Speaker 1: For sure. The scary deportations. I mean, for example, in Minnesota...

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Speaker 1: they had the highest voter turnout, right? They had like

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Speaker 1: voters crawled over broken glass because they had had ice

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Speaker 1: in their state and they knew what it was like

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Speaker 1: and they wanted nothing to do with it.

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Speaker 2: They, I mean, Minnesota, to my mind, the last sort

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Speaker 2: of grip on hope that a lot of Republicans that

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Speaker 2: I was hearing from before the election was like, we're

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Speaker 2: going to hold Florida, which They did at the state,

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Speaker 2: at the big level.

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Speaker 1: Congratulations.

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Speaker 2: LOL. It's like, Saudi Arabia maintains its monopoly on sand,

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Speaker 2: but They were like, oh, Tafoya's going to win in

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Speaker 2: Minnesota because you guys are wrong and people love Trump

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Speaker 2: in Minnesota. Because of fraud. They want security.

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Speaker 1: Because of Somali fraud. Because Donald Trump has made up

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Speaker 1: a story about Somali fraud.

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Speaker 2: Congratulations, Nick Sartor, or whatever your fucking name is.

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Speaker 1: Nick Shirley.

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Speaker 2: Shirley, yeah, yeah, yeah, yeah, yeah.

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Speaker 1: 22 years old and has never had a thought.

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Speaker 2: Right, never had a thought or a job.

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Speaker 1: Yeah.

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Speaker 2: And look, as this thing— That's how we like him

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Speaker 2: in Madness. ground on on election night, they should have said, man,

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Speaker 2: what have we done wrong here? What is it we've

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Speaker 2: done that isn't resonating with the American people? I don't know.

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Speaker 2: Maybe it was murdering people in the streets of Minnesota

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Speaker 2: or Minneapolis. Maybe. Could have been. Nah.

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Speaker 1: Nah.

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Speaker 2: Nah. Let's do more of that. So now we're going

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Speaker 2: to have a sweep through American politics in the next

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Speaker 2: couple of weeks where Miller will try to get back

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Speaker 2: the MAGA bass. He will try to get the vibe

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Speaker 2: back for the bass. He'll try to get that like,

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Speaker 2: that like crazy juice that they love so much with

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Speaker 2: the bass. And I don't see it coming back, Molly.

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Speaker 1: Here's the problem for Stephen Miller and the MAGA base.

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Speaker 1: And you tell me what you think. Donald Trump has

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Speaker 1: made three promises, right? He has said, I will, in 2024,

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Speaker 1: he said, I'm going to lower the prices. I'm going

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Speaker 1: to release the Epstein files, which is a callback to

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Speaker 1: QAnon in 2016.

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Speaker 2: Right.

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Speaker 1: I'm going to- Right, that there are bad Democrats doing

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Speaker 1: bad things and I'm going to jail them.

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Speaker 2: Right, these horrible Democrat pedophile cannibals, whatever. Right.

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Speaker 1: And so again, he was going to do that and

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Speaker 1: then he was going to end all foreign wars. And

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Speaker 1: what did he do? He did exactly the opposite. He

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Speaker 1: covered up on Epstein. He went to war with Greenland

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Speaker 1: and Canada.

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Speaker 2: Raised the prices of almost everything.

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Speaker 1: Across the board.

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Speaker 2: Right.

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Speaker 1: And so now Stephen Miller thinks that these people exclusively

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Speaker 1: voted for racism and he's going to try that.

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Speaker 2: You know, look. I mean, do you think that's right?

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Speaker 2: With Stephen Miller, he really still believes that he's in

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Speaker 2: a civilizational battle against some sort of left-wing cabal that's

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Speaker 2: trying to destroy white America.

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Speaker 1: Right.

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Speaker 2: Even though he's not white, as.

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Speaker 1: A Jew, FYI, those people don't think of us as white.

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Speaker 1: Just FYI.

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Speaker 2: Right, correct. But part of this, Molly, really comes down

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Speaker 2: to a fundamental disconnection in the mind of a lot

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Speaker 2: of people in the MAGA movement where they don't wake

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Speaker 2: up in the morning and think, you know, this is

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Speaker 2: a country with a constitution, a push and pull between

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Speaker 2: the parties and ideologies. They think, We're fighting radical Islam

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Speaker 2: and they all want to murder us. That's all they

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Speaker 2: think about every day. It's like when the Democrats propose

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Speaker 2: a health care plan, it's really about killing white people.

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Speaker 1: It's insane.

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Speaker 2: One of the things here that I have to go

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Speaker 2: back to Miller, everything in his brain, everything in his broken, weird,

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Speaker 2: moldy brain is about white nationalism. And set aside the

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Speaker 2: irony of Stephen Miller and white nationalism, all that, the

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Speaker 2: ultimate Aryan male he is not. Right. But in all

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Speaker 2: these broken-brained ideas that he has, everything, if you look

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Speaker 2: at any behavior of Stephen Miller, it will always redound

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Speaker 2: back to white mat. Always. 100% of the time. And

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Speaker 2: so that theory of the case that you could always

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Speaker 2: keep a coalition in America, he lost huge chunks of

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Speaker 2: the white vote that they took for granted. Yeah. Men moved.

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Speaker 2: to be two points positive for Democrats. Young men that

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Speaker 2: they thought they had forever blew them the fuck out.

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Speaker 1: Part of it is, I think, and I want to

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Speaker 1: talk about this because I think it's really important, is

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Speaker 1: that they really didn't deliver anything. But what I think

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Speaker 1: will be very interesting at this moment is, so we

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Speaker 1: have Trump and Stephen Miller doubling down. I think Democrats

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Speaker 1: have gotten a lot smarter about how they want to

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Speaker 1: approach this. I don't think I think there's like a

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Speaker 1: lot more of a sort of I mean, even Hakeem,

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Speaker 1: who I've been like very sort of skeptical of, had

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Speaker 1: a very good, I thought, a really smart message. And

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Speaker 1: he's been doing this actually quite a lot throughout the campaign,

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Speaker 1: which I think has been smart, which is like. Even

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Speaker 1: though it seemed so simple, I think it's smart, right? Corruption, money,

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Speaker 1: you know, affordability. They're corrupt. That's why the tariffs on Canada.

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Speaker 2: That's why you can't afford anything. Right. Hey, listen, preach

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Speaker 2: into the choir here. This is what I've been saying.

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Speaker 1: Yeah.

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Speaker 2: For about a year and a half.

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Speaker 4: Yeah.

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Speaker 2: But to have your electives- It's not because I'm a genius.

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Speaker 2: It's because it's evident. It's obvious. Right.

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Speaker 1: And so what I thought was good, I was listening

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Speaker 1: to him the other day, and he was talking about how, like,

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Speaker 1: accountability and affordability, right? Which is what Ann Applebaum talked about.

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Speaker 1: So you have to do both. You have to walk

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Speaker 1: and chew gum. You have to focus on affordability, but

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Speaker 1: you also have to focus on accountability. And those two

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Speaker 1: things are actually synonymous with each other if you do

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Speaker 1: it right. So what I think will be interesting, so

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Speaker 1: if you're looking at accountability, you're probably looking at, and again,

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Speaker 1: Thomas Massey is going to do this for Dems in

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Speaker 1: the lame duck, right?

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Speaker 2: Oh, yeah.

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Speaker 1: Because Pete Hegseth is a dead man walking. I mean,

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Speaker 1: let's talk about Pete Hegseth.

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Speaker 2: Pete Hegseth is absolutely going to face the most withering scrutiny,

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Speaker 2: and he absolutely deserves every bit of it.

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Speaker 1: Massey gave him a choice, impeachment or resign.

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Speaker 2: Yep. And the problem with Pete Hegseth is he is

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Speaker 2: so unbelievably, insanely arrogant about who and what he is.

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Speaker 2: He honestly believes that he is part of some sort of, again,

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Speaker 2: similar to Miller, that he's part of some sort of

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Speaker 2: moment in American political life that has validated his insanity

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Speaker 2: and stupidity and lack of judgment, particularly on the Iran war.

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Speaker 2: He believes, much like Miller, that I mean, his jihad

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Speaker 2: is against imaginary jihad, where he thinks that the Muslims

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Speaker 2: are here to convert your women and make them wear

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Speaker 2: burqas and blah, blah, blah. It's just, I mean, it

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Speaker 2: would be silly if it wasn't so potentially tragic. Yeah.

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Speaker 1: I want to, we've been talking about Stephen Miller, and

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Speaker 1: I think that the way it'll go, my dream is

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Speaker 1: that it will be, Pete Hegseth will be either impeached

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Speaker 1: or removed. And then after that, I think the lowest

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Speaker 1: of the low-hanging fruit is killing babies right now. The

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Speaker 1: party that loves babies, RFK Jr.

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Speaker 2: discuss. I think RFK is a dead man walking. That

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Speaker 2: has been a department that has, not only failed the

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Speaker 2: American people at a truly fundamental level, but is also

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Speaker 2: run by a bunch of grifters and weirdos. You had

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Speaker 2: one of his senior deputies who recently resigned say, I

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Speaker 2: quit because of the grift.

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Speaker 1: And he went on Joe Rogan. If the grift is

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Speaker 1: too much for.

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Speaker 2: You in Trump world, you're in the wrong business.

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Speaker 1: And he went on Joe Rogan and actually called people out,

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Speaker 1: which is Once you are starting to do a major

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Speaker 1: media tour.

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Speaker 2: Of like- Once you're there, you're going to be off

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Speaker 2: the Trump reservation at that point in some pretty fundamental ways.

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Speaker 2: And look, is RFK insanely corrupt? Of course he is.

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Speaker 2: That's not rocket science. But now you're going to see

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Speaker 2: these people who are starting to weigh the future and say, yeah,

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Speaker 2: you know, Trump's going to go.

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Speaker 1: Right.

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Speaker 2: This is over. Maybe there's a new grift ahead. Maybe

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Speaker 2: I can make the Maha thing work somehow in the future.

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Speaker 2: And I think that this moment where Miller takes over,

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Speaker 2: the ideological purist, the weirdo, the freak show guy, where

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Speaker 2: he takes over day-to-day operations of MAGA, this is not

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Speaker 2: a way where the Republican Party finds a survival strategy.

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Speaker 2: It's a suicide pact.

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Speaker 1: And again, I think that's a really important point. But

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Speaker 1: I also think it's good for Democrats. And I want

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Speaker 1: to talk about why. Because Democrats don't control the presidency.

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Speaker 1: So they control the Senate and the House. And that

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Speaker 1: means that Donald Trump can't get any Supreme Court justices in.

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Speaker 1: So if Justice Alito, if you want to resign, you

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Speaker 1: got to do it now. And he's not going to

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Speaker 1: do it now. So that's amazing.

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Speaker 2: So I have a friend who is very in... Leonard

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Speaker 2: Leo-verse in the Leo-verse.

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Speaker 1: Oh, I bet he's having a really terrible time.

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Speaker 2: They are in a state of shock and despair.

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Speaker 1: Oh, I love it.

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Speaker 2: They have always felt like they had a system.

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Speaker 1: Say more, say more.

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Speaker 2: They felt like they had a model for years. Like

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Speaker 2: we can always put together this PR campaign. We can

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Speaker 2: always put together this legislative campaign. We can always like

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Speaker 2: turn it into a culture war on Fox and we

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Speaker 2: can therefore push it through. Not with 55 or 56

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Speaker 2: Democrats you can't, because that game is over.

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Speaker 1: And FYI, I mean, like John Fetterman, the big losers, right?

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Speaker 1: John Fetterman, who thought he was going to switch parties, like,

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Speaker 1: no one gives a fuck, baby. You do you, John Fetterman.

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Speaker 2: You are a dead man walking politically.

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Speaker 1: Just completely irrelevant.

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Speaker 2: Over. Yeah.

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Speaker 1: I mean, him, I would say Haley Stevens, like talk about,

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Speaker 1: you know.

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Speaker 2: Did you see Clyburn even said he would be willing

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Speaker 2: to sanction or throw Haley Stevens out of the caucus?

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Speaker 1: I'm like, just do it now, baby. Do it now.

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Speaker 2: Don't let the door hit you on the ass, Haley.

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Speaker 1: And another thing, not to like get involved in 2028,

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Speaker 1: but in 2028, the Senate map is going to.

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Speaker 2: It's a good map. It's the good map.

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Speaker 1: You and I are like living out here, but let's

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Speaker 1: just live out on the 2028 map.

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Speaker 2: All right. First off, a whole bunch of those seats

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Speaker 2: in the 2028 map are in safe blue states. A

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Speaker 2: whole bunch of the Republican seats are now in places

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Speaker 2: where Republicans used to be very, very swaggering and very

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Speaker 2: confident and very like, hey, we'll be honest forever. And

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Speaker 2: all of a sudden they're looking at the places like

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Speaker 2: Kansas and Nebraska going, oh, what, what, what? So this

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Speaker 2: is a bad map, guys. This is not a map

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Speaker 2: you wake up and go, wow, we're going to sweep

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Speaker 2: the board. It requires the Democrats to play really smart

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Speaker 2: ball for two years.

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Speaker 1: Yeah.

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Speaker 2: And, you know, I'm going to be guardedly optimistic. I think.

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Speaker 1: And I think Trump will help them out here because he's.

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Speaker 2: I think the next two years. Yeah, exactly. I'm sorry.

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Speaker 2: That's what I'm trying to get to is your greatest

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Speaker 2: ally in the next two years is Donald Trump. He's

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Speaker 2: going to help you every day. He's going to make

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Speaker 2: he's going to make the world a better place. for

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Speaker 2: the Democratic Party in the next two years because he

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Speaker 2: can't stop talking. He can't shut his mouth.

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Speaker 1: That's right.

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Speaker 2: He will do as much damage as he can to

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Speaker 2: the GOP and not care. He will not care.

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Speaker 1: Yeah.

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Speaker 2: He can't make himself, he can't conform his behavior long

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Speaker 2: enough to care about consequences beyond tomorrow.

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Speaker 1: I just want to, as we're living out here for

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Speaker 1: a minute, like Chip Grassley- Is Chuck Grassley up? Yeah,

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Speaker 1: Chuck Grassley.

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Speaker 2: Listen, there are going to be a lot of young

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Speaker 2: Democratic candidates, including a couple that just got elected to

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Speaker 2: Congress in the great state of Iowa, who are going

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Speaker 2: to look at Chuck Grassley and go, the fuck? You're

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Speaker 2: going to be 100 years old. I don't think that

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Speaker 2: argument works like it used to work.

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Speaker 1: And Mr. MAGA, MAGA Mike Lee, is up for re-election

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Speaker 1: in Utah, which is a state that is not MAGA.

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Speaker 2: Listen, that is a state where Mike Lee's role as

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Speaker 2: a Trump sycophant, fluffer, whatever you want to call it.

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Speaker 2: And a guy who tried in his own state, by

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Speaker 2: the way, he tried to turn over all this reserve

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Speaker 2: environmental land, the Escalante National Forest or Escalante National Monument

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Speaker 2: and the Bears Ears. The most conservative people I know

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Speaker 2: in Utah were like, this is outrageous. Absolutely not. No, no, no.

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Speaker 2: He's wrong to do this. Mike Lee, is going to

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Speaker 2: be on thinner ice than he thinks. And as I

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Speaker 2: like to say, Mike Lee is just Ted Cruz without

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Speaker 2: the smoldering sensuality.

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Speaker 1: Can I just say one thing here, since we're just

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Speaker 1: like living it out here, this is like beyond Ronanon

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Speaker 1: up for reelect in the state of Wisconsin.

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Speaker 2: I will tell you something. I really want to get

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Speaker 2: out of politics at some point in my life, but

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Speaker 2: not before Ron Johnson goes down in flames. I, like

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Speaker 2: all good right-thinking Americans, Hate Ron Johnson with the fire

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Speaker 2: of a billion suns because he is truly, at a

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Speaker 2: great scale, one of the most destructive people in our politics.

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Speaker 1: Yeah, yeah, yeah. Rick Wilson.

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Speaker 2: Molly Jogfast.

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Speaker 1: Will you come back?

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Speaker 4: Of course.

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Speaker 3: Bobby, it's time for the headlines. Interestingly, Chuck Schumer seems

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Speaker 3: to be the only person running to be the head

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Speaker 3: of the Senate after Chris Van Hollen took his hat out.

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Speaker 2: Of the ring. But.

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Speaker 3: There's a lot of senators saying they don't want him.

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Speaker 2: What do you make of this?

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Speaker 1: So then this is going to be the next big

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Speaker 1: thing that happens when you're seating a new Congress is

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Speaker 1: to figure out who is going to be the leader

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Speaker 1: of the Senate and the leader of the House. And

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Speaker 1: it's a question of like, look, it's a secret ballot.

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Speaker 1: So it's not public who votes for who. It tends

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Speaker 1: to be a popularity contest as much as anything, right?

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Speaker 1: So you have to be a popular senator, someone that

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Speaker 1: people like. Harder job than it looks because you work

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Speaker 1: with these people for a long time. And then you

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Speaker 1: have this really interesting group of new senators who have

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Speaker 1: come in. And a lot of them are like pretty

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Speaker 1: untraditional candidates, right? They're coming from states where I think

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Speaker 1: a lot of people thought they might not win. So

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Speaker 1: they're not necessarily candidates. like Chuck Schumer's people.

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Speaker 3: What's the odds? A lot of them were running against

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Speaker 3: primaries against Chuck Schumer's people.

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Speaker 2: Right.

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Speaker 1: Well, not a lot. So Annie Andrews she told Fox News, again,

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Speaker 1: I think it's very cool that she's on Fox News.

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Speaker 1: I think that's really important. We saw Dr. El-Said do that, too,

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Speaker 1: that she thought that maybe it was time for generational change.

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Speaker 1: Adam Hamilton, who's from Kansas, again, a a Senate candidate,

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Speaker 1: you know, first senator, Democratic senator in Kansas in 93 years.

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Speaker 1: He is, again, so he said he's not sure about

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Speaker 1: the leadership. El-Sayed Ware is a candidate where Schumer actually

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Speaker 1: backed his opposition. Peggy Flanagan was also not a, was

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Speaker 1: not a Schumer pick. The Schumer pick was.

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Speaker 3: Angie Craig.

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Speaker 1: Angie Craig, who lost. And Troy Jackson was not a

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Speaker 1: Schumer pick. You'll remember the Schumer pick was Janet Mills.

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Speaker 1: Schumer has a job until someone else runs for it.

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Speaker 1: And right now, we don't have someone else running for it. Now,

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Speaker 1: that doesn't mean there won't be someone else running for it.

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Speaker 1: And if there is, that doesn't mean that that person

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Speaker 1: won't necessarily win. So again, it is the beginning, it's

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Speaker 1: early days with this, but you'll certainly... should certainly see

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Speaker 1: a lot, a lot of happenings behind the scenes here.

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Speaker 1: Part of what's going to need to happen in this

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Speaker 1: moment is that is that we're going to probably see

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Speaker 1: a lot of senators and activists have to work really

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Speaker 1: hard to push the party either way, you know, that

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Speaker 1: this is going to fall to a lot of these

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Speaker 1: senators to be able to do it. Now, the good news,

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Speaker 1: I think, is there's just a lot of talent in

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Speaker 1: this group. I mean, Adam Hamilton is like, I think,

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Speaker 1: a really talented politician. I think Dr. El-Sayed, who's been

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Speaker 1: on this podcast multiple times, very smart and talented and

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Speaker 1: also really a good voice for Medicare for All because

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Speaker 1: he's a doctor and a PhD and has worked in

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Speaker 1: public health in a big city where maternal fetal health

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Speaker 1: is a big issue. So I do think like these

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Speaker 1: are all really good. And Teller Rico is a really

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Speaker 1: good candidate. Ossoff was, you know, was such a good candidate.

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Speaker 1: So there's just a lot of really good candidates. So

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Speaker 1: I actually think ultimately that this is going to be

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Speaker 1: a really interesting moment for the Democratic Party.

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Speaker 2: Yeah.

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Speaker 3: And it's similar what's happening in the House with Hakeem

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Speaker 3: Jeffries as well. There's a lot of people calling saying

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Speaker 3: we don't want him. No one else is stepping up

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Speaker 3: to run. And I think you need somebody to do it.

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Speaker 1: I think it's a little different than Jeffries. How so?

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Speaker 1: I think Jeffries is in a slightly better place than Schumer.

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Speaker 3: I agree that he's definitely in a better place. But

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Speaker 3: it is interesting that there's no opposition.

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Speaker 1: And opposition would happen, I think, sort of this week,

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Speaker 1: next week. But also, you have to have somebody who

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Speaker 1: wants to run.

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Speaker 3: Yeah, that's what I'm saying. Yeah.

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Speaker 1: And you have to have someone who has the votes.

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Speaker 1: And that is a real question. I think it's still

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Speaker 1: early days. We'll see what happens. But there's definitely, there's

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Speaker 1: a lot of opinions out there.

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Speaker 3: Yeah. So, Molly, I know you were listening to all

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Speaker 3: three and a half hours of the Joe Rogan show

437
00:22:59,190 --> 00:23:03,189
Speaker 3: when he had Kaylee Means on. Always do. Yep. I

438
00:23:03,210 --> 00:23:04,090
Speaker 3: know what you do with your time.

439
00:23:04,109 --> 00:23:04,910
Speaker 1: That's right.

440
00:23:05,130 --> 00:23:08,450
Speaker 3: But let's just say my boy got a little messy

441
00:23:08,950 --> 00:23:14,390
Speaker 3: and threw around accusations like there was no tomorrow.

442
00:23:14,410 --> 00:23:14,490
Speaker 2: Yeah.

443
00:23:14,840 --> 00:23:19,280
Speaker 1: He's really messy. So he left the White House. So

444
00:23:19,320 --> 00:23:25,100
Speaker 1: this is Maha versus the versus Susie Wiles crew. Right.

445
00:23:25,240 --> 00:23:28,240
Speaker 3: I mean, Maha versus the sanctioned MAGA, really.

446
00:23:28,480 --> 00:23:34,939
Speaker 1: Yeah. But it's Susie Wiles, James Blair, Ballard Partners, Kayleigh

447
00:23:34,960 --> 00:23:35,480
Speaker 1: Means said.

448
00:23:35,520 --> 00:23:37,460
Speaker 3: Being the brother of Casey Means. Right.

449
00:23:37,510 --> 00:23:40,510
Speaker 1: The brother of Casey means both of whom were, I

450
00:23:40,530 --> 00:23:44,050
Speaker 1: guess Casey didn't get, didn't get, did she not get?

451
00:23:44,070 --> 00:23:44,470
Speaker 2: Yes.

452
00:23:44,490 --> 00:23:46,109
Speaker 3: So that they put in Kayleigh instead.

453
00:23:46,380 --> 00:23:46,620
Speaker 2: Yeah.

454
00:23:47,040 --> 00:23:53,449
Speaker 1: So Kayleigh saying that senior White House officials were, had

455
00:23:55,170 --> 00:24:01,240
Speaker 1: got pressured from tobacco lobbyists. Really? You don't say. My

456
00:24:01,260 --> 00:24:04,179
Speaker 1: favorite part of the whole story, and Todd Blanch was like,

457
00:24:04,440 --> 00:24:06,400
Speaker 1: first of all, I love Todd Blanch goes on State

458
00:24:06,440 --> 00:24:09,649
Speaker 1: of the Union and says something that someone says in

459
00:24:09,690 --> 00:24:12,510
Speaker 1: a podcast. No, I don't listen to that. Absolutely not. Well,

460
00:24:12,609 --> 00:24:15,250
Speaker 1: you guys used to really like the Joe Rogan podcast.

461
00:24:15,670 --> 00:24:23,480
Speaker 1: He calls means a disgruntled former administration official. praises Susie Wiles,

462
00:24:23,600 --> 00:24:29,420
Speaker 1: who is actually also a former official. I think what's

463
00:24:29,560 --> 00:24:32,469
Speaker 1: interesting about this story, and there's a lot here, and

464
00:24:32,609 --> 00:24:36,550
Speaker 1: I think the top line is, like, welcome to Trump 1.0,

465
00:24:36,550 --> 00:24:39,430
Speaker 1: where everyone starts throwing each other under the bus, because

466
00:24:39,450 --> 00:24:42,570
Speaker 1: that's what we're looking at here, right? That's where this is.

467
00:24:42,609 --> 00:24:45,630
Speaker 1: You know, this is where they start fighting with each

468
00:24:45,750 --> 00:24:49,120
Speaker 1: other and leaking to the press. And top line, just

469
00:24:49,490 --> 00:24:52,980
Speaker 1: in so much trouble. Like, This is just one of

470
00:24:53,010 --> 00:24:56,710
Speaker 1: the 10 million things that Todd Blanch has to deal with.

471
00:24:56,910 --> 00:25:01,629
Speaker 1: Think about the story where Kimberly Guilfoyle and Eric Dieters,

472
00:25:01,670 --> 00:25:04,650
Speaker 1: that was like two weeks ago, right? Like, it seems

473
00:25:04,690 --> 00:25:07,370
Speaker 1: like every other week or every week there's, you know,

474
00:25:07,390 --> 00:25:10,950
Speaker 1: a member of Trump World fighting with another member of

475
00:25:10,990 --> 00:25:15,399
Speaker 1: Trump World. And the allegations... Right, exactly. And the allegations

476
00:25:15,440 --> 00:25:19,760
Speaker 1: here are really bad. Like, flavored vapes, Roundup, which gives

477
00:25:19,800 --> 00:25:23,389
Speaker 1: you cancer... Like, basically, what he's saying is they couldn't

478
00:25:23,430 --> 00:25:25,100
Speaker 1: do any of the stuff they said they were going

479
00:25:25,119 --> 00:25:29,500
Speaker 1: to do because the Ballard partners and James Blair and

480
00:25:29,619 --> 00:25:33,199
Speaker 1: Susie Wiles were all in the tank for these companies.

481
00:25:33,420 --> 00:25:36,280
Speaker 1: First of all, it sounds totally believable, right? I mean,

482
00:25:36,380 --> 00:25:39,300
Speaker 1: anyone who's watching behind the scenes knew that there was

483
00:25:39,340 --> 00:25:41,460
Speaker 1: something going on here.

484
00:25:41,720 --> 00:25:43,780
Speaker 3: I think that this is going to be the funny

485
00:25:43,840 --> 00:25:46,010
Speaker 3: thing is that now we have the factionalism, especially as

486
00:25:46,030 --> 00:25:48,330
Speaker 3: you see Trump be weaker. We now have proof. There's

487
00:25:48,350 --> 00:25:50,390
Speaker 3: always this gut that Trump was weaker and some people

488
00:25:50,430 --> 00:25:53,500
Speaker 3: would turn. You'd have the Thomas Massys and the MTGs

489
00:25:53,520 --> 00:25:56,040
Speaker 3: who'd see what they were seeing on the ground. But

490
00:25:56,080 --> 00:25:59,220
Speaker 3: now we have definitive proof that Trump is that. And

491
00:25:59,240 --> 00:26:01,639
Speaker 3: we're going to see a lot of daggers being pulled

492
00:26:01,680 --> 00:26:03,290
Speaker 3: and go right into the side.

493
00:26:03,630 --> 00:26:08,510
Speaker 1: Stephen Miller's wife, Katie Miller, ended her podcast. And I

494
00:26:08,570 --> 00:26:11,310
Speaker 1: think it's like, you know, that seems like a minor thing,

495
00:26:11,350 --> 00:26:15,320
Speaker 1: but it is like they sort of know that tides

496
00:26:15,359 --> 00:26:17,280
Speaker 1: have turned really, really quickly.

497
00:26:17,780 --> 00:26:20,540
Speaker 3: And so- Did she end it because she was so

498
00:26:20,600 --> 00:26:21,430
Speaker 3: busy not voting?

499
00:26:21,730 --> 00:26:25,520
Speaker 1: Yeah, that wasn't so good. And then her excuse was

500
00:26:25,580 --> 00:26:29,320
Speaker 1: that she didn't want to be doxxed. By the way.

501
00:26:29,340 --> 00:26:31,139
Speaker 3: While living in military housing.

502
00:26:31,800 --> 00:26:32,020
Speaker 2: Yeah.

503
00:26:32,619 --> 00:26:35,440
Speaker 3: Okay. So we have more Todd Blanch news, though, because

504
00:26:35,560 --> 00:26:39,100
Speaker 3: he's not ruling out prosecuting Democrats over oversight threats.

505
00:26:39,260 --> 00:26:43,990
Speaker 1: He's going to prosecute Alyssa Slotkin, accusing her of threatening

506
00:26:44,070 --> 00:26:46,430
Speaker 1: young people who hold Republican views.

507
00:26:46,710 --> 00:26:47,030
Speaker 2: What? What?

508
00:26:47,660 --> 00:26:52,070
Speaker 1: I mean, this is Trump. So basically, Trump was tweeting

509
00:26:52,109 --> 00:26:56,740
Speaker 1: about how he wants Trump. Todd Blanche to target Alyssa Slotkin,

510
00:26:56,940 --> 00:27:03,830
Speaker 1: or he says the imprisonment should be against these extortionists,

511
00:27:04,030 --> 00:27:07,090
Speaker 1: all caps. By a copy of the statement, I'm ordering

512
00:27:07,109 --> 00:27:10,629
Speaker 1: the Department of Justice to look into this criminal act immediately.

513
00:27:10,750 --> 00:27:14,020
Speaker 1: Explanation point. Todd Blanche knows he serves at the pleasure

514
00:27:14,040 --> 00:27:16,040
Speaker 1: of Donald Trump. So if he says, no, I'm not

515
00:27:16,080 --> 00:27:19,430
Speaker 1: going to investigate those people, then Donald Trump will fire him. So,

516
00:27:19,470 --> 00:27:21,570
Speaker 1: of course, he says, yes, he's going to investigate them.

517
00:27:22,050 --> 00:27:24,590
Speaker 1: But again, one of the problems and we've seen this

518
00:27:24,670 --> 00:27:26,780
Speaker 1: again and again, and we saw this when there was

519
00:27:26,859 --> 00:27:31,600
Speaker 1: no Democratic House or Senate, is that it's very hard

520
00:27:32,200 --> 00:27:36,119
Speaker 1: to make this stuff work when it didn't happen. You know,

521
00:27:36,200 --> 00:27:39,540
Speaker 1: like think about how many times they've been think about

522
00:27:39,580 --> 00:27:42,139
Speaker 1: how many times they've been trying to investigate James Comey

523
00:27:42,220 --> 00:27:46,440
Speaker 1: on those seashells. Like all it's done is elevated Comey

524
00:27:46,540 --> 00:27:48,000
Speaker 1: and it's done nothing for anyone else.

525
00:27:48,210 --> 00:27:52,570
Speaker 3: Yep, that's correct. So lastly, Molly, you know, we talk

526
00:27:52,609 --> 00:27:55,700
Speaker 3: a lot about oversight on AI and how we want

527
00:27:55,740 --> 00:27:59,580
Speaker 3: to see these people be held accountable. And sure enough,

528
00:28:00,880 --> 00:28:04,680
Speaker 3: we now have an example of why. As NVIDIA's CEO,

529
00:28:04,740 --> 00:28:08,710
Speaker 3: Jensen Huang, has admitted an affair with a Chinese honeypot spy.

530
00:28:08,730 --> 00:28:11,129
Speaker 1: All right. First of all, he.

531
00:28:13,150 --> 00:28:15,100
Speaker 1: There's a lot of people, a lot of these men

532
00:28:15,119 --> 00:28:16,540
Speaker 1: have had affairs with Chinese spies.

533
00:28:18,180 --> 00:28:21,420
Speaker 3: Former guests of this podcast, even, that were congressmen?

534
00:28:22,200 --> 00:28:25,200
Speaker 1: I mean, from Rupert on down, there have been a

535
00:28:25,240 --> 00:28:29,340
Speaker 1: lot of people who have had relationships with Chinese spies.

536
00:28:30,280 --> 00:28:32,980
Speaker 1: I was unfaithful to my wife. My actions hurt her

537
00:28:33,060 --> 00:28:36,440
Speaker 1: and our children. This is no excuse for what I did. Look,

538
00:28:36,640 --> 00:28:39,380
Speaker 1: here's what's bad about this. What he does in his

539
00:28:39,440 --> 00:28:45,020
Speaker 1: personal life, whatever. But I think the problem is, look,

540
00:28:45,960 --> 00:28:52,840
Speaker 1: the NVIDIA chips are critical for AI. This is an

541
00:28:52,960 --> 00:28:57,400
Speaker 1: industry with no regulation. This is the head of the

542
00:28:57,620 --> 00:29:00,840
Speaker 1: big chip company. I mean, there's a lot of scary

543
00:29:00,900 --> 00:29:04,560
Speaker 1: stuff here. It's hard to parse. Though I would like

544
00:29:04,600 --> 00:29:05,560
Speaker 1: to add that.

545
00:29:07,780 --> 00:29:11,980
Speaker 1: It is still not necessarily as bad as Elon Musk,

546
00:29:12,040 --> 00:29:18,540
Speaker 1: like having crazy, you know, political views. I mean, these

547
00:29:18,580 --> 00:29:22,640
Speaker 1: are all bad choices. Like, you know, they're all bad.

548
00:29:23,280 --> 00:29:25,480
Speaker 1: One is not necessarily so much worse than, I mean,

549
00:29:25,500 --> 00:29:26,060
Speaker 1: they're all bad.

550
00:29:26,370 --> 00:29:28,710
Speaker 3: Hence why we need regulation and oversight.

551
00:29:29,050 --> 00:29:29,650
Speaker 2: Yeah, we do.

552
00:29:29,690 --> 00:29:34,710
Speaker 1: We need regulation and oversight. And it's, yeah, it's really,

553
00:29:34,730 --> 00:29:40,580
Speaker 1: that's really quite embarrassing. Owen Winter is a data journalist

554
00:29:40,640 --> 00:29:45,330
Speaker 1: at The Economist. Welcome, welcome, Owen. Hi, how are you doing?

555
00:29:45,350 --> 00:29:49,870
Speaker 1: I'm good. So we are talking about the, I think

556
00:29:49,890 --> 00:29:53,730
Speaker 1: of as like one of the biggest stories of this cycle.

557
00:29:53,840 --> 00:29:56,260
Speaker 1: And it was like a story that if you talk

558
00:29:56,300 --> 00:29:59,459
Speaker 1: to candidates, they would tell you was something that was real,

559
00:29:59,780 --> 00:30:01,920
Speaker 1: but you didn't get a ton of coverage. So you

560
00:30:01,960 --> 00:30:04,780
Speaker 1: knew it was sort of around, which is data centers

561
00:30:05,280 --> 00:30:09,850
Speaker 1: and how they shaped this midterm. So first I would

562
00:30:09,900 --> 00:30:13,640
Speaker 1: love you to tell, explain to us sort of, where

563
00:30:13,660 --> 00:30:17,420
Speaker 1: I think of them as really interesting because if you

564
00:30:17,440 --> 00:30:21,209
Speaker 1: look at the Texas governorship, among other things, data centers

565
00:30:21,290 --> 00:30:24,670
Speaker 1: are a big issue there. So talk us through sort

566
00:30:24,690 --> 00:30:28,370
Speaker 1: of what their moratorium is, but I want you first

567
00:30:28,390 --> 00:30:31,090
Speaker 1: to talk about the sort of other ways in which

568
00:30:31,470 --> 00:30:32,790
Speaker 1: they affected the elections.

569
00:30:33,150 --> 00:30:35,550
Speaker 4: Yeah, so I think it's a kind of interesting mix

570
00:30:35,610 --> 00:30:37,930
Speaker 4: of things. And maybe what made them so powerful as

571
00:30:37,950 --> 00:30:40,020
Speaker 4: a political issue was they kind of fed into a

572
00:30:40,080 --> 00:30:44,060
Speaker 4: lot of grievances that people had already had. I think

573
00:30:44,780 --> 00:30:46,980
Speaker 4: the big story of the election is that people felt

574
00:30:47,180 --> 00:30:51,780
Speaker 4: like the Trump administration was out of touch, not disinterested

575
00:30:51,800 --> 00:30:55,700
Speaker 4: in their concerns, talking about the reflecting pool or the

576
00:30:56,260 --> 00:30:58,980
Speaker 4: pool room or whatever instead of their concerns. And so

577
00:30:59,350 --> 00:31:03,270
Speaker 4: data centers kind of sits among those issues where it's

578
00:31:03,290 --> 00:31:05,350
Speaker 4: something that politicians and the media are talking about all

579
00:31:05,370 --> 00:31:07,350
Speaker 4: the time, the rise of AI, how it's going to

580
00:31:07,370 --> 00:31:10,090
Speaker 4: change the economy, et cetera, but which voters feel is

581
00:31:10,150 --> 00:31:14,709
Speaker 4: very far removed from their experience, which is, you know,

582
00:31:14,790 --> 00:31:16,810
Speaker 4: for a lot of people, it's kind of anxiety about

583
00:31:16,850 --> 00:31:20,090
Speaker 4: how AI is going to affect their jobs or the

584
00:31:20,130 --> 00:31:22,730
Speaker 4: future in general. And then the issue of data centers,

585
00:31:23,970 --> 00:31:26,410
Speaker 4: where it feels like, you know, these big corporations come

586
00:31:26,450 --> 00:31:30,380
Speaker 4: into communities and build these things that no one likes

587
00:31:30,460 --> 00:31:33,320
Speaker 4: and doesn't seem to do any good for anyone. And,

588
00:31:33,640 --> 00:31:36,280
Speaker 4: you know, what Donald Trump's talking about with AI feels

589
00:31:36,340 --> 00:31:38,500
Speaker 4: so distant from that, that all of these things kind

590
00:31:38,520 --> 00:31:41,330
Speaker 4: of get swept up into this sense that the Trump

591
00:31:41,370 --> 00:31:44,370
Speaker 4: administration is, yeah, as I say, totally out of touch

592
00:31:44,470 --> 00:31:48,030
Speaker 4: and kind of, uh, yeah, not listening to voters concerns.

593
00:31:48,510 --> 00:31:52,290
Speaker 1: Um, so, uh, talk us through the moratoriums and how

594
00:31:52,510 --> 00:31:53,390
Speaker 1: they went down.

595
00:31:53,830 --> 00:31:54,170
Speaker 2: Uh, sure.

596
00:31:54,190 --> 00:31:56,890
Speaker 4: So, uh, yeah, a lot of counties, a lot of

597
00:31:56,930 --> 00:32:01,020
Speaker 4: counties and state governments have, uh, put moratoriums on, uh,

598
00:32:01,520 --> 00:32:04,870
Speaker 4: data centers being built. I think it's a reflection of, uh,

599
00:32:04,880 --> 00:32:07,880
Speaker 4: what they're hearing from voters. And I think a lot of, uh,

600
00:32:08,310 --> 00:32:09,850
Speaker 4: a lot of politicians were kind of caught on the

601
00:32:09,870 --> 00:32:12,590
Speaker 4: wrong side of the issue because plenty who had kind

602
00:32:12,610 --> 00:32:17,030
Speaker 4: of embraced data centers first or offered tax breaks, you know,

603
00:32:17,110 --> 00:32:20,270
Speaker 4: kind of had to rewrite their policies. I think about

604
00:32:20,290 --> 00:32:24,520
Speaker 4: Joe Lombardo in Nevada or, you know, Democrats as well.

605
00:32:24,820 --> 00:32:25,100
Speaker 1: Yeah.

606
00:32:25,140 --> 00:32:27,820
Speaker 4: And I think they called him data center Joe or something. So,

607
00:32:28,180 --> 00:32:31,460
Speaker 4: you know, obviously became a big issue there. So I

608
00:32:31,500 --> 00:32:34,000
Speaker 4: don't know how politicians kind of balance that. I think

609
00:32:34,360 --> 00:32:36,860
Speaker 4: To be totally frank, some of the concerns about data

610
00:32:36,900 --> 00:32:40,480
Speaker 4: centers are overblown. And there's a little bit of a

611
00:32:40,500 --> 00:32:43,729
Speaker 4: kind of moral panic about data centers, which I think

612
00:32:43,770 --> 00:32:46,010
Speaker 4: is going to be hard for policymakers to deal with.

613
00:32:46,730 --> 00:32:49,190
Speaker 4: But at this election, I'd say Republicans are the ones

614
00:32:49,230 --> 00:32:52,010
Speaker 4: that felt the backlash in the biggest way. And that's

615
00:32:52,050 --> 00:32:57,020
Speaker 4: partly because Republicans, national Republican figures have associated themselves with

616
00:32:57,540 --> 00:33:00,900
Speaker 4: AI so closely and they're kind of big tech barons.

617
00:33:01,180 --> 00:33:05,360
Speaker 1: So let's talk about the popular vote margin here. It

618
00:33:05,730 --> 00:33:13,560
Speaker 1: is the largest popular vote margin since 1976. So how

619
00:33:13,580 --> 00:33:19,960
Speaker 1: do you sort of get that when it's not a presidential?

620
00:33:20,360 --> 00:33:23,250
Speaker 1: So the numbers are sort of dispersed. So talk us

621
00:33:23,270 --> 00:33:23,830
Speaker 1: through that.

622
00:33:24,230 --> 00:33:24,710
Speaker 2: Yeah, sure.

623
00:33:24,730 --> 00:33:27,550
Speaker 4: So it was a huge, huge blue wave. I mean,

624
00:33:28,110 --> 00:33:31,310
Speaker 4: I was up on Tuesday night, very late on UK time.

625
00:33:31,640 --> 00:33:33,180
Speaker 4: I kind of couldn't believe what I was seeing. It

626
00:33:33,200 --> 00:33:35,560
Speaker 4: was just like the scale of it was massive. I

627
00:33:35,580 --> 00:33:37,820
Speaker 4: think there's a few things that played into that. One

628
00:33:37,840 --> 00:33:40,700
Speaker 4: of them was turnout. So it seems like a lot

629
00:33:40,740 --> 00:33:44,870
Speaker 4: of Republicans stayed home and the Democratic base was fired up.

630
00:33:45,170 --> 00:33:49,370
Speaker 4: They're voting almost like it was a presidential election, particularly

631
00:33:50,170 --> 00:33:54,070
Speaker 4: minority voters, Black voters and Hispanic voters, especially seem to

632
00:33:54,110 --> 00:33:57,030
Speaker 4: turn out at much higher rates than you'd expect for

633
00:33:57,110 --> 00:34:00,550
Speaker 4: a usual midterms. And then the other part is persuasion.

634
00:34:00,610 --> 00:34:05,260
Speaker 4: Democrats clearly won a chunk of Republican voters in virtually

635
00:34:05,320 --> 00:34:08,450
Speaker 4: every race. There's very few places where they went backwards

636
00:34:08,489 --> 00:34:11,930
Speaker 4: versus 2024. So yeah, it was kind of just a

637
00:34:12,370 --> 00:34:15,110
Speaker 4: massive route and, you know, we can get into the

638
00:34:15,170 --> 00:34:18,259
Speaker 4: demographics of it or the specific races and things, but

639
00:34:18,480 --> 00:34:21,299
Speaker 4: that almost takes away from just like how big, how

640
00:34:21,340 --> 00:34:22,060
Speaker 4: big the scale.

641
00:34:21,860 --> 00:34:22,160
Speaker 3: Of it was.

642
00:34:22,550 --> 00:34:25,230
Speaker 1: Let's start with how big the scale of it was.

643
00:34:25,550 --> 00:34:28,640
Speaker 1: Just give us the sense of like compared to the

644
00:34:28,640 --> 00:34:33,180
Speaker 1: 2024 election, compared to the 2020 election. more and how

645
00:34:33,260 --> 00:34:34,550
Speaker 1: much more and what groups?

646
00:34:34,750 --> 00:34:38,850
Speaker 4: Yeah. So Democrats won this time by about 13 or

647
00:34:38,850 --> 00:34:41,109
Speaker 4: 14 points. It will kind of shake out when we've

648
00:34:41,150 --> 00:34:41,770
Speaker 4: got results.

649
00:34:41,790 --> 00:34:45,220
Speaker 1: Right, right, right. That's generic. And the polling was what?

650
00:34:45,280 --> 00:34:50,779
Speaker 1: So it was a D plus 13-ish and the polls

651
00:34:50,860 --> 00:34:54,150
Speaker 1: had predicted a D plus 11, right? Somewhere between a

652
00:34:54,200 --> 00:34:56,040
Speaker 1: D plus 8 and a D plus 11.

653
00:34:56,040 --> 00:34:58,460
Speaker 4: Yeah, I think probably most of the averages were about

654
00:34:58,500 --> 00:35:01,520
Speaker 4: D plus nine-ish, but there were definitely some polls that were.

655
00:35:02,300 --> 00:35:06,120
Speaker 1: So it was a four-point polling error in favor of

656
00:35:06,180 --> 00:35:11,870
Speaker 1: the Democrats, right? Yeah, yeah. Which is kind of amazing, but.

657
00:35:11,890 --> 00:35:13,250
Speaker 4: Yeah, it's bigger than 2016 or 2020 for Republicans, so

658
00:35:13,450 --> 00:35:13,830
Speaker 4: the scale is big.

659
00:35:18,960 --> 00:35:22,110
Speaker 1: But I want to point out in that four-point polling error,

660
00:35:22,969 --> 00:35:25,710
Speaker 1: sort of the week before, Nate Cohen from The New

661
00:35:25,730 --> 00:35:30,770
Speaker 1: York Times did in fact postulate very sort of carefully

662
00:35:31,150 --> 00:35:34,310
Speaker 1: that there was a possibility, you remember this, that the

663
00:35:34,370 --> 00:35:36,610
Speaker 1: polling error, I'm sure you read that piece, that the

664
00:35:36,650 --> 00:35:39,370
Speaker 1: polling errors could be in fact in favor of Democrats.

665
00:35:39,450 --> 00:35:43,319
Speaker 1: It was very tentative, I think, because It felt like

666
00:35:43,360 --> 00:35:45,940
Speaker 1: it was tinged with a lot of anxiety. But he did,

667
00:35:45,980 --> 00:35:49,100
Speaker 1: in fact, say that this was possible, right? Yeah.

668
00:35:49,340 --> 00:35:50,840
Speaker 4: And you're right, the anxiety. I think a lot of

669
00:35:50,900 --> 00:35:53,739
Speaker 4: pollsters were anxious, you know, were so worried about a

670
00:35:53,860 --> 00:35:57,960
Speaker 4: miss in favor of Republicans that they probably slightly overcorrected.

671
00:35:58,260 --> 00:36:00,799
Speaker 4: But I also think it's partly just that the composition

672
00:36:00,820 --> 00:36:03,739
Speaker 4: of the electorate changed so much. It's much harder for

673
00:36:03,800 --> 00:36:06,200
Speaker 4: pollsters to kind of guess who's going to vote than

674
00:36:06,219 --> 00:36:08,340
Speaker 4: it is to guess who they'll vote for. So most people,

675
00:36:08,760 --> 00:36:11,109
Speaker 4: you know, we have a good sense of which way

676
00:36:11,150 --> 00:36:13,859
Speaker 4: people are going to vote. But we don't necessarily know

677
00:36:13,880 --> 00:36:15,700
Speaker 4: where they're going to turn up. And a lot more

678
00:36:15,719 --> 00:36:17,200
Speaker 4: Democrats showed up to vote this time.

679
00:36:17,420 --> 00:36:19,500
Speaker 1: So when we talk about that, that feels like some

680
00:36:19,520 --> 00:36:23,160
Speaker 1: of that is because this administration and I know you're

681
00:36:23,180 --> 00:36:25,370
Speaker 1: a straight journalist, so you can't say this. So I'll

682
00:36:25,410 --> 00:36:28,050
Speaker 1: just say it and then you can hear me say

683
00:36:28,130 --> 00:36:31,379
Speaker 1: it and you don't have to agree or disagree. But

684
00:36:31,520 --> 00:36:35,940
Speaker 1: so many of the voters who showed up on Tuesday

685
00:36:36,200 --> 00:36:41,480
Speaker 1: were likely the were voters like in Mississippi. Democrats flipped

686
00:36:41,660 --> 00:36:44,739
Speaker 1: a Senate seat. First black. I mean, it's not it

687
00:36:44,780 --> 00:36:46,980
Speaker 1: hasn't shaken out yet, but he's definitely going to win.

688
00:36:47,020 --> 00:36:50,489
Speaker 1: I think when they get into the provisionals, first black

689
00:36:50,680 --> 00:36:57,029
Speaker 1: senator in Mississippi since Reconstruction and the reason why. is

690
00:36:57,090 --> 00:37:00,970
Speaker 1: because Mississippi is 40% people of color and those voters

691
00:37:01,130 --> 00:37:03,050
Speaker 1: turned down in large numbers, right?

692
00:37:03,350 --> 00:37:03,730
Speaker 2: Yeah, yeah.

693
00:37:03,850 --> 00:37:07,650
Speaker 4: And the black turnout in the South was so huge.

694
00:37:07,850 --> 00:37:09,450
Speaker 4: You have to kind of think it must have been

695
00:37:10,310 --> 00:37:13,820
Speaker 4: a reaction against perhaps the Supreme Court gutting voting rights.

696
00:37:14,010 --> 00:37:15,120
Speaker 1: LA, yeah.

697
00:37:15,180 --> 00:37:15,960
Speaker 3: LA, exactly.

698
00:37:15,980 --> 00:37:20,020
Speaker 4: I think Democrats were really effective at saying to black

699
00:37:20,060 --> 00:37:23,040
Speaker 4: voters in the South, these are rights that have been

700
00:37:23,080 --> 00:37:25,759
Speaker 4: around for decades that are being rolled back. you have

701
00:37:25,780 --> 00:37:27,609
Speaker 4: to show up in numbers that are kind of too

702
00:37:27,650 --> 00:37:31,170
Speaker 4: big to... Too big to rig. Yeah, too big to rig, exactly.

703
00:37:31,210 --> 00:37:33,230
Speaker 4: And I think that was a really effective message. And

704
00:37:33,270 --> 00:37:35,390
Speaker 4: it's similar, you know, it's just the same thing was

705
00:37:35,430 --> 00:37:38,700
Speaker 4: happening for Hispanic voters in Texas, for example. You know,

706
00:37:38,719 --> 00:37:43,299
Speaker 4: a lot of those neighborhoods where Immigration and Customs Enforcement, ICE,

707
00:37:43,460 --> 00:37:47,140
Speaker 4: were active, had a huge turnout of Hispanic voters this time.

708
00:37:47,540 --> 00:37:51,489
Speaker 4: The things like the 9th Congressional District, which is in Houston...

709
00:37:52,000 --> 00:37:55,500
Speaker 4: It's a majority Hispanic district, but in 2024, I think

710
00:37:55,800 --> 00:37:58,890
Speaker 4: among voters, it was only 37% Hispanic. And then they

711
00:37:58,930 --> 00:38:02,570
Speaker 4: achieved this massive swing. It was a heavily Trump district,

712
00:38:03,050 --> 00:38:06,469
Speaker 4: gerrymandered by Republicans. But so many voters turned up on

713
00:38:06,489 --> 00:38:08,640
Speaker 4: the Democratic side that it kind of overwhelmed that and

714
00:38:08,660 --> 00:38:09,440
Speaker 4: they managed to flip it.

715
00:38:09,600 --> 00:38:10,759
Speaker 1: That's Texas 9th?

716
00:38:11,360 --> 00:38:11,580
Speaker 2: 9th, yeah.

717
00:38:11,739 --> 00:38:14,300
Speaker 1: I mean, I think of all those candidates in the

718
00:38:14,500 --> 00:38:19,440
Speaker 1: Rio Grande Valley, like Bobby Polito and Johnny Garcia. And

719
00:38:19,480 --> 00:38:23,100
Speaker 1: those are candidates where those are R plus 10, R

720
00:38:23,300 --> 00:38:28,020
Speaker 1: plus 11, R plus 21. I mean, Johnny Garcia is

721
00:38:28,100 --> 00:38:30,839
Speaker 1: a redraw, but like Bobby Polito is an R plus

722
00:38:30,840 --> 00:38:31,320
Speaker 1: 21 candidate.

723
00:38:32,620 --> 00:38:35,480
Speaker 4: Yeah, so those were massive. There's districts along the southern

724
00:38:35,520 --> 00:38:38,440
Speaker 4: border and also the one in Houston. As you say,

725
00:38:38,460 --> 00:38:43,110
Speaker 4: they were gerrymandered. It was a huge gerrymander by Republicans

726
00:38:43,160 --> 00:38:45,870
Speaker 4: in Texas to try and get five additional districts. And

727
00:38:45,890 --> 00:38:48,450
Speaker 4: I think Democrats managed to win four of them off

728
00:38:48,469 --> 00:38:50,069
Speaker 4: the top of my head. And as you say, these

729
00:38:50,090 --> 00:38:52,710
Speaker 4: were districts that voted for Trump by 10, 15, 20 points,

730
00:38:52,730 --> 00:38:58,130
Speaker 4: but were majority Hispanic. They swung massively from Democrats to

731
00:38:58,170 --> 00:39:02,370
Speaker 4: Republicans in Texas. 2020 and 2024, now virtually all those

732
00:39:02,430 --> 00:39:05,049
Speaker 4: gains have been wiped out. And I mean, if you

733
00:39:05,070 --> 00:39:07,310
Speaker 4: look at Tallerico's numbers, some of the counties along the

734
00:39:07,350 --> 00:39:11,470
Speaker 4: southern border, they're like break records for the biggest swings

735
00:39:11,910 --> 00:39:17,469
Speaker 4: in decades because they go from being kind of Obama

736
00:39:17,770 --> 00:39:21,750
Speaker 4: plus 40 to Biden plus 20 to Trump plus one,

737
00:39:22,030 --> 00:39:24,790
Speaker 4: and then way back to Tallerico plus 40 again. So

738
00:39:25,930 --> 00:39:28,570
Speaker 4: the numbers on the southern border are just kind of absurd.

739
00:39:28,930 --> 00:39:33,720
Speaker 1: I wonder if you could talk to us about what

740
00:39:33,920 --> 00:39:36,629
Speaker 1: the sort of young voter turnout looked like.

741
00:39:36,930 --> 00:39:37,540
Speaker 2: Yeah, sure.

742
00:39:37,760 --> 00:39:40,819
Speaker 4: And I don't have a great set of data on

743
00:39:41,239 --> 00:39:43,180
Speaker 4: actually how many young voters turned out.

744
00:39:43,520 --> 00:39:45,509
Speaker 1: But you have a great visual.

745
00:39:46,410 --> 00:39:46,790
Speaker 2: Yes. Yes.

746
00:39:46,830 --> 00:39:49,510
Speaker 1: So which show the sort of 2024 presidential versus the

747
00:39:49,510 --> 00:40:00,600
Speaker 1: 2026 vote share. of approval, disapproval among young people and

748
00:40:00,640 --> 00:40:03,240
Speaker 1: what that looks like. Can you just talk us through,

749
00:40:03,260 --> 00:40:06,030
Speaker 1: because that'll sort of show us, I think, like more

750
00:40:06,450 --> 00:40:10,970
Speaker 1: young people went out And like non-voters, people who didn't

751
00:40:10,989 --> 00:40:13,540
Speaker 1: vote for Harris, like talk us through that. I know

752
00:40:13,560 --> 00:40:14,740
Speaker 1: you don't have the final numbers.

753
00:40:14,820 --> 00:40:19,840
Speaker 4: Yeah, yeah. So young voters turned out in unusual numbers

754
00:40:19,880 --> 00:40:22,239
Speaker 4: for Donald Trump in 2024. That was kind of a

755
00:40:22,260 --> 00:40:24,560
Speaker 4: big story of the last election was they swung away

756
00:40:24,620 --> 00:40:26,779
Speaker 4: from Democrats. But it was right at the start of

757
00:40:26,840 --> 00:40:29,359
Speaker 4: his second term that we saw in our polling that

758
00:40:29,739 --> 00:40:32,640
Speaker 4: his approval rating was kind of plummeting amongst young voters,

759
00:40:33,160 --> 00:40:36,359
Speaker 4: literally in kind of February, March, April of 2020. his

760
00:40:36,410 --> 00:40:38,989
Speaker 4: second term. And that has carried through all the way

761
00:40:39,010 --> 00:40:41,950
Speaker 4: to these midterms where some of the biggest swings in

762
00:40:41,969 --> 00:40:45,290
Speaker 4: the country have been in counties with a lot of

763
00:40:45,330 --> 00:40:48,390
Speaker 4: young voters. In the exit polls, I've seen it's young

764
00:40:48,430 --> 00:40:52,549
Speaker 4: voters that are swinging the hardest. And a lot of that,

765
00:40:52,570 --> 00:40:55,080
Speaker 4: I think, is turning out new voters. I think there

766
00:40:55,100 --> 00:40:58,359
Speaker 4: are a lot of young voters who voted for Trump

767
00:40:58,420 --> 00:41:01,340
Speaker 4: in 2024 because they were worried about inflation and the

768
00:41:01,400 --> 00:41:03,540
Speaker 4: state of the economy. There were a lot who stayed

769
00:41:03,600 --> 00:41:06,910
Speaker 4: home just because they felt like Biden and Harris didn't

770
00:41:06,950 --> 00:41:09,569
Speaker 4: really represent them. And those voters, I think, have come

771
00:41:09,610 --> 00:41:12,230
Speaker 4: back to the Democratic Party in huge numbers. So I

772
00:41:12,250 --> 00:41:14,029
Speaker 4: don't know if that's a kind of lasting trend or

773
00:41:14,050 --> 00:41:17,710
Speaker 4: it's just midterms. anger. But there's definitely a lot of

774
00:41:17,790 --> 00:41:20,460
Speaker 4: anger at the Trump administration. And we saw that on Tuesday.

775
00:41:20,640 --> 00:41:26,239
Speaker 1: So we have these turnout numbers, Latinos in Hispanic voters

776
00:41:26,560 --> 00:41:33,140
Speaker 1: in Texas, Black voters in the South. Florida Republicans were

777
00:41:33,180 --> 00:41:37,390
Speaker 1: saved by Cuban voters. That's a sort of interesting story.

778
00:41:38,670 --> 00:41:39,230
Speaker 1: Talk about that.

779
00:41:39,590 --> 00:41:42,250
Speaker 4: Yeah, from what I've seen, I think Cuban voters did

780
00:41:42,570 --> 00:41:46,640
Speaker 4: move slightly to the left this election compared to previous ones.

781
00:41:46,660 --> 00:41:49,740
Speaker 4: They've obviously been very favorable to Republicans for a long time,

782
00:41:49,900 --> 00:41:53,720
Speaker 4: but not at the same scale as particularly Mexican voters

783
00:41:53,880 --> 00:41:57,400
Speaker 4: in Texas were the ones who moved furthest amongst Latino voters.

784
00:41:58,140 --> 00:41:59,980
Speaker 4: I don't really have a good story for why that

785
00:42:00,040 --> 00:42:02,620
Speaker 4: might be. I mean, it could partly be to do

786
00:42:02,660 --> 00:42:06,149
Speaker 4: with Marco Rubio's prominence in the administration and the kind

787
00:42:06,170 --> 00:42:11,000
Speaker 4: of offline that he's pushing on Cuba. obviously what happened

788
00:42:11,020 --> 00:42:14,100
Speaker 4: in Venezuela as well. I also think Cuban voters tend

789
00:42:14,120 --> 00:42:18,650
Speaker 4: to be older than other Latino groups and older voters

790
00:42:18,690 --> 00:42:21,230
Speaker 4: in this election were much less likely to swing than

791
00:42:21,270 --> 00:42:24,450
Speaker 4: younger voters. So Florida is a very old state in general.

792
00:42:24,469 --> 00:42:26,490
Speaker 4: I think one of the only counties in the country

793
00:42:26,510 --> 00:42:29,160
Speaker 4: that swung to the right this time was the one

794
00:42:29,180 --> 00:42:32,960
Speaker 4: that includes the villages, obviously incredibly old demographic. So like,

795
00:42:33,310 --> 00:42:36,290
Speaker 4: Old people are the only group that really stayed still

796
00:42:36,370 --> 00:42:39,250
Speaker 4: since 2024. And it was young and middle-aged voters who

797
00:42:39,290 --> 00:42:42,020
Speaker 4: swung massively to the left. I wonder if Cuban voters

798
00:42:42,040 --> 00:42:43,660
Speaker 4: are kind of part of that picture as well.

799
00:42:43,880 --> 00:42:47,360
Speaker 1: That's really quite interesting. Where did you see the biggest

800
00:42:47,480 --> 00:42:52,600
Speaker 1: over-performances for Democrats that you were surprised by? I mean,

801
00:42:52,640 --> 00:42:53,700
Speaker 1: besides all of it.

802
00:42:54,480 --> 00:42:56,239
Speaker 3: Yeah, it was all huge.

803
00:42:56,340 --> 00:43:00,279
Speaker 4: I think in terms of margin, I thought Tallarico could win.

804
00:43:00,460 --> 00:43:02,859
Speaker 4: I had no idea he would win by as much

805
00:43:02,920 --> 00:43:03,339
Speaker 4: as he did.

806
00:43:03,380 --> 00:43:06,200
Speaker 1: What's the final count on that?

807
00:43:06,219 --> 00:43:07,570
Speaker 4: At the moment, the count is 55-43.

808
00:43:07,880 --> 00:43:14,550
Speaker 1: Right, that's right, because Paxton couldn't in the early vote.

809
00:43:14,770 --> 00:43:15,850
Speaker 1: He couldn't even win.

810
00:43:15,890 --> 00:43:18,149
Speaker 4: By election day, it was all over. Tallarico got more

811
00:43:18,210 --> 00:43:20,340
Speaker 4: early votes than Paxton did on the day.

812
00:43:20,739 --> 00:43:28,120
Speaker 1: But Gina Hinojosa, I thought, also did extremely well in

813
00:43:28,160 --> 00:43:28,520
Speaker 1: a sort of.

814
00:43:28,520 --> 00:43:28,600
Speaker 3: I.

815
00:43:30,350 --> 00:43:33,090
Speaker 1: Mean, she's ahead by six points or something, right? Like,

816
00:43:33,550 --> 00:43:34,670
Speaker 1: it wasn't even close.

817
00:43:34,830 --> 00:43:36,710
Speaker 4: Yeah, not really close at all. And that was another

818
00:43:36,770 --> 00:43:39,150
Speaker 4: one where the polls got it wrong. I think in

819
00:43:39,190 --> 00:43:42,580
Speaker 4: a lot of states, they kind of saw Senate candidates

820
00:43:42,640 --> 00:43:44,660
Speaker 4: running a long way ahead of governors. It's the same

821
00:43:44,700 --> 00:43:48,040
Speaker 4: in Georgia, where You know, people were pretty sure that

822
00:43:48,400 --> 00:43:50,820
Speaker 4: Ossoff was going to win the Senate seat, very unsure

823
00:43:50,860 --> 00:43:53,640
Speaker 4: about Keisha Lance Bottoms. And she was fine as well.

824
00:43:54,550 --> 00:43:56,810
Speaker 4: It wasn't really that close. So, you know, partly I

825
00:43:56,950 --> 00:43:59,029
Speaker 4: think that's because they turned out so many voters. But

826
00:43:59,469 --> 00:44:02,330
Speaker 4: voters weren't looking down the ballot and saying, you know,

827
00:44:02,550 --> 00:44:05,130
Speaker 4: is this Democrat going to do X, Y, Z on

828
00:44:05,190 --> 00:44:08,469
Speaker 4: state policies? You know, what's the record of the incumbent governor?

829
00:44:08,489 --> 00:44:11,060
Speaker 4: I think they just wanted to kick every Republican they could.

830
00:44:11,120 --> 00:44:11,980
Speaker 4: And they did.

831
00:44:12,000 --> 00:44:12,080
Speaker 2: Yeah.

832
00:44:12,870 --> 00:44:15,330
Speaker 1: One of the things that I'm struck by in Texas

833
00:44:15,410 --> 00:44:21,180
Speaker 1: was Abbott had higher– like everybody knew everyone hated Ken Paxton.

834
00:44:21,560 --> 00:44:24,920
Speaker 1: But people thought Abbott was more popular and he had

835
00:44:24,980 --> 00:44:27,980
Speaker 1: sort of better numbers. But he was running. That would

836
00:44:28,000 --> 00:44:30,960
Speaker 1: have been his fourth term. I mean why do you

837
00:44:31,080 --> 00:44:36,529
Speaker 1: think the Senate candidates sort of did better in the

838
00:44:36,550 --> 00:44:40,680
Speaker 1: polling than the gubernatorials? Because like Keeser Lance Bottoms, too–

839
00:44:40,930 --> 00:44:43,109
Speaker 1: was one where everyone was like, well, will she make

840
00:44:43,150 --> 00:44:45,480
Speaker 1: it over the line? And she won by a lot, right?

841
00:44:45,760 --> 00:44:47,420
Speaker 4: Yeah, well, I think it's probably the same thing that

842
00:44:47,480 --> 00:44:50,200
Speaker 4: caused the error in general, is that they weren't really

843
00:44:50,580 --> 00:44:53,580
Speaker 4: capturing how big the turnout gap would be between the parties. So,

844
00:44:54,100 --> 00:44:56,420
Speaker 4: you know, in the polls, it was clear that, as

845
00:44:56,450 --> 00:45:00,710
Speaker 4: you say, Abbott was more popular than Paxton, for example.

846
00:45:01,190 --> 00:45:04,290
Speaker 4: And that's probably true amongst the most engaged voters who

847
00:45:04,330 --> 00:45:06,390
Speaker 4: are most likely to take the polls. But then if

848
00:45:06,430 --> 00:45:09,360
Speaker 4: you inject into that a whole load of Democrats who...

849
00:45:10,050 --> 00:45:15,050
Speaker 4: or don't usually vote or, you know, particularly non-white Democrats

850
00:45:15,150 --> 00:45:18,830
Speaker 4: or working class Democrats who often sit out a midterm election,

851
00:45:19,230 --> 00:45:21,529
Speaker 4: they weren't making the same distinction as the kind of

852
00:45:21,950 --> 00:45:26,319
Speaker 4: super high engagement poll respondents that were making, you know,

853
00:45:26,340 --> 00:45:29,480
Speaker 4: flitting their ticket and saying different things about different candidates.

854
00:45:29,580 --> 00:45:32,980
Speaker 4: It was, yeah, the scale of democratic turnout, I think

855
00:45:33,020 --> 00:45:36,530
Speaker 4: just kind of overwhelmed any personal appeal either way. Yeah.

856
00:45:36,989 --> 00:45:41,549
Speaker 1: And I do think it is a it's such an

857
00:45:41,950 --> 00:45:50,190
Speaker 1: interesting moment because like what we see is pollsters really did.

858
00:45:50,210 --> 00:45:52,730
Speaker 1: They actually did get it right, but maybe didn't trust

859
00:45:52,750 --> 00:45:53,709
Speaker 1: themselves as much.

860
00:45:53,950 --> 00:45:54,210
Speaker 2: Right.

861
00:45:54,330 --> 00:45:57,200
Speaker 1: Because of the Republican. And part of that is like

862
00:45:57,260 --> 00:46:00,660
Speaker 1: talk us through how polls work, because they have this

863
00:46:00,800 --> 00:46:04,490
Speaker 1: interesting idea. thing that I didn't know about, which was

864
00:46:04,790 --> 00:46:07,890
Speaker 1: this where like it's an R plus 10 or an

865
00:46:08,010 --> 00:46:10,830
Speaker 1: R plus three. Explain to us how they sort of

866
00:46:10,989 --> 00:46:13,030
Speaker 1: try to budget for non-response bias.

867
00:46:13,190 --> 00:46:13,510
Speaker 2: Yeah.

868
00:46:13,550 --> 00:46:18,320
Speaker 4: So typically what pollsters do is that they'll kind of,

869
00:46:18,739 --> 00:46:21,180
Speaker 4: they'll get a sample, which they try and make reflect

870
00:46:21,219 --> 00:46:23,580
Speaker 4: the country. So either they'll ring people up from the

871
00:46:23,620 --> 00:46:27,280
Speaker 4: voter file from certain parties, or they'll get online respondents

872
00:46:27,320 --> 00:46:29,520
Speaker 4: who say they're from one party or the other. But

873
00:46:29,719 --> 00:46:32,120
Speaker 4: as we've seen in the Washington Post did a nice

874
00:46:32,160 --> 00:46:35,219
Speaker 4: analysis of it, it's much harder to get Republicans than

875
00:46:35,260 --> 00:46:37,700
Speaker 4: it is to get Democrats. So that creates one problem.

876
00:46:38,380 --> 00:46:43,100
Speaker 4: Usually that is fixed by weighting them to some known number.

877
00:46:43,180 --> 00:46:45,640
Speaker 4: So in a certain state, you might know how many

878
00:46:45,680 --> 00:46:49,120
Speaker 4: registered Republicans versus Democrats there are. That would get you

879
00:46:49,140 --> 00:46:52,350
Speaker 4: a lot of the way there. The problem that pollsters

880
00:46:52,370 --> 00:46:54,860
Speaker 4: were really worried about this election was that the They

881
00:46:54,910 --> 00:46:56,860
Speaker 4: were worried that they would be overweighting the type of

882
00:46:56,910 --> 00:46:59,790
Speaker 4: Republicans who then said they would vote for Democrats. So

883
00:47:00,090 --> 00:47:03,290
Speaker 4: they thought they might overestimate the Democrats by having too

884
00:47:03,330 --> 00:47:06,049
Speaker 4: many Republican switchers. In the end, that was not the

885
00:47:06,090 --> 00:47:09,589
Speaker 4: problem at all. The problem was that they actually had weighted... Well,

886
00:47:09,890 --> 00:47:11,250
Speaker 4: I mean, I haven't seen all the numbers.

887
00:47:11,270 --> 00:47:11,469
Speaker 2: Right.

888
00:47:11,489 --> 00:47:12,890
Speaker 1: We don't really know the specifics.

889
00:47:12,910 --> 00:47:16,109
Speaker 4: We don't really know, but based on conjecture, my.

890
00:47:17,050 --> 00:47:20,910
Speaker 1: guess is that they please come here for conjecture that's what.

891
00:47:20,810 --> 00:47:22,549
Speaker 4: We do yeah well i think i think they were

892
00:47:22,590 --> 00:47:26,670
Speaker 4: probably underweighting democrats because the turnout age edge was so

893
00:47:26,750 --> 00:47:29,630
Speaker 4: big that you know they looked at what the electorate

894
00:47:29,650 --> 00:47:32,989
Speaker 4: looked like in 2024 when polls were wrong or they

895
00:47:33,030 --> 00:47:37,160
Speaker 4: looked at uh even 2022 which was quite favorable midterms

896
00:47:37,200 --> 00:47:41,840
Speaker 4: for republicans adjusted their weights to reflect those elections and

897
00:47:41,900 --> 00:47:44,780
Speaker 4: missed that there was a huge rise in democratic turnout

898
00:47:44,820 --> 00:47:46,830
Speaker 4: this time So interesting.

899
00:47:47,890 --> 00:47:49,330
Speaker 1: Owen, thank you for joining us.

900
00:47:49,350 --> 00:47:50,690
Speaker 4: All right. Thanks very much for having.

901
00:47:52,510 --> 00:47:55,250
Speaker 2: Me. And now your moment of fuckery.

902
00:47:55,690 --> 00:47:56,310
Speaker 1: Rick Wilson.

903
00:47:56,710 --> 00:47:57,270
Speaker 3: Hello, Molly.

904
00:47:57,570 --> 00:47:59,070
Speaker 1: I want to talk to you for our moment of

905
00:47:59,110 --> 00:48:02,000
Speaker 1: fuckery about a video taken on the steps of the Pentagon,

906
00:48:02,380 --> 00:48:05,580
Speaker 1: where Hegseth portrayed himself, because if he's nothing, if not

907
00:48:05,620 --> 00:48:06,219
Speaker 1: an actor, as.

908
00:48:08,969 --> 00:48:11,370
Speaker 1: I mean, I think this is like perfect. A warrior

909
00:48:11,430 --> 00:48:15,810
Speaker 1: for Trump's agenda, suggesting his ouster would be a rebuke

910
00:48:15,870 --> 00:48:20,690
Speaker 1: to the White House and the larger MAGA movement.

911
00:48:21,170 --> 00:48:23,810
Speaker 2: Well, of course. Why wouldn't you put yourself in the

912
00:48:23,830 --> 00:48:26,450
Speaker 2: center of the frame if you're Pete Hegseth, whose ego

913
00:48:26,489 --> 00:48:31,440
Speaker 2: is not inconsiderable? I got to tell you, I watched

914
00:48:31,500 --> 00:48:35,399
Speaker 2: that and I thought this QAnon signaling he's been doing

915
00:48:35,420 --> 00:48:38,770
Speaker 2: like patriots are in control. All of it is a

916
00:48:38,830 --> 00:48:41,350
Speaker 2: guy who's going to try to play, much like Miller's

917
00:48:41,370 --> 00:48:43,290
Speaker 2: going to try to play, let's just get the base

918
00:48:43,350 --> 00:48:45,989
Speaker 2: back together, hold our votes in Congress, and no one

919
00:48:46,030 --> 00:48:49,719
Speaker 2: can defeat Trump. The numbers ain't there, Pete. You're going down.

920
00:48:49,780 --> 00:48:52,420
Speaker 2: You're going to either leave or be thrown out, one

921
00:48:52,440 --> 00:48:55,540
Speaker 2: way or the other. If I were Pete Hegseth, knowing

922
00:48:55,580 --> 00:48:58,700
Speaker 2: that he wants to run for president, which I know

923
00:48:58,739 --> 00:49:01,520
Speaker 2: is as insane as that sounds, if I were Pete Hegseth,

924
00:49:02,000 --> 00:49:04,600
Speaker 2: I'd be doing exactly this. I'd be producing videos on

925
00:49:04,620 --> 00:49:08,050
Speaker 2: the government dime, I'd be trying to make this into

926
00:49:08,130 --> 00:49:11,330
Speaker 2: a big culture war fight. I mean, he is not

927
00:49:11,410 --> 00:49:13,489
Speaker 2: the God's smartest person, but he's been raised in that

928
00:49:13,590 --> 00:49:18,670
Speaker 2: Fox slash MAGA culture. And so he's playing, it's very

929
00:49:18,730 --> 00:49:19,830
Speaker 2: predictable what he's going to do.

930
00:49:20,489 --> 00:49:25,700
Speaker 1: Whiskey Pete? I don't think that Whiskey Pete being impeached

931
00:49:26,500 --> 00:49:28,900
Speaker 1: is going to be preventable no matter what video.

932
00:49:28,960 --> 00:49:31,660
Speaker 2: I think it's going to be some great primetime amusement

933
00:49:31,739 --> 00:49:33,080
Speaker 2: for the people of the United States.

934
00:49:33,380 --> 00:49:34,020
Speaker 1: Yeah, yeah, yeah.

935
00:49:34,040 --> 00:49:35,420
Speaker 2: And we're going to learn a lot more about the

936
00:49:35,480 --> 00:49:37,900
Speaker 2: Iran war planning or lack thereof.

937
00:49:38,400 --> 00:49:38,840
Speaker 1: Right.

938
00:49:38,860 --> 00:49:42,060
Speaker 2: Because that is Whiskey Pete's claim to fame. I mean, remember,

939
00:49:42,400 --> 00:49:46,360
Speaker 2: we've decimated the Iranians. It's over. They've been utterly destroyed, obliterated.

940
00:49:46,840 --> 00:49:49,180
Speaker 1: That's right. That's why the war keeps going on. Because

941
00:49:49,200 --> 00:49:50,319
Speaker 1: you know a war is over.

942
00:49:50,340 --> 00:49:53,200
Speaker 2: We're now in month seven of a three-week war.

943
00:49:53,480 --> 00:49:56,620
Speaker 1: Yeah, exactly. Three weeks. And gas prices are going to

944
00:49:56,660 --> 00:49:57,750
Speaker 1: go way down.

945
00:49:57,830 --> 00:49:58,089
Speaker 3: Oh, yeah.

946
00:49:58,110 --> 00:50:00,670
Speaker 2: Rick Wilson. Way down. Going to be paying $ 1. 79 for

947
00:50:00,710 --> 00:50:01,850
Speaker 2: gas again. It's going to be great.

948
00:50:01,870 --> 00:50:07,380
Speaker 1: That's right. Amazing. That's it for this episode of Fast Politics.

949
00:50:07,840 --> 00:50:13,740
Speaker 1: Tune in every Monday, Wednesday, Thursday and Saturday to hear

950
00:50:13,760 --> 00:50:18,110
Speaker 1: the best minds in politics make sense of all this chaos.

951
00:50:18,610 --> 00:50:21,330
Speaker 1: If you enjoy this podcast, please send it to a

952
00:50:21,410 --> 00:50:25,390
Speaker 1: friend and keep the conversation going. Thanks for listening.