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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 Elon Musk wants to prosecute Dr

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Speaker 1: Anthony Fauci because Elon Musk is definitely not an al

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Speaker 1: to right troll. We have a jam packed show for

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Speaker 1: you today. The good liars will join us to talk

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Speaker 1: about all the fun they have attending mega rallies. Doesn't

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Speaker 1: sound that fun, but sure. Then we'll talk to the

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Speaker 1: editor in chief of The Verge, Nili Patal, about Twitter

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Speaker 1: and chat GPT. But first we have nation writer and

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Speaker 1: author of allowing me to retort, Ellie missed all. Welcome

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Speaker 1: to you Fast Politics. Ellie missed all. Molly, how are you?

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Speaker 1: I am just always so excited to have you because

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Speaker 1: you are so smart and interesting but also hilarious. No pressure,

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Speaker 1: no pressure, Yeah, exactly. Be hilarious. Nope, let's not be hilarious.

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Speaker 1: Let's talk about the Supreme Court, which is I feel

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Speaker 1: like the opposite of hilarious. It's terrifying, is what it is.

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Speaker 1: It's hilarious like a clown, right, It's a thing that

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Speaker 1: should be funny and should be dismissable. But actually is

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Speaker 1: just very creepy and scary. There are a bunch of cases.

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Speaker 1: I think we should talk about Yesterday's case where they

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Speaker 1: are toying with democracy. Yeah, so yesterday's case is the

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Speaker 1: case to legalize the coup. Essentially right, it was the

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Speaker 1: oral argument, and the case is called mor v. Harper.

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Speaker 1: It arises out of the North Carolina State legislative Republican

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Speaker 1: control North Carolina State legislature making a map that was

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Speaker 1: so partisan, so favorable Republicans basically rigged the election um

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Speaker 1: in favor of Republican corressional candidates and Republican state legislative candidates.

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Speaker 1: That the North Carolina State Supreme Court throughout the map

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Speaker 1: as a violation of the North Carolina Constitution and ordered

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Speaker 1: it to be redrawn. North Carolina Republicans appealed that decision

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Speaker 1: in that case when all is what, it went all

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Speaker 1: the way to the Supreme Court. But the theory under

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Speaker 1: which they appealed it is what's really troubling. Um. They

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Speaker 1: didn't do the normal thing of just saying, hey, the

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Speaker 1: North Carolina Supreme Court is wrong, please help us, Mr

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Speaker 1: John Like that would have been the normal Republican thing

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Speaker 1: to do, right, No, no, no, no. They propagated what's

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Speaker 1: called independent state legislature theory or doctrine, depending on which

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Speaker 1: cuckoo for cocopus person you talked to. And this doctrine

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Speaker 1: or theory says that the state legislature and the state

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Speaker 1: legislature alone, through the process of an up or down vote,

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Speaker 1: is the final arbit on every time, place, and manner

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Speaker 1: on every aspect of an election held in the state,

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Speaker 1: subject only to the Federal Constitution's supremacy clause. So while

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Speaker 1: that might not jump off the page as a huge thing,

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Speaker 1: it does not above the page. But yes, continue, it

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Speaker 1: changes everything, right. It basically means that the state legislature,

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Speaker 1: which will often be controlled by partisans, are allowed to,

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Speaker 1: for instance, draw maps that violate the state constitution, and

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Speaker 1: the state Supreme Court cannot tell them that the state

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Speaker 1: constitution has been violated because only the state legislature can

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Speaker 1: do that. It means that potentially a state board of

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Speaker 1: elections could say like, Okay, we're gonna have voting from

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Speaker 1: this day to that day and it's gonna be early

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Speaker 1: voting from here to there, and the state legislators to

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Speaker 1: say no, because only the state legislature gets determined when

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Speaker 1: the time place again in manner of elections, so we

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Speaker 1: get to decide. It could mean that when the state's

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Speaker 1: certification bureau says we have now certified our slate of

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Speaker 1: presidential electors the state legislature and only the state legislature,

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Speaker 1: it gets to say, no, no, no, we would like

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Speaker 1: to have a different slate of presidential electors. So the

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Speaker 1: entire kind of structure, the kind of nitty gritty nuts

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Speaker 1: and bolts of how democracy works, these North Carolina Republicans say,

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Speaker 1: can only be controlled, can only be be adjudicated by

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Speaker 1: the partisan upper down vote of a state legislature. It

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Speaker 1: should come as no surprise to your listeners that the

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Speaker 1: first place where this cockamamie ferry voice was in two

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Speaker 1: thousand in Bush Vigor. This theory was adopted by former

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Speaker 1: Chief Justice William Renquest in Bush Vigor. Now, if you

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Speaker 1: guys remember what and I'm gonna date myself here by

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Speaker 1: like being alive during Bush Vigor. Right, I was a

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Speaker 1: one out during Bush Vigor. So like it's stuck in

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Speaker 1: my mind a bit, right, and that when and uh,

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Speaker 1: the issue in Bush for your younger viewers listeners, is

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Speaker 1: that the Florida State Supreme Court had ordered a recount

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Speaker 1: of the general election between Gore and Bush in Florida.

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Speaker 1: That was a court ordered recount following the state courts

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Speaker 1: interpretation of Florida statutory law. It was the Republicans, who

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Speaker 1: usually are all four state rights, suddenly when the presidency

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Speaker 1: was online, didn't want to listen to states rights, didn't

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Speaker 1: want to listen to Florida wants to have a different

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Speaker 1: standard of review to find a way to stop the recount. Intellectually,

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Speaker 1: this was hard. So Renquist invented this independent state legislature

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Speaker 1: theory that said that it was actually the then Republican

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Speaker 1: controlled Florida State legislature that had the final authority on

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Speaker 1: whether or not there should be a recount, not the

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Speaker 1: Florida State Supreme Court. Now, it's important to understand that

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Speaker 1: renquests idea here was rejected by the court. That's not

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Speaker 1: the grounds on which the Republicans made George W. Bush president.

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Speaker 1: They found a different way to stop the court mandated recount.

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Speaker 1: But that's the first time this theory was put into play.

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Speaker 1: But here's the coda to that story, Molly. As I said,

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Speaker 1: it was a concurring opinion by Justice William Renquist. It

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Speaker 1: was signed only by two other justices, Antonin Scalia who

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Speaker 1: is now dead and Clarence Thomas, who was very much not.

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Speaker 1: In addition, if y'all remember one of the lawyers on

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Speaker 1: George W. Bush's team who was pushing this independent state

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Speaker 1: legislator theory was current Supreme Court Justice Brett Kavanaugh. There's

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Speaker 1: actually a clip of Kavanaugh from two thousand. Mark Joseph

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Speaker 1: Stern actually founded I saw. I saw him posted on

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Speaker 1: Twitter the other day a clip of Brett Kavanaugh in

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Speaker 1: two thousand talking to Wolf Blitzer pushing independent state legislature theory. Interesting,

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Speaker 1: that's how this cockamamie theory got started. It was Republicans

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Speaker 1: casting about for a way to make George W. Bush president,

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Speaker 1: and now it's coming back to us as a book.

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Speaker 1: Ins cast about for a way to legalize not the

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Speaker 1: previous coup, but to legalize the next coup that they're planning.

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Speaker 1: You know who else is a proponent of independent state

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Speaker 1: legislature theory. Jenny Thomas. This was one of the theories

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Speaker 1: that she was pushing in her emails to Kevin McCarthy

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Speaker 1: and all the other people that she was trying to

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Speaker 1: get to legalize the coup. So there's an entire kind

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Speaker 1: of you know, white wing, right wing ecosystem here around

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Speaker 1: the concept of pushing this theory forward so that red

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Speaker 1: state legislatures are able to decide the results of elections

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Speaker 1: as opposed to Board of Elections, nonpartisan commissions, or even

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Speaker 1: the state Supreme Court. Yeah, yeah, yeah, I want to

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Speaker 1: get back to the second. There was some pushback here,

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Speaker 1: though it didn't go as badly as I thought. Random, Yeah, y,

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Speaker 1: that's congratulate ourselves for it not going as badly as

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Speaker 1: we thought. I I was reading some piece about this,

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Speaker 1: and I was super eyes that it didn't go as

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Speaker 1: badly as we thought. Yeah, Democrats not as bad as possible. Well,

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Speaker 1: this Supreme Court, they really suck so whenever they are

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Speaker 1: slightly and I thought it was interesting. It seems like

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Speaker 1: Gorcitch was the one who was the most into the idea,

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Speaker 1: explained to us what happened. So Thomas, with his priors,

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Speaker 1: Aldo and Gorci, we're all in independence legislator theory. Gorsig

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Speaker 1: was actually particularly odious and I don't even want to

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Speaker 1: waste a lot of time on this on this guy,

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Speaker 1: because he's he He was literally just trolling. I think

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Speaker 1: all black Americans with his questioning. But that only gets

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Speaker 1: you to three. Right now, again, because of what I

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Speaker 1: said about Kavanaugh, I expected Kavanaugh to be a solid four,

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Speaker 1: but at oral arguments he was a bit weak. I

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Speaker 1: mean Kavanaugh is like constitutionally like a weak person. I

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Speaker 1: mean that's kind of his brand. But yes, continue right,

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Speaker 1: he was. He was a little bit intellectually washy. He

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Speaker 1: was trying to distinguish Bush v. Gore. And actually, just

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Speaker 1: by the way, mentioned that at the time of Bush

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Speaker 1: Vigor he was any lawyers I'm saying he was. He

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Speaker 1: was one of Bush's lawyers. Yeah, is so sketchy, and

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Speaker 1: now he runs the highest court in the land. Continue right.

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Speaker 1: The Kavanaugh tried to distinguish Bush Vigor from the case

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Speaker 1: in front of him from North Carolina. He was trying

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Speaker 1: to make a very it's a thin slice of the

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Speaker 1: legal pie. He was trying to make a distinction between

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Speaker 1: statutory interpretation, which is what was at issue when Bush

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Speaker 1: Vigor versus state constitutional interpretation, which is what is that

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Speaker 1: issue in North and Morvy Harbor. And he was trying

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Speaker 1: to say that like, those two things are different and

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Speaker 1: independent state legislature theory works for one but not for

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Speaker 1: the other. So that was a little bit surprising. I

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Speaker 1: don't know they'll have the strength of character to fall

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Speaker 1: through through with that. But the swing vote was always

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Speaker 1: gonna be Amy Coney Barrett, John Roberts, he doesn't like

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Speaker 1: this theory, like it's it's funny because part of the

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Speaker 1: reason why we're here is because of an earlier Robert's decision,

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Speaker 1: like this is a bit of chickens coming home to

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Speaker 1: roost to him. But but Roberts is not down for this,

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Speaker 1: because Roberts, this whole thing is to give more power

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Speaker 1: to judges, not take power away from judges, even at

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Speaker 1: the state court level, um and put it in the

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Speaker 1: hands of partisan legislatures. That's that's not Robert's m l.

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Speaker 1: So Roberts doesn't like independence legislature theory. But Amy Coney Barrett, like,

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Speaker 1: she has no priors on this right, She hasn't written

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Speaker 1: about this before, she hasn't ruled on this before. She

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Speaker 1: isn't married to a person who tried to support a

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Speaker 1: violent insurrection against the American government. And yet and as

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Speaker 1: far as I know, Molly, and I'm not a schooled

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Speaker 1: in theology, as I am on law, but I do

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Speaker 1: not believe Jesus has taken a strong position on independence

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Speaker 1: date legislat This is why I love you because anyway, yes,

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Speaker 1: I don't think Jesus has a right. So if so,

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Speaker 1: if she can't tie this to anything in her prior

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Speaker 1: law and to anything in Leviticus, to anything in her background,

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Speaker 1: she's in play, is what I'm saying. And based on

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Speaker 1: her questions, she seemed quite skeptical that independence legislature theory

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Speaker 1: was a thing now, and I have written about this

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Speaker 1: from the Nation and an article that's coming out a

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Speaker 1: little bit later today. You always have to be careful

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Speaker 1: with reading the tea leaves on Barrett, because in her

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Speaker 1: brief history she has actually shown herself to be a

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Speaker 1: very good, thoughtful, smart questioner, where at oral arguments she

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Speaker 1: seems to really be intellectually getting in to the meat

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Speaker 1: of the issue in front of her. But then when

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Speaker 1: it comes times to vote, all she's done is vote

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Speaker 1: for the most extreme, brain dead version of the conservative

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Speaker 1: case like she like. Whatever she does at oral arguments

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Speaker 1: has thus far not linked up to what she does

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Speaker 1: behind closed doors when it comes time kind to vote.

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Speaker 1: So I don't want to put too much faith in there,

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Speaker 1: but based on aur arguments at least, she seemed extremely

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Speaker 1: skeptical that independent state legislature theory was a thing. Right,

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Speaker 1: that's so interesting. So she is actually smarter than she votes.

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Speaker 1: I think she gets a bad rap because, you know

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Speaker 1: her her background coming into the court was so light,

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Speaker 1: and people dislike her for entirely legitimate reasons. But please

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Speaker 1: don't come at her as if she's dumb. She's not dumb.

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Speaker 1: She's not a man, basically, right, like all of the

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Speaker 1: women are the Supreme Court are smarter than all of them. Yeah,

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Speaker 1: you're laughing, but that sounds right to me. It's much

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Speaker 1: harder to succeed in these businesses as a woman, just

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Speaker 1: like it is much harder to succeed at these than

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Speaker 1: when you're black. I mean exactly. This is why the

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Speaker 1: people who get there tend to be much, much, much

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Speaker 1: smarter than the white guys. You can't be Amy Coney

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Speaker 1: Barrett but have a background like Brett Kavanaugh, Right, you can't.

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Speaker 1: You can't just fail up and be a woman, even

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Speaker 1: in kind of conservative Republican circles. You know, there are exceptions,

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Speaker 1: Carrie Lake Springs find for no reason, But she didn't win. Right,

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Speaker 1: when you're a woman and you get to the level

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Speaker 1: of Amy Coney Barrett, even within conservative circles, that there's

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Speaker 1: probably some intellectual heft there, and she has it. She's

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Speaker 1: an intelligent person. Her problem is not that she's dumb.

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Speaker 1: Her problem is that she's at least she votes like one.

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Speaker 1: Her problem is that she's a religious wing nut, or

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Speaker 1: at least she votes like like. If you're gonna come

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Speaker 1: at her, come at her for what she does. Don't

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Speaker 1: come at her for her background. Because when you listen

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Speaker 1: to her at court, she is an intelligent person who

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Speaker 1: asks and again I can't emphasize this enough. Her questions

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Speaker 1: are consistently now over the past two years, the best

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Speaker 1: version of the conservative case possible. Right, she asked the

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Speaker 1: questions correctly from the perspective of her view of you

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Speaker 1: that I obviously disagree with. Really, the only stinker was

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Speaker 1: during the dobbs Or argument where she kind of went

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Speaker 1: off on a Jesus tangent about firehouse babies and in

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Speaker 1: two years that the thing she said. I do not

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Speaker 1: go through two years and have one thing to be

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Speaker 1: the dump right, the most things you said in two years,

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Speaker 1: but other than that kind of obvious problem. She's really

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Speaker 1: intelligent and she asks really good questions. Again, when it

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Speaker 1: comes time to vote, she votes like she's a brain

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Speaker 1: dead problem right, talks like Kagan votes like Havana. That's

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Speaker 1: her problem. Right. But you, as someone who has covered

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Speaker 1: this court for a long time and who is quite smart,

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Speaker 1: I think what you're trying to tell me is that

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Speaker 1: there is that the Court is a power that changes people.

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Speaker 1: It sounds like, and again I'm maybe reading between the

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Speaker 1: lines here, but it seems like what you're saying is

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Speaker 1: that people grow into the court. I'm not quite willing

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Speaker 1: to go that far in her yet, right, but it

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Speaker 1: could you feel like it's theoretically possible. What I think

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Speaker 1: is true is that Amy Coney Barrett knows what she's doing, right.

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Speaker 1: I think what is still unknown is what she wants

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Speaker 1: to do. Right. She's got this power, she understands how

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Speaker 1: it works, she understands how this game is played. The

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Speaker 1: question is what she wants to do with that power.

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Speaker 1: And I always contrast her with Brett Kavanaugh, not just

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Speaker 1: because they are the closest in terms of their appointments,

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Speaker 1: but because Bret Kavanaugh is a drooling idiot When he

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Speaker 1: asked questions. They are the dumbest, most reductive, most most

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Speaker 1: most least, most bog standard version of the Republican argument.

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Speaker 1: Like he said, like there there is a lack of

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Speaker 1: intellectual left that you see with Brett Kavanaugh, and there's

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Speaker 1: also a sweaty desperation. She's off the man to be

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Speaker 1: liked by the media. Now. I think that that John

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Speaker 1: Roberts has gotten a completely um. He is entirely too

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Speaker 1: respected by mainstream media. They love Roberts. Everybody wants that

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Speaker 1: wants Roberts as their conservative friend. That is the wrong.

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Speaker 1: Roberts is an enemy of black people voting. It has

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Speaker 1: been for his entire career. But the way the media

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Speaker 1: treats John Roberts is the way Brett Kavanaugh would like

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Speaker 1: to be treated. The problem, of course, is that John Roberts,

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Speaker 1: you know, as far as we know, never tried to

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Speaker 1: rape anybody, right, and so like he can't kind of

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Speaker 1: can't get around that, but he would like to. There's

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Speaker 1: no world in which Kavanaugh will ever not be tainted

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Speaker 1: by what he has done exactly, but he wants to

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Speaker 1: be treated like Roberts, and so all of his all

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Speaker 1: of his little machinations are, even in this case where

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Speaker 1: he's trying he's trying to make this dissension right between

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Speaker 1: bustery goor in this case, I don't know if he'll

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Speaker 1: follow through on and I don't know if he just

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Speaker 1: did that because he wants to be liked. He wants

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Speaker 1: to sound reasonable more so than he is. So you

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Speaker 1: can really distinguish between what Kavanaugh does, which is always

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Speaker 1: kind of dumb, but also media facing and whatever versus

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Speaker 1: again what we've seen from Barrett so far, which is

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Speaker 1: to be smart, to be thoughtful, to be probative, but

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Speaker 1: then be hind closed doors, just do the Republican bidding.

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Speaker 1: So people have to see about that. Obviously, the smartest

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Speaker 1: justice on the court right now is is Katanji Brown Jackson. Whoo.

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Speaker 1: She just she comes out swinging hard, taking no prisoners.

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Speaker 1: She has been and I've said this before, Molly, but

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Speaker 1: like I'm I'm a grown as man, right, I'm a

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Speaker 1: forty four year old man. I don't need I'm beyond

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Speaker 1: the stage where I'm like, I hope that Disney character

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Speaker 1: looks like me, because i feel like I'm beyond that

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Speaker 1: right right, right, Katanji Brown Jackson is like me being

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Speaker 1: a twelve year old and seeing a black princess and

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Speaker 1: being like, maybe I could be a princess someday. Like

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Speaker 1: she's that level of affirming. It's not just what she says,

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Speaker 1: but how she says it. The way she attacks an argument,

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Speaker 1: it's exactly the way that I think. It's exactly what

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Speaker 1: I think should be done. And to hear it for

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Speaker 1: the first time in my life coming from a Supreme

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Speaker 1: Court justice, it's just been so affirming and wonderful to

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Speaker 1: to listen to her in these arguments. She gotta lose.

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Speaker 1: I mean, she lose right because the votes. Yeah, it

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Speaker 1: sucks that you have these three pretty incredible liberal justices

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Speaker 1: and then you have like the drunken beer guy. But

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Speaker 1: like it's not I mean, can you imagine like that

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Speaker 1: the women have killed themselves to get there. But lawmakers

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Speaker 1: have added a provision in the National to the National

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Speaker 1: Defense Authorization Acts. This is I'm reading a tweet from

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Speaker 1: Jane Mayer protecting Supreme Court spouses from having to reveal

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Speaker 1: any outside employer in the name of security. If it passes,

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Speaker 1: Jenny Thomas's professional entanglements would effectively be state secrets. YEP,

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Speaker 1: that sounds on brand for Republicans and Republicans. But it

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Speaker 1: also sounds like the kind of thing Joe Mansion would

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Speaker 1: be for, right, because like when you go too far

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Speaker 1: into there is a desire on the behalf of corrupt

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Speaker 1: politicians for us to not know who's paying for them. Right,

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Speaker 1: Like it's in Nascar, they at least have the dignity

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Speaker 1: to emblazon who bought them on their cars and on

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Speaker 1: their shirts, right, But in American politics, they don't even

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Speaker 1: have that level of dignity, right. They they don't want

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Speaker 1: us to know who is funding them because they don't

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Speaker 1: want us to know who actually controls their votes. And

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Speaker 1: so hiding the spouses it's not security issue, nobody's nobody's

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Speaker 1: threatening Jenny Thomas's right wing crazy conspiracy Q and on employers.

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Speaker 1: It's not a real thing, right. They're doing it so

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Speaker 1: they can hide what these people do. I remember, these

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Speaker 1: people are spouses, are you know, like Jenny Thomas, A

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Speaker 1: lot of the Supreme Court justices have spouses who were

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Speaker 1: public figures before their spouses ascended to the Supreme Court.

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Speaker 1: Unlike Jenny Thomas, they pulled back from public life to

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Speaker 1: avoid the appearance of impropriety. But John Roberts's wife, for instance,

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Speaker 1: Jane Sullivan was a fierce forced birth lawyer who ran

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Speaker 1: forced birth organizations and pushed rel Leslie the legal theories

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Speaker 1: that would eventually be used to overturn Roe v. Wade

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Speaker 1: for her whole life until Roberts to send it to

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Speaker 1: the Supreme Court. Right, super interesting, Thank you so much, Ellie,

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Speaker 1: super interesting. I hope you will come back absolutely, Molly.

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Speaker 1: You guys, you guys have a nice one. You're the best.

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Speaker 1: Thank you. Jason Selvig and dav Rom Stifler are a

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Speaker 1: comedic duo known as The Good Liars. Welcome Too Fast Politics,

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Speaker 1: The Good Liars, Thanks for having us, Thanks for having

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Speaker 1: us here. Jason, Hey, how's it going make your voice

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Speaker 1: sound distinctive? I can do it, am impression and yes,

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Speaker 1: good do Advram oppression. Now I'm not gonna do that

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Speaker 1: to da Vrom say hi to us so we can

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Speaker 1: people can Hello everyone, This is Davrom here. Jason was

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Speaker 1: a pretty good impression of me. A little it's on

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Speaker 1: the offensive side, but uh, pretty good. Good enough that

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Speaker 1: my my Syria on my phone picks him up and

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Speaker 1: thinks it's me O. Hey SERI, oh wow, my series

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Speaker 1: not going off, So that was a mistake on my part. Uh,

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Speaker 1: get what you deserve. So anyway, welcome to Fast Politics.

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Speaker 1: I wanted to have you guys on the podcast to

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Speaker 1: talk about all of the stuff that you guys do

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Speaker 1: that is pretty hilarious. I mean, I don't know where

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Speaker 1: we start here. I guess you should start by telling

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Speaker 1: people how you two found each other. Sure, yeah, de

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Speaker 1: Vron and I were friends in the city through the

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Speaker 1: comedy scene for years and years, and we didn't like

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Speaker 1: do a lot of comedy together. We mostly were friends

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Speaker 1: from playing basketball together. We would go play pick up

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Speaker 1: games in the city. And then around the time of

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Speaker 1: Occupy Wall Street, we did one video together, a comedy

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Speaker 1: video where we acted like we were investment bankers that

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Speaker 1: were down there protesting the protesters, and we called ourselves

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Speaker 1: Occupy Occupy Wall Street, and we thought we were kind

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Speaker 1: of you know, we thought it was pretty obvious that

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Speaker 1: we were joking, and we were saying ridiculous things like

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Speaker 1: if you guys keep protesting, we're gonna have to give

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Speaker 1: up our house in the Hampton's and uh, quit our

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Speaker 1: cocaine addiction. And we thought it was a pretty obvious joke.

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Speaker 1: But people thought we were real, and the news reported

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Speaker 1: on us like we were real, and real investment bankers

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Speaker 1: came and joined us in the protests. Yeah. Eventually we

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Speaker 1: we had like we had a website. We were selling

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Speaker 1: cuff links for like three hundred bucks, just like I said,

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Speaker 1: one percent on them, be part of the one percent,

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Speaker 1: and by these like everything was supposed to be a

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Speaker 1: joke was taken seriously, and then real bankers joined us

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Speaker 1: and we're saying all this that crazy stuff we were

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Speaker 1: saying along with us. It was like this out of

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Speaker 1: body experience where like we thought we were, you know,

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Speaker 1: doing caricatures of people, and now here they are joining us.

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Speaker 1: It's interesting because we had New York Times pitch on

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Speaker 1: this podcast and he said that he used to comment

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Speaker 1: on this conservative blog as a way of trolling them,

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Speaker 1: and they thought he was serious. Yeah. Yeah, it's like

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Speaker 1: the same deal where it was like the more ridiculous

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Speaker 1: we would get, the more people would be like nodding

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Speaker 1: and saying yes. And it was kind of at that

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Speaker 1: point where we realized that like both of us, I

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Speaker 1: feel comfortable, you know, for better or worse, doing these

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Speaker 1: things in public and doing ridiculous things in public and

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Speaker 1: from there, we kind of were like going. We ended

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Speaker 1: up doing a movie on the election where we kind

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Speaker 1: of got into the world of pranking politicians, and it's

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Speaker 1: kind of evolved along the way. But it's all in

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Speaker 1: the real world. We're putting ourselves in, you know, kind

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Speaker 1: of tricky situation sometimes, but all all for the sake

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Speaker 1: of comedy. I mean, do you guys have anxiety about

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Speaker 1: doing these things? I mean, I don't have any anxiety

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Speaker 1: about public speaking, but I think this might freak me

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Speaker 1: out a little bit. I think yes is the answer

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Speaker 1: to some degree. But we have gotten used to it.

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Speaker 1: Like there is as crazy as some of these situations are,

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Speaker 1: there's like a routine to it. Like we go somewhere,

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Speaker 1: we stay in a shitty hotel, we write our little

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Speaker 1: notes about what we want to say, we show up

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Speaker 1: like it is kind of tense, but because we've done

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Speaker 1: it enough, there's like a little bit of a routine

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Speaker 1: to fall back on. In a weird way. But I

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Speaker 1: think some of the craziness of the situations, you know,

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Speaker 1: doesn't go away over time because there's like a certain

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Speaker 1: amount of danger and and it will always be new

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Speaker 1: when you're dealing with real people. So yes, yes and no.

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Speaker 1: I guess. You know what's funny because I'm thinking about

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Speaker 1: we had this incredible activist on this podcast, crackhead Barney.

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Speaker 1: Oh yeah, yeah yeah, oh yeah yeah yeah, it's great.

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Speaker 1: I said, like, did you ever feel like actually scared?

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Speaker 1: Was there any place where we've ever felt scared? She

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Speaker 1: got arrested once, right, she could put in a mental

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Speaker 1: institution once. That would be scared. I would be scary

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Speaker 1: for me. Yeah, hard, yes, hard, yes, I guess would

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Speaker 1: be her answer. What is Belleview still? Does that still exist? No?

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Speaker 1: I mean, guess where was the scariest place She said

427
00:25:08,680 --> 00:25:13,960
Speaker 1: she'd ever been? Troup Rally, Staten Island. That is a

428
00:25:14,000 --> 00:25:17,520
Speaker 1: scary place, you know, scary. So so what do you guys?

429
00:25:17,560 --> 00:25:20,280
Speaker 1: I mean, where have you felt really like we're going

430
00:25:20,320 --> 00:25:24,080
Speaker 1: to get murdered? January six was scary? That was That

431
00:25:24,200 --> 00:25:26,359
Speaker 1: was That was like the thing that the event that

432
00:25:26,400 --> 00:25:29,359
Speaker 1: we went to that where it definitely took a toll

433
00:25:29,440 --> 00:25:32,520
Speaker 1: on at least my mental state. I tell us a

434
00:25:32,520 --> 00:25:35,680
Speaker 1: little bit about that experience. We got there like the

435
00:25:35,800 --> 00:25:39,439
Speaker 1: day before and it was we were staying in this

436
00:25:39,480 --> 00:25:43,680
Speaker 1: hotel and people were up all night playing like military

437
00:25:43,800 --> 00:25:46,600
Speaker 1: pump up music, and so that was like all night

438
00:25:46,640 --> 00:25:50,639
Speaker 1: the night before, so it just definitely felt like something,

439
00:25:50,800 --> 00:25:53,359
Speaker 1: something was coming. Jason interrupted a little, but I just

440
00:25:53,400 --> 00:25:56,240
Speaker 1: felt like we should mention that the January that was

441
00:25:57,280 --> 00:26:01,360
Speaker 1: almost as crazy, like Alex Jones and Roger Stone and

442
00:26:01,400 --> 00:26:03,840
Speaker 1: Ali Alexander and all those people spoke to night before

443
00:26:04,040 --> 00:26:06,880
Speaker 1: people went to that and we're just like partying and

444
00:26:07,520 --> 00:26:10,360
Speaker 1: it was that was the night before was we got

445
00:26:10,400 --> 00:26:13,480
Speaker 1: there on the fifth and we did some interviews and

446
00:26:13,960 --> 00:26:16,639
Speaker 1: talked to some people, and then we went back to

447
00:26:16,680 --> 00:26:18,719
Speaker 1: the hotel or checked in the hotel, and I remember,

448
00:26:19,160 --> 00:26:23,399
Speaker 1: and we then walked to the Alex Jones Roger Stone

449
00:26:23,800 --> 00:26:28,800
Speaker 1: speaking thing and there were people like Proud Boys or

450
00:26:28,880 --> 00:26:32,080
Speaker 1: I don't even know who, militia members in bull profess

451
00:26:32,240 --> 00:26:35,359
Speaker 1: and military gear all around the city that like had

452
00:26:35,400 --> 00:26:39,320
Speaker 1: a perimeter, that were on radios talking to other people.

453
00:26:39,640 --> 00:26:42,480
Speaker 1: They like basically like owned the city at that point,

454
00:26:42,560 --> 00:26:45,880
Speaker 1: owned d C. They like they they were the uh

455
00:26:46,000 --> 00:26:49,119
Speaker 1: quote unquote police presence there or whatever you want to

456
00:26:49,200 --> 00:26:51,560
Speaker 1: call it, their their own military presence. And then we

457
00:26:51,560 --> 00:26:54,840
Speaker 1: got to the Alex Jones event and he was speaking

458
00:26:54,920 --> 00:26:57,160
Speaker 1: and it was like the closest thing I can imagine

459
00:26:57,160 --> 00:26:59,359
Speaker 1: what it would be like to see Hitler speech speak

460
00:26:59,560 --> 00:27:03,760
Speaker 1: at in Germany in the nineteen thirties. Just the way

461
00:27:03,800 --> 00:27:05,600
Speaker 1: he was speaking. I think we have a video of

462
00:27:05,640 --> 00:27:09,119
Speaker 1: it somewhere where he's speaking and just screaming and pounding

463
00:27:09,160 --> 00:27:11,880
Speaker 1: the podium and talking about how it's a war. Yeah,

464
00:27:11,920 --> 00:27:15,040
Speaker 1: it's like indistinguishable. He's throwing his fist up and he's

465
00:27:15,119 --> 00:27:18,240
Speaker 1: like yelling, and he's pounding the podium like in rhythm

466
00:27:18,520 --> 00:27:20,800
Speaker 1: as he's speaking, and it's like it's not well lit.

467
00:27:20,840 --> 00:27:23,000
Speaker 1: I don't think they were expecting, you know, as many

468
00:27:23,000 --> 00:27:24,879
Speaker 1: people as they got there, it didn't feel like it

469
00:27:24,960 --> 00:27:29,200
Speaker 1: was very organized, like the chaos was like brewing. For sure,

470
00:27:29,920 --> 00:27:32,600
Speaker 1: it was a very unsettling night. Everything was closed, We

471
00:27:32,600 --> 00:27:36,120
Speaker 1: couldn't get dinner anywhere. Everything was boarded up. I think

472
00:27:36,119 --> 00:27:38,520
Speaker 1: people were thinking something really bad was going to happen

473
00:27:38,520 --> 00:27:41,399
Speaker 1: the next day, and then they were right to think that.

474
00:27:41,680 --> 00:27:43,480
Speaker 1: But then the next morning we went out and we

475
00:27:43,520 --> 00:27:47,919
Speaker 1: talked to people, and just everyone that we spoke to.

476
00:27:48,080 --> 00:27:50,400
Speaker 1: You know, Davram has this amazing interview where he talks

477
00:27:50,440 --> 00:27:52,280
Speaker 1: to a guy who's saying he's going to die in

478
00:27:52,400 --> 00:27:56,760
Speaker 1: his boots for Donald Trump. Like everyone was speaking like

479
00:27:56,840 --> 00:27:59,480
Speaker 1: this was an injustice and it was going to be

480
00:27:59,680 --> 00:28:04,000
Speaker 1: right that very day. This was seventeen seventy six, two

481
00:28:04,080 --> 00:28:06,879
Speaker 1: point Oh, we're going to take this thing over. And

482
00:28:06,920 --> 00:28:10,600
Speaker 1: that's exactly what they tried to do. And it when

483
00:28:10,960 --> 00:28:14,240
Speaker 1: the we wanted to get out of town. Before we started,

484
00:28:14,280 --> 00:28:17,720
Speaker 1: we started to feel like some some like as Trump

485
00:28:17,760 --> 00:28:19,320
Speaker 1: was about to be finished speaking, We're like, this is

486
00:28:19,400 --> 00:28:23,240
Speaker 1: probably the time to leave. And then um, we were

487
00:28:23,359 --> 00:28:27,200
Speaker 1: driving along whatever whatever it is constitution is that right,

488
00:28:28,520 --> 00:28:31,080
Speaker 1: trying to leave and we saw like this mob of people.

489
00:28:31,119 --> 00:28:32,640
Speaker 1: So in the time that we had gotten our car

490
00:28:32,680 --> 00:28:34,840
Speaker 1: and started to head out of town, Trump had said

491
00:28:34,880 --> 00:28:38,840
Speaker 1: we're going to the capital, and so we're like leaving town,

492
00:28:38,920 --> 00:28:41,600
Speaker 1: checking Twitter, like no way. He actually told them to

493
00:28:41,680 --> 00:28:45,120
Speaker 1: go there, and he did, and we pulled over and like, well,

494
00:28:45,320 --> 00:28:47,800
Speaker 1: whatever is about to happen, let's at least just not

495
00:28:47,960 --> 00:28:50,960
Speaker 1: miss it. And then within like five minutes they had

496
00:28:51,000 --> 00:28:54,160
Speaker 1: broken through the you know, the barricades and yeah, there

497
00:28:54,200 --> 00:28:57,719
Speaker 1: were flashbanks. Yeah. That was the thing that stuck with

498
00:28:57,760 --> 00:29:00,959
Speaker 1: me was you were seeing these cops like just like

499
00:29:01,160 --> 00:29:03,800
Speaker 1: getting the hell beat out of them by by these people,

500
00:29:03,840 --> 00:29:06,000
Speaker 1: and we were, you know, we were by the steps

501
00:29:06,000 --> 00:29:09,080
Speaker 1: in the in the back where they broke through, and

502
00:29:09,160 --> 00:29:11,120
Speaker 1: you're just seeing these just getting hit in the face

503
00:29:11,160 --> 00:29:13,920
Speaker 1: with flags, flag balls, I should say. And it was

504
00:29:14,440 --> 00:29:17,320
Speaker 1: I thought that the police, like, for the most part,

505
00:29:17,320 --> 00:29:19,880
Speaker 1: I acted completely heroically and they were just like getting

506
00:29:19,920 --> 00:29:22,760
Speaker 1: beat up. But I thought at some point one of

507
00:29:22,800 --> 00:29:25,240
Speaker 1: them was going to pull out a gun and like shoot,

508
00:29:25,640 --> 00:29:27,680
Speaker 1: because I don't know what I would do in that

509
00:29:27,720 --> 00:29:29,800
Speaker 1: situation if I was getting beat up and people were

510
00:29:29,800 --> 00:29:32,600
Speaker 1: gonna were trying to get in there to potentially kill

511
00:29:33,040 --> 00:29:35,880
Speaker 1: members of Congress or the vice president. So whenever a

512
00:29:35,920 --> 00:29:39,280
Speaker 1: flashband went off, my first instinct was, oh, bullets are

513
00:29:39,320 --> 00:29:42,160
Speaker 1: gonna fly, and I like could get shot right now,

514
00:29:42,360 --> 00:29:44,680
Speaker 1: and it's probably you know better that you know, people

515
00:29:44,720 --> 00:29:47,760
Speaker 1: didn't get shot with it, you know, one obvious exception,

516
00:29:47,920 --> 00:29:50,280
Speaker 1: but that really stuck with me where I would like

517
00:29:50,400 --> 00:29:51,760
Speaker 1: go try to go to sleep at night, and then

518
00:29:51,760 --> 00:29:54,200
Speaker 1: I would be like kind of had like PTSD for

519
00:29:54,240 --> 00:29:57,000
Speaker 1: a little bit. From that, I was I was standing

520
00:29:57,000 --> 00:29:58,680
Speaker 1: next to the police officer trying to call he was

521
00:29:58,680 --> 00:30:00,320
Speaker 1: trying to call for back up, and it was in

522
00:30:00,360 --> 00:30:03,680
Speaker 1: tears because I think as far as anyone knew, it

523
00:30:03,800 --> 00:30:06,680
Speaker 1: was like many many, many people were dying in there.

524
00:30:07,120 --> 00:30:09,960
Speaker 1: At that moment, nobody, you know, we didn't have the

525
00:30:10,000 --> 00:30:13,280
Speaker 1: benefit of any time having passed and watching on TV

526
00:30:13,440 --> 00:30:15,720
Speaker 1: and getting any reporting. It was like, oh my god,

527
00:30:15,880 --> 00:30:18,560
Speaker 1: is this happening right now? And then of course someone

528
00:30:18,640 --> 00:30:22,040
Speaker 1: saw our microphone and said we were part of the

529
00:30:22,080 --> 00:30:24,520
Speaker 1: fake news and kind of like chased us out of

530
00:30:24,560 --> 00:30:26,880
Speaker 1: there a little bit, and I think at that point

531
00:30:26,960 --> 00:30:29,120
Speaker 1: we were happy to leave. Yeah, there was. It was

532
00:30:29,160 --> 00:30:32,000
Speaker 1: like a group of like twelve guys that like circled

533
00:30:32,000 --> 00:30:35,600
Speaker 1: around us and said that we were part of the

534
00:30:35,600 --> 00:30:38,160
Speaker 1: fake news media. And then a flashband went off and

535
00:30:38,360 --> 00:30:40,560
Speaker 1: everybody turned around and we were like, all right, that's

536
00:30:40,560 --> 00:30:44,320
Speaker 1: our que let's let's let's make our way to the

537
00:30:44,360 --> 00:30:46,800
Speaker 1: exit of Washington, d C. Now we do not want

538
00:30:46,800 --> 00:30:49,320
Speaker 1: to be here for this. And oddly enough, like it

539
00:30:49,400 --> 00:30:51,959
Speaker 1: was like some people were unaware anything was going on

540
00:30:52,040 --> 00:30:55,320
Speaker 1: to like a block away. Someone's just like on a

541
00:30:55,400 --> 00:30:58,160
Speaker 1: run with their dog, the same thing they do every day.

542
00:30:58,320 --> 00:31:00,960
Speaker 1: Like at that time, it was like if you weren't

543
00:31:01,000 --> 00:31:03,680
Speaker 1: in that like block or two radius, like life was

544
00:31:03,760 --> 00:31:09,120
Speaker 1: just happening ordinarily, it was really eerie. That is really

545
00:31:09,240 --> 00:31:12,760
Speaker 1: quite interesting, So tell me what else have you done?

546
00:31:12,800 --> 00:31:17,760
Speaker 1: That's been weird and strange and interesting. Well every Trump

547
00:31:17,840 --> 00:31:21,280
Speaker 1: rally we go to is is weird and strange and interesting.

548
00:31:21,320 --> 00:31:23,400
Speaker 1: I think you could you could say all those words.

549
00:31:23,920 --> 00:31:27,240
Speaker 1: Not the actual rallies, not go in any We don't

550
00:31:27,280 --> 00:31:33,120
Speaker 1: go in. If you see there, goes on forever and ever.

551
00:31:33,320 --> 00:31:35,840
Speaker 1: I mean just endless. We usually get out of there

552
00:31:35,880 --> 00:31:40,560
Speaker 1: before he even speaks. And yeah, because it's just it's

553
00:31:40,600 --> 00:31:43,560
Speaker 1: just not worth it. He really is boring, and it's

554
00:31:43,600 --> 00:31:45,760
Speaker 1: just if you've seen one, you've you've seen them all.

555
00:31:45,880 --> 00:31:49,560
Speaker 1: In the last couple years, we were filming in Georgia

556
00:31:49,600 --> 00:31:52,920
Speaker 1: with herschel Walker and that was interesting. We were in

557
00:31:53,120 --> 00:31:56,880
Speaker 1: Kentucky and we just saw a bunch of people wearing

558
00:31:57,000 --> 00:31:59,760
Speaker 1: shirts matching shirts, and we thought, is this some sort

559
00:31:59,800 --> 00:32:02,640
Speaker 1: of like Pyramids game or something. And we walked into

560
00:32:02,720 --> 00:32:05,160
Speaker 1: this this big auditorium and it was it was like

561
00:32:05,200 --> 00:32:09,600
Speaker 1: ten am, and they were all chanting along and singing

562
00:32:09,600 --> 00:32:13,360
Speaker 1: along about some sort of like ionized water or something.

563
00:32:13,440 --> 00:32:16,840
Speaker 1: We never put a video out about to Ketones, so

564
00:32:16,880 --> 00:32:19,880
Speaker 1: I apologize that somebody a listener is a member, but

565
00:32:20,000 --> 00:32:22,520
Speaker 1: you can end up in the weirdest places on these trips.

566
00:32:22,640 --> 00:32:25,840
Speaker 1: It was like like it felt like a rave and

567
00:32:25,880 --> 00:32:28,600
Speaker 1: it was ten am and we were the only ones

568
00:32:28,680 --> 00:32:31,400
Speaker 1: not in these T shirts. The thing I'm always amazed by,

569
00:32:31,480 --> 00:32:34,479
Speaker 1: like when you are sort of going around places, is

570
00:32:34,520 --> 00:32:37,960
Speaker 1: that there's really strange stuff you can wander into. You know,

571
00:32:38,000 --> 00:32:41,040
Speaker 1: it's it's really just putting ourselves in this situation and

572
00:32:41,080 --> 00:32:43,240
Speaker 1: going out there, and the strangest place you can wander

573
00:32:43,240 --> 00:32:47,640
Speaker 1: into our Some of these people's minds though, probably like

574
00:32:47,760 --> 00:32:49,680
Speaker 1: seven out of ten people we stopped and talked to,

575
00:32:49,800 --> 00:32:54,440
Speaker 1: granted it's a self selecting group of Republicans at a

576
00:32:54,480 --> 00:32:57,000
Speaker 1: Trump rally, it's you know, it's going to be some

577
00:32:57,040 --> 00:32:58,320
Speaker 1: of the people that are more out there, but it's

578
00:32:58,360 --> 00:32:59,840
Speaker 1: like seven out of ten people are going to say

579
00:32:59,840 --> 00:33:04,560
Speaker 1: something like that is wild, like really wild stuff. It's

580
00:33:04,560 --> 00:33:07,240
Speaker 1: an actor wearing a mask. We've each heard that like

581
00:33:07,520 --> 00:33:11,360
Speaker 1: enough times at oh yeah yeah. One woman we talked

582
00:33:11,360 --> 00:33:15,480
Speaker 1: to said it was it was Jim Carey actually playing him,

583
00:33:15,560 --> 00:33:17,719
Speaker 1: and we were like, Jim Carreck and we were like,

584
00:33:17,880 --> 00:33:20,760
Speaker 1: because Biden tripped going up the stairs and it was

585
00:33:20,800 --> 00:33:24,280
Speaker 1: so funny that only a genius comedic actor like Jim

586
00:33:24,320 --> 00:33:28,080
Speaker 1: Carey could have been doing that. So wait, so if

587
00:33:28,120 --> 00:33:31,400
Speaker 1: Biden is Jim Carrey. Are they happy with that or Now?

588
00:33:31,880 --> 00:33:35,120
Speaker 1: That's the thing I can never really understand. One interview

589
00:33:35,240 --> 00:33:37,360
Speaker 1: stands out to me. It was somebody at Sea Pack,

590
00:33:37,560 --> 00:33:40,080
Speaker 1: I guess outside Sea Pack, they weren't inside the convention.

591
00:33:40,480 --> 00:33:43,720
Speaker 1: I was talking to him and he was saying that

592
00:33:43,960 --> 00:33:47,840
Speaker 1: Joe Biden isn't Joe Biden, it's an actor. Trump is

593
00:33:47,880 --> 00:33:51,640
Speaker 1: actually still the president. And then I said, well, are

594
00:33:51,640 --> 00:33:54,440
Speaker 1: you mad at Trump because of the high gas prices?

595
00:33:54,640 --> 00:33:57,640
Speaker 1: And he said no, I'm not. And I'm like, well,

596
00:33:57,640 --> 00:34:00,680
Speaker 1: whose fault is that? And he said Joe Biden. And

597
00:34:00,720 --> 00:34:02,080
Speaker 1: I was like, wait a second. You just said Joe

598
00:34:02,080 --> 00:34:05,400
Speaker 1: Biden is not the president and it's it made sense

599
00:34:05,400 --> 00:34:07,720
Speaker 1: to him. It doesn't. It still doesn't make sense to me.

600
00:34:07,920 --> 00:34:10,200
Speaker 1: But he was still blaming Joe Biden even though he

601
00:34:10,239 --> 00:34:13,040
Speaker 1: doesn't think he's president. I think the thing they think

602
00:34:13,120 --> 00:34:16,000
Speaker 1: makes sense to enough sense to them is that there

603
00:34:16,160 --> 00:34:19,759
Speaker 1: is a bigger plan. They're not concerned with the details,

604
00:34:19,800 --> 00:34:21,880
Speaker 1: but it's just important to let everyone know that, like

605
00:34:22,120 --> 00:34:25,080
Speaker 1: nothing is as it seems. And once you believe that,

606
00:34:25,719 --> 00:34:28,560
Speaker 1: then you're you just need to trust and follow the

607
00:34:28,600 --> 00:34:32,319
Speaker 1: plan and the details become like not important. I mean,

608
00:34:32,520 --> 00:34:38,480
Speaker 1: you've interviewed the guy who thinks he's jfk Jr. Vincent Fusca.

609
00:34:38,719 --> 00:34:43,680
Speaker 1: That guy doesn't look anything like jfk j even a little.

610
00:34:43,920 --> 00:34:51,640
Speaker 1: He's way too tall and handsome, exactly for those extremely online.

611
00:34:52,200 --> 00:34:54,520
Speaker 1: The guy who the Q and On people think is

612
00:34:54,640 --> 00:34:58,520
Speaker 1: jfk Jr. Is not, is like it looks like he's

613
00:34:58,560 --> 00:35:04,280
Speaker 1: a bad twenty years older and five inches shorter. Yes, yeah,

614
00:35:04,360 --> 00:35:08,040
Speaker 1: And and he's actually like some sort of financial consultant

615
00:35:08,080 --> 00:35:11,640
Speaker 1: that has like lived in Pittsburgh for thirty years. Like

616
00:35:11,680 --> 00:35:14,120
Speaker 1: there's no public records that tell you this is who

617
00:35:14,120 --> 00:35:16,120
Speaker 1: he is. That he's got kids. I think I think

618
00:35:16,160 --> 00:35:18,920
Speaker 1: I forget someone we're in into said there, you know,

619
00:35:19,080 --> 00:35:21,560
Speaker 1: some sibling went to school with his kids. Like this

620
00:35:21,600 --> 00:35:25,960
Speaker 1: guy is just not jfk Jr. Not even a little No.

621
00:35:26,080 --> 00:35:28,680
Speaker 1: But I think he's like happy to go along with it,

622
00:35:28,800 --> 00:35:31,800
Speaker 1: Like he's got this the weirdest kind of fame. People

623
00:35:31,840 --> 00:35:35,480
Speaker 1: stop him to take pictures. He's now like, you know,

624
00:35:35,600 --> 00:35:38,880
Speaker 1: he's got a special spot, you know, somewhere behind Trump

625
00:35:38,920 --> 00:35:40,719
Speaker 1: at a lot of these things. So I think he's

626
00:35:40,760 --> 00:35:44,400
Speaker 1: just rolls with it and doesn't really answer any questions.

627
00:35:44,480 --> 00:35:48,160
Speaker 1: But just like one more piece of weird shrapnel in

628
00:35:48,280 --> 00:35:50,680
Speaker 1: all of this stuff that is going on. Is that

629
00:35:50,760 --> 00:35:56,600
Speaker 1: now there's a little famous h JFK Jr. Walking around?

630
00:35:56,760 --> 00:36:02,239
Speaker 1: What about the guy who wears the brick wall suit? Jason, Oh, yeah,

631
00:36:02,280 --> 00:36:04,560
Speaker 1: I just interviewed him. Yeah, I just you know, I

632
00:36:04,560 --> 00:36:07,600
Speaker 1: don't know. It was interesting because he was saying, by

633
00:36:07,600 --> 00:36:10,400
Speaker 1: the way, for those who are not incredibly online, this

634
00:36:10,520 --> 00:36:12,400
Speaker 1: is the guy who goes to all of Trump's rallies

635
00:36:12,440 --> 00:36:15,960
Speaker 1: and wears a suit that looks like a brick wall. Hey,

636
00:36:16,040 --> 00:36:18,239
Speaker 1: so he's the build the Wall guy. He's the build

637
00:36:18,239 --> 00:36:21,480
Speaker 1: the wall guy. But he was the guy I talked to.

638
00:36:22,280 --> 00:36:24,040
Speaker 1: He was very clear. He's like, I just want to

639
00:36:24,040 --> 00:36:27,720
Speaker 1: be clear. There's two of us, two of us, and

640
00:36:28,000 --> 00:36:29,920
Speaker 1: I might not be the guy you're thinking I'm at

641
00:36:29,960 --> 00:36:32,000
Speaker 1: all the rallies, but I don't know if I might

642
00:36:32,400 --> 00:36:34,800
Speaker 1: might be thinking of the other build the Wall guy.

643
00:36:34,840 --> 00:36:37,080
Speaker 1: But I do want to disagree with what you guys

644
00:36:37,080 --> 00:36:40,400
Speaker 1: were saying, because I actually do believe that Vincent Fusca

645
00:36:40,600 --> 00:36:43,520
Speaker 1: is JFK Jr. You want that on the record, Yes,

646
00:36:43,560 --> 00:36:47,919
Speaker 1: I want that on the record. This Vincent Fusco looks

647
00:36:47,960 --> 00:36:51,839
Speaker 1: like he's significantly older than JFK would be junior if

648
00:36:51,840 --> 00:36:54,960
Speaker 1: he were still alive. It's almost like that's not him,

649
00:36:55,080 --> 00:36:59,000
Speaker 1: but you could, Jason, that's what they want you to believe.

650
00:36:59,800 --> 00:37:02,759
Speaker 1: He doesn't hive at all. No, it's very it's very,

651
00:37:02,840 --> 00:37:05,759
Speaker 1: very weird. Back to the wall guy, Jason talked to

652
00:37:05,880 --> 00:37:08,040
Speaker 1: him for kind of a long time and got him

653
00:37:08,080 --> 00:37:10,719
Speaker 1: on a kind of you know, our biggest problem is

654
00:37:10,840 --> 00:37:13,240
Speaker 1: China thing, and they went on that for a while.

655
00:37:13,360 --> 00:37:15,799
Speaker 1: And meanwhile, the the tag of the guy's suit is

656
00:37:15,840 --> 00:37:18,200
Speaker 1: just sticking out on, you know, out of the back

657
00:37:18,239 --> 00:37:21,479
Speaker 1: of his neck, and you know, Jason asks to see

658
00:37:21,560 --> 00:37:24,400
Speaker 1: the tag and the suits made in China case anybody

659
00:37:24,400 --> 00:37:26,680
Speaker 1: wanted to know out there to build the walls, but

660
00:37:26,840 --> 00:37:29,600
Speaker 1: suit when you ask them about that, what do they say?

661
00:37:29,920 --> 00:37:32,040
Speaker 1: He was like, oh, yeah, yeah, it's hard to get

662
00:37:32,040 --> 00:37:34,759
Speaker 1: anything else from another place, so I had to get

663
00:37:34,760 --> 00:37:37,440
Speaker 1: it from China. It's just like, you know, you could

664
00:37:37,480 --> 00:37:39,880
Speaker 1: spout out for a million years. And we've had that

665
00:37:39,920 --> 00:37:42,400
Speaker 1: a couple of times where people would talk about China

666
00:37:42,480 --> 00:37:44,799
Speaker 1: how much they hate it, and then do you like

667
00:37:44,880 --> 00:37:46,439
Speaker 1: be like, can I see your hat and it's made

668
00:37:46,440 --> 00:37:48,520
Speaker 1: in China. You know, it's just like it's a I

669
00:37:48,560 --> 00:37:51,520
Speaker 1: don't want to say the jokes right themselves sometimes, but

670
00:37:51,719 --> 00:37:56,080
Speaker 1: it's and I think one guy, he had all these flyers.

671
00:37:56,120 --> 00:37:59,360
Speaker 1: He was like COVID as China's fault. You know, it's terrible.

672
00:37:59,440 --> 00:38:01,920
Speaker 1: He thought it was the worst and came from China

673
00:38:01,960 --> 00:38:05,000
Speaker 1: and we needed to be holding China accountable. That was

674
00:38:05,080 --> 00:38:07,560
Speaker 1: his whole stick. He had like a table with fires.

675
00:38:07,600 --> 00:38:09,480
Speaker 1: He was handing him out. He was talking to people

676
00:38:10,080 --> 00:38:13,920
Speaker 1: and made in China hat. Oh yeah, it's amazing. Thank

677
00:38:13,920 --> 00:38:17,120
Speaker 1: you guys so much. I hope you'll come back. Oh yeah,

678
00:38:17,120 --> 00:38:22,200
Speaker 1: thanks for having us. Yeah, anytime. Nilai Patau is the

679
00:38:22,360 --> 00:38:25,360
Speaker 1: editor and cheat of The Verge and host of the

680
00:38:25,400 --> 00:38:31,360
Speaker 1: podcast Decoder. Welcome, Too Fast Politics. We're so excited to

681
00:38:31,400 --> 00:38:35,600
Speaker 1: have you. We are in this weird where it's the

682
00:38:36,040 --> 00:38:38,919
Speaker 1: it's the Twitter files, right, we got to talk about

683
00:38:38,920 --> 00:38:42,560
Speaker 1: the Twitter files. You are the editor of a tech publication.

684
00:38:43,360 --> 00:38:47,920
Speaker 1: You have spent a long time in tech journalism. What

685
00:38:47,960 --> 00:38:50,440
Speaker 1: the funk is happening? Yeah? I have sent a lot

686
00:38:50,480 --> 00:38:52,359
Speaker 1: of time in tech Journalist and the versions published a

687
00:38:52,360 --> 00:38:55,279
Speaker 1: lot of stories content moderation. Over the years. We've been

688
00:38:55,840 --> 00:38:59,760
Speaker 1: nominated like major journalism awards for big expos as content

689
00:38:59,840 --> 00:39:02,319
Speaker 1: mode ration really works. These companies. I have a lot

690
00:39:02,400 --> 00:39:06,480
Speaker 1: of sympathy for a good content moderation story. It is

691
00:39:06,480 --> 00:39:10,239
Speaker 1: a very complicated thing that big companies do. And every

692
00:39:10,239 --> 00:39:13,040
Speaker 1: time you peel back the layers of the onion, you're

693
00:39:13,080 --> 00:39:15,120
Speaker 1: like at the center of it. There's just like a

694
00:39:15,120 --> 00:39:16,560
Speaker 1: couple of people who are like, I don't know, take

695
00:39:16,600 --> 00:39:20,960
Speaker 1: it down right, like and like you build up all

696
00:39:21,000 --> 00:39:23,239
Speaker 1: of these processes to try and make it fair to

697
00:39:23,280 --> 00:39:25,279
Speaker 1: like let everybody sleep at night, and then kind of

698
00:39:25,320 --> 00:39:27,120
Speaker 1: at the end of the day it's we don't have

699
00:39:27,120 --> 00:39:28,799
Speaker 1: a rule, but it sucks. Take it down right, And

700
00:39:29,320 --> 00:39:31,279
Speaker 1: you just see this over and over again and kind

701
00:39:31,280 --> 00:39:35,880
Speaker 1: of the furthest version of this, the most like built

702
00:39:35,920 --> 00:39:38,000
Speaker 1: up version. This is like Facebook literally set up a

703
00:39:38,000 --> 00:39:40,960
Speaker 1: supreme court. They have an oversight ward that is composed

704
00:39:40,960 --> 00:39:44,120
Speaker 1: of famous lawyers and politicians that is a supreme court

705
00:39:44,160 --> 00:39:46,640
Speaker 1: for them because they don't want the responsibility. So that's

706
00:39:46,640 --> 00:39:48,840
Speaker 1: just the stage. I just want to put that out there, like,

707
00:39:48,880 --> 00:39:51,400
Speaker 1: this is a very hard thing that social media companies

708
00:39:51,440 --> 00:39:55,200
Speaker 1: has to do. There are lots and lots of pressures

709
00:39:55,200 --> 00:39:57,200
Speaker 1: placed on them to take all kinds of things down.

710
00:39:57,680 --> 00:40:00,360
Speaker 1: For some reasons are really good, right, Like some content

711
00:40:00,480 --> 00:40:03,640
Speaker 1: is illegal. Some content is like really damaging the teenage girls.

712
00:40:03,640 --> 00:40:06,200
Speaker 1: Some content is just like openly racist. Some content they're

713
00:40:06,200 --> 00:40:08,879
Speaker 1: advertisers would pay all their bills, don't like like some

714
00:40:08,920 --> 00:40:11,359
Speaker 1: content government just don't want to have existed. So they

715
00:40:11,400 --> 00:40:13,160
Speaker 1: face all these pressures and the goal in these systems.

716
00:40:13,560 --> 00:40:17,440
Speaker 1: What's hilarious about the Twitter files, Like what they are

717
00:40:17,480 --> 00:40:21,640
Speaker 1: exposing is a bunch of like diligent nerds doing bording

718
00:40:21,719 --> 00:40:26,200
Speaker 1: stuff right. Right, It's like they haven't exposed like the

719
00:40:26,640 --> 00:40:29,800
Speaker 1: Twitter would happily have told you all of the things

720
00:40:30,200 --> 00:40:32,160
Speaker 1: right and they did, right. I mean, it's in the

721
00:40:32,280 --> 00:40:34,239
Speaker 1: terms of service. It's well, it's not even in the

722
00:40:34,280 --> 00:40:37,080
Speaker 1: terms of service, like Twitter has been very open. Jack

723
00:40:37,120 --> 00:40:39,200
Speaker 1: Dorsey isn't very open. We made the wrong decision with

724
00:40:39,280 --> 00:40:41,879
Speaker 1: the Hunter Biden New York Post story. They changed their mind,

725
00:40:41,920 --> 00:40:44,000
Speaker 1: like you know, they like started allowing the list of up.

726
00:40:44,480 --> 00:40:47,600
Speaker 1: I think you have to recall that in the moment

727
00:40:47,840 --> 00:40:51,400
Speaker 1: that story was published, no one knew the providence of

728
00:40:51,440 --> 00:40:54,160
Speaker 1: that information, no one knew where it had come from,

729
00:40:54,200 --> 00:40:58,960
Speaker 1: and there had been in the past. Yeah, like these

730
00:40:58,960 --> 00:41:04,120
Speaker 1: hacken week operations beyond wiki leaks like I Cloud mccrowen

731
00:41:04,239 --> 00:41:08,200
Speaker 1: leaks mccrollan leaks, if you will, recall a bunch of

732
00:41:08,360 --> 00:41:11,080
Speaker 1: famous women had their eye cloud accounts act and their

733
00:41:11,160 --> 00:41:13,040
Speaker 1: nudes leaked. Like that's the real thing that has happened

734
00:41:13,040 --> 00:41:16,160
Speaker 1: in the past. Sony had its email systems breached, and

735
00:41:16,239 --> 00:41:19,719
Speaker 1: the president's emy, Pascal Head her emails leaked all over

736
00:41:19,719 --> 00:41:23,359
Speaker 1: the place, including like a very intimate shopping list on Amazon. Right. Yeah,

737
00:41:23,360 --> 00:41:26,360
Speaker 1: but that's because they got North Koreans really really mad.

738
00:41:27,000 --> 00:41:30,080
Speaker 1: It's true. But so all of these platforms like developed

739
00:41:30,160 --> 00:41:32,399
Speaker 1: these policies that are like, you know what, like we're

740
00:41:32,440 --> 00:41:37,040
Speaker 1: complicit in this thing, and like we're complicit in someone

741
00:41:37,160 --> 00:41:40,560
Speaker 1: hacks your system and says pay us the ransom and bitcoin,

742
00:41:40,680 --> 00:41:42,759
Speaker 1: or like leak all your emails. We don't want to

743
00:41:42,760 --> 00:41:45,799
Speaker 1: be complicit. It's like they developed all these sort of

744
00:41:45,840 --> 00:41:50,239
Speaker 1: like hair trigger shut it down systems. I got in

745
00:41:50,320 --> 00:41:54,239
Speaker 1: that context, overheated, overdone, and then they changed your mind,

746
00:41:54,640 --> 00:41:56,920
Speaker 1: and it's like, yeah, okay, that sucks. You made their

747
00:41:56,920 --> 00:42:00,080
Speaker 1: own decision, But they weren't. None of those executives are

748
00:42:00,120 --> 00:42:02,040
Speaker 1: running around saying this was the right decision. They all

749
00:42:02,080 --> 00:42:04,160
Speaker 1: admit it was wrong decision. And so I think what

750
00:42:04,200 --> 00:42:07,040
Speaker 1: you're seeing with the Twitter files, particularly a second set

751
00:42:07,080 --> 00:42:10,400
Speaker 1: where it's like some people on Twitter had their reach suppressed.

752
00:42:10,960 --> 00:42:13,920
Speaker 1: It's like, yeah, dude, like they they say it out

753
00:42:13,960 --> 00:42:17,160
Speaker 1: loud like they were they were not unclear about what

754
00:42:17,200 --> 00:42:18,880
Speaker 1: they were trying to do. They're trying to make the

755
00:42:18,880 --> 00:42:24,200
Speaker 1: platform or pleasant. It's just it is being presented as

756
00:42:24,280 --> 00:42:28,120
Speaker 1: though it is this ongoing scandal. Is things are revealed,

757
00:42:28,120 --> 00:42:30,319
Speaker 1: and I think what everyone is going to come to learn,

758
00:42:30,360 --> 00:42:32,920
Speaker 1: what we have come to learn doing hundreds of content

759
00:42:32,960 --> 00:42:35,759
Speaker 1: moderation stories over the years, is once you fall all

760
00:42:35,760 --> 00:42:38,040
Speaker 1: the way down the rabbit hole of talking on content moderation,

761
00:42:38,520 --> 00:42:41,640
Speaker 1: but people just tune out, it's just boring. Yeah, I mean,

762
00:42:41,680 --> 00:42:44,080
Speaker 1: I think that's right. What I think is pretty interesting

763
00:42:44,120 --> 00:42:48,319
Speaker 1: about Elon's Twitter purchase is that we've seen him he's

764
00:42:48,400 --> 00:42:52,600
Speaker 1: like literally living out loud, like he's doing everything that

765
00:42:52,640 --> 00:42:55,680
Speaker 1: Twitter has done. Right. He went back and you know,

766
00:42:55,800 --> 00:42:58,560
Speaker 1: he said, we're going to just let everyone get verified. Okay, no,

767
00:42:58,680 --> 00:43:00,960
Speaker 1: we're not going to do that. We're gonna do no

768
00:43:01,160 --> 00:43:03,600
Speaker 1: content moderation. Oh well, we're not going to do that.

769
00:43:03,680 --> 00:43:05,920
Speaker 1: I mean, it does seem like he gets to the

770
00:43:06,040 --> 00:43:10,239
Speaker 1: same place that Twitter is anyway. Yeah, so I just

771
00:43:10,280 --> 00:43:13,040
Speaker 1: talked to Matt mollen White, who's the CEO of Coming

772
00:43:13,080 --> 00:43:15,640
Speaker 1: called Automatic. They're the holding company that owns WordPress. They

773
00:43:15,640 --> 00:43:18,280
Speaker 1: also owned Tumbler. He's the CEO of Tumbler as well.

774
00:43:18,400 --> 00:43:21,880
Speaker 1: And you know, they bought Tumbler from Verizon for like nothing,

775
00:43:21,960 --> 00:43:24,400
Speaker 1: for like three million dollars. And it was like, he

776
00:43:24,520 --> 00:43:27,960
Speaker 1: said to me, this has been the most humbling experience

777
00:43:27,960 --> 00:43:30,240
Speaker 1: of my career owning a social network. Like we bought

778
00:43:30,239 --> 00:43:33,000
Speaker 1: it for nothing, Like we bought it for the smallest

779
00:43:33,000 --> 00:43:35,960
Speaker 1: amount of money that anybody would feel good about. It

780
00:43:36,040 --> 00:43:39,240
Speaker 1: was losing money and has all these users Tumblings famous

781
00:43:39,239 --> 00:43:42,719
Speaker 1: content moderation problems kicked off the app store already. And

782
00:43:42,760 --> 00:43:44,840
Speaker 1: he's like, our roadmap is basically Twitter short map. We

783
00:43:44,840 --> 00:43:46,839
Speaker 1: want to reduce our reliance on advertising income. We want

784
00:43:46,880 --> 00:43:48,760
Speaker 1: to build some subscriptions when you created all the stuff

785
00:43:48,760 --> 00:43:50,359
Speaker 1: that he wants is he wants to do. And he's like,

786
00:43:50,400 --> 00:43:53,000
Speaker 1: Ellen is quickly going to get to the place where

787
00:43:53,040 --> 00:43:56,080
Speaker 1: he already was, because once you start going down the road,

788
00:43:56,600 --> 00:43:59,160
Speaker 1: you could be as libertarian about speech as you want,

789
00:43:59,680 --> 00:44:02,840
Speaker 1: but there is all this stuff that is horrible that

790
00:44:02,960 --> 00:44:05,799
Speaker 1: drives away your users, that drives away your advertisers, that

791
00:44:05,920 --> 00:44:08,879
Speaker 1: limits your ability to grow, that makes you feel bad

792
00:44:09,400 --> 00:44:11,480
Speaker 1: about being the CEO of a social network that you're

793
00:44:11,480 --> 00:44:13,719
Speaker 1: gonna want to just not have the example he gave,

794
00:44:14,160 --> 00:44:17,319
Speaker 1: I think this is like impossible to argue with. There

795
00:44:17,440 --> 00:44:20,359
Speaker 1: is a community of people out there who are pro anorexia,

796
00:44:20,600 --> 00:44:23,840
Speaker 1: and they run around telling people to be interacting, specifically

797
00:44:23,880 --> 00:44:26,600
Speaker 1: young women, and like, you should just not surface that content,

798
00:44:26,600 --> 00:44:28,239
Speaker 1: You should not give that content reach. You should not

799
00:44:28,280 --> 00:44:32,200
Speaker 1: feel good about running a service where that content is

800
00:44:32,360 --> 00:44:35,960
Speaker 1: allowed to proliferate. And at some point, no matter who

801
00:44:36,000 --> 00:44:38,600
Speaker 1: you are, what your beliefs, you're just going to run

802
00:44:38,600 --> 00:44:43,160
Speaker 1: into the reality of millions of people are using my service.

803
00:44:43,239 --> 00:44:45,759
Speaker 1: They're chaotic, they cannot be controlled, and I have to

804
00:44:45,800 --> 00:44:49,080
Speaker 1: make some decisions about what will happen here or or

805
00:44:49,120 --> 00:44:52,200
Speaker 1: what I won't allow. And it is not for nothing

806
00:44:52,280 --> 00:44:55,520
Speaker 1: that every major social network in this country more or

807
00:44:55,640 --> 00:44:59,600
Speaker 1: less arrives at the exact same position around content moderation,

808
00:45:00,040 --> 00:45:02,880
Speaker 1: because that's just the reality of the constraints if you

809
00:45:02,880 --> 00:45:06,520
Speaker 1: want to grow your business. So now, this very white

810
00:45:06,640 --> 00:45:10,600
Speaker 1: thing I thought was interesting. They were so breathless, like

811
00:45:11,120 --> 00:45:13,879
Speaker 1: you know, they're like and then every reveal I mean

812
00:45:13,920 --> 00:45:16,680
Speaker 1: and you saw it with that to do that, every

813
00:45:16,760 --> 00:45:20,759
Speaker 1: reveal was like and then they said, I mean like

814
00:45:20,800 --> 00:45:25,040
Speaker 1: there was no smoking gun yet, right. Yeah. My favorite

815
00:45:25,040 --> 00:45:27,880
Speaker 1: part of the very tweet storm, I'm pretty sure it

816
00:45:27,880 --> 00:45:31,200
Speaker 1: was hers, was she was like a secret committee composed

817
00:45:31,200 --> 00:45:34,280
Speaker 1: of Jack Dorsey, the CEO, the head of trust and safety,

818
00:45:34,400 --> 00:45:36,440
Speaker 1: the company's lewner And it's like, yeah, that's the top

819
00:45:36,480 --> 00:45:40,839
Speaker 1: of the company. That's the that's the company, like the

820
00:45:40,920 --> 00:45:44,240
Speaker 1: C suite, right, Yeah, that's the c suite of the company.

821
00:45:44,239 --> 00:45:47,960
Speaker 1: Like you're saying that problems with like tricky problems in

822
00:45:48,000 --> 00:45:50,959
Speaker 1: content moderation were escalated to a committee at the top

823
00:45:51,000 --> 00:45:52,960
Speaker 1: of the company that then made a decision and they

824
00:45:52,960 --> 00:45:55,840
Speaker 1: were somewhat unaccountable. Like have you ever worked at a company?

825
00:45:55,840 --> 00:45:58,920
Speaker 1: Didn't you work at The New York Times, like one

826
00:45:58,920 --> 00:46:02,759
Speaker 1: of the most famously hierarchical and political media companies. Okay,

827
00:46:02,800 --> 00:46:06,800
Speaker 1: I don't. Like, there's just an element of people reacting

828
00:46:06,880 --> 00:46:09,240
Speaker 1: to the world as it is as though it's a scandal,

829
00:46:09,280 --> 00:46:12,359
Speaker 1: which is built into all of this, and I think

830
00:46:12,360 --> 00:46:15,400
Speaker 1: that's fine. You know, like I was once a teenager

831
00:46:15,400 --> 00:46:17,319
Speaker 1: who reacted to the state of the world as though

832
00:46:17,440 --> 00:46:19,839
Speaker 1: it was an ongoing scandal, right, I just don't think

833
00:46:19,840 --> 00:46:23,120
Speaker 1: you're going to get a lot of traction with you know,

834
00:46:23,160 --> 00:46:27,279
Speaker 1: the sort of mainstream consumer of Twitter. By saying did

835
00:46:27,320 --> 00:46:31,320
Speaker 1: you know that the CEO of Twitter often makes capricious decisions?

836
00:46:31,360 --> 00:46:33,960
Speaker 1: It's like, yeah, that's been that is the experience of

837
00:46:33,960 --> 00:46:36,520
Speaker 1: these Your Twitter has been for a decade. Did you

838
00:46:36,560 --> 00:46:40,719
Speaker 1: know Mark Zuckerberg is very powerful? Yeah? Great? Do you

839
00:46:40,800 --> 00:46:44,359
Speaker 1: think that Ellen will just keep going and keep doing these?

840
00:46:44,400 --> 00:46:47,640
Speaker 1: I mean, they get attention from a certain group, They

841
00:46:47,640 --> 00:46:50,319
Speaker 1: don't necessarily move the needle. They do seem to be

842
00:46:50,400 --> 00:46:54,520
Speaker 1: getting more and more boring. The endgame here? Again, I

843
00:46:54,640 --> 00:46:57,319
Speaker 1: have heard from people, and again I don't believe this,

844
00:46:57,400 --> 00:47:01,120
Speaker 1: but I've heard that Ellen's goal is to drive down

845
00:47:01,360 --> 00:47:03,560
Speaker 1: the value of the company so he doesn't have to

846
00:47:03,600 --> 00:47:06,799
Speaker 1: service the debt. I don't know how he could do that,

847
00:47:06,920 --> 00:47:10,879
Speaker 1: because I think the distressed investors, the investors who are

848
00:47:11,000 --> 00:47:15,000
Speaker 1: good at getting money for people who buy stuff that

849
00:47:15,120 --> 00:47:17,640
Speaker 1: they can't afford. In this case, he can't afford it,

850
00:47:17,640 --> 00:47:19,359
Speaker 1: but he doesn't want to pay for it. I don't

851
00:47:19,360 --> 00:47:20,960
Speaker 1: think they're going to let him do that. But do

852
00:47:21,000 --> 00:47:23,439
Speaker 1: you does that look like the end game to you here?

853
00:47:23,560 --> 00:47:26,120
Speaker 1: Or do you think he's just like having fun. I

854
00:47:26,120 --> 00:47:28,600
Speaker 1: think mostly he's having fun. I don't think the endgame

855
00:47:28,719 --> 00:47:31,880
Speaker 1: is the failure. And Ellen is a risk taker. I

856
00:47:31,920 --> 00:47:35,440
Speaker 1: think he enjoys the gamble. And you know that people

857
00:47:35,440 --> 00:47:38,520
Speaker 1: who surrounds himself with their metaphors for everything or like

858
00:47:38,640 --> 00:47:41,880
Speaker 1: high stakes poker. Fine. I actually applaud the idea that

859
00:47:41,880 --> 00:47:44,920
Speaker 1: you should take immense risks with the products that you make.

860
00:47:44,960 --> 00:47:46,560
Speaker 1: I think that's cool. I think there's like an element

861
00:47:46,560 --> 00:47:48,920
Speaker 1: that it's fun. I don't think his goal is failures.

862
00:47:48,960 --> 00:47:51,480
Speaker 1: You can get out of financial obligation, and certainly there

863
00:47:51,520 --> 00:47:54,000
Speaker 1: are reports this week that some of the banks are

864
00:47:54,000 --> 00:47:57,400
Speaker 1: willing to accept testless stock and revalue the loans, and

865
00:47:57,800 --> 00:48:00,680
Speaker 1: we'll see there's just some financial engineering here. I do

866
00:48:00,840 --> 00:48:04,200
Speaker 1: think there's a lot of people in Silicon Valley, a

867
00:48:04,239 --> 00:48:06,480
Speaker 1: lot of investors who are like, all of these companies

868
00:48:06,520 --> 00:48:09,480
Speaker 1: are too bloated, and this is like the first time

869
00:48:09,520 --> 00:48:11,480
Speaker 1: we're ever going to run an experiment like this where

870
00:48:11,520 --> 00:48:15,319
Speaker 1: we just destroy a company's culture and rip out all

871
00:48:15,360 --> 00:48:19,920
Speaker 1: of the fluff indiscriminately and see what happens. Like what

872
00:48:20,080 --> 00:48:23,960
Speaker 1: if Meta could be fifty of its size and still

873
00:48:24,200 --> 00:48:28,200
Speaker 1: operate like that company would just produce profits like massive

874
00:48:28,280 --> 00:48:31,120
Speaker 1: profits would have. Google could be half the size of

875
00:48:31,160 --> 00:48:34,360
Speaker 1: itself and still be Google. That company would just print

876
00:48:34,440 --> 00:48:37,040
Speaker 1: money because it wouldn't be paying for all this stuff

877
00:48:37,080 --> 00:48:38,359
Speaker 1: that you know a lot of people in the valley

878
00:48:38,400 --> 00:48:41,399
Speaker 1: think or is meaningless or useless. And Twitter is much

879
00:48:41,400 --> 00:48:44,080
Speaker 1: smaller than those companies. I remind everybody all the time

880
00:48:44,400 --> 00:48:47,280
Speaker 1: when you talk about big tech, it is in error

881
00:48:47,280 --> 00:48:50,160
Speaker 1: a category air to say Twitter is part of big tech.

882
00:48:50,160 --> 00:48:52,680
Speaker 1: Twitter is teeny tiny. He fired fifty percent of the

883
00:48:52,680 --> 00:48:55,480
Speaker 1: people and he still on me has two thousands some people.

884
00:48:55,600 --> 00:48:58,520
Speaker 1: It's like nothing. Twitter in a year makes less money

885
00:48:58,520 --> 00:49:01,279
Speaker 1: than Facebook. Facebook makes a right. So this is a

886
00:49:01,280 --> 00:49:04,240
Speaker 1: tiny company, but it is the first and only time

887
00:49:04,280 --> 00:49:07,160
Speaker 1: and experiment like this has been run where you just

888
00:49:07,239 --> 00:49:10,000
Speaker 1: gut the company, you reset the culture, you get rid

889
00:49:10,040 --> 00:49:15,440
Speaker 1: of all the institutional knowledge that most smart business experts

890
00:49:15,440 --> 00:49:18,000
Speaker 1: would tell you it was important to sustain a company,

891
00:49:18,120 --> 00:49:21,520
Speaker 1: and you see what happens, and thus far the experiment

892
00:49:21,520 --> 00:49:23,760
Speaker 1: has been working out right, at least on one level.

893
00:49:23,960 --> 00:49:26,359
Speaker 1: I experience all kinds of weird errors and Twitter left

894
00:49:26,360 --> 00:49:29,840
Speaker 1: and right. I feel personally less motivated to tweet, to

895
00:49:30,040 --> 00:49:34,359
Speaker 1: make content for free, for the service but but it's

896
00:49:34,360 --> 00:49:37,239
Speaker 1: still up, it's still working, it's it's surviving the World Cup.

897
00:49:37,480 --> 00:49:40,920
Speaker 1: So I think there's just an element of just experimentation.

898
00:49:41,000 --> 00:49:43,799
Speaker 1: Like I think everyone's curious, and I think because of

899
00:49:43,840 --> 00:49:47,439
Speaker 1: the people involved, they want to go and they wanted

900
00:49:47,520 --> 00:49:50,040
Speaker 1: to succeed, so they can go out to the other

901
00:49:50,040 --> 00:49:52,080
Speaker 1: companies are invested in, or the other companies they were

902
00:49:52,080 --> 00:49:54,080
Speaker 1: on and say, you know what, what if we just

903
00:49:54,200 --> 00:49:57,919
Speaker 1: delete it? That's just sort of fascinating and a little

904
00:49:57,920 --> 00:50:00,440
Speaker 1: bit dystopian. I always thought of Twitter are as the

905
00:50:00,440 --> 00:50:02,720
Speaker 1: people worked at Twitter were always sort of the most

906
00:50:03,200 --> 00:50:06,680
Speaker 1: kind of upstanding, in the least evil, you know, they

907
00:50:06,719 --> 00:50:09,919
Speaker 1: were earnest in a way. So I see what you're saying,

908
00:50:09,920 --> 00:50:12,760
Speaker 1: but it's very depressing, and that earnest this is probably

909
00:50:12,760 --> 00:50:17,040
Speaker 1: why Twitter was like a bad company. Criticizing Elon's administration,

910
00:50:17,080 --> 00:50:19,879
Speaker 1: Twitter is in no way praise for the previous administration

911
00:50:20,520 --> 00:50:23,439
Speaker 1: or the administration before that, Like this has not been

912
00:50:23,600 --> 00:50:27,480
Speaker 1: a well run company. Often the earnestness that you describe,

913
00:50:27,760 --> 00:50:30,759
Speaker 1: or the sincerity, and I certainly have experienced that sincerity

914
00:50:31,120 --> 00:50:34,920
Speaker 1: fully got in Twitter's way when it came to being

915
00:50:35,080 --> 00:50:38,799
Speaker 1: a well run company with a path forward to be

916
00:50:38,920 --> 00:50:43,960
Speaker 1: successful and actually ellen buying the company. One person showing

917
00:50:44,040 --> 00:50:46,239
Speaker 1: up and being like, yeah, I'll just buy the whole thing,

918
00:50:46,560 --> 00:50:50,279
Speaker 1: like doesn't usually happen, right, And a board of directors

919
00:50:50,400 --> 00:50:52,520
Speaker 1: does not usually look at an offer like this and

920
00:50:52,520 --> 00:50:55,200
Speaker 1: be like, you know what, we have no fucking plan

921
00:50:55,800 --> 00:50:58,359
Speaker 1: to get to evaluation of forty four billion dollars. There's

922
00:50:58,400 --> 00:51:02,920
Speaker 1: nothing we can do, and this guy sucks and there's

923
00:51:02,920 --> 00:51:05,839
Speaker 1: no one else screw it, Like, let's take the cash

924
00:51:05,880 --> 00:51:08,720
Speaker 1: and walk, which is the decision the board of Rector's

925
00:51:08,719 --> 00:51:11,680
Speaker 1: Twitter made, And that to me is as damning as anything.

926
00:51:11,960 --> 00:51:15,160
Speaker 1: They had no ideas for how to get to this valuation.

927
00:51:15,160 --> 00:51:17,600
Speaker 1: So yes, it's true as a company, as a culture,

928
00:51:17,640 --> 00:51:22,040
Speaker 1: as individuals, very earnest, very sincere right, but not earnest

929
00:51:22,200 --> 00:51:24,520
Speaker 1: enough not to cash out, right, They weren't, like, my

930
00:51:24,640 --> 00:51:28,440
Speaker 1: principles are worth more than that this moron who's overpaying.

931
00:51:28,440 --> 00:51:30,120
Speaker 1: I mean he's not a moron, obviously, but you know

932
00:51:30,120 --> 00:51:34,000
Speaker 1: what I mean, he's overpaying for the privilege of taking over. Yeah,

933
00:51:34,120 --> 00:51:36,200
Speaker 1: and maybe he'll crash the car off the cliff and

934
00:51:36,239 --> 00:51:38,760
Speaker 1: maybe he won't. But like Twitter was successful despite itself,

935
00:51:38,800 --> 00:51:41,400
Speaker 1: it was successful because it attracted so many users, but

936
00:51:41,440 --> 00:51:43,560
Speaker 1: it was not successful as a business. I say, you

937
00:51:43,600 --> 00:51:46,720
Speaker 1: will let Meta and metas like the comparison here, because

938
00:51:46,719 --> 00:51:51,279
Speaker 1: it's the other large social networking company. Mark Zuckerberg has conviction, right,

939
00:51:51,440 --> 00:51:54,560
Speaker 1: and like he's the god emperor of that company. You

940
00:51:54,600 --> 00:51:57,120
Speaker 1: can't get rid of him, and it's going to do

941
00:51:57,160 --> 00:52:00,680
Speaker 1: whatever he wants. And He's key is clear that like

942
00:52:00,719 --> 00:52:02,120
Speaker 1: what he wants is for all of us to like

943
00:52:02,160 --> 00:52:03,840
Speaker 1: put our brains in vats and live in the metaverse.

944
00:52:03,840 --> 00:52:06,360
Speaker 1: I'm like, fine, he's gonna try to do it. Twitter

945
00:52:06,400 --> 00:52:09,480
Speaker 1: had no conviction, right, It had no center. It was

946
00:52:09,560 --> 00:52:11,840
Speaker 1: just it continued to exist to spite itself. And I

947
00:52:11,840 --> 00:52:16,160
Speaker 1: think that to me is it is is damning of

948
00:52:16,320 --> 00:52:19,560
Speaker 1: the administration of Twitter, the previous administration, as it is

949
00:52:19,640 --> 00:52:22,279
Speaker 1: damning of Musk, Who's like, screw it. I'm gonna throw

950
00:52:22,280 --> 00:52:24,640
Speaker 1: it all off the cliff, including the good parts. I'm

951
00:52:24,640 --> 00:52:26,520
Speaker 1: not even to evaluate the parts of this that we're working.

952
00:52:26,920 --> 00:52:29,479
Speaker 1: I'm just saying, screw it. Now. It's a payment set

953
00:52:30,080 --> 00:52:33,000
Speaker 1: that's delightfully dystopian. We need to talk about the c

954
00:52:33,160 --> 00:52:37,120
Speaker 1: g I chatbot that's going to destroy all of our

955
00:52:37,160 --> 00:52:40,080
Speaker 1: careers and replace us. Are we going to be replaced

956
00:52:40,080 --> 00:52:43,799
Speaker 1: by c g I, chat bots, GPTs. What you're talking about, yes,

957
00:52:43,920 --> 00:52:47,920
Speaker 1: gbs whatever many initials put together. Our cameras are off

958
00:52:47,920 --> 00:52:49,799
Speaker 1: what I could personally use some c g I. So

959
00:52:51,719 --> 00:52:54,840
Speaker 1: maybe that's pretty good. Yeah, I think is really fascinating.

960
00:52:55,000 --> 00:52:58,279
Speaker 1: Will you explain to my dad what it is? Sure

961
00:52:58,320 --> 00:53:00,239
Speaker 1: if you took a robot and you said, I want

962
00:53:00,239 --> 00:53:03,799
Speaker 1: you to read the entire Internet, and then you said, now,

963
00:53:03,840 --> 00:53:08,120
Speaker 1: what you are is the world's fanciest Microsoft word autocomplete system,

964
00:53:08,160 --> 00:53:10,520
Speaker 1: and I'm gonna ask you a question, and You're just

965
00:53:10,520 --> 00:53:12,840
Speaker 1: gonna guess what the next word of the sentence should

966
00:53:12,840 --> 00:53:15,799
Speaker 1: be in response to that question. And that's really what

967
00:53:15,880 --> 00:53:19,319
Speaker 1: it's doing. It's not alive, it's not thinking. It just

968
00:53:19,600 --> 00:53:22,360
Speaker 1: has a map of all of the language on the

969
00:53:22,400 --> 00:53:26,560
Speaker 1: Internet and the probability that some words come after another

970
00:53:26,920 --> 00:53:29,440
Speaker 1: in response to prompts, and so it's just it's just

971
00:53:29,480 --> 00:53:32,240
Speaker 1: a really really fancy auto complete and obviously very impressive

972
00:53:32,239 --> 00:53:36,680
Speaker 1: autocomplete and in some cases deeply hilarious autocomplete. But it's autocomplete,

973
00:53:37,280 --> 00:53:41,520
Speaker 1: and so there's limits to it, right, and there's limits

974
00:53:41,520 --> 00:53:43,439
Speaker 1: that are imposed by the people who make it. Open

975
00:53:43,480 --> 00:53:45,600
Speaker 1: eye it won't it won't answer some questions. It will

976
00:53:45,600 --> 00:53:47,560
Speaker 1: happily tell you it's not a search engine all the time.

977
00:53:47,760 --> 00:53:49,840
Speaker 1: But then there are things, uh. And this is like

978
00:53:49,880 --> 00:53:53,120
Speaker 1: a great phrase that our reporter James Vincent, who covers

979
00:53:53,120 --> 00:53:55,160
Speaker 1: AI for the Vergi, told me that there's amazing phrase.

980
00:53:55,200 --> 00:53:58,640
Speaker 1: It's called the capability overhang, which is you invent a

981
00:53:58,680 --> 00:54:03,799
Speaker 1: system like this. The AI researchers and developers they can't.

982
00:54:03,840 --> 00:54:06,239
Speaker 1: They're not as creative as the whole internet. So then

983
00:54:06,280 --> 00:54:07,719
Speaker 1: you give it to the whole internet and you start

984
00:54:07,760 --> 00:54:10,239
Speaker 1: discovering all the stuff that you had no idea could do.

985
00:54:10,560 --> 00:54:12,640
Speaker 1: And then you were like off on the cliff being like, well,

986
00:54:12,719 --> 00:54:14,239
Speaker 1: we didn't think about that. Should we slow it down?

987
00:54:14,440 --> 00:54:16,279
Speaker 1: And I think that's the that's the thing we're all

988
00:54:16,280 --> 00:54:19,399
Speaker 1: experiencing right now. These models have existed for years. In fact,

989
00:54:19,400 --> 00:54:23,560
Speaker 1: the core technology of chat GPT is what they made

990
00:54:23,640 --> 00:54:25,880
Speaker 1: was a user interface for it. That's really fun, right,

991
00:54:25,920 --> 00:54:28,720
Speaker 1: They made it a chat bot. But Google has demoed

992
00:54:28,719 --> 00:54:30,560
Speaker 1: this kind of technology for a long time. Microsoft has

993
00:54:30,560 --> 00:54:32,360
Speaker 1: demoed it. I don't know if you recalled this, but

994
00:54:32,400 --> 00:54:34,239
Speaker 1: a couple of months ago there's a Google engineer named

995
00:54:34,239 --> 00:54:36,920
Speaker 1: Blake Lemoyne who was like Google's chat bot is alive.

996
00:54:38,160 --> 00:54:40,400
Speaker 1: Be fired because Google is like, this thing is definitely

997
00:54:40,440 --> 00:54:45,240
Speaker 1: not alive, right, it's super not alive. There was. Actually

998
00:54:45,280 --> 00:54:47,800
Speaker 1: Google had another controversy with a woman named timnet Gibreu

999
00:54:48,280 --> 00:54:51,040
Speaker 1: was one of their ethics researchers, and she published a

1000
00:54:51,160 --> 00:54:54,279
Speaker 1: paper was about a concept called stochastic parents, which is

1001
00:54:54,280 --> 00:54:56,920
Speaker 1: fundamentally like, if you train about like this on the

1002
00:54:57,000 --> 00:54:59,200
Speaker 1: on the text of the Internet, it will just be

1003
00:54:59,440 --> 00:55:03,440
Speaker 1: it will re flect the biases of the Internet at large. Right,

1004
00:55:03,600 --> 00:55:05,840
Speaker 1: It can only reflect to you what is on the Internet,

1005
00:55:05,880 --> 00:55:08,600
Speaker 1: and that means it will be sexist, it will be racist.

1006
00:55:08,680 --> 00:55:12,799
Speaker 1: But then people who talk to the robot will assume

1007
00:55:12,920 --> 00:55:15,560
Speaker 1: that the robot is more accurate than the Internet at large, right,

1008
00:55:15,600 --> 00:55:17,719
Speaker 1: so that you will ask it some question, it will

1009
00:55:17,719 --> 00:55:20,480
Speaker 1: deliver you from like racist or wrong answer, and it

1010
00:55:20,520 --> 00:55:22,840
Speaker 1: would be so confident that people will like assume that

1011
00:55:22,920 --> 00:55:24,880
Speaker 1: that is correct. And she got run out of Google

1012
00:55:24,960 --> 00:55:27,840
Speaker 1: for this paper. So Google has like put this technology

1013
00:55:27,840 --> 00:55:30,160
Speaker 1: that they haven't in a box right there. Their version

1014
00:55:30,239 --> 00:55:32,560
Speaker 1: is called Lambda, and they kind of like kind of

1015
00:55:32,560 --> 00:55:34,200
Speaker 1: afraid to use it. And then this other company is

1016
00:55:34,239 --> 00:55:37,240
Speaker 1: like here you go, like, let's burned money like running

1017
00:55:37,239 --> 00:55:40,399
Speaker 1: GPS to do AI for people, And I think that's

1018
00:55:40,400 --> 00:55:42,319
Speaker 1: really fascinating, right, And I think we're just a moment

1019
00:55:42,360 --> 00:55:44,480
Speaker 1: where it's like very fabish and very fun. There are

1020
00:55:44,520 --> 00:55:46,520
Speaker 1: some of these that are like hilarious. Like I saw

1021
00:55:46,600 --> 00:55:49,400
Speaker 1: one that's like, tell me why I shouldn't put a

1022
00:55:49,440 --> 00:55:51,920
Speaker 1: banana and a VCR, or it was peanut butter sandwich.

1023
00:55:51,960 --> 00:55:53,320
Speaker 1: It was like, tell me how to get a peanut

1024
00:55:53,320 --> 00:55:54,919
Speaker 1: butter sandow which out of the VCR in the style

1025
00:55:54,920 --> 00:55:57,600
Speaker 1: of the King James Bible. And that's actually, on its

1026
00:55:57,640 --> 00:56:00,839
Speaker 1: face like a hilarious improv prompt, right, And then the

1027
00:56:00,960 --> 00:56:03,840
Speaker 1: robot is like just spits out a Bible verse about

1028
00:56:03,920 --> 00:56:07,520
Speaker 1: peanut butter sandwiches and VCRs Like that's great. I think like,

1029
00:56:07,680 --> 00:56:09,160
Speaker 1: who's going to really use it as a bunch of

1030
00:56:09,200 --> 00:56:13,840
Speaker 1: like mid level content marketers to be like, here's a

1031
00:56:13,840 --> 00:56:19,239
Speaker 1: blog about health insurance, right, and my fine, that's fine.

1032
00:56:19,280 --> 00:56:22,520
Speaker 1: But what it cannot do is come up with new ideas, right,

1033
00:56:22,520 --> 00:56:25,160
Speaker 1: because it is just an autocomplete. It's only guessing the

1034
00:56:25,200 --> 00:56:27,960
Speaker 1: next word in a sentence based on the words that

1035
00:56:27,960 --> 00:56:29,759
Speaker 1: already exist on the Internet, which means you can do

1036
00:56:30,000 --> 00:56:32,920
Speaker 1: like meet things like pretend to write code. But if

1037
00:56:33,000 --> 00:56:35,680
Speaker 1: you are the sort of person who like makes new sentences,

1038
00:56:35,800 --> 00:56:37,960
Speaker 1: I think you're safe for now. Oh good, well, I'm

1039
00:56:37,960 --> 00:56:40,120
Speaker 1: glad to hear my job is safe. Thank you so

1040
00:56:40,200 --> 00:56:42,439
Speaker 1: much for coming on. I hope you will come back

1041
00:56:42,560 --> 00:56:46,719
Speaker 1: and talk to us as the world ends slowly. It's

1042
00:56:46,760 --> 00:56:55,120
Speaker 1: super fun. Thank you for having me on, Molly John Fast,

1043
00:56:55,600 --> 00:57:02,120
Speaker 1: Jesse Cannon. These republic in the night out in New York,

1044
00:57:02,160 --> 00:57:04,279
Speaker 1: they always lead to so much for our first there's

1045
00:57:04,320 --> 00:57:06,759
Speaker 1: the proud Boys. Now that it's the I would have

1046
00:57:06,800 --> 00:57:10,720
Speaker 1: done the insurrection right speech. I did not see it coming.

1047
00:57:10,960 --> 00:57:14,120
Speaker 1: I'm sorry that was really bad. But Marjorie Taylor Green

1048
00:57:14,360 --> 00:57:17,120
Speaker 1: is of course going to be our moment of fucory today.

1049
00:57:17,360 --> 00:57:20,520
Speaker 1: New York Young Republican Club had their annual gala on

1050
00:57:20,600 --> 00:57:24,200
Speaker 1: Saturday night, where the group's president declared total war on

1051
00:57:24,320 --> 00:57:28,040
Speaker 1: perceived enemies. He said, we want to cross the rubicon.

1052
00:57:28,160 --> 00:57:30,800
Speaker 1: We want total war. Let me just tell you what's

1053
00:57:30,840 --> 00:57:35,040
Speaker 1: not happening total war. Marjorie Taylor Green, the intellectual, the

1054
00:57:35,120 --> 00:57:39,720
Speaker 1: head of the intellectual dark Web of the GOP Congressional

1055
00:57:39,720 --> 00:57:43,960
Speaker 1: Caucus UM told the group, I will tell you something

1056
00:57:44,040 --> 00:57:47,000
Speaker 1: if Steve Bannon and I organized that she's talking about

1057
00:57:47,080 --> 00:57:49,960
Speaker 1: January six. We would have won, she said as an

1058
00:57:49,960 --> 00:57:52,920
Speaker 1: attend as the attendees erupted in cheers and a pause,

1059
00:57:53,240 --> 00:57:56,440
Speaker 1: not to mention it would have been armed. This was

1060
00:57:56,560 --> 00:57:58,840
Speaker 1: right after they gave her the Richard M. Nixon Award,

1061
00:57:58,880 --> 00:58:02,440
Speaker 1: which is really really something. Yeah, you'd think Richard Nixon

1062
00:58:02,560 --> 00:58:06,560
Speaker 1: would want better, but we know that he would not.

1063
00:58:08,320 --> 00:58:13,120
Speaker 1: It's very on brand for the Richard Nixon Award, and

1064
00:58:13,320 --> 00:58:17,280
Speaker 1: for that they are our moment of glory. That's it

1065
00:58:17,480 --> 00:58:21,000
Speaker 1: for this episode of Fast Politics. Tune in every Monday,

1066
00:58:21,080 --> 00:58:24,120
Speaker 1: Wednesday and Friday to your the best minds in politics

1067
00:58:24,200 --> 00:58:27,280
Speaker 1: makes sense of all this chaos. If you enjoyed what

1068
00:58:27,320 --> 00:58:29,960
Speaker 1: you've heard, please send it to a friend and keep

1069
00:58:30,000 --> 00:58:33,120
Speaker 1: the conversation going. And again, thanks for listening.