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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 an ap Pole says, we really

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Speaker 1: half of all Americans say it's harder than usual to

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Speaker 1: afford the things they like to give as holiday gifts.

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

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Speaker 1: Lincoln Projects owned Rick Wilson joins us to discuss the

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Speaker 1: economic delusions of Trump as we enter the holiday season.

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Speaker 1: Then we'll talk to The New York Times own Lydia

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Speaker 1: Polgreen about the disastrous United States immigration policy and the

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Speaker 1: toll it's taking on all of us. But first the news, Milly.

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Speaker 2: One of our really really bad i'd say it's a

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Speaker 2: cancer growing in America is sports gimboig and how many

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Speaker 2: casinos we're putting everywhere? And we have two charts that

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Speaker 2: show a lot of evidence for this.

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Speaker 1: Yeah, so this is a change in average calls to

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Speaker 1: National Problem Gambling Helpline since January twenty seventeen. And that

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Speaker 1: was when we started legalizing sports gambling. And now we

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Speaker 1: gamble on everything because gambling is great and regulation is bad.

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Speaker 2: Ah, did you see this insane thing that they showed

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Speaker 2: that this guy has this crazy prediction record of predicting

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Speaker 2: open AI things. And they're like, yeah, it's because he's

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Speaker 2: an employee there and he's betting on the schedules of releases.

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Speaker 2: He's putting out like it's unbelievable that anyone is participating

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Speaker 2: in this shit.

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Speaker 1: This is what happens when you don't regulate an industry,

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Speaker 1: you get corruption. And so here we go. This is

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Speaker 1: like our central thesis, right you and I, is that

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Speaker 1: you have to regulate things in order to make them fair. Well,

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Speaker 1: here we are so young people are already struggling with

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Speaker 1: all sorts of addictions, drugs, phones, and inability to afford

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Speaker 1: their lifestyles. Well, now online gambling bankruptcy rates rose twenty

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Speaker 1: eight percent. You just have people who are in debt. Now,

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Speaker 1: it's an argument that you're going to hear from the

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Speaker 1: right is that this tax revenue collected by states from

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Speaker 1: sports betting is humongous. And it is. It's big. It's

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Speaker 1: like a billion dollars per corridor, and I mean, I

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Speaker 1: think that's persont. I don't know exactly how that works

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Speaker 1: because what state This chart is a little bit hard

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Speaker 1: to understand, but there certainly is a lot states are

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Speaker 1: making money on taxing this, but the problem is, like,

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Speaker 1: I mean, here's a great example from the Journal. An

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Speaker 1: eighteen year old student at the University of Rhode Island

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Speaker 1: told the economists that he struggles to enjoy watching sports

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Speaker 1: without the high of betting. Young Men are also making

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Speaker 1: risky bets on crypto memestocks and the production market. So

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Speaker 1: here you have a group that's really struggling, that is

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Speaker 1: now being offered things that are addictive and financially could

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Speaker 1: crush them. And there we go. So I think there's

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Speaker 1: a pretty good case here that regulation could keep these

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Speaker 1: people out of trouble. Since we have a Republican president

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Speaker 1: and a Republican Congress, we won't a Republican Supreme Court,

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Speaker 1: we won't see this, but would be nice to see it.

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Speaker 2: Yeah, I particularly get disturbed as I see people who

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Speaker 2: work for me skipping meals to have money to gamble

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Speaker 2: on sports. It's just a horrible behavior that when we say, oh, well,

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Speaker 2: we get tax money for other things from it, it's like, yes,

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Speaker 2: you're getting tax money from literally the people at the

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Speaker 2: lowest end of the economy getting hurt by an addiction.

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Speaker 3: That's not a good method of taxation now.

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Speaker 1: And it's also and I think the other thing that

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Speaker 1: I think is so important to realize here is like

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Speaker 1: sports betting doesn't do anything right. It doesn't help people,

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Speaker 1: it doesn't help athletes, it doesn't you know, it's just

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Speaker 1: an addiction for the sake of an addiction. Which it's

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Speaker 1: really crazy that they won't regulate this.

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Speaker 2: It's like, well, speaking of our other favorite subjects, w

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Speaker 2: vanders waving its hand to say hello with Texas as

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Speaker 2: we see that they may not have drawn these maps.

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Speaker 1: Well, there's a great part of the story, like it's

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Speaker 1: so multi layered and amazing. But my favorite part of

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Speaker 1: the story is that Donald Trump he pardons Henry Kuailar,

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Speaker 1: who is wildly popular but runs in a very purpoly district.

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Speaker 1: He pardons him because he thinks that if he pardons him,

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Speaker 1: he'll run as a Republican. Instead, he continues as a Democrat,

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Speaker 1: thus pretty much assuring that Democrats will keep that seat,

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Speaker 1: despite the fact that he really probably should not have

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Speaker 1: been pardoned. But look, Trump came to Republicans, he said,

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Speaker 1: give me five seats. I want those five seats. This

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Speaker 1: is my state. They were like, no problem. They I

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Speaker 1: guess they thought that Trump would still have the twenty

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Speaker 1: twenty four margins. It's funny because like if you had

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Speaker 1: pulled back just for a second and been like, even

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Speaker 1: in twenty sixteen, Donald Trump did not keep those same

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Speaker 1: margins in the midterms. He didn't even keep those same

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Speaker 1: margins in twenty twenty. So like the idea that somehow

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Speaker 1: he was going to do the kind of numbers he

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Speaker 1: did with working people too was just kind of like

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Speaker 1: it just totally flew in the face of all history

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Speaker 1: and all evidence of the contrary. So now the question

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Speaker 1: is five seats, Well, they get five seats. You know,

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Speaker 1: by the way, I've said this before because I think

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Speaker 1: it's really important. Democrats have had a really hard time

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Speaker 1: pushing back against Trump, and one of the things that

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Speaker 1: I think, like, you know, you and I may not

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Speaker 1: like totally love every one of his policies, but Gavin

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Speaker 1: Newsom delivered those five seats, and he did it in

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Speaker 1: an astonishing way. He set up a special election and

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Speaker 1: he put it together, he raised the money to do it,

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Speaker 1: and he won by big margins, and that was like

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Speaker 1: pretty fucking amazing. And so you know, when he did that,

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Speaker 1: that meant that had shut down the dummymander the way

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Speaker 1: that Trump had wanted it to go, And I think

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Speaker 1: that's meaningful and important.

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Speaker 2: Oh you know, there was one very funny thing that

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Speaker 2: you see Rad Paul on Meet the Press this morning.

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Speaker 2: What he said about this, Okay, let me let me

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Speaker 2: read it to you since I'm not going to play it. So,

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Speaker 2: speaking of the mid decade redistrict in Texas, he said,

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Speaker 2: I think it's going to lead to more civic tenshion

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Speaker 2: and possibly more violence in our country.

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Speaker 3: Think about it.

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Speaker 2: If thirty five percent of Texas is solidly Democrat and

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Speaker 2: they have zero representation, how does that make Democrats feel?

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Speaker 2: I think it makes them feel like they're not represented.

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Speaker 1: Well, I mean he's I mean, it's not dumb. It's awesome.

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Speaker 1: I mean it's dumb, but it's also amazing.

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Speaker 2: What yeah, I mean the logical stretch of that Democrats

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Speaker 2: are going to get violent over it is something.

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Speaker 1: But sharply, I don't think they're gonna.

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Speaker 2: I mean, his his neighbor may have been a Democrat

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Speaker 2: who got violent with him, but that's a different story.

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Speaker 1: Well, I just think, like Paul, I don't know, I

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Speaker 1: have very complicated feelings about Ram Paul because of any

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Speaker 1: number of them, is it.

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Speaker 2: The two pay that really?

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Speaker 4: Like?

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Speaker 3: Just maybe draws alting view in.

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Speaker 1: Your Okay, that is not the worst to pay in Congress, that's.

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Speaker 3: That is great.

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Speaker 2: Okay, we got fish to fry, though, Trump and these

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Speaker 2: selling Nvidia chips to China feel a little fishy.

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Speaker 3: Huh.

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Speaker 1: Listen. Turns out, in case you're wondering, trade wars are

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Speaker 1: bad and easy to lose, as shown by Donald J. Trump,

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Speaker 1: who is now desperately trying to make nice with China.

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Speaker 1: So Elizabeth Warren is mad that Donald Trump is selling

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Speaker 1: out in the video he's selling these chips to China.

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Speaker 1: But what Elizabeth Warren does not understand is that Donald

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Speaker 1: Trump has a trade war disaster on his hands and

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Speaker 1: he will do anything he can to try to make

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Speaker 1: China like him. It's just wild, right, I mean, like

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Speaker 1: he knows it's bad. We know it's bad. Elizabeth Warren

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Speaker 1: knows it's bad. But like the problem is, China is

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Speaker 1: a superpower. We are in a trade war with them.

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Speaker 1: They are not. I think it's not unfair to say

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Speaker 1: we are losing the trade wars, and that is why

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Speaker 1: Donald Trump wants to make nice with Navidio over at

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Speaker 1: our YouTube channel. We have some really exciting stuff. Jesse

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Speaker 1: and I have made a documentary. It's called Project twenty

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Speaker 1: twenty nine, A Reimagining. It's a series about how Democrats

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Speaker 1: can deliver popular policies when they regain power. The first

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Speaker 1: episode is up now. It's the first part of the series,

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Speaker 1: and we're going to examine what went wrong with some

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Speaker 1: of the Biden administration's approach to policies that may have

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Speaker 1: prevented Democrats from being able to deliver the broad anti

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Speaker 1: corruption legislation that needed to happen, and by the way,

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Speaker 1: that would have prevented some of the corruption we're seeing

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Speaker 1: right now. The first episode dives deep into the first

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Speaker 1: step of fixing American politics. It may sound unsexy, but

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Speaker 1: it's a big, important issue you and that big subject

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Speaker 1: is campaign finance reform. For this episode, we talk to

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Speaker 1: some of the smartest people we know, professors and academics,

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Speaker 1: people like Lawrence Lessig, Tiffany Muller, Michael Waldman, and Tom Moore.

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Speaker 1: Republicans were prepared for when they got the levers of power.

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Speaker 1: Democrats need that same kind of preparation. That's why we

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Speaker 1: need to start the conversation on how democrats can do

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Speaker 1: the same thing. So please head over to our YouTube

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Speaker 1: channel and search Molly John Fast Project twenty twenty nine

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Speaker 1: or go to the Fast Politics YouTube channel page and

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Speaker 1: you'll find it there and help us spread the word

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Speaker 1: and stay tuned for more episodes. Rick Wilson is the

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Speaker 1: founder of the Lincoln Project and the host of the

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Speaker 1: Enemy's List. Rick Wilson, Hey, Molly, So Trump was told

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Speaker 1: to campaign on affordability, so he is going to campaign

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Speaker 1: on unaffordability. I'm going to play a clip for you.

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Speaker 1: So we're building an arc to triumph. It will be

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Speaker 1: like the one in Paris, but it will blow it away.

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Speaker 3: Yeah. Look, I think that the two things America is

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Speaker 3: struggling working class and middle class voters are crying out

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Speaker 3: for right now are a golden ballroom decorated in the

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Speaker 3: style of LIBERACEI slash Saddam and a triumphal arc in DC.

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Speaker 3: Other than that, food prices, gas prices, rent insurance, health insurance,

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Speaker 3: none of those things really matter. It's the arc that's

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Speaker 3: going to really make the sale.

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Speaker 1: What do you think is going on here?

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Speaker 3: I think he is unable to focus on anything beyond

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Speaker 3: the stuff that he likes the day to day, minute

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Speaker 3: to minute stuff that he likes. Honestly, Molly, I know

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Speaker 3: we're all amateur neurologists and amateur gerontologists lately, but this

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Speaker 3: is the old man who wants to sit there and

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Speaker 3: have us putting at lunch and talk about the good

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Speaker 3: old days. And that's what he's doing now. All this

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Speaker 3: stuff is it's like this weird, twisted version of a legacy.

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Speaker 3: None of these things are going to get built. We're

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Speaker 3: not building an arc, we're not building a ballroom. It's

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Speaker 3: not gonna happen. But he's going to try to make

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Speaker 3: those things the center. And I think in some ways

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Speaker 3: it also he's also trying to go back to the

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Speaker 3: old days, like he's thinking about when did his brand

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Speaker 3: really take off, and he's thinking to himself, I'm going

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Speaker 3: to be the Trump brand of the eighties or the nineties.

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Speaker 3: It's gold, it's luxury, it's class. None of it is

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Speaker 3: any of those things, but he is going to try

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Speaker 3: to play that idea. I feel like the decline and

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Speaker 3: fall of Trump is now increasing in both frequency and

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Speaker 3: amplitude about the degree to which all this stuff is

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Speaker 3: crazy and it's so disconnected from where people are. I mean,

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Speaker 3: we are getting screaming alarm bells about the economy on

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Speaker 3: every single front right now. We are getting nut real

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Speaker 3: improvement on inflation, we're getting no real improvement on jobs.

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Speaker 3: All this stuff is really the exact opposite of showing

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Speaker 3: some contrition and saying, you know what, I'm going to

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Speaker 3: be right about tariffs in the end. I know it's

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Speaker 3: hurting you right now, so we're gonna try to find

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Speaker 3: ways to He he won't say that. He can't say that.

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Speaker 3: The only reason he's done the ad thing is because

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Speaker 3: he's afraid of getting blown out even further in the midterms.

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Speaker 3: So I think we're in a situation where he doesn't

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Speaker 3: care about anything else. We're seeing him talk about the

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Speaker 3: things he cares about, and that's very narrow.

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Speaker 1: The only thing they have is history. I always say,

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Speaker 1: the one thing you can compete with. But eventually we

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Speaker 1: will have history too discuss.

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Speaker 3: I would like to inform the president, depending on which

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Speaker 3: of the French republics you want to start counting from,

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Speaker 3: they have been around a little longer than we have,

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Speaker 3: not by much. As a republic. He thinks of history

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Speaker 3: as tourism, not as the passage of time and the

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Speaker 3: recorded nature of it. This is a pathetic display more

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Speaker 3: than anything. People should not be losing their minds, like, oh,

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Speaker 3: it's a side of fact, Yeah, sure it is. That's

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Speaker 3: just coincidental. This is a sign of a pathetic old

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Speaker 3: man who needs something. He's trying to find something he

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Speaker 3: can call a victory, something he can call a win.

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Speaker 3: Something he has failed economically. He has sold America out

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Speaker 3: around the world. He has embarrassed us on the global stage.

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Speaker 3: He has handed China the competitive economic advantage it always

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Speaker 3: craved and could never quite get to before Trump. And

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Speaker 3: so he's desperate to do something to leave a mark,

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Speaker 3: to leave a legacy, that thing will not have ground

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Speaker 3: broken on it and be finished before he dies. And

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Speaker 3: he wants something to stay in Washington forever.

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Speaker 1: Well, he will have the ballroom.

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Speaker 3: He's going to have the ballroom. I don't think he's

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Speaker 3: going to get it done. Construc tis time.

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Speaker 1: He did fire the guy he brought in. You do

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Speaker 1: the architecture because the guy said he didn't want it

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Speaker 1: East Wing to be bigger than the actual White House.

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Speaker 3: Correct look architects, even ones who are willing to work

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Speaker 3: for a vulgar like Donald at some point follow a

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Speaker 3: set of generally specifically more traditionalist architects, follow a set

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Speaker 3: of guidelines about proportions and grace and style, and they

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Speaker 3: don't wake up in the morning and say, you know,

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Speaker 3: what I'm really after is the largest most vulgar display

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Speaker 3: of Trump's Verian tastes possible. And this guy said, you know,

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Speaker 3: it wouldn't fit the scene, the design the rest of

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Speaker 3: the compound by building a giant super ballroom from hell.

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Speaker 3: So trum Trump got mad. He's pissed off about it,

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Speaker 3: and he fires the guy. And they didn't fire him,

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Speaker 3: they layered him, like we're bringing in another another architectural first.

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Speaker 1: Okay, I wonder if we could just talk for a

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Speaker 1: minute about where Donald Trump finds himself in at this moment,

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Speaker 1: because I think we're coming into Christmas. The pulling is

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Speaker 1: people are upset about implation things are more expensive or

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Speaker 1: in this trade war with China, Trump is selling the

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Speaker 1: video chips to China. Probably because he's trying to win

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Speaker 1: the trade war.

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Speaker 3: He started selling those, so there was all there's a

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Speaker 3: theory with China that you give them just a bit

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Speaker 3: of access to these sort of technologies, right, you don't

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Speaker 3: cut them off. The theory used to be you don't

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Speaker 3: cut them off completely or is able to develop them

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Speaker 3: organically at home. But the theory now is we're going

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Speaker 3: to give them the most advanced AI chips. Now. Look,

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Speaker 3: China's exports have in the last fifteen years have increased

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Speaker 3: by about forty five percent. Their imports have increased by

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Speaker 3: about one percent, and they're not coming from US. China

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Speaker 3: has set out to be completely independent of America when

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Speaker 3: it comes to developing the high end technologies that we

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Speaker 3: still are a leader on. The problem here for US

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Speaker 3: is they're going to get those Age two hundreds and

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Speaker 3: they're going to reverse engineer them. And this is the

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Speaker 3: greatest short term pennies on the dollar foolishness I think

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Speaker 3: I've ever seen in my life.

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Speaker 1: Well, Trump is an impossible situation, though, right, because he's

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Speaker 1: losing the trade war and he desperately needs China to

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Speaker 1: negotiate with him.

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Speaker 3: What Trump ended up doing was in negotiating with Nvidia

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Speaker 3: instead of with China. Right, So he's getting a twenty

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Speaker 3: five percent cut of sales from Nvidia, but not putting

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Speaker 3: the Chinese in any position where they're like, well that

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Speaker 3: Trump showed us we better open the rest of our

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Speaker 3: markets or we won't get any more Nvidia chips. Jensen

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Speaker 3: Wang shows up wearing a leather jacket and saying you're

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Speaker 3: the greatest, mister President, and ends up handing we handed

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Speaker 3: Nvidia another very large client, and we hand China an

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Speaker 3: absolute triumph in the economic market that they want to dominate,

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Speaker 3: which is AI.

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Speaker 1: Do you think that Trump is just such a bad

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Speaker 1: negotiator and that that's how we got to this moment?

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Speaker 3: Well, I think he's surrounded by corrupt assholes who are

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Speaker 3: getting paid off by Russia, China and god knows who

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Speaker 3: else we're negotiating, And that to me is like the

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Speaker 3: Oukham's raisor of this thing is like, is it Trump

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Speaker 3: making bad deals or are the people around Trump, like

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Speaker 3: Jared getting basically a very sweet set of payoffs erd

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Speaker 3: in Whitcoff's kids becoming the people who are going to

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Speaker 3: quote redevelop Ukraine. What a strange and remarkable coincidence that is.

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Speaker 1: Do you think that Trump has sort of backed himself

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Speaker 1: into a corner though with the economy, because like, clearly

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Speaker 1: we all see what's going and it's where it's going,

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Speaker 1: and it's going bad.

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Speaker 3: It's going south and sideways, as they say, in a

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Speaker 3: big hurry. And and Trump doesn't have a selling proposition, Molly,

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Speaker 3: at this moment where he gets to walk out of

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Speaker 3: this problem, he has really screwed himself by declaring the

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Speaker 3: trade war is going to pay Americans a tariff dividend?

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Speaker 3: Where would that be? Donald? When's that gonna happen? It's

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Speaker 3: gonna pay us a doged dividend? And if it's going

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Speaker 3: to happen, the whole thing has gotten so kafukata because

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Speaker 3: he can't deliver what he promised in terms of a

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Speaker 3: golden age in America, because the fund mental center of

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Speaker 3: everything he's doing economically is screwing his own.

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Speaker 1: Voters, right, And that's what we see with the farming.

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Speaker 3: You can pay the farmers a twelve billion dollar, give

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Speaker 3: him a twelve billion dollar bailout, but they would much

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Speaker 3: rather have sold their soybeans, right either have stayed in business.

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Speaker 1: Yeah, we got Trump. He has this trade war. He's

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Speaker 1: trying to negotiate. Things are more expensive. He thinks the

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Speaker 1: release valve is lowering interest rates, but it really isn't.

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Speaker 3: No, it isn't. And one of the things we know

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Speaker 3: right now is we're on a clock. The economy is

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Speaker 3: on a clock because forty percent of the S and

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Speaker 3: P now is AI companies, Oracle, the guy Larry Elison

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Speaker 3: who is the guy behind buying all this media for

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Speaker 3: the right wing. Larry Elison made a deal with open

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Speaker 3: Ai for sixty billion dollars. They have to start paying

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Speaker 3: that deal back in twenty twenty seven. Beginning of twenty

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Speaker 3: to thirty seven. Now open Ay's entire revenue is seventeen

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Speaker 3: billion dollars. They're buying it on credit default swaps. So

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Speaker 3: if anybody who remembers what credit of false swaps were,

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Speaker 3: go back and watch the big short. Now these things

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Speaker 3: are collateralized by Sam Altman doing the woo woo dance

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Speaker 3: at TED Talk. You know, the bullshit tornado is about

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Speaker 3: to die. We're about to the AI bubble. And look,

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Speaker 3: it doesn't mean that AI can't still do trimend as

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Speaker 3: good and trimidous harm, but that bubble economically is getting

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Speaker 3: really shaky. Trump will have no way to manage his

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Speaker 3: way out of that bubble. There's no chance he can

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Speaker 3: get out of that without America being as close to

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Speaker 3: a depression as we've ever been since the Depression.

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Speaker 1: But fox has said it's.

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Speaker 3: A great economy, you just don't appreciate it.

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Speaker 1: Molly, Well, that is one of the things it said.

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Speaker 1: But it's also said that American should go without Christmas

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Speaker 1: tree is so that those Christmas tree farms can be

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Speaker 1: used as data data SARMs for data sentences discuss.

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Speaker 3: I'm pro Christmas tree and I vote. You know, this

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Speaker 3: is one more of their bizarre Foxy and kind of

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Speaker 3: affectations about you know, we're going to find some way

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Speaker 3: to spin that Donald Trump isn't a complete economic disaster

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Speaker 3: in every dimension. But I think this is kind of unspinnable.

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Speaker 3: I think you can't make this one work.

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Speaker 1: But just curious for a second. Like I've heard Scott

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Speaker 1: Jennings also talk about how great data centers are and

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Speaker 1: how it's going to win the war with China. But

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Speaker 1: just explain to me why we need data centers everywhere.

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Speaker 3: What we're doing on the data centers is we're providing

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Speaker 3: essentially energy subsidies and zoning permissions to tech companies so

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Speaker 3: they can build data centers, so that the American taxpayer

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Speaker 3: is paying in part for the facilities they use to

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Speaker 3: train their ais. Why because we live in a kleptocracy

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Speaker 3: now and everything is crooked? How about that?

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Speaker 1: Why should I pay for Elon Musk's power electricity?

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Speaker 3: You should not. And the other question is why should

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Speaker 3: you pay for a loan guarantee for open Ai, which

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Speaker 3: Donald Trump is considering doing a loan guarantee for open

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Speaker 3: AI for one point seven trillion dollars And I'm curious.

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Speaker 3: I got a lot of questions about whether anybody there

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Speaker 3: was no private market facility that wanted to give him

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Speaker 3: a one point seven trillion dollars weird, why would that?

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Speaker 1: We're gonna He's going to crash the economy, right.

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Speaker 3: Oh yeah, Sam Altman's going to crash the economy.

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Speaker 1: Sam Oltin' going to crash the economy? Or is Donald

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Speaker 1: Trump going to crash you?

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Speaker 3: Well, Donald Trump's gonna crash it, but Sam's gonna get

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Speaker 3: He's going to collateralize everything humanly possible for the benefit

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Speaker 3: of open AI before he does.

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Speaker 1: I'm excited to work on a potato farm. I feel

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Speaker 1: like I will I will not be a good potato farmer.

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Speaker 3: No, I'm always I love you, Dearly, but I do

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Speaker 3: not believe you are meant for agricultural labor.

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Speaker 1: So let's just go for a minute. You know who's

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Speaker 1: having a worse time than Donald Trump.

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Speaker 3: Who's that?

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Speaker 1: Mike Johnson?

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Speaker 3: Oh, poor Mike Johnson.

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Speaker 1: And she has just a few more weeks with Marjorie

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Speaker 1: Taylor Green in Congress. She has promised that she's going

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Speaker 1: to just end his career. So what's going on there?

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Speaker 3: I think Marge has decided she's gonna Kama Cozie. What's

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Speaker 3: left of Mike Johnson's sanity? Yeah, I would do it

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Speaker 3: to fuck with Johnson. And here's what I would do

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Speaker 3: if I were March. I would caucus with the Democrats

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Speaker 3: for one day. Yeah, call for a vote for speaker

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Speaker 3: a let pa Keem Jeffries.

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Speaker 1: It's probably not going to do that, but you she could.

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Speaker 3: I would not put a lot past her at this point.

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Speaker 1: One of the things that I think is interesting about

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Speaker 1: Marjorie Taylor Green doing this is that it mirrors what

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Speaker 1: we saw Mac Gates do. Like Matt Gates was on

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Speaker 1: his way out and he was like, fuck you, Kevin McCarthy,

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Speaker 1: You're going to lose your job.

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Speaker 3: The departing Kamakauze attack is now becoming, I think a

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Speaker 3: sort of Republican brand standard, and I kind of love it. Honestly,

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Speaker 3: I kind of love it. It does not bother me

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Speaker 3: the way that they're encountering this terrible set of outcomes.

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Speaker 1: Do you think Marjorie Taylor Green does something huge like

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Speaker 1: writes reads the names on the floor could.

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Speaker 3: Be Hey, listen, I don't think you should think that

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Speaker 3: she has an upper boundary right now. She really clearly

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Speaker 3: has decided she gonna burn it all down.

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Speaker 1: I have a lot of respect for that.

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Speaker 3: I you know what, Again, as we've talked about before,

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Speaker 3: I'm not inviting Marjorie over for cocktails or anything. But

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Speaker 3: when the enemy of my enemy is doing the shit

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Speaker 3: that she's doing, I'll allow it.

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Speaker 1: So we're gonna probably see more Ebstein stuff.

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Speaker 3: Well, four days in the time, people hear this is

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Speaker 3: when the Epstein fowls are due. I would like to

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Speaker 3: make you a side bet. I don't think we're gonna

433
00:23:43,359 --> 00:23:43,880
Speaker 3: see shit.

434
00:23:44,600 --> 00:23:45,280
Speaker 1: Well, we know the.

435
00:23:45,280 --> 00:23:48,320
Speaker 3: House is gonna go, the DOJ is gonna go. Oh no,

436
00:23:48,720 --> 00:23:51,120
Speaker 3: we must protect the victims. And it's too shocking.

437
00:23:51,720 --> 00:23:53,800
Speaker 1: The DOJ is definitely going to give you a hard time.

438
00:23:53,840 --> 00:23:56,679
Speaker 1: But oversight has a bunch of overstation.

439
00:23:57,080 --> 00:24:00,439
Speaker 3: But let's keep in mind, everybody, what Oversight has is

440
00:24:00,560 --> 00:24:06,640
Speaker 3: different from what the DJ has. Yeah, and the DOJ

441
00:24:06,880 --> 00:24:09,639
Speaker 3: pretending they don't know any of them, that all this

442
00:24:09,760 --> 00:24:11,400
Speaker 3: Oversight stuff is brand new and they have to vet

443
00:24:11,400 --> 00:24:13,679
Speaker 3: it for two or three years before they'll release it

444
00:24:13,760 --> 00:24:15,240
Speaker 3: is going to be their next play you watch.

445
00:24:15,400 --> 00:24:19,159
Speaker 1: Yeah, but Oversight has tons and tons of photos and

446
00:24:19,280 --> 00:24:21,760
Speaker 1: videos and they are not sure how they can release

447
00:24:21,800 --> 00:24:23,800
Speaker 1: them while protecting the victims.

448
00:24:24,160 --> 00:24:26,000
Speaker 3: There's a lot of c SAM material in there that

449
00:24:26,040 --> 00:24:29,120
Speaker 3: they are trying to consider how they either redact it

450
00:24:29,920 --> 00:24:32,600
Speaker 3: or summarize it so that it doesn't have to be

451
00:24:33,320 --> 00:24:36,240
Speaker 3: out there. But you know, a lot of the victims

452
00:24:36,359 --> 00:24:39,640
Speaker 3: who are in this material are saying, I still want

453
00:24:39,640 --> 00:24:40,920
Speaker 3: it out there. I want to know the truth. I

454
00:24:40,960 --> 00:24:42,800
Speaker 3: want the truth. I want people to understand what would

455
00:24:42,800 --> 00:24:43,280
Speaker 3: happened to me.

456
00:24:44,000 --> 00:24:45,159
Speaker 1: Yeah, it's pretty dark.

457
00:24:45,440 --> 00:24:47,280
Speaker 3: It's pretty dark. It's dark.

458
00:24:47,800 --> 00:24:50,239
Speaker 1: It's not a great situation for anyone to be in.

459
00:24:50,960 --> 00:24:55,320
Speaker 3: No, no, no, nobody likes this this outcome. No, nobody

460
00:24:55,359 --> 00:24:56,600
Speaker 3: likes where it's at. It's bad.

461
00:24:56,800 --> 00:25:01,800
Speaker 1: Rick Wilson, thank you. Lydia Polgrim is a New York

462
00:25:01,840 --> 00:25:06,200
Speaker 1: Times columnist. Welcome Too Fast Politics, Lydia.

463
00:25:06,359 --> 00:25:08,439
Speaker 4: Paul Green, It's great to be back.

464
00:25:08,880 --> 00:25:12,440
Speaker 1: I'm so excited to have you. We have podcasted together

465
00:25:12,480 --> 00:25:14,600
Speaker 1: at The New York Times, but it's fun to have

466
00:25:14,640 --> 00:25:17,080
Speaker 1: you on my podcast, which is much shorter.

467
00:25:20,280 --> 00:25:22,360
Speaker 4: Never let it be said that The New York Times

468
00:25:22,440 --> 00:25:24,600
Speaker 4: is selling anyone short. You know, people can get money

469
00:25:24,760 --> 00:25:26,560
Speaker 4: to read, watch, and listen to the New York Times,

470
00:25:26,640 --> 00:25:28,119
Speaker 4: so we got to give them good value.

471
00:25:28,240 --> 00:25:30,320
Speaker 1: That's right. Well, the first I wanted to start by

472
00:25:30,359 --> 00:25:34,360
Speaker 1: talking about the incredible stuff you're doing with immigration. You're

473
00:25:34,400 --> 00:25:39,080
Speaker 1: writing these really great reported columns about immigration. But I

474
00:25:39,080 --> 00:25:41,320
Speaker 1: would love you to start by talking about your central

475
00:25:41,440 --> 00:25:45,560
Speaker 1: thesis about immigration, because I think it's a very counter

476
00:25:45,880 --> 00:25:50,840
Speaker 1: intuitive or it's not counterintuitive exactly. It's sort of intuitive,

477
00:25:51,000 --> 00:25:54,879
Speaker 1: but it's not where anyone in Trump world is coming

478
00:25:54,960 --> 00:25:55,680
Speaker 1: to immigration.

479
00:25:56,200 --> 00:25:58,400
Speaker 4: Yeah, I mean, I think when we think about immigration

480
00:25:58,640 --> 00:26:00,840
Speaker 4: in the current moment, it's all about how do we

481
00:26:00,920 --> 00:26:03,560
Speaker 4: keep people out? And there are too many people coming

482
00:26:03,560 --> 00:26:05,840
Speaker 4: into the United States, there are too many people coming

483
00:26:05,880 --> 00:26:09,199
Speaker 4: into Europe. And the assumption is that this policy of

484
00:26:09,240 --> 00:26:12,879
Speaker 4: restriction is sort of universally popular and that the harder

485
00:26:12,920 --> 00:26:15,159
Speaker 4: you go, the more popular you're going to be. And

486
00:26:15,200 --> 00:26:17,600
Speaker 4: I think this just flies in the face of the reality,

487
00:26:17,680 --> 00:26:19,760
Speaker 4: which is that you know, we are looking at a

488
00:26:19,800 --> 00:26:23,880
Speaker 4: world in which the countries that currently control most of

489
00:26:24,000 --> 00:26:27,399
Speaker 4: the wealth and power actually have the fewest people in

490
00:26:27,440 --> 00:26:30,680
Speaker 4: the slowest population growth rates. And you know, we're used

491
00:26:30,680 --> 00:26:34,400
Speaker 4: to thinking about a world in which the scarcest things

492
00:26:34,600 --> 00:26:39,440
Speaker 4: are you know, oil or electricity or land or things

493
00:26:39,480 --> 00:26:41,359
Speaker 4: like that, but we might actually be looking at a

494
00:26:41,359 --> 00:26:45,000
Speaker 4: future in which actually the scarcest resources people. And you know,

495
00:26:45,200 --> 00:26:49,040
Speaker 4: as all of these societies are not reproducing at replacement rate,

496
00:26:49,480 --> 00:26:52,879
Speaker 4: or we're seeing demographic cliffs everywhere, including China, which you

497
00:26:52,920 --> 00:26:55,879
Speaker 4: know has its own kind of separate demographic disaster. So

498
00:26:56,320 --> 00:26:58,560
Speaker 4: I think that we need to be thinking about migration

499
00:26:58,800 --> 00:27:01,920
Speaker 4: in a much more holistic way and thinking about how

500
00:27:01,920 --> 00:27:05,320
Speaker 4: we're going to manage the politics of that change as

501
00:27:05,359 --> 00:27:07,760
Speaker 4: we move forward. And I think a lot of countries

502
00:27:07,760 --> 00:27:10,879
Speaker 4: are being very shortsighted and thinking about these kind of

503
00:27:10,920 --> 00:27:15,760
Speaker 4: maximum restriction policies without without considering what the longer term

504
00:27:15,800 --> 00:27:16,720
Speaker 4: impact is going to be.

505
00:27:17,040 --> 00:27:20,080
Speaker 1: I love this thesis for any number of reasons, but

506
00:27:20,320 --> 00:27:24,280
Speaker 1: also because you even see it. It's not you even

507
00:27:24,440 --> 00:27:28,199
Speaker 1: see Trump world trying to grapple with it. So do

508
00:27:28,280 --> 00:27:32,280
Speaker 1: you have people like Jadie Vance and Victor Orbon and

509
00:27:32,520 --> 00:27:35,520
Speaker 1: you have people who truly see it coming, right. They

510
00:27:35,680 --> 00:27:39,200
Speaker 1: know that people are not having the number of children

511
00:27:39,240 --> 00:27:41,400
Speaker 1: they need. I mean, they're not having children at all,

512
00:27:41,800 --> 00:27:43,560
Speaker 1: but when they are, they're not having the number of

513
00:27:43,640 --> 00:27:45,880
Speaker 1: children they need to keep the population growing to keep

514
00:27:45,880 --> 00:27:49,240
Speaker 1: the economy going. And so you see them a little

515
00:27:49,280 --> 00:27:52,040
Speaker 1: bit trying to grapple with that, but obviously not But

516
00:27:52,240 --> 00:27:54,480
Speaker 1: do you, I mean, can you talk us through that?

517
00:27:55,119 --> 00:27:55,359
Speaker 3: Yeah?

518
00:27:55,440 --> 00:27:57,399
Speaker 4: I mean I think that there is this kind of

519
00:27:57,520 --> 00:28:02,440
Speaker 4: fantasy that somehow the wealthy countries of the world are

520
00:28:02,480 --> 00:28:05,720
Speaker 4: going to be able to command the human talent that

521
00:28:05,760 --> 00:28:08,160
Speaker 4: they need to come to the places where they need

522
00:28:08,200 --> 00:28:10,840
Speaker 4: them on the terms that are favorable and agreeable to

523
00:28:10,880 --> 00:28:13,199
Speaker 4: those wealthy countries. You know, I noticed in the new

524
00:28:13,280 --> 00:28:17,280
Speaker 4: National Security document that the Trump administration put out, they

525
00:28:17,359 --> 00:28:20,639
Speaker 4: said that actually their immigration policy is not going to

526
00:28:20,680 --> 00:28:23,080
Speaker 4: be built just on merit, so not just you know,

527
00:28:23,080 --> 00:28:25,320
Speaker 4: who are the most talented people on the globe, but

528
00:28:25,359 --> 00:28:28,480
Speaker 4: they're going to take other considerations into view. And I

529
00:28:28,480 --> 00:28:30,600
Speaker 4: think that that's you know, we can assume that that's

530
00:28:30,680 --> 00:28:33,320
Speaker 4: just code for you know, we want you know, white

531
00:28:33,440 --> 00:28:36,720
Speaker 4: European Christians, we don't want people from India, or we

532
00:28:36,720 --> 00:28:39,960
Speaker 4: don't want people from Africa, but the reality is that

533
00:28:39,960 --> 00:28:41,880
Speaker 4: that's where people are, and in fact, that's where a

534
00:28:41,920 --> 00:28:44,640
Speaker 4: lot of human talent is. And the United States has

535
00:28:44,680 --> 00:28:49,080
Speaker 4: been the beneficiary of a huge amount of human migration

536
00:28:49,240 --> 00:28:51,640
Speaker 4: to the United States some of the most talented people

537
00:28:51,680 --> 00:28:53,640
Speaker 4: in the world. You know, I was just in Houston

538
00:28:53,640 --> 00:28:55,880
Speaker 4: where I was doing some reporting for an upcoming peace

539
00:28:55,920 --> 00:28:58,360
Speaker 4: about the Indian American experience. And we can talk about

540
00:28:58,360 --> 00:29:00,880
Speaker 4: the crazy anti Indian races that has just kind of

541
00:29:00,880 --> 00:29:04,400
Speaker 4: exploded on the right in recent days. But Indian Americans

542
00:29:04,680 --> 00:29:07,240
Speaker 4: have the highest household income of any group in the

543
00:29:07,320 --> 00:29:10,120
Speaker 4: United States. They're the most likely to be educated, with

544
00:29:10,400 --> 00:29:13,760
Speaker 4: a bachelor's degree or higher. They're extremely unlikely to commit

545
00:29:13,800 --> 00:29:16,560
Speaker 4: any crimes. They you know, tend to live in two

546
00:29:16,640 --> 00:29:19,160
Speaker 4: household families, you know, with mom and dad and kids.

547
00:29:19,240 --> 00:29:21,920
Speaker 4: By any metric that one might look at this group,

548
00:29:22,040 --> 00:29:24,200
Speaker 4: you can't really say like, oh, these are folks who

549
00:29:24,200 --> 00:29:25,920
Speaker 4: are coming to take advantage of America. There are people

550
00:29:25,920 --> 00:29:28,880
Speaker 4: whok coming and contributing to America. And if anyone should

551
00:29:28,920 --> 00:29:31,560
Speaker 4: know that, it should be jd Vance, whose own wife

552
00:29:31,600 --> 00:29:34,200
Speaker 4: is the product of such a family. And you know,

553
00:29:34,240 --> 00:29:37,000
Speaker 4: we've been very fortunate in the United States to be

554
00:29:37,160 --> 00:29:39,880
Speaker 4: the place where the smartest and most ambitious people in

555
00:29:39,920 --> 00:29:42,600
Speaker 4: the world want to come, and the Republican Party seems

556
00:29:42,640 --> 00:29:46,240
Speaker 4: absolutely determined to make sure that that changes and that

557
00:29:46,440 --> 00:29:49,280
Speaker 4: we drive away all of those talented people. And it's

558
00:29:49,320 --> 00:29:51,520
Speaker 4: not just Indian Americans. I mean, one of the fastest

559
00:29:51,520 --> 00:29:54,080
Speaker 4: growing groups of immigrants is African immigrants. And when people

560
00:29:54,080 --> 00:29:56,360
Speaker 4: think of African immigrants, they think, oh, you know, poor

561
00:29:56,400 --> 00:29:58,840
Speaker 4: shit hold countries, things like that. But we're talking about

562
00:29:58,920 --> 00:30:01,840
Speaker 4: Nigerian doctors and physicists, and I mean, I think that

563
00:30:02,080 --> 00:30:03,960
Speaker 4: we need all kinds of talent in this country. We

564
00:30:04,000 --> 00:30:07,680
Speaker 4: need nurses, we need home health as, we need construction workers.

565
00:30:07,840 --> 00:30:09,840
Speaker 4: You know, it's not just about elites.

566
00:30:09,560 --> 00:30:11,360
Speaker 1: But even we just need people.

567
00:30:11,560 --> 00:30:14,520
Speaker 4: Yeah, the reality is that there's no way to predict

568
00:30:14,640 --> 00:30:16,960
Speaker 4: who is going to be successful as an immigrant in

569
00:30:17,000 --> 00:30:20,000
Speaker 4: this country. But the very fact that someone is an

570
00:30:20,000 --> 00:30:23,320
Speaker 4: immigrant means that they have taken the initiative to do

571
00:30:23,360 --> 00:30:25,480
Speaker 4: something very difficult, which is to leave the place that

572
00:30:25,520 --> 00:30:27,400
Speaker 4: you're from and go to a new place in search

573
00:30:27,400 --> 00:30:30,760
Speaker 4: of opportunity. And a person who's willing to sacrifice in

574
00:30:30,840 --> 00:30:33,640
Speaker 4: order to do that is kind of, by definition a

575
00:30:33,680 --> 00:30:36,400
Speaker 4: person of extraordinary ambition. We tend to think, oh, anybody

576
00:30:36,440 --> 00:30:38,200
Speaker 4: who could come to the United States would come here.

577
00:30:38,240 --> 00:30:41,760
Speaker 4: But the reality is that globally, just five percent of

578
00:30:41,800 --> 00:30:44,480
Speaker 4: the population of the world lives outside the country in

579
00:30:44,480 --> 00:30:47,560
Speaker 4: which they were born. So it's actually a very select

580
00:30:47,640 --> 00:30:50,440
Speaker 4: few number of people who migrate. And so, you know,

581
00:30:50,480 --> 00:30:52,880
Speaker 4: I think in the past, American policy has been centered

582
00:30:52,880 --> 00:30:54,760
Speaker 4: on how to bring the best in the brightest to

583
00:30:54,840 --> 00:30:57,440
Speaker 4: this country, and now that all seems to be breaking apart.

584
00:30:57,680 --> 00:31:00,520
Speaker 1: Yeah, it's so interesting. I'm so glad you talk this

585
00:31:00,760 --> 00:31:04,960
Speaker 1: because it's so important, but it's also I mean, five percent.

586
00:31:05,000 --> 00:31:07,080
Speaker 1: It's funny because I think about my family and I

587
00:31:07,120 --> 00:31:10,040
Speaker 1: feel like my great grandparents came to this country. It

588
00:31:10,120 --> 00:31:14,320
Speaker 1: was so traumatic. There were four generations in and people

589
00:31:14,360 --> 00:31:16,880
Speaker 1: are still talking about it, you know, like it's a

590
00:31:16,920 --> 00:31:17,560
Speaker 1: big deal.

591
00:31:17,880 --> 00:31:19,920
Speaker 4: It's a really big deal. And I think that that's

592
00:31:19,960 --> 00:31:23,600
Speaker 4: something that we tend to underappreciate. People like to stay

593
00:31:23,680 --> 00:31:25,920
Speaker 4: where they know everybody, where they know the language, where

594
00:31:25,920 --> 00:31:27,840
Speaker 4: they know the system, where they know the culture, and

595
00:31:27,920 --> 00:31:30,000
Speaker 4: so it's a huge sacrifice. I mean, the other thing

596
00:31:30,000 --> 00:31:32,520
Speaker 4: that's really interesting about this right now, Molly, is that

597
00:31:32,680 --> 00:31:35,600
Speaker 4: you know, we saw in the twenty four election that

598
00:31:35,640 --> 00:31:39,680
Speaker 4: there was a significant swing of immigrant origin voters towards

599
00:31:39,720 --> 00:31:43,920
Speaker 4: the GOP. And one sort of assumption in American politics

600
00:31:44,000 --> 00:31:48,360
Speaker 4: until fairly recently was that non white voters were kind

601
00:31:48,360 --> 00:31:51,400
Speaker 4: of automatically going to become Democrats. You know, maybe Cubans

602
00:31:51,400 --> 00:31:53,440
Speaker 4: are the one exception to that rule, but you know,

603
00:31:53,440 --> 00:31:56,520
Speaker 4: there was a sense that, you know, immigrant origin voters

604
00:31:56,600 --> 00:31:59,640
Speaker 4: are Democratic voters, and therefore there's you know, that's that's

605
00:31:59,760 --> 00:32:01,400
Speaker 4: that's just kind of how it's always going to be.

606
00:32:01,560 --> 00:32:04,200
Speaker 4: But instead, what we saw was actually some of the

607
00:32:04,240 --> 00:32:08,240
Speaker 4: sharpest shifts towards the GOP came particularly among young men

608
00:32:08,400 --> 00:32:11,640
Speaker 4: of immigrant origin. And you know, there is just no

609
00:32:11,840 --> 00:32:17,920
Speaker 4: hope of recreating the Trump coalition without keeping those Latino voters,

610
00:32:17,960 --> 00:32:21,680
Speaker 4: without keeping those Indian American Asian American voters who trended

611
00:32:21,720 --> 00:32:23,960
Speaker 4: towards Trump. And what we've seen in the election since

612
00:32:23,960 --> 00:32:26,360
Speaker 4: twenty twenty four is that some of those voters, and

613
00:32:26,360 --> 00:32:29,400
Speaker 4: particularly in places where you can really see the population concentration,

614
00:32:29,680 --> 00:32:32,960
Speaker 4: they very sharply moved back towards the Democrats. So, you know,

615
00:32:33,040 --> 00:32:35,240
Speaker 4: you you went and spent some time in New Jersey

616
00:32:35,680 --> 00:32:39,280
Speaker 4: on the gubernatorial race, there Mikey Cheryl and the precincts

617
00:32:39,280 --> 00:32:41,320
Speaker 4: within central New Jersey where there's some of the highest

618
00:32:41,320 --> 00:32:44,600
Speaker 4: concentration of Indian Americans living in the United States. There

619
00:32:44,800 --> 00:32:47,880
Speaker 4: was a huge and sharp shift back towards Democrats, and

620
00:32:47,920 --> 00:32:50,400
Speaker 4: Mikey Cheryl, you know, she went to you know, an

621
00:32:50,480 --> 00:32:53,080
Speaker 4: Indian restaurant and talked to you know, small business owners

622
00:32:53,120 --> 00:32:55,920
Speaker 4: about how, you know, the tariffs were effected there. These

623
00:32:55,920 --> 00:32:58,520
Speaker 4: are fundamentally swing voters, right, They're not they don't have

624
00:32:58,560 --> 00:33:03,520
Speaker 4: a strong ideological or orientation. They're interested in, you know, affordability,

625
00:33:03,600 --> 00:33:07,200
Speaker 4: cost of living. They're often focused on immigration and dealing

626
00:33:07,280 --> 00:33:10,360
Speaker 4: with you know, with with unauthorized immigration because they see

627
00:33:10,360 --> 00:33:12,440
Speaker 4: themselves as people who followed the rules and came the

628
00:33:12,520 --> 00:33:15,360
Speaker 4: right way. So, you know, they're not automatically going to

629
00:33:15,400 --> 00:33:17,360
Speaker 4: be on the side of the Democrats, but they're certainly

630
00:33:17,400 --> 00:33:19,640
Speaker 4: not automatically going to be on the side of the Republicans.

631
00:33:19,640 --> 00:33:21,720
Speaker 4: So this is a demographic, you know, a series of

632
00:33:21,720 --> 00:33:24,120
Speaker 4: demographics that are really very much up for grabs, and

633
00:33:24,160 --> 00:33:25,640
Speaker 4: I think that that's going to be a really big

634
00:33:25,640 --> 00:33:27,360
Speaker 4: problem for the Republicans. We're already seeing it in the

635
00:33:27,400 --> 00:33:30,880
Speaker 4: Ohio governor's race. Howurday have you been following Vivic Ramaswami.

636
00:33:31,040 --> 00:33:33,920
Speaker 1: Yeah, but I want you to first talk about Houston

637
00:33:34,040 --> 00:33:36,440
Speaker 1: and then let's talk about the Ohio governors, right, yea,

638
00:33:36,560 --> 00:33:39,160
Speaker 1: sure you were in Texas and I don't actually know

639
00:33:39,280 --> 00:33:40,800
Speaker 1: what's happening in Texas. Yeah.

640
00:33:40,960 --> 00:33:42,960
Speaker 4: I decided to go to Texas because you know, the

641
00:33:43,000 --> 00:33:46,440
Speaker 4: Houston area has been the recipient of huge numbers of

642
00:33:46,600 --> 00:33:49,760
Speaker 4: Indian immigrants over the you know, I would say past

643
00:33:49,800 --> 00:33:51,960
Speaker 4: sixty years. You know, in the Indian immigants really could

644
00:33:52,000 --> 00:33:54,240
Speaker 4: only start coming to the United States in nineteen sixty five.

645
00:33:54,440 --> 00:33:56,720
Speaker 4: They have really prospered there. You know, a lot of

646
00:33:56,720 --> 00:33:59,440
Speaker 4: engineers who work in the oil and gas industry. You know,

647
00:33:59,480 --> 00:34:02,680
Speaker 4: there's a huge, huge kind of medical complex in Houston,

648
00:34:02,920 --> 00:34:05,840
Speaker 4: m D Anderson, all of those universities. There so many,

649
00:34:05,880 --> 00:34:08,920
Speaker 4: many many Indian doctors, you know, top researchers and things

650
00:34:09,000 --> 00:34:11,919
Speaker 4: like that. And this is a community that's actually done really,

651
00:34:11,960 --> 00:34:13,839
Speaker 4: really well. You know. I spent a lot of time

652
00:34:13,880 --> 00:34:16,839
Speaker 4: in a particular suburb called sugar Land where you know,

653
00:34:17,040 --> 00:34:20,560
Speaker 4: I visited this huge Hindu temple that has as existed

654
00:34:20,600 --> 00:34:23,000
Speaker 4: for twenty five years, and they a couple of years

655
00:34:23,040 --> 00:34:26,799
Speaker 4: ago they inaugurated this like huge, beautiful, ninety foot tall

656
00:34:26,880 --> 00:34:31,120
Speaker 4: statue of the of the god Hanuman, and recently some

657
00:34:31,480 --> 00:34:35,360
Speaker 4: you know, kind of maga adjacent chuds were you know,

658
00:34:35,400 --> 00:34:36,960
Speaker 4: went on the local news and were like, why are

659
00:34:37,040 --> 00:34:39,480
Speaker 4: you putting why are these devil worshippers putting up these

660
00:34:39,600 --> 00:34:42,200
Speaker 4: you know, these evil you know, monkey statues in our

661
00:34:42,360 --> 00:34:44,759
Speaker 4: Christian country. And I think that really took a lot

662
00:34:44,800 --> 00:34:47,719
Speaker 4: of people there by surprise, because for decades they've been

663
00:34:47,760 --> 00:34:51,719
Speaker 4: living you know, really happily and have integrated into the

664
00:34:51,760 --> 00:34:54,880
Speaker 4: life and the lifestyle of that part of Texas and

665
00:34:54,920 --> 00:34:56,880
Speaker 4: become really part of the fabric. I mean, they're probably

666
00:34:56,880 --> 00:34:59,400
Speaker 4: a quarter of a million Indian Americans living in the

667
00:34:59,400 --> 00:35:01,799
Speaker 4: Houston area, and so, you know, I think there's just

668
00:35:01,840 --> 00:35:04,560
Speaker 4: been a real shock at how this is all unfolded,

669
00:35:04,600 --> 00:35:07,000
Speaker 4: and there's just so much nastiness online and I know

670
00:35:07,360 --> 00:35:09,720
Speaker 4: Twitter isn't real life, but you know, once I started

671
00:35:09,760 --> 00:35:11,720
Speaker 4: looking for this stuff, the things that would be fed

672
00:35:11,760 --> 00:35:14,319
Speaker 4: to me about you know, oh all Indians are low

673
00:35:14,360 --> 00:35:17,719
Speaker 4: iq all and it's just really really nasty stuff. And

674
00:35:18,000 --> 00:35:21,600
Speaker 4: so you know, in talking to for example, PhD students

675
00:35:21,600 --> 00:35:24,480
Speaker 4: from India who are thinking about making a career. So

676
00:35:24,520 --> 00:35:27,040
Speaker 4: I talked to one young physicist. She's, you know, finishing

677
00:35:27,080 --> 00:35:30,000
Speaker 4: up her PhD. And she's working on technology that would

678
00:35:30,040 --> 00:35:35,120
Speaker 4: turn seawater into hydrogen for green energy. Pretty useful. It

679
00:35:35,160 --> 00:35:37,759
Speaker 4: seems like it'd be pretty useful, you know. And she's like,

680
00:35:37,800 --> 00:35:39,640
Speaker 4: you know what, I'm just not sure that I'm actually

681
00:35:39,680 --> 00:35:41,480
Speaker 4: going to be able to get an H one B

682
00:35:41,640 --> 00:35:43,840
Speaker 4: visa to work here in the United States. And you know,

683
00:35:43,880 --> 00:35:45,680
Speaker 4: I'd love to make a career here, but you know,

684
00:35:45,719 --> 00:35:47,440
Speaker 4: I might end up going to Germany or going to

685
00:35:47,480 --> 00:35:50,920
Speaker 4: Singapore somewhere else. And it's a really vivid example of

686
00:35:51,080 --> 00:35:54,480
Speaker 4: like how we lose. And the assumption is that no

687
00:35:54,560 --> 00:35:57,719
Speaker 4: matter how badly we treat people who are coming from

688
00:35:57,719 --> 00:36:00,799
Speaker 4: countries that are poor from US, those people automatically want

689
00:36:00,840 --> 00:36:02,279
Speaker 4: to come to the US. Who doesn't want to come

690
00:36:02,320 --> 00:36:04,759
Speaker 4: to America. But if you're going to face racism, if

691
00:36:04,800 --> 00:36:07,279
Speaker 4: you're going to face religious bigotry, if you're going to

692
00:36:07,320 --> 00:36:10,160
Speaker 4: be jerked around in getting your visa and getting a

693
00:36:10,200 --> 00:36:13,200
Speaker 4: green card, maybe you'll go someplace else. And there are

694
00:36:13,239 --> 00:36:14,279
Speaker 4: lots of other places to go.

695
00:36:14,640 --> 00:36:18,279
Speaker 1: I think about that Hyundai factory in Georgia where they

696
00:36:18,320 --> 00:36:22,040
Speaker 1: sent the South Korean highly trained workers to work in

697
00:36:22,080 --> 00:36:25,480
Speaker 1: the factory to train Americans and then ICE sent them

698
00:36:25,480 --> 00:36:28,080
Speaker 1: to a detention center and then put them on a plane.

699
00:36:28,360 --> 00:36:31,960
Speaker 4: Totally crazy. I mean, it's just it's just really really nuts.

700
00:36:32,080 --> 00:36:34,480
Speaker 4: And you know, it just kind of elevates this question

701
00:36:34,600 --> 00:36:37,319
Speaker 4: of like, if we don't want these folks who are

702
00:36:37,440 --> 00:36:40,160
Speaker 4: just a tiny fraction of the kind of talent that

703
00:36:40,200 --> 00:36:41,799
Speaker 4: we need in the United States, then who do we

704
00:36:41,840 --> 00:36:43,799
Speaker 4: want and what kind of country are we going to be?

705
00:36:44,120 --> 00:36:46,319
Speaker 4: And I think there's a tendency to think, well, you know,

706
00:36:46,440 --> 00:36:50,080
Speaker 4: this is just one administration. Things could change, could cool down.

707
00:36:50,600 --> 00:36:53,360
Speaker 4: But migration is such an interesting phenomenon. You know, it

708
00:36:53,600 --> 00:36:56,280
Speaker 4: tends to build on itself, right. You know, for sixty years,

709
00:36:56,320 --> 00:36:58,120
Speaker 4: Indians have been able to come to the United States

710
00:36:58,160 --> 00:37:01,320
Speaker 4: and become part of our country on to their own heritage,

711
00:37:01,320 --> 00:37:04,840
Speaker 4: but also become Americans and contribute all of you know, culturally,

712
00:37:05,080 --> 00:37:08,040
Speaker 4: you know, economically, in all these different ways. But you

713
00:37:08,120 --> 00:37:10,440
Speaker 4: also know, in moving to a place like Houston or

714
00:37:10,440 --> 00:37:12,360
Speaker 4: moving to a place like Central New Jersey or to

715
00:37:12,400 --> 00:37:14,840
Speaker 4: Silicon Valley, you know, I'm going to find other folks

716
00:37:14,840 --> 00:37:16,279
Speaker 4: like me who are going to help me find my

717
00:37:16,320 --> 00:37:19,520
Speaker 4: way and those patterns are being built elsewhere. You know.

718
00:37:19,640 --> 00:37:22,080
Speaker 4: I talked to one German entrepreneur who just happened to

719
00:37:22,080 --> 00:37:23,960
Speaker 4: be in Houston, and he was like, oh, yeah, no, no,

720
00:37:24,000 --> 00:37:26,040
Speaker 4: we've been recruiting a lot of people from India, and

721
00:37:26,080 --> 00:37:28,759
Speaker 4: as soon as people here, you know, oh, actually there

722
00:37:28,800 --> 00:37:30,680
Speaker 4: are other Indians here, so you're not going to be

723
00:37:30,680 --> 00:37:32,759
Speaker 4: the only one, and there are people who can help

724
00:37:32,760 --> 00:37:35,280
Speaker 4: you find your way and settle in, and your kid's

725
00:37:35,280 --> 00:37:36,880
Speaker 4: not going to be the only one at school and

726
00:37:36,960 --> 00:37:40,319
Speaker 4: so on. Then those patterns and pathways change, and once

727
00:37:40,360 --> 00:37:42,239
Speaker 4: they do, I think it's really hard to change them,

728
00:37:42,320 --> 00:37:43,399
Speaker 4: to switch them back.

729
00:37:43,640 --> 00:37:47,279
Speaker 1: Right, and immigration has like a long tail, right absolutely,

730
00:37:47,440 --> 00:37:50,640
Speaker 1: even if you had an admin come in and say

731
00:37:51,040 --> 00:37:53,040
Speaker 1: we are pro immigrant, we're going to do this, We're

732
00:37:53,040 --> 00:37:55,360
Speaker 1: going to do that, it's going to take decades to

733
00:37:55,560 --> 00:37:57,600
Speaker 1: shift that kind of Well, then who's going.

734
00:37:57,520 --> 00:38:00,279
Speaker 4: To trust America? Right broadly? You know, there's been so

735
00:38:00,520 --> 00:38:03,600
Speaker 4: much sudden and sharp change, And I think that what

736
00:38:03,680 --> 00:38:07,399
Speaker 4: people see is that American voters were willing to roll

737
00:38:07,400 --> 00:38:09,560
Speaker 4: the dice on this, and so it's not even just Trump.

738
00:38:09,640 --> 00:38:11,879
Speaker 4: It's like, if American voters are willing to roll their dice,

739
00:38:11,960 --> 00:38:14,319
Speaker 4: vill of the dice in and accept this. What else

740
00:38:14,320 --> 00:38:15,840
Speaker 4: could come down the pike? You know, you could have

741
00:38:15,840 --> 00:38:19,280
Speaker 4: a president who's just relentlessly backed by the Supreme Court,

742
00:38:19,360 --> 00:38:23,120
Speaker 4: who has Congress in his bucket, and you know, so

743
00:38:23,400 --> 00:38:25,520
Speaker 4: I think, you know, Trump is a singular figure, but

744
00:38:25,600 --> 00:38:28,080
Speaker 4: I think that it's not just losing faith in one

745
00:38:28,120 --> 00:38:32,080
Speaker 4: particular president. It's you know, really kind of losing faith

746
00:38:32,080 --> 00:38:34,680
Speaker 4: in the wisdom of American voters such as it is.

747
00:38:35,200 --> 00:38:37,800
Speaker 1: Yeah, which we should all have lost faith in American

748
00:38:37,920 --> 00:38:42,040
Speaker 1: voters too, probably. I wonder if we can talk for

749
00:38:42,080 --> 00:38:46,799
Speaker 1: a minute about immigration just more broadly, like Trump has

750
00:38:46,920 --> 00:38:50,680
Speaker 1: been I don't believe this, but there is a conventional

751
00:38:50,760 --> 00:38:55,080
Speaker 1: pundit wisdom which says that voters were mad about the

752
00:38:55,120 --> 00:38:58,919
Speaker 1: border and Biden and that Trump came in and that

753
00:38:58,920 --> 00:39:02,040
Speaker 1: that was something that voters liked him on. Maybe not

754
00:39:02,600 --> 00:39:05,200
Speaker 1: liberal voters, but voters liked him on. I actually don't

755
00:39:05,200 --> 00:39:08,520
Speaker 1: think it's true. But whatever, But now this ice stuff,

756
00:39:08,600 --> 00:39:11,279
Speaker 1: like people don't like him, even his people are not

757
00:39:11,400 --> 00:39:14,680
Speaker 1: happy with this. Do you think that see changed like

758
00:39:14,960 --> 00:39:17,040
Speaker 1: you know that and the economy have been like his

759
00:39:17,520 --> 00:39:19,279
Speaker 1: you know, where people were like, you have to hand

760
00:39:19,320 --> 00:39:21,239
Speaker 1: it to him on the economy. So I mean, do

761
00:39:21,280 --> 00:39:24,680
Speaker 1: you think that changes the trajectory? Obviously won't change the

762
00:39:24,760 --> 00:39:27,160
Speaker 1: trajectory for immigrants, but do you think that changes the

763
00:39:27,200 --> 00:39:30,280
Speaker 1: trajectory for the Republican Party writ large.

764
00:39:30,800 --> 00:39:32,840
Speaker 4: I mean, it's a great question, Molly. I mean, I

765
00:39:32,880 --> 00:39:36,480
Speaker 4: think that a lot of people were upset about the border, right,

766
00:39:36,560 --> 00:39:38,839
Speaker 4: I mean, I think that seems clear, and I think

767
00:39:38,880 --> 00:39:41,000
Speaker 4: there was a feeling and this again coming back to

768
00:39:41,000 --> 00:39:43,680
Speaker 4: this idea that like people of immigrant origin are really

769
00:39:43,760 --> 00:39:45,239
Speaker 4: kind of swing voters, because I think a lot of

770
00:39:45,239 --> 00:39:47,560
Speaker 4: people of immigrant origin were mad about the border too,

771
00:39:47,760 --> 00:39:51,760
Speaker 4: you know, because nobody competes for jobs, you know, with immigrants,

772
00:39:51,920 --> 00:39:54,120
Speaker 4: like the immigrants who arrived two years ago. You know,

773
00:39:54,440 --> 00:39:56,799
Speaker 4: like that's a very real phenomenon. So I do think

774
00:39:56,800 --> 00:39:58,799
Speaker 4: that that anger was real. You know a lot of

775
00:39:58,840 --> 00:40:00,440
Speaker 4: the stuff that was done at the bord order and

776
00:40:00,440 --> 00:40:02,719
Speaker 4: that the you know, the border crossing's really plummeted in

777
00:40:02,800 --> 00:40:04,960
Speaker 4: the toward the tail end of the Biden administration, but

778
00:40:05,000 --> 00:40:06,920
Speaker 4: it was too little, too late. And so I do

779
00:40:06,960 --> 00:40:09,279
Speaker 4: think that that anger was real. But I think that

780
00:40:09,840 --> 00:40:14,399
Speaker 4: there is really just a very small constituency for the

781
00:40:14,520 --> 00:40:17,319
Speaker 4: thing that that that the Trump administration is doing now,

782
00:40:17,440 --> 00:40:20,520
Speaker 4: and honestly, sometimes I wonder if that constituency even includes

783
00:40:20,560 --> 00:40:22,360
Speaker 4: Donald Trump. You know, he's kind of all over the

784
00:40:22,400 --> 00:40:24,520
Speaker 4: place on this stuff, right, I mean, he's he sort

785
00:40:24,560 --> 00:40:26,279
Speaker 4: of says, oh, yeah, you know, we need the h

786
00:40:26,360 --> 00:40:29,239
Speaker 4: one B. You know, we we you know, people need jobs.

787
00:40:29,280 --> 00:40:31,240
Speaker 4: We don't have the talent in the United States. Meanwhile,

788
00:40:31,280 --> 00:40:34,000
Speaker 4: you have you know, JD. Vance out there and Stephen

789
00:40:34,040 --> 00:40:37,360
Speaker 4: Miller saying these, you know, kind of really hardline things

790
00:40:37,440 --> 00:40:39,680
Speaker 4: about how immigrants are the reason that you can't buy

791
00:40:39,680 --> 00:40:41,640
Speaker 4: a house, you know, or that they're the reason you

792
00:40:41,640 --> 00:40:43,840
Speaker 4: can't find but you know, find a job. And so

793
00:40:44,120 --> 00:40:46,279
Speaker 4: I think there's a lot of kind of there. It

794
00:40:46,280 --> 00:40:48,680
Speaker 4: feels a little schizophrenic the moves that we're seeing with

795
00:40:48,719 --> 00:40:51,320
Speaker 4: the rhetoric that we're seeing within the administration. But I

796
00:40:51,360 --> 00:40:54,200
Speaker 4: think that the relentless forward march of these you know,

797
00:40:54,360 --> 00:40:59,680
Speaker 4: really indiscriminate, quite violent, you know, frankly fascist operations of

798
00:40:59,760 --> 00:41:02,720
Speaker 4: I in the United States, and the kind of wanton

799
00:41:02,840 --> 00:41:05,799
Speaker 4: cruelty of it, I think is something that the overwhelming

800
00:41:05,800 --> 00:41:08,560
Speaker 4: majority of Americans just do not like. And a lot

801
00:41:08,600 --> 00:41:11,279
Speaker 4: of my reporting around the world about migration, whenever I

802
00:41:11,360 --> 00:41:14,200
Speaker 4: talk to people who are angry about immigrants. I would

803
00:41:14,280 --> 00:41:17,200
Speaker 4: ask them, would you rather be from a country that

804
00:41:17,280 --> 00:41:19,960
Speaker 4: people flee from or a country that people flee to?

805
00:41:20,320 --> 00:41:22,680
Speaker 4: And you know, of course everyone everyone wants to think

806
00:41:22,680 --> 00:41:25,280
Speaker 4: that their place is the greatest place in the world.

807
00:41:25,440 --> 00:41:28,600
Speaker 4: And I think for Americans it's even much deeper than that.

808
00:41:28,840 --> 00:41:33,239
Speaker 4: You know, the idea that the American brand as it were,

809
00:41:33,480 --> 00:41:37,320
Speaker 4: as a destination, as a repository for people's dreams would

810
00:41:37,320 --> 00:41:40,080
Speaker 4: be tarnished, I think is something that really gets to

811
00:41:40,120 --> 00:41:42,840
Speaker 4: the core of the American psyche, you know, and I

812
00:41:42,840 --> 00:41:45,480
Speaker 4: think people can be mad because they, you know, feel

813
00:41:45,480 --> 00:41:47,680
Speaker 4: like things are disorderly, or they think that, you know,

814
00:41:47,719 --> 00:41:49,480
Speaker 4: the border is a mess, or you know, they are

815
00:41:49,480 --> 00:41:51,719
Speaker 4: these bus loads of migrants coming from the border to

816
00:41:51,800 --> 00:41:54,200
Speaker 4: northern cities and you know they're they're sleeping on the

817
00:41:54,200 --> 00:41:57,240
Speaker 4: streets of Chicago or whatever. You can confront that disorder

818
00:41:57,239 --> 00:41:58,880
Speaker 4: and be like, something's got to be done about this,

819
00:41:58,960 --> 00:42:00,799
Speaker 4: But that doesn't mean that you signed up for the

820
00:42:00,800 --> 00:42:03,920
Speaker 4: agenda that this administration is putting out there. And so

821
00:42:04,000 --> 00:42:06,560
Speaker 4: I think there's just a huge disconnect between those things.

822
00:42:06,719 --> 00:42:09,480
Speaker 4: And you know, the reality is that you know, Trump

823
00:42:09,480 --> 00:42:11,480
Speaker 4: won the presidency by the skin of his teeth, and

824
00:42:11,520 --> 00:42:14,600
Speaker 4: he won it with the supportive people who wanted, as

825
00:42:14,640 --> 00:42:17,840
Speaker 4: they say in Congress, kind of common sense immigration reform.

826
00:42:18,120 --> 00:42:21,200
Speaker 4: You know, they did not want to, you know, start

827
00:42:21,480 --> 00:42:25,720
Speaker 4: subjecting Indian doctors, you know, who are frankly desperately needed,

828
00:42:25,800 --> 00:42:28,440
Speaker 4: particularly in rural areas of the United States.

829
00:42:28,320 --> 00:42:29,480
Speaker 1: Especially in Texas.

830
00:42:29,880 --> 00:42:32,400
Speaker 4: No totally to be subjected to one hundred thousand dollars

831
00:42:32,520 --> 00:42:34,400
Speaker 4: fee to get an H one B visa, which is

832
00:42:34,440 --> 00:42:37,239
Speaker 4: just a non starter, you know. But it's funny, right because, like,

833
00:42:37,440 --> 00:42:39,239
Speaker 4: you know, on the one hand, you have all this

834
00:42:39,360 --> 00:42:41,799
Speaker 4: rhetoric that you know, these people are you know, they

835
00:42:41,800 --> 00:42:43,799
Speaker 4: have lower IQ's than the US, they come from a

836
00:42:43,840 --> 00:42:46,279
Speaker 4: poor country and blah blah blah. But you know, if

837
00:42:46,320 --> 00:42:48,160
Speaker 4: that's the case, like why are you afraid of them

838
00:42:48,200 --> 00:42:49,040
Speaker 4: taking your jobs?

839
00:42:49,360 --> 00:42:49,560
Speaker 3: You know?

840
00:42:49,840 --> 00:42:52,600
Speaker 4: Like what does that say about you?

841
00:42:53,280 --> 00:42:55,840
Speaker 1: Lydia? Will you come back? And also can we do

842
00:42:55,920 --> 00:42:56,640
Speaker 1: more stuff together?

843
00:42:56,719 --> 00:42:57,960
Speaker 4: I love you anytime.

844
00:43:00,719 --> 00:43:06,279
Speaker 3: No moment, Rick Wilson, Mali Jong Fast.

845
00:43:06,239 --> 00:43:07,399
Speaker 1: What is your moment of fuck?

846
00:43:07,480 --> 00:43:10,360
Speaker 3: Ray my momonni fuckery this week is the social media

847
00:43:10,400 --> 00:43:12,799
Speaker 3: stuff that the government is now going to demand of

848
00:43:12,800 --> 00:43:15,880
Speaker 3: everyone who enters the country where they're now saying to

849
00:43:16,000 --> 00:43:18,839
Speaker 3: anybody who wants to come here on a tourist visa.

850
00:43:19,280 --> 00:43:20,640
Speaker 3: So if you want to come over here to go

851
00:43:20,680 --> 00:43:25,720
Speaker 3: to Disney World, now you have to provide the federal

852
00:43:25,719 --> 00:43:29,520
Speaker 3: government with five years of your social media history, all accounts,

853
00:43:29,920 --> 00:43:33,960
Speaker 3: all your email accounts, all your IP addresses. And I

854
00:43:33,960 --> 00:43:37,840
Speaker 3: find the whole thing just grotesque because we do monitor

855
00:43:37,880 --> 00:43:40,520
Speaker 3: people who are extremists around the world, but we do

856
00:43:40,560 --> 00:43:43,359
Speaker 3: it because we have a cause to So some guy

857
00:43:43,440 --> 00:43:46,360
Speaker 3: is like, yes, I'm going to America to commit jihad

858
00:43:46,400 --> 00:43:49,560
Speaker 3: and burn down a daycare center. We order that guy

859
00:43:49,960 --> 00:43:53,080
Speaker 3: because we already do those things. But this idea that

860
00:43:53,160 --> 00:43:55,880
Speaker 3: everybody who's coming here, the seventy five year old grandmother

861
00:43:55,960 --> 00:43:59,840
Speaker 3: with her grandchildren coming from France who comes to the US,

862
00:44:00,200 --> 00:44:03,319
Speaker 3: needs to turn over social media. This is feeding the

863
00:44:03,400 --> 00:44:07,319
Speaker 3: pal and teer machine. This is not about anything other

864
00:44:07,360 --> 00:44:10,399
Speaker 3: than that. This is about giving Alice carp and Peter

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00:44:10,480 --> 00:44:13,600
Speaker 3: Thiel more of the data from people around the world.

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00:44:13,880 --> 00:44:16,279
Speaker 3: And I find it to just be like, I find

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00:44:16,280 --> 00:44:20,800
Speaker 3: it deeply offensive that we are suddenly going to say

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00:44:21,239 --> 00:44:24,600
Speaker 3: that your private speech is going to be monitored and

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00:44:24,719 --> 00:44:26,279
Speaker 3: sucked down. If you want to come to the US,

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00:44:26,320 --> 00:44:29,480
Speaker 3: it's going to be terrible for business, terrible for tourism,

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00:44:29,600 --> 00:44:32,000
Speaker 3: terrible for our reputation. So that's my moont.

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00:44:31,760 --> 00:44:33,680
Speaker 4: Tofuckerat small government.

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00:44:33,960 --> 00:44:37,399
Speaker 3: Very small government, the smalls small. Thank you, Rick, I'll

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00:44:37,400 --> 00:44:38,280
Speaker 3: see you next time.

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Speaker 1: That's it for this episode of Fast Politics. Tune in

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00:44:43,600 --> 00:44:49,040
Speaker 1: every Monday, Wednesday, Thursday and Saturday to hear the best

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00:44:49,120 --> 00:44:53,399
Speaker 1: minds and politics make sense of all this chaos. If

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00:44:56,920 --> 00:45:00,080
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