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

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Speaker 1: where we discussed the top political headlines with some of

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Speaker 1: today's best minds, and a news survey from the European

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Speaker 1: Council on Foreign Relations says only eleven percent of Europeans.

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Speaker 2: View the United States as an ally.

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

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Speaker 1: now is Steph Rules Stop Spy to talk about Trump

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Speaker 1: loving inflation, and we'll talk to heat Map's own Robbinson

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Speaker 1: Meyer about the opposition to data centers and AI in America.

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Speaker 1: The first of the Newsmike.

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Speaker 3: We had not very surprising results in the primaries last night.

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Speaker 3: Graham Patner is a nominee. But I think that the

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Speaker 3: really interesting thing is that three out of the four

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Speaker 3: Republicans who forced the release of the Epstein files are

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Speaker 3: now out of jobs.

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Speaker 1: Yeah. I mean there's a lot of really interesting takeaways

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Speaker 1: from this from Tuesday's election results. First, is it Graham

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Speaker 1: Vlatner won his primary? What does that mean? It could

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Speaker 1: mean any number of things. So far, his scandals have

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Speaker 1: not hurt him. His campaign has had a bunch of revelations,

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Speaker 1: but so far he's been okay. Early returns show him

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Speaker 1: with about seventy two percent of the vote. That's pretty

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Speaker 1: close to his poll numbers before these headlines. That means

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Speaker 1: that really he has so far, and a preview of

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Speaker 1: some of the ads that are coming down the pike

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Speaker 1: are pretty scary. The Republicans are all in on saying

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Speaker 1: that he's a racist, sexist, Nazi loving domestic abuser. Platner

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Speaker 1: is a combat veteran. I mean, I don't know what

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Speaker 1: happens here. You know, there are people who are comparing

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Speaker 1: this to Trump and Hillary right that Collins is like

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Speaker 1: a longtime DC person and Plattner is an anti establishment firebrand.

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Speaker 1: I don't really think it's the same thing, but there's

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Speaker 1: some you know, history rhyming here. The question of whether

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Speaker 1: or not pla wins, I think is really about whether

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Speaker 1: he can get the low frequency voters, the populist voters,

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Speaker 1: the ones and twos, the white men who feel they've

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Speaker 1: been abandoned, the people who voted for Trump and voted

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Speaker 1: for Zoren. If those voters register and those voters come

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Speaker 1: out for Platner, and then platter wins. If they don't,

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Speaker 1: then he doesn't, and that is going to be the

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Speaker 1: big thing.

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Speaker 3: I'd be curious though, do you think though a lot

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Speaker 3: of people criteria like after that Sean trendy stuff after

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Speaker 3: twenty sixteen, thought that Trump's people just wanted their way,

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Speaker 3: They wanted the madness to stop, the swamp to be drained.

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Speaker 3: And it seems like the Democrats are having that moment

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Speaker 3: with Platner. They don't really care about his flaws, they

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Speaker 3: just want him to vote the way he says he's

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Speaker 3: going to vote.

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Speaker 1: Well, we don't know, right, I mean, this is the thing,

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Speaker 1: especially in Maine with the polls have been so screwy,

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Speaker 1: and we saw the candidate Sarah Gedion who ran against

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Speaker 1: Susan Collins last time, she was way up. She was

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Speaker 1: up like eight points. So the answer is, I don't know.

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Speaker 1: I still think Maine is this state where it's like

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Speaker 1: two states. It's this very poor rural state that's a

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Speaker 1: little bit like West Virginia and then this very affluent

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Speaker 1: coastal state that's a little bit like Massachusetts. And so

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Speaker 1: the question is like does the more rural part of

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Speaker 1: the state come out from And one of the things

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Speaker 1: that remember, we talked to someone who was a main

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Speaker 1: based reporter who was talking about how there's this thing

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Speaker 1: about Maine. Are you from Maine? Are you a manor?

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Speaker 1: And his father was sort of a country house manor,

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Speaker 1: but he was born in Maine, and they feel that

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Speaker 1: he's a manor. So and Sarah Gideon, there was a

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Speaker 1: lot of complaints that she was not a maynor. So again, remember,

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Speaker 1: these races are about controlling the Senate, but they're also

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Speaker 1: about how people feel about their Senate candidates and their

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Speaker 1: congressional candidates. And so you know, it's possible that he

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Speaker 1: gets people to show up for him and he wins

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Speaker 1: and none of this matters, and it's possible that it

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Speaker 1: just totally derails him, and that we have months and

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Speaker 1: months and months to go.

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Speaker 3: So let's go down to a totally different elector at

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Speaker 3: South Carolina where Nancy Mace not going to miss her

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Speaker 3: at all. She finished in fifth place. Unbelievable.

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Speaker 1: Nancy Mace is like a very very chaotic chaos agent.

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Speaker 1: I think that's fair.

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Speaker 2: A sweat wave of describe it gets like.

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Speaker 1: I heard her speed probably four years ago and thought like, oh,

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Speaker 1: this person is really smart. Like she used to give

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Speaker 1: a speech wheice she talked about the most important Supreme

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Speaker 1: Court justice to her was better Ginsburg because she made

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Speaker 1: it so she could go to the citadel. Is that nuts?

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Speaker 3: It's nuts? And you know, I think this is another

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Speaker 3: case of the worst ingredient you can add to a

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Speaker 3: lot of people's personality as fame, and she decided to

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Speaker 3: try to add that ingredient and it went very bad.

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Speaker 1: Yeah, I mean, I also think there's a fair amount

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Speaker 1: of like bad behavior and drinking. So anyway, she runs.

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Speaker 1: She does not get the endorsement of Trump. Somebody else

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Speaker 1: gets it. She posts an old endorsement. We've seen other

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Speaker 1: people do this. He gets mad. She comes in festh

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Speaker 1: it's South Carolina, man, It's not. You know, like there

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Speaker 1: are states like Iowa where Donald Trump is clearly in trouble. Right,

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Speaker 1: he endorses someone, the candidate loses. This is not that.

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Speaker 1: This is a state that is very rad and that

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Speaker 1: is very angry and is still very trumpy. So Mace

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Speaker 1: comes in fest which is a really good sign that

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Speaker 1: also Twitter is not real life, right, like, this is

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Speaker 1: a very online candidate, but while online, she is not

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Speaker 1: a South Carolina favorite. So she has lost her congressional seat,

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Speaker 1: just like Marjorie Taylor Green. So who knows what happens next.

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Speaker 1: But a complicated figure like Madison, Cosshorn, all these members

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Speaker 1: of Congress, just in and out yep.

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Speaker 3: Okay. So, as we know, mister Trump is laser focused

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Speaker 3: on the economy, and by that I mean making his

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Speaker 3: sons and his friend's money. So what we've seen here

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Speaker 3: is the FEDS are going to formally allow sports trading

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Speaker 3: quote unquote trading on prediction markets like Polymarket and Calci.

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Speaker 3: I wonder why.

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Speaker 1: Yeah, so Dodd Junior's advisor of both poly Markets and Calshie.

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Speaker 1: The whole idea that we're doing this, that we're turning

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Speaker 1: America into a casino, it's so bad, it's so stupid,

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Speaker 1: It's so incredibly pointless and dangerous and terrible.

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Speaker 3: So there is this blockbuster New York Times reporting about

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Speaker 3: how Trump tried to silence the disclosure of the Epstein files.

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Speaker 3: Now he is big mad at The New York Times

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Speaker 3: about it.

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Speaker 1: Maggie Haberman and John is in Swad with a forty

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Speaker 1: seven minute long the story. I listened to it this

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Speaker 1: morning about the Epstein files melt down and how it

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Speaker 1: is worth listening to. I mean, it is like they

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Speaker 1: were in the fucking room. Like you hear everyone who

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Speaker 1: was there, and every sentence they said to each other.

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Speaker 1: I mean it is just you really do see that,

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Speaker 1: like this white House sleeks like a fucking sev and

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Speaker 1: also like those two are just so good at what

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Speaker 1: they I mean, it is literally, like you think about

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Speaker 1: so much. Reporting is just like a sense of what

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Speaker 1: we think is happening, and here is like literally it's

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Speaker 1: like a novel. It's like there's Susie Wilds says this,

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Speaker 1: and then Dan Bongino says that, and then this and

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Speaker 1: then that. I mean, just incredible worth reading. And Donald

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Speaker 1: Trump is very mad and I cannot imagine why he's

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Speaker 1: very mad that everyone in his White House is totally leaky,

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Speaker 1: like I said, But my favorite part is early Wednesday,

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Speaker 1: Morning Joe mentioned the report on his show. Less than

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Speaker 1: an hour later, Trump and we had thought that Trump

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Speaker 1: might watch Morning Joe. He is, in fact watching Morning Joe,

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Speaker 1: which is a little scary. One of the most inaccurate

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Speaker 1: detailers of truthful facts on television, truthful facts as opposed

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Speaker 1: to untruthful facts. He has a serious case of Trump

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Speaker 1: Derangement syndrome, often referred to as TDS. So isn't it TDS?

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Speaker 1: TBSS a network laughingstock among those who know what's going

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Speaker 1: on in the wonderful world of television? Trump posted, So, yeah,

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Speaker 1: he's very mad. They all suffer from low ratings disease.

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Speaker 1: Thank you, President dj T. There you go terrible. And

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Speaker 1: Mike and the show is being cut because of his

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Speaker 1: ratings are terrible. And Mike and Willie, who wants to

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Speaker 1: be his father and doesn't have the talent, are falling

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Speaker 1: further and further into the TDS swamp swampy anyway, So

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Speaker 1: Donald Trump still obsessed with Joe Scarborough.

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Speaker 3: Street really read like one of the classics about creating

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Speaker 3: Carder's party is no longer being hot.

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Speaker 1: It really feels like something you put on a needle

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Speaker 1: Foyd Pillow.

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Speaker 3: Yeah, so I had to like check that this headline

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Speaker 3: was not a trick on me. The House passes the

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Speaker 3: Faster Labor Contracts Act, which is a pro union bill,

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Speaker 3: after twenty Republicans defy the leadership.

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Speaker 1: Yeah, in case you're wondering, Mike Johnson has completely lost

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Speaker 1: control of the ship and that's why shit's just passing through.

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Speaker 1: Mike Johnson has just completely utterly My man is just

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Speaker 1: a complete disaster, unmissed. We got this discharge petition, we

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Speaker 1: got Faster labor contracts. This is David Norcross across from

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Speaker 1: New Jersey. This is the kind of stuff I love.

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Speaker 1: You know that this is It's Donald Norcross, Donald Norcross.

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Speaker 1: I'm sorry, you guys, I'm very sick, but I'm still

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Speaker 1: I'm rallying. Baby. This is the kind of stuff I'm

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Speaker 1: obsessed with because it's like, I love it when Democrats

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Speaker 1: do shit like this where they get smart and they

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Speaker 1: get around Republicans to day, and I just love this

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Speaker 1: so much. The measure would amend the National Labor Rate

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Speaker 1: Relations Act and require employers to begin contract negotiations with

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Speaker 1: newly certified unions within ten days of receiving a written request.

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Speaker 1: You know who doesn't like this, my man, Donald L. Trump,

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Speaker 1: Donald Trump, but also like Russ Bott, like this is

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Speaker 1: the kind of thing Russ Bot stays up at night

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Speaker 1: worrying about. Steph Rule is the host of MS Has

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Speaker 1: Money Power Politics with Stephanie Rule, airing weekdays from nine

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Speaker 1: am to eleven am Eastern. Welcome, Steph Arrel. You are

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Speaker 1: on the precipice of having two hours of live television

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Speaker 1: every single day.

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Speaker 4: I want you to picture a twenty twenty six version

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Speaker 4: of both Solid Gold and dance Fever.

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Speaker 2: That's what we're going to be doing starting Monday.

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Speaker 4: If you can remember that, it's like a little bit

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Speaker 4: of Dan Ontario, a little bit of Dionne Warwick, and

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Speaker 4: a whole lot of me.

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Speaker 2: Like that's what you're in for.

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Speaker 4: Money Power of Politics on MS now starting June fifteenth

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Speaker 4: at nine am, So get ready.

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Speaker 1: Nine to eleven and it will also you'll also be

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Speaker 1: able to listen to it in your car one hundred percent.

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Speaker 4: And to be honest, people keep saying like, oh my gosh,

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Speaker 4: how do you feel about a different time slot?

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Speaker 2: And I said, first of all, I couldn't be more excited.

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Speaker 2: But I think that the way you know this better

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Speaker 2: than me. We consume media.

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Speaker 4: Now, it's not about really, well, what's so different. It's

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Speaker 4: about making a great, smart show. It's going to help

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Speaker 4: people be informed and engage. It's about having a relationship,

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Speaker 4: a trust in relationship.

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Speaker 2: With an audience.

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Speaker 4: And it's all of our collective goals to increase our audiences,

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Speaker 4: not just because yay, we want our shows to be

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Speaker 4: a success, because this is the most important time I

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Speaker 4: think in our lives in terms of news, whether you're

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Speaker 4: talking current events, political news, economic news, to I'm not

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Speaker 4: even saying I care who you vote for, but we

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Speaker 4: must be informed and engaged because the moment of disinformation

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Speaker 4: and misinformation, the goal for many is to create so

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Speaker 4: much chaos that.

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Speaker 2: People want to tune out.

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Speaker 4: Think about after the election, the dip of people saying

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Speaker 4: I just don't want to pay.

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Speaker 2: Attention, baby boy, that's what people want.

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Speaker 4: They want you to say, I'm not paying and to

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Speaker 4: say I feel like tuning out of the news is

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Speaker 4: like a really, I don't know.

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Speaker 2: Privileged is the world I'm looking for.

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Speaker 4: I just don't want to feel like this is our country,

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Speaker 4: this is a democracy, this is our future.

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Speaker 2: The reason we choose.

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Speaker 4: To do this reason I I want to live in

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Speaker 4: a world it's better than the world my parents gave me.

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Speaker 2: I'm worried it is not.

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Speaker 4: And so I think I'm super excited we're going to

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Speaker 4: make this show at nine am starting on Mondays. But

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Speaker 4: I also think for all of us, it's we have

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Speaker 4: gone past the universe of Rachel and Ross and must

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Speaker 4: see TV at eight pm on Thursdays, and it's much

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Speaker 4: more about making the best, smartest content that we can

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Speaker 4: make that works for linear TV that works and social

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Speaker 4: that works on digital. So when people sit down and

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Speaker 4: say what do I need to understand today, doesn't even

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Speaker 4: matter what time of day around there saying I need

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Speaker 4: to know this true.

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Speaker 1: And also you are a person who has you know,

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Speaker 1: who does a lot of financial stuff and it was

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Speaker 1: very focused on the markets and has been and has

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Speaker 1: a lot of smart takes and do.

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Speaker 2: But actually the.

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Speaker 4: Reason I want to do this at nine am is

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Speaker 4: because the economy is so important right now, Right in

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Speaker 4: two days, SPACEXIPO is going to happen. Elon Musk will

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Speaker 4: be a trillionaire when it.

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Speaker 2: Happens, I think.

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Speaker 4: I think it's like I'm going to get the number

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Speaker 4: slightly wrong, but I think four thousand employees are.

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Speaker 2: Going to be instant millionaires.

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Speaker 4: And so we already talk about sort of the K

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Speaker 4: shaped economy and the point oh oh one percent.

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Speaker 2: We're sixteen years out from Citizens United.

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Speaker 4: And have an unimaginable, unimaginable amount of money in politics.

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Speaker 2: So people aren't just getting richer and richer, I mean

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Speaker 2: the richest people.

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Speaker 4: The influence they have over policy is greater than I

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Speaker 4: think we've seen in our lifetime. And Donald Trump has

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Speaker 4: absolutely created a permission structure where people can achieve that.

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Speaker 4: So I think it's not just like, Oh, my background

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Speaker 4: is in finance and I want to cover markets. I

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Speaker 4: actually want to cover the economy, not for just a

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Speaker 4: business news audience. And I love a business news audience,

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Speaker 4: but that audience speaks finance. Their focus is on markets,

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Speaker 4: and when CEOs come on television to talk, they're just talking,

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Speaker 4: in my opinion, about their quarterly results and information that's

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Speaker 4: going to move their stock up or down that day. Instead,

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Speaker 4: I would actually like to hear from experts in all

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Speaker 4: sorts of business verticals and education vertical to just explain

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Speaker 4: to us where do they see the world. I would

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Speaker 4: love to hear from I don't know the biggest employer

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Speaker 4: in the country right it could be Walmart, and I'd

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Speaker 4: love to get their perspective on how they know where

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Speaker 4: they think the economy is what they're hearing from their

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Speaker 4: labor force. And I just think that's a component did

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Speaker 4: we hear less and less from. And one of the

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Speaker 4: things that's hard is we were at a place where

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Speaker 4: we were hearing from a lot more people of influence

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Speaker 4: years ago, and now the president has thrown the bautlet

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Speaker 4: down so hard. The influence he wants to have over business,

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Speaker 4: the influence he wants to have over media.

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Speaker 2: It's getting harder and harder for people.

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Speaker 4: Of influence to speak up and I think, and not

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Speaker 4: even speak out against him, but to just speak their mind.

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Speaker 4: And that's why I think it's really important to cover

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Speaker 4: the real economy.

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Speaker 1: Now, we had some inflation numbers today.

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Speaker 2: We did, We did, and I would love.

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Speaker 1: You to first of all explain them to us, because

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Speaker 1: they were a little bit complicated.

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Speaker 4: I don't even think we need to go into deep detail.

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Speaker 4: What we need to understand is inflation is not going away. Okay,

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Speaker 4: just yesterday the President was saying, look, gas prices are

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Speaker 4: lower than they were during Biden. They're a little bit

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Speaker 4: lower than where they were the summer of twenty twenty

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Speaker 4: two when Russia invaded Ukraine and the American people struggled

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Speaker 4: because of it. One of the reasons Joe Biden and

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Speaker 4: subsequently Kamala Harris did not win the last presidential election

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Speaker 4: was persistent inflation. We are not out of that inflationary environment.

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Speaker 4: And it's why it's super tricky because remember Donald Trump,

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Speaker 4: who originally appointed j. Powell, ran a very public right,

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Speaker 4: you know, beauty pageant to choose the next FED chair,

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Speaker 4: and Kevin worsh who is highly qualified for the job,

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Speaker 4: really wanted this. And the President, while he has said

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Speaker 4: it tongue in cheek or maybe serious, has said has

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Speaker 4: made it very clear, I want you, I expect you

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Speaker 4: to lower interest rates. This is not an easy economy.

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Speaker 2: It's not.

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Speaker 4: And the difference between this president and whenever the economy

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Speaker 4: is good or bad, whoever's in the White House, that's

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Speaker 4: what the American people blame.

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Speaker 2: And usually that president.

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Speaker 4: Like will have like a tangential impact or like they're

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Speaker 4: just the person who's been charged during this moment in time.

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Speaker 4: But when you think think about the economic struggles that

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Speaker 4: we're facing right now, many of them they're a direct

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Speaker 4: result of Trump's policies. Mass immigration, the tariffs, the war

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Speaker 4: in Iran. Right, these are three things that are impacting business,

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Speaker 4: impacting our labor force, and they fall at the feet

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Speaker 4: of Donald John Trump.

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Speaker 1: Mm hmm no. And and I think it's a really

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Speaker 1: good point. And I also think that said Donald Trump

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Speaker 1: said that he loves inflation.

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Speaker 4: What I don't even understand what I mean. On one hand,

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Speaker 4: I guess he makes the argument. Listen, people are out

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Speaker 4: there spending that's a good thing.

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Speaker 5: You know.

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Speaker 4: We heard from Kevin Hasset weeks ago and he said,

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Speaker 4: look at credit cards spending.

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Speaker 5: It's up.

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Speaker 2: People are out there spending that's not that's not good.

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Speaker 1: Right.

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Speaker 4: That means people have to spend more money to buy

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Speaker 4: their basic goods any other thing that we sometimes forget,

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Speaker 4: and it's something that worsens inflation. This goes back to

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Speaker 4: the K shaped economy. Okay, a lot of people maybe

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Speaker 4: ten of four to spend more money. So they are spending,

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Speaker 4: you know, when things are more expensive, so they are

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Speaker 4: spending more money. They're forced into it. Right, that's the

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Speaker 4: top universe of people who don't like spending more, but

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Speaker 4: they can so they are. Then you have an entire

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Speaker 4: bottom core tile that cannot afford to and we hear a.

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Speaker 2: Story after story.

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Speaker 4: We don't even talk anymore about increased health care costs.

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Speaker 4: Remember the big debate at the end of last year

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Speaker 4: where they going to extend the Obamacare subsidies. The government

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Speaker 4: said no, people still had their healthcare spiking. And so

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Speaker 4: I've said this to you before. I really think that,

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Speaker 4: especially before the warn around, the president was in somewhat

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Speaker 4: of a gilded cage. He spends his time at the

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Speaker 4: White House, he spends his time at Mar A Lago.

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Speaker 4: He has made an extraordinary amount of money. He is

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Speaker 4: surrounded by people who.

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Speaker 2: Are in that point one percent, and they are making

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Speaker 2: an extraordinary amount of money, and he's sort of shruggling

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Speaker 2: off those who are struggling, and a lot of people are.

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Speaker 4: And so I think while the economy isn't that tricky

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Speaker 4: for Trump, it's tricky for every single person running in

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Speaker 4: the midterms, by the way, not just Republicans, but Democrats.

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Speaker 4: Do For Democrats, they've got two things here, accountability and affordability.

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Speaker 4: How if inflation, if the economy is going to be

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Speaker 4: the number one issue, which I think it will be

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Speaker 4: in the midterms, then what are democrats How are Democrats explaining?

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Speaker 4: What are they saying the solution is going to be

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Speaker 4: in terms of how they're going to address affordability? And

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Speaker 4: B They can't just say Donald Trump and the griff,

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Speaker 4: Donald Trump and the corruption he's doing X, Y and this.

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Speaker 4: They have to say, here's our plan on how we're.

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Speaker 2: Addressing the corruption.

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Speaker 4: Because what surprises me is business leaders, at least that

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Speaker 4: I cover, seem so drunk and happy on the access

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Speaker 4: that they can get, or the fact that Trump is

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Speaker 4: so transactional they're just in it to win it.

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Speaker 2: The only business leader I could even point.

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Speaker 4: To was Jamie Diamond who said during the ballroom fundraising,

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Speaker 4: we're not going to to write a big check because

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Speaker 4: when he's not in office anymore, and we look back

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Speaker 4: on this moment, another administration is going to call this corruption.

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Speaker 2: Yeah.

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Speaker 5: Now.

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Speaker 4: And the thing is, though, Molly, we always say, like

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Speaker 4: this is such a great time for businesses.

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Speaker 2: They can just pay and get what they want.

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Speaker 4: Lots of businesses cannot afford to pay and get what

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Speaker 4: they want. Just because you run a big business does

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Speaker 4: not mean your flush with cash or hotels to donate

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Speaker 4: to Trump or airplanes degenerate to Just because you can

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Speaker 4: call Trump or Howard Lutnik doesn't necessarily mean you have

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Speaker 4: the currency.

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Speaker 2: That they want in order to get the deal that

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Speaker 2: you got you want.

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Speaker 1: I think there's uncertainty in the market, so people don't

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Speaker 1: know whether they can build a factory. I mean, like

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Speaker 1: with the.

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Speaker 4: Tariffs, Okay, there's so much you know, people think there's

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Speaker 4: not uncertainty because the markets you know, have been soaring, soaring, soaring,

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Speaker 4: which they have. But the markets are soaring, largely on

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Speaker 4: AI right. It's it's the magnificent seven tech companies that

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Speaker 4: are doing extraordinary, really well but also are not impacted

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Speaker 4: by mass layoffs or tariffs, and they're such huge and

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Speaker 4: pending investment in the AI space because it's going to

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Speaker 4: change everything. But then you have a giant universe, a

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Speaker 4: fortune five hundred of public companies under that that are

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Speaker 4: struggling or these are uncertain times and it's very difficult

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Speaker 4: for them to make financial decisions. But I think lots

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Speaker 4: of businesses are kind of just keeping their head down.

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Speaker 4: It's very hard to plan a business when you have

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Speaker 4: a president who's so erratic. But there's others who have

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Speaker 4: figured out. I mean, look at look at Michael Dell.

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Speaker 4: You I don't even know, but I don't even know, Molly,

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Speaker 4: if you look at it and you admire it and

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Speaker 4: say that player's play in the game, or you are

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Speaker 4: repulsed by it, and see this is correct, right, So

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Speaker 4: Michael Dell, Michael and Decision Dell are right in the center.

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Speaker 4: When we create the Trump accounts, which many people can say,

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Speaker 4: this is a great idea, creating an investment account for children,

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Speaker 4: you know, babies in America.

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Speaker 1: Just a little bit of money that goes in. How

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Speaker 1: much money is it? And it goes in for everyone.

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Speaker 2: Yes, and it invests over time.

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Speaker 4: And the idea is everyone, whether you're wealthy or not,

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Speaker 4: should have this idea to invest a little money and

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Speaker 4: over time, your children are really going to have, you know,

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Speaker 4: money in the long term. And Michael and student Dell

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Speaker 4: donated six billion dollars of their own personal money, and

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Speaker 4: so you could say, wow, this is great, I'm so

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Speaker 4: glad they did that. Donald Trump then publicly says you

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Speaker 4: should go out there and buy a Dell. Donald Trump

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Speaker 4: himself bought thousands and thousands of shares of the company.

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Speaker 4: And then in the last few weeks and again I

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Speaker 4: am not drawing a red or a black line. I'm

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Speaker 4: saying this happens.

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Speaker 2: So this happened. I'm saying these things happens. Happened.

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Speaker 4: The next thing that happened is that Dell won a

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Speaker 4: nine and a half billion dollar government contract. Right, and

439
00:22:52,040 --> 00:22:55,879
Speaker 4: let me be clear, a government contract in general, but

440
00:22:55,960 --> 00:22:59,760
Speaker 4: a government contract of that magnitude is sort of like

441
00:23:00,600 --> 00:23:02,640
Speaker 4: is it a tick? What kind of thing bites you

442
00:23:02,840 --> 00:23:04,800
Speaker 4: that when it digs in you can't pull it?

443
00:23:04,880 --> 00:23:06,080
Speaker 2: Back out. Is that a tick?

444
00:23:06,640 --> 00:23:09,320
Speaker 4: It's like, okay, yeah, so once you get in there,

445
00:23:09,680 --> 00:23:14,040
Speaker 4: even if Trump's not the president anymore, it's very.

446
00:23:13,640 --> 00:23:16,919
Speaker 2: Government contracts aren't like a government contract isn't like you're.

447
00:23:16,800 --> 00:23:19,880
Speaker 4: The buyer at Sacks and this season you know you're

448
00:23:20,240 --> 00:23:23,000
Speaker 4: you're loving Valentino and next Sea and you say you're

449
00:23:23,040 --> 00:23:25,840
Speaker 4: out once you've got a government contract, and it doesn't

450
00:23:25,880 --> 00:23:26,959
Speaker 4: pull out that quickly.

451
00:23:27,560 --> 00:23:30,640
Speaker 2: So I'm just saying somebody like Michael Dell. You could

452
00:23:30,680 --> 00:23:34,120
Speaker 2: say this guy, I don't even know whether he loves her.

453
00:23:34,280 --> 00:23:37,480
Speaker 2: He figured out how to play that game. Now that's so.

454
00:23:38,119 --> 00:23:39,440
Speaker 2: And you could say this is great for.

455
00:23:39,440 --> 00:23:41,680
Speaker 4: Dell, This is great for Dell computers, This is great

456
00:23:41,680 --> 00:23:45,000
Speaker 4: for Dell shareholders. Maybe they even deserve the government contract.

457
00:23:45,200 --> 00:23:49,879
Speaker 4: But where this is so dangerous for our country, for

458
00:23:49,960 --> 00:23:54,360
Speaker 4: our democracy, for national security. You are now co mingling

459
00:23:54,480 --> 00:23:58,000
Speaker 4: a president who himself is making a huge amount of

460
00:23:58,040 --> 00:24:02,720
Speaker 4: money a business person who's making a huge donation.

461
00:24:03,200 --> 00:24:04,480
Speaker 2: This isn't how it supposed to work.

462
00:24:04,480 --> 00:24:08,320
Speaker 4: We've had previous administrations who do make direct investments in companies.

463
00:24:08,400 --> 00:24:11,760
Speaker 4: We obviously saw it in times of crisis, right autobailout,

464
00:24:12,480 --> 00:24:15,640
Speaker 4: financial industry, bailout and then the government, and I would

465
00:24:15,680 --> 00:24:18,959
Speaker 4: just say this, like we've seen this, Bernie Sanders has

466
00:24:19,080 --> 00:24:22,920
Speaker 4: argued before, why is it that, you know, the government

467
00:24:23,080 --> 00:24:27,479
Speaker 4: funds a huge amount of medical research, right, and then

468
00:24:27,560 --> 00:24:30,359
Speaker 4: somebody comes up with a drug or a formula, and

469
00:24:30,400 --> 00:24:32,200
Speaker 4: then they go out and send.

470
00:24:32,080 --> 00:24:34,640
Speaker 1: And make the money billions and billions of dollars.

471
00:24:34,720 --> 00:24:37,919
Speaker 4: One could arguey, wait a minute, this started with government

472
00:24:37,960 --> 00:24:38,680
Speaker 4: funded research.

473
00:24:38,720 --> 00:24:40,320
Speaker 2: So why I'm not even saying.

474
00:24:40,359 --> 00:24:44,560
Speaker 4: Well, you government do that with their totally and so

475
00:24:44,720 --> 00:24:46,560
Speaker 4: I'm not even saying we shouldn't.

476
00:24:46,720 --> 00:24:48,320
Speaker 2: You know, the government shouldn't be a one.

477
00:24:48,200 --> 00:24:50,399
Speaker 4: Way street of just saying we're going to give you

478
00:24:50,440 --> 00:24:53,320
Speaker 4: a grant and all you need to do is invite

479
00:24:53,400 --> 00:24:56,600
Speaker 4: us to your dinner when you sell your company for

480
00:24:56,600 --> 00:24:59,520
Speaker 4: a zillion dollars. But what I am saying is the

481
00:24:59,560 --> 00:25:03,960
Speaker 4: way these businesses are getting selected, the way they're tied

482
00:25:04,000 --> 00:25:06,639
Speaker 4: to the president, the way they're tied to the president's family.

483
00:25:07,040 --> 00:25:10,800
Speaker 4: This is so concerning, and what voters I think would

484
00:25:10,840 --> 00:25:14,679
Speaker 4: like to hear from or should hear from Democrats to say,

485
00:25:15,040 --> 00:25:17,560
Speaker 4: this isn't normal, this isn't okay, and this is what

486
00:25:17,600 --> 00:25:20,879
Speaker 4: we're going to do. Because Trump just was it yesterday

487
00:25:21,600 --> 00:25:24,000
Speaker 4: said when he was sort of asked about kind of

488
00:25:24,040 --> 00:25:25,840
Speaker 4: like the you know, how much money he's making, he

489
00:25:25,960 --> 00:25:28,399
Speaker 4: was basically like, the people don't care. As long as

490
00:25:28,440 --> 00:25:30,959
Speaker 4: you're going on, the people don't care. And then they

491
00:25:31,000 --> 00:25:32,720
Speaker 4: do people and some people out.

492
00:25:32,640 --> 00:25:34,639
Speaker 2: There be like, oh, I don't know, you know, don't

493
00:25:34,640 --> 00:25:37,920
Speaker 2: all lawmakers do it? Come on, now, they don't they?

494
00:25:38,040 --> 00:25:40,680
Speaker 4: And if we if we normalize Trump saying that people

495
00:25:40,680 --> 00:25:42,400
Speaker 4: don't care, we shouldn't people do care.

496
00:25:42,800 --> 00:25:45,560
Speaker 1: And you know, stock trading, not wanting members of Congress

497
00:25:45,600 --> 00:25:49,120
Speaker 1: to trade sex is like an eighty twenty issue. So clearly,

498
00:25:49,880 --> 00:25:50,560
Speaker 1: and you know.

499
00:25:50,480 --> 00:25:53,400
Speaker 2: Trump, you know what do Molly bullshit, it's an eighty

500
00:25:53,440 --> 00:25:54,119
Speaker 2: twenty issue.

501
00:25:54,160 --> 00:25:58,040
Speaker 4: But it's not because I've been talking about banning or

502
00:25:58,119 --> 00:26:02,000
Speaker 4: limiting or regulating stop trading in Congress for ten years.

503
00:26:02,320 --> 00:26:05,040
Speaker 4: And even though the average lots of people in eighty twenty,

504
00:26:05,080 --> 00:26:07,240
Speaker 4: as you people are like, hell, yeah, we need to

505
00:26:07,280 --> 00:26:11,720
Speaker 4: stop that, it never changes because it's not a high

506
00:26:11,840 --> 00:26:13,080
Speaker 4: enough issue for voters.

507
00:26:13,080 --> 00:26:14,719
Speaker 2: And I'm not saying it should be. Voters. So like

508
00:26:15,040 --> 00:26:16,359
Speaker 2: I need to put food on the table, I need

509
00:26:16,400 --> 00:26:17,440
Speaker 2: a good school, I need help here.

510
00:26:17,640 --> 00:26:20,280
Speaker 4: It's not a high enough issue the voters say, I'm

511
00:26:20,280 --> 00:26:23,359
Speaker 4: going to vote you out of office, right unless you

512
00:26:23,440 --> 00:26:26,080
Speaker 4: do this. So the Abigail Spamberger of the world keep

513
00:26:26,119 --> 00:26:29,760
Speaker 4: putting four bills to regulate or limit congressional stock trading,

514
00:26:29,840 --> 00:26:31,200
Speaker 4: and then it just never happens.

515
00:26:31,440 --> 00:26:33,359
Speaker 1: But one of the things with Trump, and if you

516
00:26:33,480 --> 00:26:36,960
Speaker 1: look at his like there was this amazing graph about

517
00:26:37,040 --> 00:26:39,640
Speaker 1: Trump's stock trading, and it showed that he basically did

518
00:26:39,640 --> 00:26:44,480
Speaker 1: not stockgraft well exactly, that he really just didn't trade

519
00:26:44,520 --> 00:26:47,800
Speaker 1: stocks until the second term. And I wonder, like, if

520
00:26:47,800 --> 00:26:50,120
Speaker 1: you are on the board of a company, aren't there

521
00:26:50,280 --> 00:26:51,919
Speaker 1: rules against trading stock?

522
00:26:52,080 --> 00:26:54,719
Speaker 2: Oh my god, Mollie, there are. First of all, if

523
00:26:54,760 --> 00:26:56,359
Speaker 2: you're a business reporter.

524
00:26:56,119 --> 00:26:59,080
Speaker 4: And you cover specific industries, you can't trade those individual

525
00:26:59,119 --> 00:27:02,040
Speaker 4: stocks you're on the board. If you're an officer at

526
00:27:02,080 --> 00:27:04,440
Speaker 4: a publicly traded company, there's so many limitations.

527
00:27:04,600 --> 00:27:05,400
Speaker 2: But here's the thing.

528
00:27:05,600 --> 00:27:10,560
Speaker 4: Our founding fathers trusted that the president of the United States,

529
00:27:10,600 --> 00:27:13,800
Speaker 4: a person who would choose to be the president of

530
00:27:13,800 --> 00:27:17,440
Speaker 4: the United States. It's almost like beneath them to have

531
00:27:17,480 --> 00:27:20,680
Speaker 4: this conversation and put rules almost insulting, do you know

532
00:27:20,760 --> 00:27:21,160
Speaker 4: what I mean?

533
00:27:23,000 --> 00:27:25,200
Speaker 2: But alas here we are.

534
00:27:25,280 --> 00:27:28,280
Speaker 4: But I will just say this yeah. Do you remember

535
00:27:28,480 --> 00:27:31,919
Speaker 4: after Trump's first term, when Joe Biden was running, he

536
00:27:32,119 --> 00:27:36,280
Speaker 4: ran and said all of this self dealing and the

537
00:27:36,320 --> 00:27:39,080
Speaker 4: self dealing was child's play compared to what it is now.

538
00:27:39,760 --> 00:27:42,800
Speaker 4: We called self dealing the first term when Kelly and

539
00:27:42,880 --> 00:27:45,879
Speaker 4: Codway did an interview on sixty Minutes and said of

540
00:27:45,960 --> 00:27:49,840
Speaker 4: loves Blue, Saul, don't you like my bracelet from Avanka Desanda?

541
00:27:50,200 --> 00:27:51,240
Speaker 2: That was an incident.

542
00:27:51,560 --> 00:27:54,160
Speaker 4: And we heard from Joe Biden in his campaign saying

543
00:27:54,200 --> 00:27:55,760
Speaker 4: We're not gonna let this happen again.

544
00:27:55,800 --> 00:27:58,000
Speaker 2: We're going to change the rules, We're going to close

545
00:27:58,040 --> 00:28:01,679
Speaker 2: these loopholes, so this will happen again. Well, lo and behold,

546
00:28:01,760 --> 00:28:03,280
Speaker 2: they didn't. They did not.

547
00:28:03,920 --> 00:28:06,720
Speaker 4: And so Trump rolls back in and feels more in

548
00:28:06,800 --> 00:28:10,560
Speaker 4: bolden than ever, and he has an administration that's like, yep,

549
00:28:10,640 --> 00:28:12,800
Speaker 4: do whatever you want. And I don't know this for

550
00:28:12,880 --> 00:28:15,000
Speaker 4: a fact, but it sure seems like many many of

551
00:28:15,040 --> 00:28:18,359
Speaker 4: those people are also making an enormous amount of money.

552
00:28:18,520 --> 00:28:20,840
Speaker 4: And so I think Democrats have to show the American

553
00:28:20,880 --> 00:28:24,679
Speaker 4: people this is what's happening, this is the self dealing,

554
00:28:24,720 --> 00:28:27,000
Speaker 4: this is how it's putting us at risk, and here's

555
00:28:27,040 --> 00:28:28,040
Speaker 4: what we're going to do.

556
00:28:28,119 --> 00:28:28,480
Speaker 2: About it.

557
00:28:28,560 --> 00:28:30,439
Speaker 1: I think that's a great point. And there's so many

558
00:28:30,600 --> 00:28:33,600
Speaker 1: other things like this that are tangentially like this. I

559
00:28:33,680 --> 00:28:38,440
Speaker 1: think about the Ukrainian drones. So Ukraine has figured out

560
00:28:38,440 --> 00:28:41,600
Speaker 1: a way to do drones very cheaply, and they have

561
00:28:41,800 --> 00:28:45,440
Speaker 1: held off Russia and it's sort of a miracle. And

562
00:28:45,560 --> 00:28:48,480
Speaker 1: what are we doing in America where you're spending billions

563
00:28:48,480 --> 00:28:52,040
Speaker 1: of dollars using technology that we don't need to be using.

564
00:28:53,240 --> 00:28:55,120
Speaker 1: So I think that's a really good example of it.

565
00:28:55,320 --> 00:28:57,920
Speaker 4: Okay, that's a great example, but the fact that we're

566
00:28:57,960 --> 00:29:02,000
Speaker 4: doing that is a bigger example of the whole world

567
00:29:02,560 --> 00:29:06,000
Speaker 4: now has to change what they're doing because everything has

568
00:29:06,000 --> 00:29:11,160
Speaker 4: shifted from globalization and actual partnerships to we got to

569
00:29:11,240 --> 00:29:13,640
Speaker 4: take care of ourselves, right, And there is an argument

570
00:29:13,720 --> 00:29:17,000
Speaker 4: to be made, like I believe we should invest we

571
00:29:17,040 --> 00:29:20,400
Speaker 4: should invest in rare earth minerals in the United States.

572
00:29:20,440 --> 00:29:24,480
Speaker 4: We should not be solely dependent on China.

573
00:29:24,680 --> 00:29:26,320
Speaker 2: But we're going so far.

574
00:29:26,240 --> 00:29:30,240
Speaker 4: Beyond that then now not just US, but basically every

575
00:29:30,320 --> 00:29:32,640
Speaker 4: region in the world has to figure out how do

576
00:29:32,720 --> 00:29:35,880
Speaker 4: I how do I operate as an island?

577
00:29:36,400 --> 00:29:39,640
Speaker 1: Yeah? No, I mean it's that's the sort of net

578
00:29:39,720 --> 00:29:43,440
Speaker 1: net of Trumpism is that there's no longer this global cooperation.

579
00:29:43,560 --> 00:29:46,680
Speaker 1: It's everyone out for themselves, and we have a superpower

580
00:29:46,720 --> 00:29:49,600
Speaker 1: that no longer cares about any of the norms.

581
00:29:49,920 --> 00:29:52,560
Speaker 2: But perhaps the Stephen Miller's of the world or the

582
00:29:52,600 --> 00:29:55,280
Speaker 2: president now says, well, fine, we're all just going to

583
00:29:55,320 --> 00:29:57,360
Speaker 2: take care of our own. It's going to be hard

584
00:29:57,400 --> 00:29:59,760
Speaker 2: and more expensive, but we're going to do it better

585
00:29:59,800 --> 00:30:02,680
Speaker 2: than the rest of the world. Where he's mistaken is

586
00:30:02,920 --> 00:30:05,360
Speaker 2: the rest of the world isn't just going out of alone.

587
00:30:05,480 --> 00:30:08,000
Speaker 2: They're forming partnerships without us.

588
00:30:08,240 --> 00:30:09,800
Speaker 1: Yeah. I think that's totally right.

589
00:30:09,880 --> 00:30:11,760
Speaker 2: It's strengthened. It has strengthened China.

590
00:30:12,080 --> 00:30:14,440
Speaker 1: When you look at these inflation numbers, what will be

591
00:30:14,520 --> 00:30:17,280
Speaker 1: the second order effects of these numbers?

592
00:30:17,600 --> 00:30:21,280
Speaker 4: Listen, It remains to be seen. It's like listen, it's

593
00:30:21,440 --> 00:30:25,400
Speaker 4: very difficult to tell. But the President wants to act

594
00:30:25,520 --> 00:30:28,160
Speaker 4: as though we're on the precipice of signing a peace

595
00:30:28,200 --> 00:30:30,480
Speaker 4: deal in Iran and everything is going to be hunky dory.

596
00:30:30,880 --> 00:30:33,800
Speaker 4: It's just not okay. The impact of this war, the

597
00:30:33,800 --> 00:30:35,920
Speaker 4: impact of closing the Strait of Foremost, even if we

598
00:30:36,000 --> 00:30:37,960
Speaker 4: reopen to tomorrow, which we are not going to do.

599
00:30:38,160 --> 00:30:40,320
Speaker 4: You don't just trying to spick it back on. And

600
00:30:40,520 --> 00:30:42,680
Speaker 4: people here could say, well, gas prisons are high, but

601
00:30:42,680 --> 00:30:44,520
Speaker 4: they're not as high as they've been. This is the

602
00:30:44,560 --> 00:30:47,800
Speaker 4: global economy and this jet fuel shortage that we are

603
00:30:47,840 --> 00:30:50,800
Speaker 4: seeing around the world is having an impact, right go

604
00:30:51,040 --> 00:30:53,360
Speaker 4: you know, talk to a farmer who is spending over

605
00:30:53,400 --> 00:30:56,080
Speaker 4: five dollars a gallon for diesel. Talk to them about

606
00:30:56,120 --> 00:30:58,600
Speaker 4: how this is impact and the cost of fertilizer. I mean,

607
00:30:58,680 --> 00:31:01,640
Speaker 4: I think you are real issue. And do I think

608
00:31:01,680 --> 00:31:04,760
Speaker 4: that we're seeing this crazy giant spike in inflation.

609
00:31:04,920 --> 00:31:05,960
Speaker 2: No, not necessarily.

610
00:31:06,520 --> 00:31:08,880
Speaker 4: But when you couple that with what Trump wants to

611
00:31:08,920 --> 00:31:11,040
Speaker 4: do with cut, cut, cut, I just don't see how

612
00:31:11,080 --> 00:31:12,160
Speaker 4: Kevin Worrish is going to do it.

613
00:31:12,760 --> 00:31:15,920
Speaker 1: Yeah, step Rule, thank you, thank you, thank you.

614
00:31:16,360 --> 00:31:16,680
Speaker 5: Thank you.

615
00:31:16,760 --> 00:31:22,600
Speaker 1: Sister Robins admire as a founding executive of PEEDBAP and

616
00:31:22,640 --> 00:31:27,240
Speaker 1: the host of shift Key. Welcome, Welcome, Robin Than, thank

617
00:31:27,280 --> 00:31:29,400
Speaker 1: you for having me. We're talking about like the one

618
00:31:29,600 --> 00:31:33,200
Speaker 1: thing I want to talk about all the time, which

619
00:31:33,240 --> 00:31:36,160
Speaker 1: is hating data centers. I hate them. So one of

620
00:31:36,200 --> 00:31:38,440
Speaker 1: my kids went to college in New Mexico. I'm like,

621
00:31:38,840 --> 00:31:41,080
Speaker 1: spend a lot of time in New Mexico, not anymore.

622
00:31:41,160 --> 00:31:43,120
Speaker 1: But one of my best friends lives in New Mexico.

623
00:31:43,480 --> 00:31:46,400
Speaker 1: We hate data centers. There's a humongous data center in

624
00:31:46,440 --> 00:31:49,880
Speaker 1: New Mexico that is humming and doing all sorts of

625
00:31:49,960 --> 00:31:52,800
Speaker 1: terrible shit, you hear it. I haven't been there, but

626
00:31:53,040 --> 00:31:56,800
Speaker 1: I've heard that it's loud and it's bullshit, and it's

627
00:31:57,080 --> 00:32:00,960
Speaker 1: making noise and using electricity and do you know whatever

628
00:32:01,280 --> 00:32:04,240
Speaker 1: killing wildlife. I mean, it's like, we're just so fucked.

629
00:32:04,280 --> 00:32:07,760
Speaker 1: So talk to us about the data center backlash and

630
00:32:07,800 --> 00:32:09,040
Speaker 1: where we are at this moment.

631
00:32:09,400 --> 00:32:10,040
Speaker 5: Yeah, totally.

632
00:32:10,080 --> 00:32:10,160
Speaker 1: So.

633
00:32:10,240 --> 00:32:15,040
Speaker 5: I'm a executive editor at heatmat News. We cover climate, energy, decarbonization,

634
00:32:15,280 --> 00:32:18,600
Speaker 5: and increasingly we cover data centers, not because not even

635
00:32:18,600 --> 00:32:21,840
Speaker 5: because data centers have environmental harms or don't, but because

636
00:32:22,040 --> 00:32:26,200
Speaker 5: they are transforming the US electricity system. And you know,

637
00:32:26,240 --> 00:32:29,480
Speaker 5: in climate what we care about is electricity. So much

638
00:32:29,640 --> 00:32:32,400
Speaker 5: of fighting climate change is going to require electrifying big

639
00:32:32,440 --> 00:32:34,760
Speaker 5: parts of the economy, and so we kind of have

640
00:32:34,800 --> 00:32:36,840
Speaker 5: to care about how the power grid works. And the

641
00:32:36,960 --> 00:32:39,520
Speaker 5: biggest change to how the power grid works in twenty

642
00:32:39,600 --> 00:32:41,920
Speaker 5: years is data centers right now. And so we started

643
00:32:41,960 --> 00:32:44,280
Speaker 5: tracking them at heatmap and kind of tracking public sentiment

644
00:32:44,360 --> 00:32:48,040
Speaker 5: around them, and what we found is really a shockingly

645
00:32:48,240 --> 00:32:53,280
Speaker 5: fast turnaround in public opinion. And so we periodically at

646
00:32:53,280 --> 00:32:56,360
Speaker 5: heat Map poll thousands of Americans about how they feel

647
00:32:56,400 --> 00:33:01,560
Speaker 5: about energy issues, about forms of industrial development, electricity, and

648
00:33:02,200 --> 00:33:05,160
Speaker 5: last year in August, we started polling people about whether

649
00:33:05,200 --> 00:33:07,160
Speaker 5: they would want a data center in their area. At

650
00:33:07,200 --> 00:33:08,840
Speaker 5: the time, maybe it was something that not many people

651
00:33:08,920 --> 00:33:11,200
Speaker 5: had thought about, not something that people had heard of.

652
00:33:11,280 --> 00:33:14,640
Speaker 5: But we found last August that Americans were pretty split.

653
00:33:14,840 --> 00:33:18,200
Speaker 5: So about half of Americans but forty three percent they

654
00:33:18,200 --> 00:33:20,920
Speaker 5: would support a data center in their area and a

655
00:33:20,920 --> 00:33:23,200
Speaker 5: little more than that, maybe forty five percent, forty eight

656
00:33:23,240 --> 00:33:26,520
Speaker 5: percent said that they would not support it, and fifteen

657
00:33:26,520 --> 00:33:27,960
Speaker 5: percent of Americans didn't really know.

658
00:33:28,320 --> 00:33:31,040
Speaker 1: Was it split on bartisan lines or now you know.

659
00:33:31,120 --> 00:33:33,120
Speaker 5: This is not an issue that we've found to be

660
00:33:33,560 --> 00:33:37,720
Speaker 5: terribly partisan. Okay, Democrats were slightly more against data centers

661
00:33:37,920 --> 00:33:41,280
Speaker 5: back in August than Republicans. I think Republicans were the

662
00:33:41,280 --> 00:33:43,880
Speaker 5: most pro data center group, but it was not like

663
00:33:43,960 --> 00:33:48,120
Speaker 5: a resounding split one way or another. We pulled it

664
00:33:48,200 --> 00:33:51,719
Speaker 5: again in February and this time same question. You know,

665
00:33:52,040 --> 00:33:54,560
Speaker 5: data centers are facilities that house the servers, the power,

666
00:33:54,600 --> 00:33:57,200
Speaker 5: the Internet apps and AI. Would you support or oppose

667
00:33:57,240 --> 00:33:59,560
Speaker 5: the data center being built near where you live? In February,

668
00:33:59,840 --> 00:34:04,040
Speaker 5: just slightly over half Americans were against building a data center,

669
00:34:04,080 --> 00:34:06,320
Speaker 5: but I would say it was still most, you know.

670
00:34:06,640 --> 00:34:09,160
Speaker 5: The other half didn't know or were in support. Twenty

671
00:34:09,200 --> 00:34:11,440
Speaker 5: one percent said they weren't sure. We pulled that question

672
00:34:11,600 --> 00:34:14,880
Speaker 5: in late May, and the turnaround is really shocking. So

673
00:34:15,320 --> 00:34:18,160
Speaker 5: fifty five percent of Americans, an outright majority, said they

674
00:34:18,160 --> 00:34:21,560
Speaker 5: would strongly oppose a data center being built in the region.

675
00:34:21,840 --> 00:34:24,880
Speaker 5: Another sixteen percent said they would somewhat oppose it, and

676
00:34:25,000 --> 00:34:27,920
Speaker 5: only twenty one percent said that they would support the

677
00:34:27,960 --> 00:34:31,320
Speaker 5: project form. And so in just nine months, the public

678
00:34:31,360 --> 00:34:35,200
Speaker 5: has swung forty nine points against data centers. And what

679
00:34:35,560 --> 00:34:38,400
Speaker 5: is shocking, I think is that to your question, this

680
00:34:38,560 --> 00:34:41,919
Speaker 5: swing is like across parties, and so we would say

681
00:34:42,040 --> 00:34:45,680
Speaker 5: data centers were fourteen points above water with Republicans in August.

682
00:34:45,760 --> 00:34:48,480
Speaker 5: They are now thirty three points above water with Democrats.

683
00:34:48,520 --> 00:34:50,879
Speaker 5: In Independence, they were a little unpopular. They were five

684
00:34:50,920 --> 00:34:54,960
Speaker 5: points underwater for Independents and eight points underwater for Democrats

685
00:34:55,000 --> 00:34:58,319
Speaker 5: back in August. Now they're sixty four points underwater with

686
00:34:58,480 --> 00:35:02,320
Speaker 5: Democrats and fifty six points underwater with independents. I think

687
00:35:02,440 --> 00:35:04,719
Speaker 5: the other thing that I would note we're so used to,

688
00:35:05,000 --> 00:35:08,279
Speaker 5: I think the public opinion breaking a lot, as you said,

689
00:35:08,280 --> 00:35:12,120
Speaker 5: partisan lines, but not only do we see this swing

690
00:35:12,719 --> 00:35:16,719
Speaker 5: kind of reflected across parties, but actually we see it

691
00:35:16,760 --> 00:35:21,680
Speaker 5: reflected across geographies, and so rural voters were actually who

692
00:35:21,719 --> 00:35:24,480
Speaker 5: have been, you know, where the locusts of the big

693
00:35:24,520 --> 00:35:27,040
Speaker 5: Republican shift of the past few decades or past decade

694
00:35:27,120 --> 00:35:29,359
Speaker 5: or so are actually more opposed to data centers than

695
00:35:29,480 --> 00:35:31,319
Speaker 5: urban or suburban Americans are.

696
00:35:31,360 --> 00:35:34,120
Speaker 1: And there's some reason for that, because they're being put

697
00:35:34,200 --> 00:35:35,000
Speaker 1: in rural.

698
00:35:34,760 --> 00:35:40,000
Speaker 5: Areas exactly Exactly. Young people are also particularly opposed to

699
00:35:40,160 --> 00:35:43,920
Speaker 5: data centers, So we see eighty percent of Americans ages

700
00:35:43,960 --> 00:35:46,000
Speaker 5: eighteen to thirty four would oppose a new data center

701
00:35:46,080 --> 00:35:48,279
Speaker 5: knew where they live. I think what's interesting is that

702
00:35:48,360 --> 00:35:51,160
Speaker 5: there are ways that this big shift kind of reflects

703
00:35:51,160 --> 00:35:54,479
Speaker 5: classic partisan trends. And of course, like the President has

704
00:35:54,600 --> 00:35:57,600
Speaker 5: completely embraced AI industry, and I think the data center

705
00:35:57,640 --> 00:36:00,000
Speaker 5: build out in ways that is driving part of their partisanship.

706
00:36:00,400 --> 00:36:02,839
Speaker 5: But it's not like doesn't match the patterns that we've

707
00:36:02,880 --> 00:36:05,640
Speaker 5: seen for other big shifts in public opinion, and it's

708
00:36:05,800 --> 00:36:09,040
Speaker 5: more geographically dispersed, a little more spread out that way.

709
00:36:09,200 --> 00:36:14,560
Speaker 1: Do you think, though, that when the right starts messaging

710
00:36:14,640 --> 00:36:18,080
Speaker 1: on data centers that they'll be able to convince their

711
00:36:18,120 --> 00:36:19,160
Speaker 1: people to like them.

712
00:36:19,480 --> 00:36:21,759
Speaker 5: I think it's a really interesting question. I don't think

713
00:36:21,760 --> 00:36:23,759
Speaker 5: we fully know yet. I would say, right now, what

714
00:36:23,880 --> 00:36:28,040
Speaker 5: we're tracking is like Republican candidates are opposed to data

715
00:36:28,040 --> 00:36:32,440
Speaker 5: centers wow, or are interested in pulling out tax and

716
00:36:32,480 --> 00:36:34,279
Speaker 5: centives for data centers, which is a little bit of

717
00:36:34,280 --> 00:36:38,160
Speaker 5: a medium point of view, right, and some Republican jurisdictions

718
00:36:38,200 --> 00:36:40,400
Speaker 5: are going to say, sure, come in, You're just going

719
00:36:40,440 --> 00:36:41,920
Speaker 5: to have to pay a lot of taxes. I mean,

720
00:36:41,960 --> 00:36:44,759
Speaker 5: the question for me is does this follow the contours

721
00:36:44,800 --> 00:36:48,240
Speaker 5: of like the fracking story. Right, fracking with a huge

722
00:36:48,239 --> 00:36:51,240
Speaker 5: form of economic development, exploded in the early twenty teens.

723
00:36:51,560 --> 00:36:55,279
Speaker 5: Democratic states banned it pretty quickly. Republican states it was

724
00:36:55,360 --> 00:36:58,359
Speaker 5: unpopular in some rural areas, but mostly did not ban it,

725
00:36:58,719 --> 00:37:01,239
Speaker 5: and over time it created a coalition that supported it.

726
00:37:01,480 --> 00:37:03,480
Speaker 5: And at this point I would say that you know,

727
00:37:03,719 --> 00:37:07,040
Speaker 5: most racking is happening in Pennsylvania and Purple State or

728
00:37:07,320 --> 00:37:10,400
Speaker 5: kind of Republican Midwestern or south Central States.

729
00:37:10,719 --> 00:37:14,000
Speaker 1: Part of what happened with racking, though, is a lot

730
00:37:14,040 --> 00:37:16,560
Speaker 1: of the stuff that it did, they sort of figured

731
00:37:16,560 --> 00:37:21,120
Speaker 1: out how to mitigate it, right, Like, your water isn't catching.

732
00:37:20,760 --> 00:37:23,720
Speaker 5: Fire, that's right, that's right. Well, there was that famous

733
00:37:23,840 --> 00:37:26,400
Speaker 5: video and gas Land of the water catching fire. I

734
00:37:26,440 --> 00:37:29,320
Speaker 5: think though that the question for me is, like data

735
00:37:29,320 --> 00:37:33,120
Speaker 5: center developers think that they're getting better at managing the

736
00:37:33,360 --> 00:37:37,360
Speaker 5: harms of these facilities, right, so they are shifting to

737
00:37:37,440 --> 00:37:39,960
Speaker 5: processing their water on site. We can talk about where

738
00:37:39,960 --> 00:37:41,839
Speaker 5: the electricity is going to come from. I think that's

739
00:37:42,040 --> 00:37:44,840
Speaker 5: a tricky one. What makes AI different?

740
00:37:44,920 --> 00:37:45,000
Speaker 4: Right?

741
00:37:45,160 --> 00:37:45,359
Speaker 1: Yeah?

742
00:37:45,840 --> 00:37:48,239
Speaker 5: I think water has like played an interesting role in

743
00:37:48,239 --> 00:37:52,920
Speaker 5: this debate because just to like anticipating future partisan movement here, right,

744
00:37:53,200 --> 00:37:56,239
Speaker 5: water is like the one resource that Americans across the

745
00:37:56,280 --> 00:37:58,320
Speaker 5: political spectrum think is important.

746
00:37:58,560 --> 00:37:58,680
Speaker 3: Right.

747
00:37:58,760 --> 00:38:01,239
Speaker 5: Republicans don't care about carbon missions, but they do care

748
00:38:01,239 --> 00:38:03,680
Speaker 5: about water. And they care about water because of farms.

749
00:38:03,680 --> 00:38:06,359
Speaker 5: They care because residential development, because people have to drink,

750
00:38:06,400 --> 00:38:09,640
Speaker 5: they've want And so if water is like more neutralized,

751
00:38:09,719 --> 00:38:12,440
Speaker 5: I don't know whether we'll start to see some you know,

752
00:38:12,600 --> 00:38:15,680
Speaker 5: artisan identity shift. So like the other thing is like

753
00:38:16,080 --> 00:38:18,600
Speaker 5: one big question I have and one reason I think

754
00:38:18,600 --> 00:38:21,000
Speaker 5: it may not follow fracking is that, like we at

755
00:38:21,000 --> 00:38:24,520
Speaker 5: heat map cover these things as giant electricity users, and

756
00:38:24,560 --> 00:38:28,399
Speaker 5: they are enormous electricity users. Curious about them and does

757
00:38:28,400 --> 00:38:30,840
Speaker 5: some degree, like when I have no as like an

758
00:38:30,960 --> 00:38:33,280
Speaker 5: energy person, when I think about this, I think about

759
00:38:33,480 --> 00:38:36,960
Speaker 5: a data center primarily as a big electricity user, And

760
00:38:37,040 --> 00:38:39,560
Speaker 5: so I think when people do policy around data centers,

761
00:38:39,560 --> 00:38:41,719
Speaker 5: they're thinking about how can we mitigate the effects on

762
00:38:41,760 --> 00:38:43,640
Speaker 5: the power grid, how can we use these things to

763
00:38:43,719 --> 00:38:46,520
Speaker 5: like build the infrastructure that we want and also make

764
00:38:46,560 --> 00:38:48,840
Speaker 5: it so they're not terrible to live near. But like

765
00:38:49,200 --> 00:38:52,920
Speaker 5: there's this whole other category of policy, right that's about

766
00:38:53,120 --> 00:38:56,080
Speaker 5: round AI and what is effect on society and the economy.

767
00:38:56,080 --> 00:38:58,000
Speaker 5: Who is going to be and is it going to

768
00:38:58,080 --> 00:38:59,640
Speaker 5: get you know, get rid of jobs? Is going to

769
00:38:59,680 --> 00:39:01,080
Speaker 5: be good for people? It is going to make our

770
00:39:01,080 --> 00:39:03,080
Speaker 5: lives hell? Is it going to drive human extinction?

771
00:39:03,440 --> 00:39:03,600
Speaker 1: Right?

772
00:39:03,840 --> 00:39:06,399
Speaker 5: All these questions and like to some degree, the data

773
00:39:06,440 --> 00:39:09,800
Speaker 5: center conversations happening over here, and like the AI policy

774
00:39:09,800 --> 00:39:12,839
Speaker 5: conversation that is happening over here, And like I can

775
00:39:12,840 --> 00:39:15,160
Speaker 5: be a good energy journalist to tell you, like, well,

776
00:39:15,239 --> 00:39:19,000
Speaker 5: here's ways you could mitigate data centers, but like even

777
00:39:19,280 --> 00:39:23,000
Speaker 5: the biggest data center more Toryan proposals like say Bernie's,

778
00:39:23,239 --> 00:39:26,880
Speaker 5: are as much about the effects of AI on society

779
00:39:27,080 --> 00:39:29,680
Speaker 5: as they are about the fact that they use all

780
00:39:29,680 --> 00:39:33,800
Speaker 5: this electricity. And I think when Americans think about data centers,

781
00:39:33,800 --> 00:39:36,680
Speaker 5: they think about them as the physical manifestation of AI.

782
00:39:37,120 --> 00:39:39,839
Speaker 5: Number one and number two is like a particular form

783
00:39:39,840 --> 00:39:42,880
Speaker 5: of economic development that can be you know, positive or

784
00:39:42,880 --> 00:39:45,520
Speaker 5: negative depending on how you manage it. And so I

785
00:39:45,600 --> 00:39:47,279
Speaker 5: just think this is like what maybe is going to

786
00:39:47,280 --> 00:39:50,319
Speaker 5: make it different from fracking is like, and maybe is

787
00:39:50,400 --> 00:39:53,959
Speaker 5: going to make the partisan dynamics still kind of unpredictable.

788
00:39:54,160 --> 00:39:56,120
Speaker 1: I'd love you to explain to us, like, in a

789
00:39:56,200 --> 00:40:00,080
Speaker 1: state like New Mexico, it's a very small state, how

790
00:40:00,160 --> 00:40:03,480
Speaker 1: much of the power give me like data center to

791
00:40:04,239 --> 00:40:06,879
Speaker 1: normal people watching television.

792
00:40:07,480 --> 00:40:10,440
Speaker 5: There's like one distinction I think is worth making at

793
00:40:10,440 --> 00:40:13,200
Speaker 5: the beginning of this conversation, which is we are actually

794
00:40:13,200 --> 00:40:15,400
Speaker 5: talking about two different kinds of things when we talk

795
00:40:15,440 --> 00:40:19,360
Speaker 5: about data centers. There are what are called cloud data centers,

796
00:40:19,760 --> 00:40:23,959
Speaker 5: and those exist primarily to service local internet demand. We're

797
00:40:24,000 --> 00:40:25,839
Speaker 5: like using one right now, you know, as we talk

798
00:40:25,880 --> 00:40:29,399
Speaker 5: on Zoom, and those are kind of actually all over

799
00:40:29,400 --> 00:40:31,880
Speaker 5: the place. They don't use that much electricity. They're pretty efficient.

800
00:40:32,120 --> 00:40:35,479
Speaker 5: For instance, I have heard that Netflix has three data

801
00:40:35,520 --> 00:40:38,840
Speaker 5: centers within New York City, basically like an uptown, downtown,

802
00:40:38,840 --> 00:40:39,960
Speaker 5: and Brooklyn data center.

803
00:40:40,160 --> 00:40:44,280
Speaker 1: And you want those because otherwise you can't watch Netflix exactly.

804
00:40:44,320 --> 00:40:46,640
Speaker 5: And that is how they position literally the bits and

805
00:40:46,719 --> 00:40:49,400
Speaker 5: as post as they can, right. Yeah, and then you

806
00:40:49,480 --> 00:40:52,600
Speaker 5: have AI data centers, which are this new class of

807
00:40:52,719 --> 00:40:56,919
Speaker 5: facility being built, and those use, specifically if they're doing

808
00:40:56,920 --> 00:41:03,120
Speaker 5: the training, staggering amounts of electricity. And so the example

809
00:41:03,239 --> 00:41:07,000
Speaker 5: for me is that we are beginning to see one

810
00:41:07,080 --> 00:41:11,880
Speaker 5: gigawatt or seven hundred megawat sized data centers. You know,

811
00:41:11,960 --> 00:41:15,600
Speaker 5: let's say five hundred megawatts to one gigawat. So let's

812
00:41:15,600 --> 00:41:19,640
Speaker 5: put that in the context of how much power is used.

813
00:41:19,840 --> 00:41:23,799
Speaker 5: New York City draws like six gigawatts of power at

814
00:41:23,800 --> 00:41:25,680
Speaker 5: any one time. Do you think about electricity is a

815
00:41:25,680 --> 00:41:27,799
Speaker 5: little bit of a confusing thing because we really have

816
00:41:27,840 --> 00:41:29,120
Speaker 5: two units to talk about it.

817
00:41:29,280 --> 00:41:33,000
Speaker 1: I need a very stupid answer here. Okay, you're being

818
00:41:33,080 --> 00:41:33,880
Speaker 1: too smart.

819
00:41:34,120 --> 00:41:37,960
Speaker 5: One AI data center is capable of using roughly as

820
00:41:38,040 --> 00:41:41,839
Speaker 5: much electricity as a small city at one perfect. And

821
00:41:41,920 --> 00:41:44,440
Speaker 5: what makes this so crazy.

822
00:41:44,239 --> 00:41:46,800
Speaker 1: Is because they don't turn it off, right, there's no bedtime.

823
00:41:47,160 --> 00:41:50,719
Speaker 5: It's a very compact land footprint, right, It's not much land,

824
00:41:50,760 --> 00:41:53,839
Speaker 5: and it's using all this electricity. And in terms of

825
00:41:53,880 --> 00:41:57,319
Speaker 5: like the history of technology, history of factories, there's not

826
00:41:57,400 --> 00:42:00,080
Speaker 5: that many things that use that much electricity. It's like

827
00:42:00,160 --> 00:42:03,759
Speaker 5: an aluminum smelter and an AI data center. And so

828
00:42:04,400 --> 00:42:07,439
Speaker 5: you actually it's really hard to get that much electricity

829
00:42:07,600 --> 00:42:11,799
Speaker 5: in one place without designing a system around it.

830
00:42:12,360 --> 00:42:12,480
Speaker 1: Uh.

831
00:42:12,719 --> 00:42:14,719
Speaker 5: And so that's why you see a lot of these

832
00:42:14,800 --> 00:42:19,120
Speaker 5: data centers building on site not only the facility to

833
00:42:19,160 --> 00:42:22,520
Speaker 5: actually power the chips, but now a lot of batteries

834
00:42:22,600 --> 00:42:26,240
Speaker 5: and also you know, increasingly some natural gas or diesel

835
00:42:26,400 --> 00:42:30,200
Speaker 5: generation turbines which can back up or augment or just

836
00:42:30,360 --> 00:42:33,960
Speaker 5: outright generate the full amount of electricity that's needed to

837
00:42:34,000 --> 00:42:37,120
Speaker 5: power that data center. And so I think why this

838
00:42:37,200 --> 00:42:38,440
Speaker 5: is such a big deal is that so many of

839
00:42:38,440 --> 00:42:42,520
Speaker 5: these facilities are kind of small power plants as well

840
00:42:42,560 --> 00:42:46,440
Speaker 5: as being you know, big warehouses with computer chips in them.

841
00:42:46,800 --> 00:42:50,880
Speaker 1: I want you to talk about the screw worms. Here's

842
00:42:50,920 --> 00:42:55,960
Speaker 1: what happened. I googled screwworms and I was like, oh fuck,

843
00:42:56,320 --> 00:42:59,799
Speaker 1: because I was like, screwworms cattle Texas is never going

844
00:42:59,880 --> 00:43:03,200
Speaker 1: to to me. And then I was like, oh fuck,

845
00:43:03,360 --> 00:43:05,440
Speaker 1: this is like something from a nightmare.

846
00:43:06,000 --> 00:43:11,840
Speaker 5: Yes, so screwworms are these parasites. They are bad for cattle.

847
00:43:12,040 --> 00:43:15,600
Speaker 5: They're not that harmful for humans, but they are parasites

848
00:43:15,640 --> 00:43:17,799
Speaker 5: that can infect and kill cattle. And they used to

849
00:43:17,800 --> 00:43:21,400
Speaker 5: be all over North America, and then gradually in the

850
00:43:21,480 --> 00:43:26,200
Speaker 5: nineteen fifties, the US government did this huge project where

851
00:43:26,239 --> 00:43:31,879
Speaker 5: it air dropped sterile male screwworms across the country and

852
00:43:31,960 --> 00:43:33,160
Speaker 5: like pushed.

853
00:43:32,880 --> 00:43:35,000
Speaker 1: At a great expense, a great.

854
00:43:34,760 --> 00:43:39,239
Speaker 5: Expense, a great expense pushed because sterile male screwworms, you

855
00:43:39,320 --> 00:43:42,120
Speaker 5: drop them, they out compete. You have so many of them,

856
00:43:42,160 --> 00:43:44,640
Speaker 5: they mate with the female screwworms and then they don't

857
00:43:44,680 --> 00:43:49,120
Speaker 5: produce any offspring, right, So they use this to push

858
00:43:49,239 --> 00:43:53,480
Speaker 5: the screwworm out of the United States, down into Mexico,

859
00:43:53,680 --> 00:43:58,000
Speaker 5: out of Mexico, and then basically down out of Central America,

860
00:43:58,400 --> 00:44:02,120
Speaker 5: and they eliminated them from Central America. And then for

861
00:44:02,200 --> 00:44:07,120
Speaker 5: decades the US government would air drop over Panama over

862
00:44:07,160 --> 00:44:11,280
Speaker 5: the dairying gap. The narrow strip of rainforest that separates

863
00:44:11,320 --> 00:44:16,120
Speaker 5: South America from Central America would just air drop regularly

864
00:44:16,360 --> 00:44:20,480
Speaker 5: sterile male screwworms and it basically created this biological wall

865
00:44:20,760 --> 00:44:23,919
Speaker 5: over Panama. And then in twenty twenty one, we don't

866
00:44:23,920 --> 00:44:28,080
Speaker 5: really know how it happened, screwworms somehow got away through

867
00:44:28,200 --> 00:44:30,880
Speaker 5: this wall. It might have been because there were animals

868
00:44:30,920 --> 00:44:33,760
Speaker 5: that were infected that were taken through the Darien Gap.

869
00:44:33,880 --> 00:44:35,799
Speaker 1: Who even fucking knows, And.

870
00:44:35,880 --> 00:44:39,200
Speaker 5: They have been gradually working their way through up Central

871
00:44:39,239 --> 00:44:42,920
Speaker 5: America since then. Now, there was a program to monitor

872
00:44:43,040 --> 00:44:45,800
Speaker 5: the spread of screworms in Central America that does seem

873
00:44:45,800 --> 00:44:48,839
Speaker 5: to have been canceled by the Department of Government Efficiency.

874
00:44:49,200 --> 00:44:55,480
Speaker 1: What no way, so Elon Musk screwworms.

875
00:44:54,840 --> 00:44:57,359
Speaker 5: As part of the huge effort to.

876
00:44:58,080 --> 00:45:02,600
Speaker 1: Cost us the taxpayers more mind, Elon Musk has, you know.

877
00:45:02,760 --> 00:45:05,279
Speaker 5: Pull out projects without necessarily knowing what they did and

878
00:45:05,280 --> 00:45:09,480
Speaker 5: why they were important. Right there, does seem like DOGE

879
00:45:09,960 --> 00:45:14,080
Speaker 5: canceled a screwworm surveillance contract. But I will say they

880
00:45:14,120 --> 00:45:18,359
Speaker 5: were coming north and the US government has is working allegedly.

881
00:45:18,600 --> 00:45:22,120
Speaker 5: The issue is once they escape containment, are like sterile

882
00:45:22,239 --> 00:45:26,160
Speaker 5: screw worm manufacturing facility where we blast these things with

883
00:45:26,320 --> 00:45:31,160
Speaker 5: radiation so they can't reproduce, was overwhelmed once they escaped north,

884
00:45:31,320 --> 00:45:34,799
Speaker 5: and so allegedly the USDA is opening a new screwworm

885
00:45:34,840 --> 00:45:37,359
Speaker 5: factory somewhere in Texas. It's going to produce a lot more.

886
00:45:37,600 --> 00:45:39,920
Speaker 5: Last week we started to detect them in Texas. They

887
00:45:39,960 --> 00:45:42,959
Speaker 5: finally worked their way all the way through Mexico. They're

888
00:45:43,160 --> 00:45:46,200
Speaker 5: just across the border. They're in southern Texas. They seem

889
00:45:46,239 --> 00:45:48,480
Speaker 5: to be a little bit in New Mexico. We are

890
00:45:48,640 --> 00:45:51,239
Speaker 5: supposedly opening this new factory soon. Whether it's going to

891
00:45:51,280 --> 00:45:53,239
Speaker 5: be enough to push them south, who knows. I mean,

892
00:45:53,280 --> 00:45:56,480
Speaker 5: it's a nightmare for cattle farmers. Yeah, if it's speaking

893
00:45:56,520 --> 00:45:59,600
Speaker 5: of Republican I mean, listen, it's bad as a parasite

894
00:45:59,640 --> 00:46:03,640
Speaker 5: back speaking of the Republican coalition. Cattle move constantly while

895
00:46:03,640 --> 00:46:05,920
Speaker 5: they're being farmed. You take them to slaughterhouses, you take

896
00:46:05,960 --> 00:46:09,799
Speaker 5: their branches, they're traded. And all the USDA can do

897
00:46:09,840 --> 00:46:12,040
Speaker 5: once these things are in the in the wild, so

898
00:46:12,120 --> 00:46:14,919
Speaker 5: to speak, is like ask branch or it's not to move

899
00:46:14,960 --> 00:46:18,560
Speaker 5: their cattle. And they're already asking folks that to do that.

900
00:46:18,640 --> 00:46:22,120
Speaker 5: In southern Texas. But deep prices, speaking of inflation, speaking

901
00:46:22,120 --> 00:46:24,359
Speaker 5: of Republican voters, are already super high, and it's just

902
00:46:24,400 --> 00:46:26,880
Speaker 5: going to push these things higher. And meanwhile, this kind

903
00:46:26,880 --> 00:46:29,800
Speaker 5: of nightmarish parasite is spreading across the United States.

904
00:46:29,920 --> 00:46:32,480
Speaker 1: Are they going to have to kill the cows who

905
00:46:32,560 --> 00:46:34,359
Speaker 1: have it? Or can you treat it?

906
00:46:35,840 --> 00:46:38,319
Speaker 5: We're at the edge of my notedge. You can treat it,

907
00:46:38,400 --> 00:46:41,319
Speaker 5: but I think it's more that it is a if

908
00:46:41,320 --> 00:46:43,920
Speaker 5: a cow's already sick or already has a cut, than

909
00:46:44,320 --> 00:46:47,319
Speaker 5: they infest that cut, and so I think you can

910
00:46:47,360 --> 00:46:49,279
Speaker 5: treat it, but it's like expensive.

911
00:46:50,480 --> 00:46:53,920
Speaker 1: Oh, we're so fucked. Thank you, thank you, sank you.

912
00:46:54,480 --> 00:46:56,319
Speaker 5: Of course, I'm happy to be here. Thank you for

913
00:46:56,360 --> 00:46:56,759
Speaker 5: having me.

914
00:47:00,040 --> 00:47:06,120
Speaker 3: Exactly Jesse Cannon, Somali, we have a moment of concerning fuckery.

915
00:47:06,440 --> 00:47:10,160
Speaker 3: This headline postal service won't deliver mail ballots for states

916
00:47:10,160 --> 00:47:11,879
Speaker 3: that don't hand over voterless.

917
00:47:12,280 --> 00:47:16,319
Speaker 1: Yeah, this is fucked. And again, by the way, this

918
00:47:16,480 --> 00:47:17,920
Speaker 1: is the kind of thing they try to do and

919
00:47:17,960 --> 00:47:21,760
Speaker 1: it's not gonna work. Fuckers like this will go even

920
00:47:21,880 --> 00:47:25,800
Speaker 1: the Trump Supreme Court, which is filled with Magaska fans.

921
00:47:26,000 --> 00:47:29,000
Speaker 1: Even that court will not go along with and even

922
00:47:29,040 --> 00:47:33,080
Speaker 1: though Justice Elito and Justice Thomas will absolutely go along

923
00:47:33,080 --> 00:47:36,240
Speaker 1: with this, the rest of the normals will not, even

924
00:47:36,520 --> 00:47:40,239
Speaker 1: Amy Cony Barrack, Justice Amy will not go along with this.

925
00:47:40,440 --> 00:47:42,799
Speaker 1: And that is why you got to push back. You

926
00:47:42,800 --> 00:47:44,960
Speaker 1: got to take them to court and you got to win.

927
00:47:45,560 --> 00:47:49,120
Speaker 1: Like this, they're going to always try stuff and you

928
00:47:49,480 --> 00:47:53,400
Speaker 1: just have to push back. You have to say no die.

929
00:47:53,920 --> 00:47:57,480
Speaker 1: And it's very annoying and it's very fucked up, but

930
00:47:57,760 --> 00:48:00,080
Speaker 1: like this is not going to happen for them, and

931
00:48:00,120 --> 00:48:03,840
Speaker 1: they're gonna try and we just all have to keep

932
00:48:03,960 --> 00:48:07,520
Speaker 1: going and keep pushing back on this stupid crap. Yep,

933
00:48:08,760 --> 00:48:13,080
Speaker 1: that's it for this episode of Fast Politics. Tune in

934
00:48:13,400 --> 00:48:18,840
Speaker 1: every Monday, Wednesday, Thursday, and Saturday to hear the best

935
00:48:18,920 --> 00:48:23,200
Speaker 1: minds and politics make sense of all this chaos. If

936
00:48:23,280 --> 00:48:26,320
Speaker 1: you enjoy this podcast, please send it to a friend

937
00:48:26,760 --> 00:48:29,960
Speaker 1: and keep the conversation going. Thanks for listening.