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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 President Trump's approval rating among gen

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Speaker 1: Z voters has fallen to a new low of twenty

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Speaker 1: five percent, with sixty seven percent disapproving, according to the

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Speaker 1: Latest Economist. Yougo falling, congratulations, Donald, We have such a

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Speaker 1: great show for you today. The Jim Acostas Show's own

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Speaker 1: Jim Acosta stops by to talk Pam Bondy taking the

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Speaker 1: stand to defend her indefensible handling of the Epstein files.

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Speaker 1: Ben we'll talk to Princeton Samwang about his run for

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Speaker 1: Congress in New Jersey's twelfth district. But first we have.

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Speaker 2: The news Somalia Gallup will no longer measure presidential approval

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Speaker 2: after eighty eight years. What do you think could have

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Speaker 2: spurred this decision?

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Speaker 1: Is Trump sending Gallup to Gitmo? We need to know more.

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Speaker 1: Probably not. The Gallup approval rating has for decades been

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Speaker 1: among the top barometers cited by media outlets measuring public

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Speaker 1: opinion of the president's performance. Trump has seen his ratings

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Speaker 1: slip from the agency, peaking at forty seven percent last February,

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Speaker 1: dipping to lower than thirty seven percent in December those

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Speaker 1: watching at home, that is ten percentage points. The change

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Speaker 1: is part of a broader, ongoing effort to align all

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Speaker 1: of Gallup's public work with its mission, which is who Knows.

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Speaker 1: Asked by The Hill if Gallup had received any feedback

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Speaker 1: from Donald Trump's White House and if they were in

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Speaker 1: fact being held hostage right now, the Gallop bravely said,

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Speaker 1: this is a strategic, solid shift based solely on we

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Speaker 1: are not being held hostage right now by the Trump administration,

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Speaker 1: So believe them, don't believe them? Who knows? Former President

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Speaker 1: John F. Kennedy experienced some of the highest average ratings

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Speaker 1: that Gallup had seventy one percent in January nineteen sixty one.

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Speaker 1: Eisenhower too super popular, more popular than Donald Trump.

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Speaker 2: Yeah, fun stuff. Really great week or so for journalism

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Speaker 2: in the last week. So in other news of where optimism,

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Speaker 2: where our justice keeps holding up is in these grand

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Speaker 2: jury indictments and a grand jury declined to indict Democratic

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Speaker 2: lawmakers who urged service members to disobey legal Trump orders.

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Speaker 2: This included a bunch of senators, including Mark Kelly, who,

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Speaker 2: Pete Higsetth, decided to punish.

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Speaker 1: So a bunch of former service members who were in

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Speaker 1: Congress and in the Senate made an advertisement together and

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Speaker 1: in the video with a ninety second video clip, they said,

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Speaker 1: if you are ordered to do something illegal, you don't

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Speaker 1: have to do it. This would seem, in a sane world,

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Speaker 1: to be not only not illegal, but just normal common sense. Right,

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Speaker 1: somebody tells you to do something illegal, don't do it.

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Speaker 1: As someone who thinks that that's pretty good advice, military

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Speaker 1: or not military, the whole idea that this would be

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Speaker 1: illegal is insane. Here's what happened. Trump got really mad.

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Speaker 1: The fact that Trump identifies this stuff as a being

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Speaker 1: about him is almost a bigger tell. Like during the

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Speaker 1: super Bowl, Trump was offended because Bad Bonnie talked about

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Speaker 1: how hate was bad, and Trump was like, hey, hey hate,

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Speaker 1: that's me. And with this, it's like illegal orders, but

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Speaker 1: I might want to do something illegal. So I think

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Speaker 1: it's really important just to pull back and see that tel.

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Speaker 1: But it's also important to remember that traditionally, grand juries

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Speaker 1: have been so famous for their interest in indicting that

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Speaker 1: there is that saying that you could find a grand

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Speaker 1: jury that would indict a ham sandwich clearly, not because

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Speaker 1: Senator Slockin, Senator Kelly and a bunch of members of

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Speaker 1: Congress were not indicted in the district of Columbia, but

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Speaker 1: also because the first Amendment. I don't know if you

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Speaker 1: know about this, it's not the one the Republicans love.

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Speaker 1: That's the second one, that's the guns. The first one

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Speaker 1: actually says that you can do videos like this because

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Speaker 1: they are protected under the free speech clause of the Constitution.

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Speaker 2: I thought it was pretty interesting the analysis that senior

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Speaker 2: officials would not take this up, but you know who

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Speaker 2: would take it up. Judge Box of Wine.

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Speaker 1: Yeah, Judge Box of Wine. And one of the things

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Speaker 1: I enjoy about her is that she is just about

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Speaker 1: as bad a prosecutor as you would think. It turns

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Speaker 1: out that being good on Fox News does not actually

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Speaker 1: make you good at the law.

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Speaker 2: So Molly, you know, it is a basically like a

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Speaker 2: red moon. It's so rare that I praise the strategy

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Speaker 2: democratic leadership. I think this is quite smart that the

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Speaker 2: House Democrats are going to do a ton of anti

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Speaker 2: teriff votes to make it so that this albatross is

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Speaker 2: tied to the House members of the Republican sides neck.

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Speaker 1: Yeah, so Democrats in Congress, and I really believe this.

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Speaker 1: I think they're doing good stuff in there, even though

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Speaker 1: they don't have the majority, though they only have a

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Speaker 1: one seat minority. So here's what happened. The White House

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Speaker 1: had wanted a rule put in with the rules that

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Speaker 1: would say that like they couldn't do more questions or

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Speaker 1: votes on tariffs. Remember, the White House is on the

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Speaker 1: back foot on tariffs. And here's why the Supreme Court

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Speaker 1: is looking at this big tariff case that might take

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Speaker 1: away Trump's emergency powers. Tariffs are not popular. We just

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Speaker 1: saw this week some news that tariffs cost approximately one

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Speaker 1: thousand dollars of family, that they're making things more expensive,

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Speaker 1: they're inflationary, which we've talked about endlessly. So this administration

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Speaker 1: is on the back foot on the tariffs. Republicans are

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Speaker 1: nine months from a midterm. They have a very unpopular president,

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Speaker 1: and this is an opportunity for them. And one of

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Speaker 1: the things that I want you to think about is

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Speaker 1: like you will see Republicans behaving bravely at this moment.

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Speaker 1: That is not because they are brave. Do you know

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Speaker 1: why that is? It is because they can read polls

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Speaker 1: and they want to keep their jobs. So you're going

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Speaker 1: to see votes on tariffs. You're going to see them

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Speaker 1: break with Trump. You're going to see this stuff, and

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Speaker 1: you're going to see it because these Republicans want to

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Speaker 1: keep their jobs and they're worried about losing their jobs.

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Speaker 1: And there's no reason to believe that these people have

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Speaker 1: had a change of heart, and there's every reason to

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Speaker 1: believe that this is just craven opportunism. So what's going

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Speaker 1: to happen now is we're going to have votes and

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Speaker 1: members of Congress are going to stand up to this,

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Speaker 1: and Trump may lose on some of these votes, though

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Speaker 1: I think ultimately the Supreme Court will have the power

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Speaker 1: they want to block the tariffs.

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Speaker 2: So a judge has dismissed for the third time this year,

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Speaker 2: the DOJ's lawsuit seeking Michigan's voter role data. I wonder

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Speaker 2: why the dog keeps going after this voter world data.

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Speaker 1: Yeah, because Donald Trump wants to fix the midterms and

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Speaker 1: this is the only way he thinks he could do it.

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Speaker 1: Thank god that he doesn't have the votes and the

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Speaker 1: Save Act also we're seeing he doesn't have the votes

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Speaker 1: when it comes to Save Act, which is good. He

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Speaker 1: knows he's unpopular. He knows it's nine months to the midterms,

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Speaker 1: he knows he's going to get impeaged, and he wants

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Speaker 1: to do anything he can to prevent it. So we're

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Speaker 1: going to see a lot more of this kind of

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Speaker 1: fuckorate when it comes to elections and what Democrats need

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Speaker 1: to do, if they need to stand up for our

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Speaker 1: elections and for the rule of flow. Jim Acosta is

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Speaker 1: the author of The Substack, The Jim Acosta Show, and

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Speaker 1: the author of the New York Times' bestseller Enemy of

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Speaker 1: the People, A Dangerous Time to Tell the Truth in America. Welcome, Welcome,

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Speaker 1: Jim Acosta.

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Speaker 3: Hi, Mollie, Hi, I'm at the airport.

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Speaker 1: Coming to me from la where he is the consummate professional.

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Speaker 3: The seventh level of Dante's Inferno, recording to you live.

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Speaker 1: That's right. I don't know if you saw that. Pam

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Speaker 1: Bondy is Oh.

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Speaker 3: My god, she is not happy. What the hell?

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Speaker 1: How dare you? How dare you watch this hearing and

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Speaker 1: criticize President Trump discuss.

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Speaker 3: I know, the greatest president of all time. I don't

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Speaker 3: know that this may be too low browber of a

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Speaker 3: reference for you, but she reminds me of Ricky Bobby's

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Speaker 3: wife from Talladega Knights. You know when she says, I'm

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Speaker 3: a racer's wife, I don't work, you know, that's what

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Speaker 3: she reminds me of. I apologize for the lowbrow reference,

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Speaker 3: but that is what I hear every time she opens

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Speaker 3: her mouth. What a shameful performance just to get serious

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Speaker 3: for a moment, like the survivors of Jeffrey Epstein's stand up.

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Speaker 1: She won't look at them.

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Speaker 3: She won't look at them. They won't meet with them.

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Speaker 3: The Justice Department has not met with these survivors and

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Speaker 3: victims and their families. It's appalling.

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Speaker 1: So let's talk about this. Representative Ji Paul. You know,

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Speaker 1: congressional hearings are so interesting because a lot of times

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Speaker 1: there's just a lot of grandstanding, but occasionally there's really

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Speaker 1: effective questioning. So Representative Jipaul to the survivors in the room,

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Speaker 1: if you're willing, please stand, and if you're willing, please

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Speaker 1: raise your hand. If you have not been able to

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Speaker 1: meet with the DOJ. Please note for the record that

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Speaker 1: every single survivor has raised their hands. So what do

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Speaker 1: you think about that?

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Speaker 3: How much worse is this going to get? How many

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Speaker 3: more indignities are they going to have to suffer through

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Speaker 3: these survivors and these victims. I know we've said this,

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Speaker 3: we've stipulated this, that shamelessness is their superpower, but my god,

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Speaker 3: this is like next level Jedi master shamelessness. I mean,

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Speaker 3: it's just unreal to me. The only way to do

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Speaker 3: this is to keep the pressure on. When Rocanna went

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Speaker 3: to the floor of the house and read those names

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Speaker 3: out loud, that felt like, for a moment, the beginning

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Speaker 3: of some sort of light at the end of the tunnel,

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Speaker 3: some ray of hope that came into all of this,

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Speaker 3: and I just think that, you know, they're just going

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Speaker 3: to have to keep charging forward. My sense of it

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Speaker 3: is is that at some point something the damn is

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Speaker 3: going to break. And when I saw you know, the

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Speaker 3: FA restrictions going up Overell Passo this morning, I was like,

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Speaker 3: are we going to def con you know one when

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Speaker 3: it comes to Trump and the Epstein files. Is that

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Speaker 3: what's happening here? The final chapters of this have not

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Speaker 3: been written, And it just seems to me like when

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Speaker 3: Howard Lutnik is like, oh, yeah, I brought my family.

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Speaker 3: I brought my four kids and the nannies to Epstein Island,

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Speaker 3: How has he not been fired? How is he still there?

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Speaker 1: Howard Lutnik has a really interesting dejectory. We see him

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Speaker 1: on a podcast say that he went to Epstein's house

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Speaker 1: in two thousand and four, thought Epstein was a creep,

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Speaker 1: didn't want anything to do with him. Then we hear

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Speaker 1: that's actually a lie. He's in the files endlessly. He

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Speaker 1: did an LLC with Ebstein. He's got these emails from

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Speaker 1: his wife to Epstein's assistant saying, we're gonna come. Here

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Speaker 1: are the kids. We have four kids, lists their ages,

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Speaker 1: which if I were taking my kids to the home

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Speaker 1: of a sex predator and my friend's kids, I would

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Speaker 1: probably not list their I mean, I think listing their

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Speaker 1: ages is the least of it. But yeah, okay, And

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Speaker 1: then there's this weird email about the nanny. So the

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Speaker 1: Latnick nanny is going to meet with Jeffrey Epstein. Now

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Speaker 1: there is some other nanny talk in other pieces of

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Speaker 1: the file, emails and such about men getting nanny's from

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Speaker 1: Epstein and they're not nanny's for the kids.

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Speaker 3: Right now, this is like, could it get any worse? Honestly,

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Speaker 3: and to me, you know, Howard Lutnik, I was looking

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Speaker 3: to see is he worth like threety billion dollars or

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Speaker 3: something like that.

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Speaker 1: All these people right now are worth ten brillion dollars

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Speaker 1: because they're totally grubbed.

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Speaker 3: And how many people in Donald Trump's orbit? I mean,

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Speaker 3: first of all, he's in there tens of thousands of times.

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Speaker 1: Grump is in there more than a million times.

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Speaker 3: I heard Scott Galloway say that that Trump is in

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Speaker 3: the Epstein files more than Jesus is in the Bible.

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Speaker 3: I think, is that is that maybe him come up with.

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Speaker 1: Where Harry Poliner is in the Harry Potter books.

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Speaker 3: I mean, at some point, so we need to start

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Speaker 3: asking the question, Donald Trump, how is it that you

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Speaker 3: are in the Epstein files so many times? What the

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Speaker 3: hell is going on? When are you going to finally

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Speaker 3: offer a full explanation for all of this? And one

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Speaker 3: of my friends in the press is going to grill

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Speaker 3: his ass on this, I'm sorry, Molly. In any other administration,

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Speaker 3: and any other administration, I mean, any other timeline, any

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Speaker 3: other on Earth one, two, three, and four, he would

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Speaker 3: have been crucified over this ship. A president would have

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Speaker 3: been crucified over this shit.

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Speaker 1: Aren't they scared of losing press room access?

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Speaker 4: Yes?

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Speaker 3: But who gives a fuck at this point, Go for

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Speaker 3: the gold. It's the goddamn Olympics. Go for the fucking

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Speaker 3: White House for Responsor Association gold. And Grill has ass

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Speaker 3: on this. When he starts to insult, keep going, and

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Speaker 3: the other recorders of the room keep going. How is

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Speaker 3: it Howard Lutnik, the Secretary of the Navy, Elon Musk,

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Speaker 3: Steve Bannon over and over again.

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Speaker 1: Kimball, Musk got girls from Epstein, Grill his ass.

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Speaker 3: I'm sorry, I just don't get it.

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Speaker 1: If you're a Tesla stockholder, your board has Elon and Kimball,

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Speaker 1: both of whom have been I mean, like, the thing is,

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Speaker 1: don't we want in the rest of the world where

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Speaker 1: people are normal. They are firing people in the Epstein files.

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Speaker 1: They are right leaving from government. They are talking about

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Speaker 1: removing the Prime Minister of Britain, not because he was

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Speaker 1: in the Epstein files, merely because he hired someone in

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Speaker 1: the Epstein files. You know who is in the Epstein files, Donald.

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Speaker 3: The President, I know, And I just think that maybe

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Speaker 3: this is just going to come down to a voter

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Speaker 3: revolt in the fall, and that'll be where the accountability starts.

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Speaker 3: But it just seems to me that I mean, Pam

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Speaker 3: Bonnie the performance that she does anybody buy this? Does

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Speaker 3: anybody see this as credible? When she gets up there

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Speaker 3: and that this Kobookie Theater with Jim Jordan and the

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Speaker 3: other Republicans on the can, it's just like that, no

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Speaker 3: one's buying this, it's all worship. They're covering this up

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Speaker 3: and they're covering his ass.

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Speaker 1: But Pam Bondi only is performing for one person, and

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Speaker 1: she is. It's funny because when I was watching her,

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Speaker 1: I was thinking about Justice bred Kavanaugh. And you'll remember

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Speaker 1: that when Justice Fred Kavanaugh got up there, the first day,

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Speaker 1: he was normal and Trump hated it, and the second

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Speaker 1: day he was insane and Trump loved it. And that's

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Speaker 1: what we're seeing with Bondi is her whole thing is

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Speaker 1: that she wants me Trump happy.

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Speaker 3: Well, and that's that's all well and good, and they

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Speaker 3: can hit option to the faux outrage button and put

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Speaker 3: on that performance for the audience of one. But I

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Speaker 3: still think that these hearings are productive in that we

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Speaker 3: are memorializing for all time the lunacy of what they

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Speaker 3: are putting this country through. And it just seems to

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Speaker 3: me this is creeping up further and further. And I

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Speaker 3: know we've all banned. You know, we can't say the

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Speaker 3: expression the walls are closing in or you know, this

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Speaker 3: is it.

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Speaker 1: Because we did a year four years of it.

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Speaker 3: And you should be publicly flogged for saying so. But

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Speaker 3: there is a there there with the Epstein files in

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Speaker 3: Donald Trump, and until the public scratches that itch and

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Speaker 3: gets to the bottom of this, his ass is still

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Speaker 3: in hot water, it seems to me.

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Speaker 1: But I also think a really important point of this

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Speaker 1: story is that, like, you never know what's going to

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Speaker 1: come from hearings, and I'm thinking about Cash fatal during

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Speaker 1: those hearings when he said Jeffrey Epstein never traffied anyone,

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Speaker 1: unbelievable And I saw that and I thought, how what?

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Speaker 1: But it was really important because it showed that Cash

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Speaker 1: Patau was lying to protect someone, because the head of

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Speaker 1: the FBI getting up there and saying that, you don't

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Speaker 1: say that unless you're trying to protect someone, and so

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Speaker 1: it really does you know it's on the record.

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Speaker 3: Well, and Molly. The other thing too, is this is

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Speaker 3: why Donald Trump surrounds himself with the kinds of people

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Speaker 3: who really can't get these kinds of gigs anywhere else.

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Speaker 3: He puts people in these jobs who know they depend

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Speaker 3: on him for five star treatments. They get the private

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Speaker 3: cars and the you know, the primetime appearances on Fox

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Speaker 3: and Handity and so on. They don't get this without

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Speaker 3: cowtowing to Donald Trump. And that is why he gets

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Speaker 3: away with this, because he surrounded in himself with I mean,

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Speaker 3: the twenty fifth and that is just completely useless at

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Speaker 3: this point, because he has surrounded himself with the kind

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Speaker 3: of people who will never invoke the twenty fifth Amendment

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Speaker 3: or exercise the twenty fifth Amendment. Their livelihoods, everything that

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Speaker 3: they hold dear is because of Donald Trump, and so

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Speaker 3: we can't expect them to behave in any sort of

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Speaker 3: fashion that you know, we would find normal in any

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Speaker 3: other administration. And you know, to me, like Cash Bettel

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Speaker 3: saying that that's going to have to go down, that's

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Speaker 3: one of the worst lies ever in the presidency of

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Speaker 3: Donald Trump first or second administration.

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Speaker 1: It is just shameful, and it is worse just pulling

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Speaker 1: back and looking at how unusual all of this is.

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Speaker 1: This is not how any of it's supposed to work.

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Speaker 1: And I think this is particularly important is that the

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Speaker 1: Epstein files have a number of places where other people

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Speaker 1: have been redacted. And one of the things that we've

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Speaker 1: heard members of Congress talk about is that there are

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Speaker 1: two sets of production. So there are the redactions, the

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Speaker 1: dj already got files that were already redacted, and those

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Speaker 1: redactants have not been taken away, and those were the

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Speaker 1: Trump Protection redactions. So what we're seeing now are some

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Speaker 1: other files that they felt were safe enough to release.

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Speaker 1: So I wonder, like when we see stories like yesterday

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Speaker 1: there was a story about how Trump warned the chief

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Speaker 1: of police yeah in Palm Beach, that Jeffrey Epstein was bad.

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Speaker 3: And that Gallainne Maxwell was evil, you know all of that.

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Speaker 1: I'm very suspicious of that story. When I saw that story,

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Speaker 1: I thought, that's the only story about coming out about

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Speaker 1: Donald Trump. What do you think? I mean?

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Speaker 3: Yes, and Julie Brown has done some amazing reports.

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Speaker 1: Amazing Yeah, but it's the one file that was released

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Speaker 1: that was easy to track down, that wasn't heavily redacted.

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Speaker 3: But there were there were members of Congress coming out

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Speaker 3: of viewing these unredacted files who were, you know, singing

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Speaker 3: like a canary, And there were some Republicans who were

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Speaker 3: coming out of the look at those viewing sessions, absolutely

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Speaker 3: shell shocked. And there was the congresswoman from Vermont. It

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Speaker 3: was Blint if I've seen the name Becker ballanced balanced,

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Speaker 3: excuse me, and she's, you know, she's being asked by

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Speaker 3: reporters about this, and she says that, well, the whole

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Speaker 3: lie that Trump told about kicking Jeffrey Epstein out of

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Speaker 3: the club is a lie. It's a lie, and so

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Speaker 3: he's been lying repeatedly about this over the years.

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Speaker 1: Yeah, I'm really curious to know what she saw. The

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Speaker 1: other one, you'll remember, Cynthia Loomis Senator from Wyoming not

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Speaker 1: running for reelection, which I think is important to mention,

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Speaker 1: says that she now understands after seeing the unredacted files,

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Speaker 1: what a big deal this all is, which begs the question,

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Speaker 1: what did she see? What did any of those people say?

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Speaker 3: I know? And why is it that they've released some

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Speaker 3: of the files but not all of the files, and

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Speaker 3: that some of the files are unredacted for the numbers

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Speaker 3: of Congress And I have to say, like, why aren't

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Speaker 3: there like career prosecutors going through this stuff and sharpening

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Speaker 3: their knives and figuring out that they have some cases

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Speaker 3: that can be brought against some real sick people who

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Speaker 3: need to be put behind bars. I mean, this is

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Speaker 3: not how our Justice department. I mean, you could talk

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Speaker 3: to all of the wonderful experts that you and I

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Speaker 3: talk to Molly all the time on this subject. This

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Speaker 3: should be a prosecutorial shopping spree here. You know, like

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Speaker 3: you know, you get a conviction, you get a conviction

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Speaker 3: that you know, and it's unreal and it's not happening.

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Speaker 1: Well, if the Trump administration wanted to solve these crimes,

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Speaker 1: or even anyone else, not even the Trump administration, just

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Speaker 1: like members of Congress, wouldn't one of the first people

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Speaker 1: you would call to testify be Mornen Kombe, who was

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Speaker 1: working on this case. I mean, there are so many

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Speaker 1: points in which you see that these victims have really

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Speaker 1: nobody cares about the victims.

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Speaker 3: It's all a shamp bringing in the Clintons, and the

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Speaker 3: Clintons like, well, we'll testify in front of the cameras.

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Speaker 3: Oh no, no, no, we don't want that. But they don't

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Speaker 3: want Donald Trump to be the ghost.

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Speaker 1: I understand Bill, he's in the photos, he's in the files. Great,

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Speaker 1: have Bill testify have him testify, have Trump testify, have

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Speaker 1: all these men testified. But Hillary never was on the plane.

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Speaker 1: She was never. I mean, I understand they're obsessed with her,

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Speaker 1: but like it's a pretty good case of like where

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Speaker 1: you say, this is just partisan hacker, Like get Bill,

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Speaker 1: get Doug banned, Bill's body man, who got a feleip attack.

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Speaker 3: Watch right, right, right right.

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Speaker 1: Yeah, you get that guy, But like, get people where

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Speaker 1: they actually are in the files. Don't get people where

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Speaker 1: you are going to be able to you know, just grandstand.

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Speaker 3: And you and I have talked about this, and it

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Speaker 3: should be said again, you and I, people on the

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Speaker 3: center left, the sanity based world, we don't give a

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Speaker 3: fuck if something bad happens to Bill Clinton and all

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Speaker 3: of this. At the end of the day, we don't care.

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Speaker 3: And so the same standard what's good for the goose

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Speaker 3: is good for the gander. And it seems to be

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Speaker 3: it's a shrinking universe of people who are saying I

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Speaker 3: stand with Donald Trump and why he's behaving when it

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Speaker 3: comes to the Epstein files, the ice is cracking, their

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Speaker 3: fissures forming I just think, faster your seatbelt. It just

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Speaker 3: seems to me, and again we're not allowed to say

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Speaker 3: the walls are closing in and so on.

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Speaker 1: They seem like they're on the back foot here.

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Speaker 3: It is not going in the right direction for the

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Speaker 3: forty seventh president. Of you that experience, I'll just say that, yeah, and.

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Speaker 1: Quite frankly, it shouldn't be, because a lot of this

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Speaker 1: stuff he's trying to get away with here is really unprecedented.

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Speaker 3: And maybe we start to use the word evil. I mean,

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Speaker 3: I mean, some of this is just it's just evil.

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Speaker 3: It just is.

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Speaker 1: And it's also just not okay. You are definitely seeing Republicans,

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Speaker 1: some Republicans break with Trump on this. Do you think

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Speaker 1: this is opportunism?

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Speaker 3: I think folks are starting to put their finger in

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Speaker 3: the wind. And I talked to a long time Republican

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Speaker 3: fundraiser just the other day who was telling me this

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Speaker 3: is something I've known for years. In Washington, they're predicting

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Speaker 3: a wipeout. They are feeling at this point that they're

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Speaker 3: just going to get wiped out, and that not just

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Speaker 3: the House isn't obviously in jeopardy, that the Senate is

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Speaker 3: in jeopardy, and that they could lose in places like

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Speaker 3: Iowa and alaskap this is the doomsday scenario that people

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Speaker 3: like you and I have been warning about for some time,

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Speaker 3: that the chickens will eventually come home to roost. He

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Speaker 3: doesn't get away scott free. That's not how this ends.

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Speaker 3: I don't cook that that's going to be the case.

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Speaker 3: I think that they see what's coming, and you know,

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Speaker 3: nature finds a way as a wiseman once set in

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Speaker 3: Jurassic Park. And the Republicans are starting to figure this out.

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Speaker 3: I don't know how they don't go Thelman Louise off

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Speaker 3: the cliff with him at this point, kwant honestly, I

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Speaker 3: don't see how do you make that pivot? I mean,

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Speaker 3: just can somebody draw me that picture between now and November.

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Speaker 3: They don't, They're right or die, so they go over

431
00:23:21,000 --> 00:23:21,439
Speaker 3: the cliff.

432
00:23:21,920 --> 00:23:24,919
Speaker 1: Jim Acosta, don't miss your flight.

433
00:23:25,240 --> 00:23:27,720
Speaker 3: Thank you, Thank you so much. Owe you on.

434
00:23:29,680 --> 00:23:33,640
Speaker 1: Sam Wang is a neuroscientist and a professor at Princeton

435
00:23:33,720 --> 00:23:39,280
Speaker 1: University and a candidate for the Democratic nomination for New

436
00:23:39,400 --> 00:23:46,400
Speaker 1: Jersey's twelfth congressional district. Welcome too Fast Politics, Sam.

437
00:23:46,160 --> 00:23:47,600
Speaker 3: Oh, Mollie, good to see you.

438
00:23:47,640 --> 00:23:50,080
Speaker 1: So you come in to talk to us a lot

439
00:23:50,240 --> 00:23:55,960
Speaker 1: about this jerrymandering, but today you are talking about something else,

440
00:23:56,040 --> 00:23:57,720
Speaker 1: So talk us through it today.

441
00:23:57,800 --> 00:23:58,680
Speaker 3: I have something new.

442
00:23:58,840 --> 00:24:01,359
Speaker 4: So as you know, I've been looking for gosh twenty

443
00:24:01,440 --> 00:24:05,119
Speaker 4: years on helping people be more powerful, fighting gerrymandering, you know,

444
00:24:05,240 --> 00:24:07,520
Speaker 4: different ways to put my science to work. Today I

445
00:24:07,560 --> 00:24:12,879
Speaker 4: am entering politics myself. I'm standing for office for Congress

446
00:24:13,000 --> 00:24:15,360
Speaker 4: here in the twelfth District of New Jersey. So I'm

447
00:24:15,400 --> 00:24:17,119
Speaker 4: throwing my hat in the ring. I'm trying to get

448
00:24:17,119 --> 00:24:18,160
Speaker 4: the Democratic nomination.

449
00:24:18,520 --> 00:24:21,639
Speaker 1: Talk us through what the twelfth district in New Jersey

450
00:24:21,680 --> 00:24:22,119
Speaker 1: looks like.

451
00:24:22,480 --> 00:24:24,359
Speaker 4: It looks like a lot of things. The twelfth District

452
00:24:24,400 --> 00:24:26,280
Speaker 4: is one of the most diverse districts in the country.

453
00:24:26,400 --> 00:24:28,520
Speaker 4: Princeton where I teaches, in the middle of it, but

454
00:24:28,560 --> 00:24:31,560
Speaker 4: it stretches from Trenton and Ewing, which is in the

455
00:24:31,560 --> 00:24:35,960
Speaker 4: southwest black and Hispanic populations, to Plainfield in the northeast,

456
00:24:36,040 --> 00:24:39,240
Speaker 4: again black and Hispanic, and in the middle of highly

457
00:24:39,320 --> 00:24:44,159
Speaker 4: educated Princetonian's Asian East and South Asian communities of Montgomery

458
00:24:44,119 --> 00:24:47,360
Speaker 4: and West Windsor and so Plainsboro and South Brunswick. As

459
00:24:47,359 --> 00:24:49,119
Speaker 4: you can tell from the way I'm sounding, Usually we

460
00:24:49,119 --> 00:24:52,000
Speaker 4: talk about gerrymandering like at a national level we talk

461
00:24:52,040 --> 00:24:54,920
Speaker 4: about reform and the damage to our democracy. This is new.

462
00:24:55,000 --> 00:24:57,800
Speaker 4: This is me getting into the specifics of a district.

463
00:24:57,840 --> 00:25:00,520
Speaker 4: There's an open seat, and I think Congress someone who

464
00:25:00,600 --> 00:25:03,040
Speaker 4: understands the science of what's gone wrong, and so I

465
00:25:03,040 --> 00:25:04,840
Speaker 4: want to get in there and try to fix the

466
00:25:04,880 --> 00:25:07,320
Speaker 4: damage and help make sure it never happens again.

467
00:25:07,560 --> 00:25:09,760
Speaker 1: So the history of this seat is it was a

468
00:25:09,800 --> 00:25:12,440
Speaker 1: Republican seat, and then it was a Democrat seat. Right,

469
00:25:12,520 --> 00:25:13,960
Speaker 1: it's been mixed.

470
00:25:14,400 --> 00:25:17,359
Speaker 4: Years ago, it was I think at one point a

471
00:25:17,359 --> 00:25:20,119
Speaker 4: Republican seat, but let's say about as recently as fifteen

472
00:25:20,160 --> 00:25:23,240
Speaker 4: years ago, it was represented by a plasma physicist, Rush

473
00:25:23,240 --> 00:25:26,960
Speaker 4: Holt oh Cool Russia is a physicist, five time Jeopardy winner,

474
00:25:27,320 --> 00:25:29,639
Speaker 4: and so he won a very very close race against

475
00:25:29,680 --> 00:25:31,800
Speaker 4: a Republican. And then for the last twelve years it's

476
00:25:31,800 --> 00:25:33,520
Speaker 4: been represented by Bonnie Watson Coleman.

477
00:25:33,640 --> 00:25:34,160
Speaker 3: Congresswoman.

478
00:25:34,240 --> 00:25:38,000
Speaker 4: Coleman's turning eighty one this year pretty soon, and so

479
00:25:38,080 --> 00:25:40,919
Speaker 4: she's stepping down to a well earned retirement. And it's

480
00:25:40,960 --> 00:25:45,920
Speaker 4: an open seat, and because of some regressive ballot laws

481
00:25:45,920 --> 00:25:48,399
Speaker 4: that I play a small role in overturning, it's a

482
00:25:48,440 --> 00:25:51,919
Speaker 4: free for all. Anybody can run and there's eighteen candidates it's.

483
00:25:51,760 --> 00:25:52,560
Speaker 3: A wild ride.

484
00:25:52,680 --> 00:25:55,320
Speaker 1: I want you to talk about the regressive ballot laws

485
00:25:55,359 --> 00:25:57,399
Speaker 1: in New Jersey because a lot of states have this

486
00:25:57,680 --> 00:26:02,639
Speaker 1: sort of weird low stuff. New Jersey had one that

487
00:26:02,680 --> 00:26:04,399
Speaker 1: we discovered. I'd let me talk about it.

488
00:26:04,560 --> 00:26:07,320
Speaker 4: As an neuroscientist, I think of it as a cognitive trick.

489
00:26:07,680 --> 00:26:09,800
Speaker 4: Most ballots you just have the office and then a

490
00:26:09,840 --> 00:26:12,680
Speaker 4: list of candidates. But the ballot in New Jersey has

491
00:26:12,760 --> 00:26:15,560
Speaker 4: this thing where candidates are listed in different places than

492
00:26:15,560 --> 00:26:18,399
Speaker 4: the ballot, and there's this one column of names, and

493
00:26:18,440 --> 00:26:21,880
Speaker 4: then one column of names is the party's preferred candidate

494
00:26:22,000 --> 00:26:25,840
Speaker 4: for Congress, for president, for Senate, for local council. And

495
00:26:25,880 --> 00:26:27,960
Speaker 4: this is a thing called the county line. And so

496
00:26:28,160 --> 00:26:30,360
Speaker 4: this funny arrangement has a lot of blank spaces on it,

497
00:26:30,440 --> 00:26:34,280
Speaker 4: and it fools the eye, and it gives as much

498
00:26:34,320 --> 00:26:37,320
Speaker 4: as a thirty eight point bonus to whoever the party elders,

499
00:26:37,600 --> 00:26:40,159
Speaker 4: you know, who their preferred candidate is. So that was

500
00:26:40,200 --> 00:26:42,040
Speaker 4: the law until a couple of years ago. There was

501
00:26:42,040 --> 00:26:44,240
Speaker 4: a lawsuit in which a number of US brought evidence

502
00:26:44,240 --> 00:26:46,080
Speaker 4: into federal court and we said, look, this is a

503
00:26:46,119 --> 00:26:48,640
Speaker 4: trick that it gets played on voters. It's against free

504
00:26:48,640 --> 00:26:51,359
Speaker 4: and fair elections, and this judge overturned it. So at

505
00:26:51,359 --> 00:26:54,000
Speaker 4: this point, the power of the party machine has gotten broken.

506
00:26:54,200 --> 00:26:56,480
Speaker 4: Last week in the district north of US in the

507
00:26:56,480 --> 00:27:00,320
Speaker 4: eleventh district, actually that district turned out very different. The

508
00:27:00,320 --> 00:27:03,679
Speaker 4: party endorsed candidates in that district came in third and fourth.

509
00:27:03,960 --> 00:27:07,200
Speaker 4: The next congresswoman from that district is probably going to

510
00:27:07,200 --> 00:27:11,080
Speaker 4: be Ana Lilia Mahia, and she came out of seemingly

511
00:27:11,119 --> 00:27:14,640
Speaker 4: nowhere and ran a populist campaign and was not endorsed,

512
00:27:14,960 --> 00:27:17,760
Speaker 4: and so you know, it feels like a new day

513
00:27:17,760 --> 00:27:20,440
Speaker 4: in New Jersey for more equitable elections.

514
00:27:20,680 --> 00:27:23,480
Speaker 1: So that was a special election to fill the Mikey

515
00:27:23,560 --> 00:27:24,399
Speaker 1: share All seat.

516
00:27:24,600 --> 00:27:26,200
Speaker 2: Yeah, it was a primary, right.

517
00:27:26,160 --> 00:27:29,280
Speaker 1: Special primary. Your primary is not going to be until June.

518
00:27:29,440 --> 00:27:30,120
Speaker 4: That's right, it's June.

519
00:27:30,200 --> 00:27:30,480
Speaker 2: Second.

520
00:27:30,720 --> 00:27:34,440
Speaker 1: It sounds like it's a pretty reliably blue seat, especially

521
00:27:34,960 --> 00:27:38,680
Speaker 1: in the coming cycle where Donald Trump has ice agents

522
00:27:38,720 --> 00:27:42,760
Speaker 1: occupying various American cities. So it is going to be

523
00:27:42,920 --> 00:27:46,320
Speaker 1: really an election that takes place in the primary. Oh yes,

524
00:27:46,640 --> 00:27:49,680
Speaker 1: so talk us through you know, these kind of primaries

525
00:27:49,720 --> 00:27:53,040
Speaker 1: is kind of very crowded. Primaries make very interesting contests.

526
00:27:53,080 --> 00:27:57,280
Speaker 1: So how are you campaigning in that primary? And you know,

527
00:27:57,320 --> 00:28:02,479
Speaker 1: they're ultimately usually kind of a math, right, these primaries.

528
00:28:01,920 --> 00:28:04,920
Speaker 4: With eighteen candidates, I mean, is technically possible to win

529
00:28:04,960 --> 00:28:07,840
Speaker 4: with ten percent of the vote. Realistically probably takes more

530
00:28:07,920 --> 00:28:10,320
Speaker 4: like twenty twenty five percent of the vote. So the

531
00:28:10,359 --> 00:28:13,320
Speaker 4: way to campaign, it's crazy because there's so many candidates,

532
00:28:13,359 --> 00:28:14,960
Speaker 4: and the question is how to rise up out of

533
00:28:14,960 --> 00:28:17,480
Speaker 4: the noise. A lot of the candidates in our district

534
00:28:17,520 --> 00:28:19,480
Speaker 4: are you know, the kind of people you might expect, Like,

535
00:28:19,520 --> 00:28:22,440
Speaker 4: you know, people have done local service, a local assembly woman,

536
00:28:22,680 --> 00:28:26,000
Speaker 4: several local mayors, people who are known to the small

537
00:28:26,000 --> 00:28:29,160
Speaker 4: sectors of the district. So we have one outsider who

538
00:28:29,200 --> 00:28:31,639
Speaker 4: lives outside the district who lost in the seventh district

539
00:28:31,640 --> 00:28:34,440
Speaker 4: two years ago to Tom Kain Junior, and so she's

540
00:28:34,520 --> 00:28:37,320
Speaker 4: moving closer to the district and so she's hoping to run.

541
00:28:37,800 --> 00:28:40,440
Speaker 4: It'll be her first time running for office. About half

542
00:28:40,480 --> 00:28:43,280
Speaker 4: the people running our first time candidates who have never

543
00:28:43,360 --> 00:28:44,200
Speaker 4: run for office before.

544
00:28:44,200 --> 00:28:45,160
Speaker 2: And I'm one of those people.

545
00:28:45,320 --> 00:28:47,520
Speaker 4: And yeah, how to stick out? I mean, I've made

546
00:28:47,520 --> 00:28:50,240
Speaker 4: a career out of both neuroscience and also working on

547
00:28:50,280 --> 00:28:53,520
Speaker 4: democracy reform, trying to empower people as an outsider, and

548
00:28:53,720 --> 00:28:55,280
Speaker 4: I'm the only one in the race has done that.

549
00:28:55,800 --> 00:28:59,280
Speaker 1: Talk us through the ways in which you have tried

550
00:28:59,320 --> 00:29:04,680
Speaker 1: to reform jerrymandering to make elections more fair and free.

551
00:29:04,840 --> 00:29:05,280
Speaker 3: Yeah, you know.

552
00:29:05,400 --> 00:29:07,840
Speaker 4: I use my science to try to find ways to

553
00:29:07,880 --> 00:29:11,720
Speaker 4: equalize voter power. And I years ago started analyzing the

554
00:29:11,760 --> 00:29:14,880
Speaker 4: electoral college to understand how voters are more powerful in

555
00:29:14,960 --> 00:29:17,280
Speaker 4: some states than others. And I tried to use that

556
00:29:17,320 --> 00:29:19,480
Speaker 4: to encourage people to go campaign and get out the

557
00:29:19,560 --> 00:29:22,160
Speaker 4: vote and so on in places where they could make

558
00:29:22,200 --> 00:29:24,360
Speaker 4: the most difference with their federal citizens. As you and

559
00:29:24,360 --> 00:29:26,959
Speaker 4: I have talked about before, I work on jerrymandering at Princeton.

560
00:29:27,000 --> 00:29:29,959
Speaker 4: I've started the Princeton Jerrymandering Project where we've come up

561
00:29:30,000 --> 00:29:32,880
Speaker 4: with a report card to grade offenses, and it's a

562
00:29:32,920 --> 00:29:36,040
Speaker 4: non partisan report card that calls out offenses on both sides.

563
00:29:36,200 --> 00:29:39,400
Speaker 4: And using that report card, we've discovered offenses all over

564
00:29:39,440 --> 00:29:42,600
Speaker 4: the country. And that report card has demonstrated that there

565
00:29:42,640 --> 00:29:45,400
Speaker 4: is a national law that if we enacted, it could

566
00:29:45,440 --> 00:29:49,080
Speaker 4: stop this gerrymandering war that we see independent citizen commissions.

567
00:29:49,360 --> 00:29:53,000
Speaker 4: And so our math and analytics show that independent citizen

568
00:29:53,040 --> 00:29:55,959
Speaker 4: commissions are the very best way to end jerrymandering. So

569
00:29:56,000 --> 00:29:58,360
Speaker 4: that's the thing that I would like to do in Congress.

570
00:29:59,200 --> 00:30:02,000
Speaker 4: And now I actually these days off campus, I have

571
00:30:02,560 --> 00:30:05,920
Speaker 4: a nonprofit organization, the Electoral Innovation Lab, and the Electoral

572
00:30:05,920 --> 00:30:08,600
Speaker 4: Innovation Lab again is trying to take all these ideas

573
00:30:08,800 --> 00:30:12,080
Speaker 4: out of the academic halls and put them into practice,

574
00:30:12,280 --> 00:30:15,280
Speaker 4: so help people bring about ranked choice voting, help people

575
00:30:15,800 --> 00:30:19,160
Speaker 4: defend elections where they're under attack. We've been doing analytics

576
00:30:19,480 --> 00:30:23,040
Speaker 4: to show where attacks on elections might lead to the

577
00:30:23,040 --> 00:30:27,480
Speaker 4: most damage, like in Minnesota, in North Carolina.

578
00:30:27,560 --> 00:30:28,200
Speaker 2: In Texas.

579
00:30:28,600 --> 00:30:31,440
Speaker 4: So again it's all my basic game has been trying

580
00:30:31,440 --> 00:30:34,600
Speaker 4: to understand the whole system, find the weak points, stop

581
00:30:34,680 --> 00:30:37,640
Speaker 4: damage this year, and build a better system so we

582
00:30:37,720 --> 00:30:38,600
Speaker 4: never see this again.

583
00:30:38,920 --> 00:30:42,200
Speaker 1: Talk to me about ranked choice voting because I have questioned.

584
00:30:42,000 --> 00:30:44,880
Speaker 4: Yeah, ranked choice voting is when you have a lot

585
00:30:44,920 --> 00:30:47,760
Speaker 4: of choices. So in a plurality election where whoever gets

586
00:30:47,800 --> 00:30:50,440
Speaker 4: the most where everyone picks one candidate and then whichever

587
00:30:50,480 --> 00:30:53,000
Speaker 4: candidate gets the most vote wins, that's called plurality voting.

588
00:30:53,480 --> 00:30:57,000
Speaker 4: That tends to build two political parties. And one big

589
00:30:57,080 --> 00:30:58,880
Speaker 4: driver of why we have two parties that are so

590
00:30:58,960 --> 00:31:02,040
Speaker 4: far apart is choice voting and a really determined faction

591
00:31:02,560 --> 00:31:04,920
Speaker 4: can take over one party and drive it over the

592
00:31:05,040 --> 00:31:07,400
Speaker 4: edge and make them crazy, and you know we're living

593
00:31:07,400 --> 00:31:09,560
Speaker 4: through that. So ranked choice voting is a way to

594
00:31:10,600 --> 00:31:12,560
Speaker 4: let you mark your first choice and your second choice

595
00:31:12,560 --> 00:31:15,400
Speaker 4: in your third choice, and it's a way of counting

596
00:31:15,440 --> 00:31:18,280
Speaker 4: those votes to find out who the consensus choice is,

597
00:31:18,640 --> 00:31:22,200
Speaker 4: and it prevents the possibility of a really small faction

598
00:31:22,760 --> 00:31:26,720
Speaker 4: taking over the whole system. So today, you know, Republicans

599
00:31:26,720 --> 00:31:29,400
Speaker 4: and Democrats are each one third of the voters, About

600
00:31:29,440 --> 00:31:31,640
Speaker 4: one third can take over one of those parties, and

601
00:31:31,720 --> 00:31:34,080
Speaker 4: so one ninth of the voters can like completely up

602
00:31:34,160 --> 00:31:36,080
Speaker 4: end the system. And ranked choice voting is a way

603
00:31:36,080 --> 00:31:37,520
Speaker 4: of preventing that.

604
00:31:37,680 --> 00:31:41,880
Speaker 1: I would love you to talk about your district and

605
00:31:42,240 --> 00:31:44,480
Speaker 1: how you're going to win this primary.

606
00:31:44,960 --> 00:31:47,680
Speaker 4: So it has been interesting to me to take, you know,

607
00:31:47,760 --> 00:31:50,960
Speaker 4: my broad knowledge as an outsider, as an analyst and say, okay,

608
00:31:50,960 --> 00:31:52,600
Speaker 4: you know what, let's apply it to one district. So

609
00:31:52,920 --> 00:31:55,800
Speaker 4: this district, which is in Central Jersey, is pretty diverse.

610
00:31:55,920 --> 00:31:59,080
Speaker 4: It's fifty nine percent non white. The largest non white

611
00:31:59,080 --> 00:32:01,920
Speaker 4: group is Asian American Pacific Islanders. And you may have

612
00:32:01,960 --> 00:32:04,760
Speaker 4: noticed that I'm Asian, and so that's a community in

613
00:32:04,800 --> 00:32:08,240
Speaker 4: which they automatically noticed me. So my theory of winning

614
00:32:08,400 --> 00:32:11,440
Speaker 4: is that Asian American voters are about twenty five percent

615
00:32:11,520 --> 00:32:13,520
Speaker 4: of the voting age population of the district.

616
00:32:13,800 --> 00:32:14,520
Speaker 3: That's a lot.

617
00:32:14,600 --> 00:32:16,960
Speaker 4: And so these towns that I was reeling off before,

618
00:32:17,040 --> 00:32:20,960
Speaker 4: Montgomery West windsor Plainsboro, South Brunswick, these places are close

619
00:32:21,000 --> 00:32:24,120
Speaker 4: to fifty percent Asian East Asians and South Asians. And

620
00:32:24,160 --> 00:32:26,680
Speaker 4: so you know, I had this idea of like going

621
00:32:26,720 --> 00:32:29,320
Speaker 4: and visiting these places because I've lived here twenty six years,

622
00:32:29,520 --> 00:32:31,240
Speaker 4: and I thought, why don't I go to all these

623
00:32:31,240 --> 00:32:34,640
Speaker 4: great restaurants and places in these towns, And I want

624
00:32:34,680 --> 00:32:37,920
Speaker 4: to like basically go and like shoot like little TikTok

625
00:32:38,040 --> 00:32:41,840
Speaker 4: videos of these cool places that have like Moroccan food

626
00:32:41,880 --> 00:32:45,120
Speaker 4: and Chinese food and Korean food and Soul food and

627
00:32:45,160 --> 00:32:48,040
Speaker 4: Mexican food, and just like say, look, guys, this is

628
00:32:48,080 --> 00:32:51,760
Speaker 4: a fantastic diverse district. We all need protection, we all

629
00:32:51,760 --> 00:32:54,760
Speaker 4: need to thrive, and look, hey, here's some Moroccan food.

630
00:32:55,200 --> 00:32:56,760
Speaker 4: So I thought maybe something like that.

631
00:32:57,200 --> 00:32:59,840
Speaker 1: You also are part of Princeton, which is a huge,

632
00:33:00,160 --> 00:33:05,200
Speaker 1: incredible university in New Jersey that is involved in civic life,

633
00:33:05,240 --> 00:33:08,960
Speaker 1: and your work for Princeton has been largely about improving

634
00:33:09,040 --> 00:33:10,000
Speaker 1: civic life, right.

635
00:33:10,280 --> 00:33:12,560
Speaker 4: Yeah, And you know, Princeton's been a really wonderful home

636
00:33:12,600 --> 00:33:15,560
Speaker 4: to me. They recruited me to do my neuroscience. It's

637
00:33:15,640 --> 00:33:19,160
Speaker 4: led me to make discoveries and autism. They encourage civic engagement.

638
00:33:19,200 --> 00:33:22,120
Speaker 4: I mean, I speak for myself and not for the university.

639
00:33:22,280 --> 00:33:24,720
Speaker 4: I'm always my own person, and they encourage that. But

640
00:33:24,720 --> 00:33:26,600
Speaker 4: they also they want me to be that I don't

641
00:33:26,600 --> 00:33:28,320
Speaker 4: speak for the school. I speak for myself.

642
00:33:28,680 --> 00:33:32,080
Speaker 1: At a moment, though, when this administration is at war

643
00:33:32,680 --> 00:33:37,880
Speaker 1: with universities, oh yes, and with funding for research, I

644
00:33:37,920 --> 00:33:41,960
Speaker 1: wonder if you could speak to we've seen like war

645
00:33:42,040 --> 00:33:46,640
Speaker 1: with scientific research, war with science just more generally, I'd

646
00:33:46,640 --> 00:33:49,600
Speaker 1: love you to talk about what you have seen over

647
00:33:49,640 --> 00:33:51,760
Speaker 1: the last year and what you think.

648
00:33:51,760 --> 00:33:52,480
Speaker 3: Here at Princeton.

649
00:33:52,880 --> 00:33:56,080
Speaker 4: Our president, Crisis Scruper, has fought back and stood up

650
00:33:56,120 --> 00:33:58,960
Speaker 4: for academic freedom and for science. I think we've held

651
00:33:59,040 --> 00:34:01,800
Speaker 4: up better than a lot of places. But look, everybody's

652
00:34:01,880 --> 00:34:04,680
Speaker 4: under attack. And so first there was an attack to

653
00:34:05,080 --> 00:34:07,600
Speaker 4: cut drastically overhead costs that we can't even keep the

654
00:34:07,680 --> 00:34:08,120
Speaker 4: lights on.

655
00:34:08,800 --> 00:34:12,840
Speaker 1: Right. That was Elon Musk deciding that NIH grants should

656
00:34:12,880 --> 00:34:14,480
Speaker 1: only be fifteen percent.

657
00:34:14,880 --> 00:34:15,279
Speaker 4: That's right.

658
00:34:15,280 --> 00:34:16,240
Speaker 3: And then the next attack.

659
00:34:16,560 --> 00:34:20,040
Speaker 4: One of the attacks is happening now is forcing NIH grants,

660
00:34:20,040 --> 00:34:22,719
Speaker 4: which are the lifeblood of American biomedical research, to force

661
00:34:22,760 --> 00:34:25,279
Speaker 4: these multi year grants to be paid out all in

662
00:34:25,320 --> 00:34:27,279
Speaker 4: the current fiscal years. So if I get a grant

663
00:34:27,280 --> 00:34:29,680
Speaker 4: from NIH and I get NH money, if I get

664
00:34:29,719 --> 00:34:32,160
Speaker 4: an NIH grant for multiple years, all the money has

665
00:34:32,200 --> 00:34:34,120
Speaker 4: to come out this year. And what that does is

666
00:34:34,160 --> 00:34:36,560
Speaker 4: it spends down all the money this year, and it

667
00:34:36,640 --> 00:34:38,399
Speaker 4: starves other laboratories.

668
00:34:38,600 --> 00:34:40,600
Speaker 1: Why are they doing it, well, lord.

669
00:34:40,440 --> 00:34:41,960
Speaker 3: I do not know why they're doing.

670
00:34:42,880 --> 00:34:45,920
Speaker 4: The charitable interpretation is not thinking about the impacts on

671
00:34:45,960 --> 00:34:49,640
Speaker 4: American science. And the more sinister interpretation is to weaken

672
00:34:49,680 --> 00:34:52,840
Speaker 4: American science because American science in some ways is considered

673
00:34:52,840 --> 00:34:56,160
Speaker 4: a threat to building more equal society, and so because

674
00:34:56,520 --> 00:35:01,240
Speaker 4: damaging science hurts that goal, therefore it's good to weaken science,

675
00:35:01,520 --> 00:35:04,839
Speaker 4: you know, and also has the secondary consequence of hurting universities.

676
00:35:04,880 --> 00:35:07,320
Speaker 4: I mean, I do research here, I teach my students.

677
00:35:07,520 --> 00:35:09,719
Speaker 4: If I can't do my research, then I can no

678
00:35:09,800 --> 00:35:12,879
Speaker 4: longer be a leading researcher. I can no longer teach

679
00:35:12,920 --> 00:35:15,760
Speaker 4: my students about what's happening. It makes for a worse environment.

680
00:35:15,840 --> 00:35:17,280
Speaker 4: And this is you know, this is true at private

681
00:35:17,360 --> 00:35:22,000
Speaker 4: universities like Princeton, public universities like Rutgers and University of California.

682
00:35:22,040 --> 00:35:24,239
Speaker 4: But you know the deep why, I mean, why are

683
00:35:24,280 --> 00:35:27,600
Speaker 4: all our systems under attack by a runaway executive branch?

684
00:35:27,960 --> 00:35:31,080
Speaker 1: So why we certainly see that, I mean not a lot,

685
00:35:31,160 --> 00:35:33,560
Speaker 1: but some of this research is being picked up by

686
00:35:33,640 --> 00:35:37,560
Speaker 1: other countries. Have you seen that in the field? And

687
00:35:37,640 --> 00:35:38,480
Speaker 1: what does that look like?

688
00:35:39,080 --> 00:35:42,160
Speaker 4: Usually science is a team sport and it's international, and

689
00:35:42,719 --> 00:35:46,200
Speaker 4: generation after generation people have come from other countries to

690
00:35:46,320 --> 00:35:48,920
Speaker 4: study in the United States and sometimes they stay. I mean,

691
00:35:49,000 --> 00:35:51,239
Speaker 4: I can't count the number of star graduate students and

692
00:35:51,280 --> 00:35:53,480
Speaker 4: postdocs who have come to my lab, and then many

693
00:35:53,560 --> 00:35:55,480
Speaker 4: of them I'm stayed in the United States. And that

694
00:35:55,600 --> 00:35:58,040
Speaker 4: is a pipeline. I mean, look look at the CEOs

695
00:35:58,080 --> 00:36:00,320
Speaker 4: of all these tech companies, like most of them actually

696
00:36:00,560 --> 00:36:02,799
Speaker 4: came from other countries. In the last year, it's become

697
00:36:02,880 --> 00:36:05,480
Speaker 4: much harder to persuade scholars from other countries to study

698
00:36:05,480 --> 00:36:08,320
Speaker 4: in the United States. I've had even people from countries

699
00:36:08,320 --> 00:36:11,400
Speaker 4: that are not in the crosshairs, like the Netherlands or Spain,

700
00:36:11,840 --> 00:36:14,480
Speaker 4: express reluctance about coming to the US to study. So

701
00:36:14,520 --> 00:36:16,920
Speaker 4: it's hard on those grounds. I know people who are

702
00:36:16,960 --> 00:36:20,160
Speaker 4: talking about leaving the United States. Only a few have

703
00:36:20,239 --> 00:36:22,439
Speaker 4: done so. But people in the United States are looking

704
00:36:22,480 --> 00:36:25,520
Speaker 4: for ways to build their research careers. And you know,

705
00:36:25,680 --> 00:36:28,960
Speaker 4: someplace like Europe is a more nurturing place for researchers

706
00:36:29,000 --> 00:36:32,560
Speaker 4: now and China right, and China, yeah, and morale is down.

707
00:36:32,600 --> 00:36:34,760
Speaker 4: I mean, I got to say, we've had town halls

708
00:36:34,800 --> 00:36:37,480
Speaker 4: here where graduate students and post docs want to know, like,

709
00:36:37,560 --> 00:36:39,600
Speaker 4: what's going to become of us? Is anyone going to

710
00:36:39,640 --> 00:36:42,520
Speaker 4: protect us? And so yeah, it's actually a really tough

711
00:36:42,560 --> 00:36:45,319
Speaker 4: time for scientists, and scientists who are usually not very

712
00:36:45,320 --> 00:36:47,520
Speaker 4: political are starting to notice the problem.

713
00:36:47,640 --> 00:36:50,120
Speaker 1: I want you to say more about China because that

714
00:36:50,320 --> 00:36:53,360
Speaker 1: blows my mind. I mean, how worried are you about

715
00:36:53,360 --> 00:36:57,800
Speaker 1: a world where China is a better home descientist in America?

716
00:36:57,840 --> 00:36:58,840
Speaker 1: And is it still not?

717
00:36:59,000 --> 00:36:59,320
Speaker 3: Really?

718
00:36:59,719 --> 00:37:02,200
Speaker 4: China science has gotten better and better. You know, it

719
00:37:02,280 --> 00:37:04,480
Speaker 4: used to be not so many years ago that a

720
00:37:04,520 --> 00:37:06,200
Speaker 4: lot of the work was not so high grade. But

721
00:37:06,480 --> 00:37:09,640
Speaker 4: I've had friends who have visited China and seen great

722
00:37:09,680 --> 00:37:12,640
Speaker 4: research institutions being built up there. There are some problems

723
00:37:12,719 --> 00:37:16,480
Speaker 4: with Chinese research journals, some of them are kind of sketchy,

724
00:37:16,680 --> 00:37:19,440
Speaker 4: but the best research coming out of China is absolutely

725
00:37:19,440 --> 00:37:22,080
Speaker 4: world class. And yeah, if we think of China as

726
00:37:22,080 --> 00:37:24,480
Speaker 4: an adversary for the United States, then guess what that

727
00:37:24,600 --> 00:37:27,760
Speaker 4: adversary is becoming pretty good at technology, pretty good at science,

728
00:37:27,960 --> 00:37:29,480
Speaker 4: and you know, and here we are not cooperating.

729
00:37:29,560 --> 00:37:32,520
Speaker 1: I mean, I mean, we are making them when if

730
00:37:32,520 --> 00:37:35,440
Speaker 1: we're an adversary of China, which I don't necessarily think

731
00:37:35,480 --> 00:37:37,799
Speaker 1: we are, but if we are, there are scientists who

732
00:37:37,800 --> 00:37:40,240
Speaker 1: are going over there because it's better.

733
00:37:40,600 --> 00:37:41,120
Speaker 2: Oh yeah.

734
00:37:41,320 --> 00:37:44,160
Speaker 4: Like I had one colleague who's driven out of his position,

735
00:37:44,360 --> 00:37:46,840
Speaker 4: a leading research in New York City, and he immigrated

736
00:37:46,880 --> 00:37:49,759
Speaker 4: to the United States decades ago from China, and then

737
00:37:49,800 --> 00:37:51,719
Speaker 4: he got caught up in the what I think he

738
00:37:51,840 --> 00:37:54,719
Speaker 4: was part of the China initiative, this thing to crack

739
00:37:54,760 --> 00:37:58,680
Speaker 4: down on bad interactions, and he got investigated and he

740
00:37:58,760 --> 00:38:01,480
Speaker 4: now is running a thriving lab in China, you know,

741
00:38:01,520 --> 00:38:03,680
Speaker 4: and he's doing great work there. But it was hard

742
00:38:03,680 --> 00:38:05,160
Speaker 4: on him and bad for us.

743
00:38:05,400 --> 00:38:08,880
Speaker 1: Yeah, it's sort of self inflicted anti science wound.

744
00:38:09,160 --> 00:38:11,040
Speaker 4: I mean, yeah, like my parents immigrated to the US

745
00:38:11,040 --> 00:38:13,200
Speaker 4: in the nineteen sixties, and when I was born. In

746
00:38:13,239 --> 00:38:15,360
Speaker 4: my baby book, my father said I want him to

747
00:38:15,360 --> 00:38:18,000
Speaker 4: get his PhD in science by the age twenty five.

748
00:38:18,160 --> 00:38:18,680
Speaker 2: And I saw that.

749
00:38:18,719 --> 00:38:21,520
Speaker 4: I'm like, okay, Dad, But you know, he came wanting

750
00:38:21,760 --> 00:38:23,960
Speaker 4: me to do this. And so immigrants make it possible

751
00:38:24,120 --> 00:38:26,880
Speaker 4: for science to grow, including from China. Yeah, Like normally

752
00:38:26,880 --> 00:38:28,680
Speaker 4: you talk about Jerry Mannering here, I was talking about

753
00:38:28,719 --> 00:38:31,120
Speaker 4: my parents, and so there's there's sort of a candidate

754
00:38:31,160 --> 00:38:33,160
Speaker 4: thing where people have to talk about themselves more and

755
00:38:33,160 --> 00:38:34,239
Speaker 4: I'm kind of getting used to that.

756
00:38:34,600 --> 00:38:38,160
Speaker 1: Yeah, it's a differend to be to go from being

757
00:38:38,280 --> 00:38:40,680
Speaker 1: a republic intellectual to a candidate.

758
00:38:40,960 --> 00:38:42,840
Speaker 4: Well, you know, I'm used to talking about ideas about

759
00:38:42,920 --> 00:38:45,200
Speaker 4: how we can make democracy work better. And I'm just

760
00:38:45,239 --> 00:38:49,319
Speaker 4: realizing that people are so oriented towards personality and individuals

761
00:38:49,360 --> 00:38:51,760
Speaker 4: that I think that it's I can make more progress

762
00:38:51,840 --> 00:38:54,520
Speaker 4: by getting out there into the fray and being the

763
00:38:54,520 --> 00:38:57,040
Speaker 4: person talking about it. And so for me, this is

764
00:38:57,080 --> 00:39:00,800
Speaker 4: not just representing my district, which is essential, but also

765
00:39:00,920 --> 00:39:03,200
Speaker 4: hopefully I don't know, there's people who are big thinkers

766
00:39:03,200 --> 00:39:06,160
Speaker 4: like Jamie Raskin or Don Byer, and I'm hoping to

767
00:39:06,200 --> 00:39:08,319
Speaker 4: be a junior partner to them and be right there

768
00:39:08,520 --> 00:39:09,080
Speaker 4: to help out.

769
00:39:09,280 --> 00:39:13,120
Speaker 1: I love that so much. Talk about the gerrymandering wars,

770
00:39:13,320 --> 00:39:13,799
Speaker 1: well just.

771
00:39:13,920 --> 00:39:17,080
Speaker 4: I mean, it's just continuing, right. It's weirdly that it's

772
00:39:17,120 --> 00:39:19,080
Speaker 4: come to a bit of a stalemate, and it started

773
00:39:19,120 --> 00:39:22,239
Speaker 4: in Texas, spread to California. Thankfully, a few states held back,

774
00:39:22,280 --> 00:39:25,080
Speaker 4: like Indiana, and so you know, we did analytics showing

775
00:39:25,200 --> 00:39:27,480
Speaker 4: that something like one out of three people in Indiana

776
00:39:27,520 --> 00:39:29,520
Speaker 4: would be moved to a new district, and so a

777
00:39:29,560 --> 00:39:32,960
Speaker 4: few states have hung back, Indiana and Kansas. That's good.

778
00:39:33,200 --> 00:39:35,680
Speaker 4: Looks like Maryland's trying, but I think the Democrats there

779
00:39:35,760 --> 00:39:38,000
Speaker 4: might step back from the brank, so that's good. But

780
00:39:38,120 --> 00:39:40,880
Speaker 4: now you know, looks like Virginia might move forward, and

781
00:39:40,920 --> 00:39:43,279
Speaker 4: that's going to be a multi seat jerrymander for Democrats.

782
00:39:43,400 --> 00:39:45,280
Speaker 4: Probably in the next few months. It's going to be Florida.

783
00:39:45,400 --> 00:39:47,960
Speaker 4: So I would say right now Democrats are actually a

784
00:39:48,000 --> 00:39:50,560
Speaker 4: head by a few seats. Once Florida jumps in the mix,

785
00:39:50,600 --> 00:39:53,040
Speaker 4: it'll probably be even again. But in the end we're

786
00:39:53,040 --> 00:39:55,120
Speaker 4: going to talk about it'll be like eight states.

787
00:39:55,320 --> 00:39:59,520
Speaker 1: But what about the Voting Rights Act? Isn't the Supreme

788
00:39:59,560 --> 00:40:02,400
Speaker 1: Court won't they roll on the voting right to act?

789
00:40:02,760 --> 00:40:05,959
Speaker 4: Yes, the Calais case or however you sent, that case

790
00:40:06,040 --> 00:40:09,200
Speaker 4: is going to possibly open the gates for racial gerrymandering.

791
00:40:09,320 --> 00:40:12,399
Speaker 4: And so that case might make it much harder for

792
00:40:12,520 --> 00:40:16,320
Speaker 4: communities of color to be represented in Congress, in legislatures,

793
00:40:16,520 --> 00:40:18,640
Speaker 4: and like you know, in county government, like up and

794
00:40:18,680 --> 00:40:21,320
Speaker 4: down the ticket, up and down all levels of government.

795
00:40:21,400 --> 00:40:24,920
Speaker 4: That case could be a pretty big deal for minority representation.

796
00:40:25,160 --> 00:40:28,480
Speaker 1: And would that happen this year? Would it be for

797
00:40:28,560 --> 00:40:30,200
Speaker 1: these midterms or for next?

798
00:40:30,560 --> 00:40:32,400
Speaker 4: I think a lot of that would be for next

799
00:40:32,440 --> 00:40:34,600
Speaker 4: And so there's a few things that have already happened.

800
00:40:34,680 --> 00:40:36,960
Speaker 4: Depending on how that goes, it's going to foreclose lawsuits

801
00:40:36,960 --> 00:40:41,000
Speaker 4: in Texas, Louisiana, and upcoming Florida. But that's those maps

802
00:40:41,080 --> 00:40:43,480
Speaker 4: either have been already enacted or will be enacted soon.

803
00:40:43,800 --> 00:40:47,399
Speaker 4: There's a handful of seats that could affect in Alabama, Mississippi,

804
00:40:47,480 --> 00:40:51,359
Speaker 4: some other places. I think, honestly, at some level, as

805
00:40:51,440 --> 00:40:54,839
Speaker 4: bad as that case is in the long term this year,

806
00:40:55,560 --> 00:40:58,560
Speaker 4: it's already been priced in because many of those laws

807
00:40:58,600 --> 00:41:01,799
Speaker 4: have already been enacted or there's not enough time. That's

808
00:41:01,800 --> 00:41:05,120
Speaker 4: a long term problem, and so there might be time six, Yeah,

809
00:41:05,360 --> 00:41:07,480
Speaker 4: could be fixed. We need a Voting Rights Act every

810
00:41:07,480 --> 00:41:09,560
Speaker 4: twenty years or so. We need to renew that act.

811
00:41:09,760 --> 00:41:12,440
Speaker 4: And so we need to put in racial protections. You know,

812
00:41:12,520 --> 00:41:15,160
Speaker 4: section two and section five. One of John Roberts's bits

813
00:41:15,200 --> 00:41:17,759
Speaker 4: of logic is it only applies to states with a

814
00:41:17,840 --> 00:41:20,919
Speaker 4: track record of racial discrimination, so therefore it doesn't treat

815
00:41:20,920 --> 00:41:23,480
Speaker 4: all states equally. And of course this makes my head explode.

816
00:41:23,560 --> 00:41:25,360
Speaker 4: But the way to fix that is, Okay, let us

817
00:41:25,400 --> 00:41:28,040
Speaker 4: apply section two and section five to every state. Done,

818
00:41:28,080 --> 00:41:29,920
Speaker 4: and then we will have that. And so, yeah, new

819
00:41:30,000 --> 00:41:31,680
Speaker 4: Voting Rights Act, if we can make it through this

820
00:41:31,760 --> 00:41:34,360
Speaker 4: November's election, I think the New Voting Rights Act should

821
00:41:34,360 --> 00:41:37,279
Speaker 4: be very top of agenda and hopefully can undo some

822
00:41:37,320 --> 00:41:39,600
Speaker 4: of the damage that we've seen to voting rights from

823
00:41:39,600 --> 00:41:40,360
Speaker 4: the Supreme Court.

824
00:41:40,600 --> 00:41:43,319
Speaker 1: Sam, thank you, thank you, thank you. I hope you'll

825
00:41:43,360 --> 00:41:43,799
Speaker 1: come back.

826
00:41:44,080 --> 00:41:44,760
Speaker 3: Thank you, Molly.

827
00:41:44,920 --> 00:41:47,600
Speaker 4: I have delighted to come back anytime next time, maybe

828
00:41:47,600 --> 00:41:48,480
Speaker 4: as a nominee.

829
00:41:50,280 --> 00:41:55,480
Speaker 1: No normal, Jesse Cannon bid fast.

830
00:41:55,560 --> 00:41:58,920
Speaker 2: So Republicans, there's this whole brand for as long as

831
00:41:58,920 --> 00:42:00,920
Speaker 2: you and I have been alive, that debt we have,

832
00:42:01,120 --> 00:42:04,040
Speaker 2: Oh my god, it's the ruin of our country. This debt.

833
00:42:04,280 --> 00:42:06,600
Speaker 2: We can't be standing for this. Yeah, I'm going to

834
00:42:06,640 --> 00:42:11,200
Speaker 2: shock you. Under the physical responsibility of mister Deal's president,

835
00:42:11,280 --> 00:42:14,120
Speaker 2: The federal debt has hit record levels and the Budget

836
00:42:14,120 --> 00:42:16,560
Speaker 2: Office has sent to a stirred warding to mister Trump.

837
00:42:16,800 --> 00:42:19,680
Speaker 1: Yeah, you're going to be shocked to hear this. But

838
00:42:20,120 --> 00:42:23,759
Speaker 1: Donald Trump, the man who is famous for having bankrupt

839
00:42:23,840 --> 00:42:27,240
Speaker 1: numerous businesses, is not so interested in the debt. And

840
00:42:27,280 --> 00:42:30,200
Speaker 1: I think there's a pretty good reason for that. Basically,

841
00:42:30,280 --> 00:42:35,319
Speaker 1: Trump has tried to reshape the economy, but when it

842
00:42:35,320 --> 00:42:38,560
Speaker 1: comes to budget, it's been a wash, and the country

843
00:42:38,680 --> 00:42:42,440
Speaker 1: is still basically on track to borrow what economists consider

844
00:42:42,480 --> 00:42:45,120
Speaker 1: an alarming amount of money in the coming years. Now,

845
00:42:45,239 --> 00:42:48,239
Speaker 1: there's a big problem here, which is the dollar. As

846
00:42:48,280 --> 00:42:52,719
Speaker 1: the dollar gets less valuable, it gets more expensive to

847
00:42:52,960 --> 00:42:57,319
Speaker 1: borrow money, and debt gets more expensive to service. And

848
00:42:57,360 --> 00:43:01,839
Speaker 1: that is a big problem, and we are on track

849
00:43:01,840 --> 00:43:04,520
Speaker 1: for that. Also, I want to point out Donald Trump

850
00:43:04,719 --> 00:43:08,640
Speaker 1: spends like a drunken saler. The funding for ICE is like,

851
00:43:08,960 --> 00:43:11,960
Speaker 1: you know what, four times most armies. I mean, it's

852
00:43:12,000 --> 00:43:16,080
Speaker 1: some eighty seven billion dollars, some crazy number. Buying of

853
00:43:16,200 --> 00:43:19,319
Speaker 1: ware hases, I mean, the idea that Ice is not

854
00:43:19,520 --> 00:43:22,520
Speaker 1: doing some sketchy stuff with all that money is I

855
00:43:22,520 --> 00:43:25,840
Speaker 1: would be shocked if people were not profiting off that money.

856
00:43:25,920 --> 00:43:29,600
Speaker 1: You know. Luckily you have Corey Lubandowski who's known to

857
00:43:29,680 --> 00:43:32,880
Speaker 1: be very honest and a straight shooter and someone who's

858
00:43:32,960 --> 00:43:36,400
Speaker 1: never ever, ever gotten in any kind of legal trouble

859
00:43:36,520 --> 00:43:40,760
Speaker 1: in any which way. So he's clearly operating on the level.

860
00:43:41,080 --> 00:43:43,160
Speaker 2: Oh yeah, that sounds exactly right to me.

861
00:43:43,280 --> 00:43:47,319
Speaker 1: That's really to be a good actor. They actually don't

862
00:43:47,360 --> 00:43:50,200
Speaker 1: even say sober as a judge. They say sober as

863
00:43:50,320 --> 00:43:51,520
Speaker 1: a Lubandowski.

864
00:43:51,800 --> 00:43:54,480
Speaker 2: You know. They call him much how we've rebranded wife

865
00:43:54,480 --> 00:43:57,080
Speaker 2: beaters two partner respectors. That's what they call him too.

866
00:43:57,200 --> 00:44:01,640
Speaker 1: Yeah, he's a big partner respector. And his boss, the

867
00:44:01,800 --> 00:44:06,320
Speaker 1: very smart and intelligent Christy Nome, has this under control.

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Speaker 1: So my guess is that they are not doing sketchy

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Speaker 1: stuff with all that money, no doubt. That's it for

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Speaker 1: this episode of Fast Politics. Tune in every Monday, Wednesday,

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00:44:20,160 --> 00:44:24,640
Speaker 1: Thursday and Saturday to hear the best minds and politics

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00:44:24,719 --> 00:44:29,000
Speaker 1: make sense of all this chaos. If you enjoy this podcast,

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00:44:29,360 --> 00:44:32,320
Speaker 1: please send it to a friend and keep the conversation

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00:44:32,480 --> 00:44:34,360
Speaker 1: going Thanks for listening,