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Speaker 1: Hi, I'm Mollie 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 Marjorie Taylor Green was laughed at

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Speaker 1: by Democrats when she asked for them to observe decorum.

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Speaker 1: We have such a great show today. Representatives Summerly talks

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Speaker 1: to us about representing western Pennsylvania with progressive values. Then

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Speaker 1: we'll talk to author Doug Rushkoff about AI and Ronda Santis'

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Speaker 1: failure to launch. But first we have Washington Post columnist

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Speaker 1: Dana Milbank. Welcome back to Fast Politics, one of my

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Speaker 1: favorite guests and writers.

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Speaker 2: Dana Milbank, Well, thank you very much, pleasure to be

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Speaker 2: with you.

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Speaker 1: Yeah, we started the Republican primary cycle last night Putting

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Speaker 1: fingers announced discuss.

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Speaker 3: Yes, and quite an announcement it was. And I think

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Speaker 3: that was a bit of a metaphor the failure to

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Speaker 3: launch on Twitter. But it's been a failure to launch overall.

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Speaker 3: Here we are. It's twenty sixteen all over again. Got

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Speaker 3: a huge field would be Republican candidates, and Donald Trump

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Speaker 3: is in a perfect position to pick them off one

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Speaker 3: by one. And that's what happened last time. Nobody got

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Speaker 3: a clear shot at him. It looked like DeSantis might

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Speaker 3: have been that guy. But then came the indictment and

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Speaker 3: the rally around Trump effect. DeSantis bungled it and it's

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Speaker 3: been sort of a disaster for him ever since. So

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Speaker 3: I think the launch, which was sort of a you know,

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Speaker 3: in microcosm, the disaster that his campaign has been. So look,

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Speaker 3: I don't you know, we've learned a long time ago

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Speaker 3: not to make predictions, particularly as regards anything where Trump

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Speaker 3: is in the sentence. But you know, history is definitely

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Speaker 3: rhyming here. I can see the path again for Donald Trump.

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Speaker 1: Yeah, I mean that is I think a really good point.

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Speaker 1: I mean, what's amazing about Trump and Trumps in general

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Speaker 1: is like there were never secrets in trump Ism, right

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Speaker 1: or in Trump He just says what he wants. So

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Speaker 1: he's like everyone should jump in, you know, And he's

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Speaker 1: encouraging these people because he knows that this is the

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Speaker 1: not Trump lane of the primary. And maybe if you

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Speaker 1: had one candidate, you might be able to run a

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Speaker 1: real challenge to Trump. But you have a gazillion candidates.

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Speaker 3: Yeah, and you'd think that maybe the rest of the

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Speaker 3: Republican Party would have learned a lesson from Jeb and

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Speaker 3: Marco can Cruz and all the others back in twenty sixteen.

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Speaker 3: By the same token. You would think that some of

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Speaker 3: us in the media would have learned our lesson from

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Speaker 3: twenty sixteen. But we're starting to cover him exactly the

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Speaker 3: same way we covered him last time, with just a

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Speaker 3: whole lot of credulity and free airtime. It's kind of

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Speaker 3: nauseating to see it all happening again. You know, look,

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Speaker 3: we don't know, you know, what other indictments await, and

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Speaker 3: maybe Republicans can get wise this time. But you know,

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Speaker 3: I've lost a lot of money betting on the wisdom

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Speaker 3: of Republicans.

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Speaker 1: I mean, what's interesting is if you'll get twenty twenty right.

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Speaker 1: The primary field was messy, right, and in fact, Biden

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Speaker 1: lost those first two primary contests, right and Mayor Pete.

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Speaker 1: It was messy. But then there was a moment Democrats

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Speaker 1: came together and they supported Biden and it wasn't like

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Speaker 1: there was really it was really I've never seen Democrats

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Speaker 1: do that. You know, they all got together and they

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Speaker 1: were like, you know, he's maybe not my guy, but

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Speaker 1: we got to get rid of Trump. And Republicans have

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Speaker 1: not come into it with that same kind of steadfastness.

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Speaker 3: Not so far still, Trump commands overwhelming support from the

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Speaker 3: Republican base, and you know, just the tribal nature of things,

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Speaker 3: you know, if it looks like he's going to be

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Speaker 3: the nominee, then all of them will once again the

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Speaker 3: other way and accept it. That's just the nature of

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Speaker 3: our politics. It doesn't matter whether it was Trump or

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Speaker 3: DeSantis or somebody else. They're going to rally around whoever

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Speaker 3: it is. But just looks like we're already seeing that

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Speaker 3: rallying effect this far out. I think the Democratic experience

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Speaker 3: around Biden will surely. I mean, I think there's going

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Speaker 3: to be a whole lot of unity around the Democrat

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Speaker 3: Joe Biden in twenty twenty four. These notions of sort

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Speaker 3: of dissatisfaction with his age, I think those largely vanished

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Speaker 3: for the same reason that it's you know, if it's

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Speaker 3: him or it's Donald Trump. It's not like people are

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Speaker 3: going to be on the Democratic side are going to

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Speaker 3: be holding out saying if only somebody else.

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Speaker 2: Had run, right.

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Speaker 1: I also think, you know, Biden versus Trump is very

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Speaker 1: different than Biden versus someone else.

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Speaker 3: Absolutely, Yeah, I mean, you know, a younger candidate a DeSantis.

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Speaker 3: May his campaign rest in peace. No, that's absolutely. I mean,

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Speaker 3: you know, the best thing for Biden, if not for America,

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Speaker 3: is Trump as the nominee. He was made to be

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Speaker 3: the anti Trump. So this, you know, this, this extinguishes

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Speaker 3: a lot of the doubts that would have bubbled up

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Speaker 3: from Democrats. And Democrats are very good at, you know,

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Speaker 3: bubbling up doubts, as you know we're seeing with the

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Speaker 3: debt limit and everything else. They're just I mean, it

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Speaker 3: is a it is a doubt bubbling factory.

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Speaker 1: Yeah. Oh no, I mean that's the brand. I feel like,

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Speaker 1: you know, that's what it is.

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Speaker 2: Right.

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Speaker 1: The thing that I'm so struck by with Democrats is

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Speaker 1: the dead ceiling. Like I do think there were a

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Speaker 1: lot of Democrats who before the twenty twenty two election

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Speaker 1: were saying, we probably should just fix this fucking debt

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Speaker 1: ceiling bullshit. There were people who were saying it, why

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Speaker 1: didn't he get done?

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Speaker 2: Well?

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Speaker 3: I mean there were, you know, some obvious reasons like

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Speaker 3: Joe Manchin and Kristen Cinema and the lack of ten

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Speaker 3: Republicans who would go along with it. And remember it

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Speaker 3: was already a very heavy lift to get there, you know,

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Speaker 3: end of the year legislation through, So that may very

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Speaker 3: well have sunk it. You know, I don't see that

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Speaker 3: as a real possibility. And you know, I think part

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Speaker 3: of the reason they were able to get their deal

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Speaker 3: through at the at the end of the year is

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Speaker 3: explicitly because because of that, because the Republican said, all right,

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Speaker 3: we're going to have another bite at the apple after

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Speaker 3: McCarthy takes over, and that's exactly what they've done. Was

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Speaker 3: there really anybody out there who thought that Biden would say, well,

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Speaker 3: I'm not going to negotiate and they're going to pass

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Speaker 3: a clean debt limit. I mean, of course that's what

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Speaker 3: you say. That's a negotiating tactic. But the people now

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Speaker 3: who are shocked and stunned that Biden has offered up

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Speaker 3: a freeze of spending. I mean, that's how this game

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Speaker 3: is played. I don't think anybody should be surprised. This

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Speaker 3: is just the way it works.

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Speaker 1: Yeah, no, no, And I think that's a good point.

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Speaker 1: I mean it's true watching it is so interesting and

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Speaker 1: also quite scary. I mean that's the thing is like

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Speaker 1: with this debtlimit stuff. The stakes this is not like

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Speaker 1: a government shutdown. This is like a catastrophe.

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Speaker 3: Yeah, I've got my column coming out for this weekend

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Speaker 3: on the subject. You know, in foreign affairs, Richard Nixon

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Speaker 3: pioneered the madman theory, and the idea is if the

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Speaker 3: other side thinks you're just crazy enough to launch a

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Speaker 3: nuclear attack, well it gives you a lot of leverage

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Speaker 3: and they'll be down. And I think that's exactly what's

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Speaker 3: happening here, except I don't think it's really a theory.

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Speaker 3: I think there are a lot of mad men in

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Speaker 3: the House Republican Party who think, well, you know, the

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Speaker 3: government is not going to default on June first, you know,

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Speaker 3: maybe default isn't a real thing after all. And then

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Speaker 3: you've got you know, people like Matt Gates saying, you know,

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Speaker 3: I don't think we should negotiate over this hostage. He's

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Speaker 3: acknowledging that this is a hostage situation. And if you

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Speaker 3: really don't care, you know, or in the sense that

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Speaker 3: you think, well, okay, the economy tanks, and guess what,

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Speaker 3: they blame the incumbent president when the economy tanks, that's

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Speaker 3: somewhat rational. I mean, it's not rational to bring about

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Speaker 3: economic cataclysm, but it's rational to think that the incumbent

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Speaker 3: president would be blamed for it. Then the leverage really

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Speaker 3: is on the Republican side, and it is one hundred

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Speaker 3: percent up to the administration to avoid this economic calamity.

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Speaker 1: Right you cannot assume that voters will not blame you

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Speaker 1: even if you didn't do it.

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Speaker 3: A lot of the Democratic handring right now is over

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Speaker 3: the messaging, and Biden isn't out there countering. I don't

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Speaker 3: think the messaging back and forth over the last ten

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Speaker 3: days is going to matter a whole lot if we

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Speaker 3: get to the point of an actual default, because then

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Speaker 3: it's presumably going to be some sort of an apocalyptic situation.

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Speaker 3: Right right now, we're not going to wonder what was

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Speaker 3: Biden saying on Tuesday. We've got much bigger problems at

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Speaker 3: that point. You know, the White House is playing this.

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Speaker 3: They've decided that they're going to work the negotiation, and

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Speaker 3: they're not going to be out there jawboning, you know,

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Speaker 3: attacking the Republicans on this. They're actually trying to get

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Speaker 3: a deal through.

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Speaker 1: You Know, one of the things I was always shocked

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Speaker 1: by was when you would have Trump negotiate something and

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Speaker 1: he'd be out there saying stuff where you'd be like,

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Speaker 1: how is this ever going to work? You know, he'd

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Speaker 1: be out there blowing it up, you know. So I

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Speaker 1: do respect that, Like with the Biden administration, you do

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Speaker 1: feel like there's not an insane person who could come

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Speaker 1: in in the last minute and blow everything up for

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Speaker 1: no reason. But unfortunately that is the GOP base now, right.

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Speaker 3: I mean, you've got Trump explicit saying don't give in

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Speaker 3: unless you've got everything you possibly wanted to, including the

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Speaker 3: kitchen sink, and you've got the Freedom Caucus saying don't

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Speaker 3: negotiate unless it's everything that we passed as part of

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Speaker 3: our budget.

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Speaker 1: They can't agree on what everything is.

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Speaker 3: Right cause that you keep getting things added in immigration

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Speaker 3: and other things. So I do believe there'll be a deal,

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Speaker 3: and whatever the deal will be will not be Biden's

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Speaker 3: surrendering on five trillion dollars and essentially everything that he

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Speaker 3: enacted over the last three years. But you know, people

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Speaker 3: need to be rational and understand that they're going to

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Speaker 3: get their freeze or something.

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Speaker 1: The stakes are incredibly high here.

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Speaker 3: Right, because you bring about this kind of a catastrophe,

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Speaker 3: it's going to do a whole lot more damage than

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Speaker 3: freezing spending at twenty twenty three levels or even slightly

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Speaker 3: below that.

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Speaker 2: Yeah.

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Speaker 1: Yeah, this is one of these ways in which the

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Speaker 1: Republican Party has really taken a normal, non political you know,

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Speaker 1: it's really just meant to be a kind of piece

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Speaker 1: of government legislative slature, and made it into the potential

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Speaker 1: for catastrophe.

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Speaker 3: Yeah. I mean we've seen versions of this before, you know,

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Speaker 3: after twenty ten and lesser one subsequently. But there was

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Speaker 3: always somebody, you know, there was a John Baynor, Paul

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Speaker 3: Ryan or somebody saying all right, guys, you can't actually default.

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Speaker 3: And I'm not sure. I mean, you have Mitch McConnell

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Speaker 3: saying we won't default. He's not actually doing anything to

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Speaker 3: prevent that. So I think they've kept you know, I

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Speaker 3: think it'd be giving it a little too much credit

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Speaker 3: to say this has been a deliberate negotiating strategy, to

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Speaker 3: say we're dangling the threat of default out there. I

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Speaker 3: think that it's a genuine threat of default out there,

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Speaker 3: because there are plenty of people who would be perfectly

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Speaker 3: happy seeing that happen.

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Speaker 1: That I think is pretty scary.

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Speaker 3: That is new. That is not what you had after

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Speaker 3: twenty ten. That's what has taken this fairly mundane thing.

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Speaker 3: And you know, as McConnell says, yes, we have these

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Speaker 3: fights all the time, but there is something different here,

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Speaker 3: and that you've got significant amount of craziness in the

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Speaker 3: House Republican Party and McCarthy isn't necessarily one of them himself,

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Speaker 3: but he can't lose more than five of them or

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Speaker 3: he's out of a job. So that's the difference here,

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Speaker 3: and he cares I would venture to say more about

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Speaker 3: keeping that job than whatever happens to the world economy.

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Speaker 1: Oh, no question. But I also think that it does

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Speaker 1: seem to me. And again, tell me if you think

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Speaker 1: I'm wrong that McCarthy doesn't really have control over these people.

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Speaker 3: It certainly doesn't. You know, based on his public pronouncements.

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Speaker 3: You know he'll be saying things are terrific in negotiations,

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Speaker 3: the Freedom Caucus says, don't negotiate, and then he comes

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Speaker 3: out and says we're nowhere near a deal.

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Speaker 2: Right.

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Speaker 4: I mean, that's the thing.

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Speaker 1: I mean, I don't want to feel bad for him

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Speaker 1: because he put himself in this position and he helped

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Speaker 1: destroy the Republican Party and make it this weird tea

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Speaker 1: party disaster. It is today. But you definitely get the

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Speaker 1: sense that the guy is whistling in the dark to

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Speaker 1: keep himself from being a friend.

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Speaker 3: Yeah, he's a follower, not a leader. Now that doesn't

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Speaker 3: mean it won't work in the sense that Biden's sitting

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Speaker 3: across the table from McCarthy knows what he's up against

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Speaker 3: and that we will do anything to keep his job,

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Speaker 3: even if that means placating the Freedom Caucus into going

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Speaker 3: off the cliffs. So it gives him a whole lot

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Speaker 3: of leverage. Right, whether or not that was what it

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Speaker 3: was intended, that's the way it works out.

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Speaker 1: I want to have two seconds with you on what

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Speaker 1: you think this summer, this is like the last summer,

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Speaker 1: this sort of sleepy summer before the twenty twenty four

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Speaker 1: armageddon where we have you know, democracy on the ticket again.

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Speaker 1: Do you what do you first see happening right now?

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Speaker 1: What are you watching? What is on the horizon? What

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Speaker 1: is keeping you up at night?

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Speaker 3: Well, I love the idea that you could think it

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Speaker 3: would be a sleepy summer that when we could actually

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Speaker 3: have the first ever to fall. So I'm not taking

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Speaker 3: you know, I'm still holding out the possibility that that

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Speaker 3: occurs if we survive if we survive the debt standoff,

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Speaker 3: which also presumably reduces the threat of government shut down

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Speaker 3: at you know, when the fiscal year ends in the fall,

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Speaker 3: then I think we returned to status quo ante, which

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Speaker 3: is basically ninety percent of the time fighting about Hunter Byte,

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Speaker 3: which would be kind of you know, endearing, right, and

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Speaker 3: Jim Jory can have multiple weaponization hearings with you know,

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Speaker 3: featuring January sixth insurrectionists and.

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Speaker 1: A whistle blower they can't find.

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Speaker 3: Can't find the whistleblower, youre the ones they can find

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Speaker 3: seem to seem to be a little bit suspect of,

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Speaker 3: you know, is it criminal criminal conduct? The FBI said

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Speaker 3: we can have some good old fashioned fun with Jim

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Speaker 3: Jordan and James Coher because the stakes are lower. But

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Speaker 3: you know, I mean I say that in Jess, because

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Speaker 3: the stakes aren't at all low. You see what's going

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Speaker 3: on with you know, abortion restrictions around the country, Efforts

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Speaker 3: to rein in the referendum process to prevent people, you know,

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Speaker 3: actual voters from right.

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Speaker 1: You know, that's a bill in Ohio that comes up

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Speaker 1: in August. It's called Proposition eight, and it raises the

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Speaker 1: so basically Republicans have decided that these ballot initiatives are

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Speaker 1: too popular and they don't want people doing democracy if

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Speaker 1: it doesn't work for them, and so they're raising them

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Speaker 1: standard the minimum to sixty percent.

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Speaker 3: Exactly. Basically, we step back from the precipice of instant

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Speaker 3: worldwide destruction and go into more of the sort of steady,

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Speaker 3: day to day erosion of our democracy, which is really

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Speaker 3: very satisfying and relaxing by comparison.

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Speaker 1: I have to tell you that the stuff they're doing

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Speaker 1: in Ohio. I just wrote about this in my Vatty

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Speaker 1: Fair column this week. Everyone took it out. In it,

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Speaker 1: I talk about how there is you know, in Ohio

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Speaker 1: they have a bill, I mean, Republican legislature legislators are

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Speaker 1: unbelievably stupid and like at the state level, it's like incredible,

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Speaker 1: and so they have a bill that academics cannot endorse

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Speaker 1: controversial ideas. Amazing.

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Speaker 3: Yeah, no, that's excellent. That's that. That is the end

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Speaker 3: of the education process.

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Speaker 1: Not only are they going to get rid of tenure

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Speaker 1: in Texas you had Dan Patrick is in a war

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Speaker 1: with academics about tenure, but you're going to get rid

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Speaker 1: of academics having controversial idea terrific right.

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Speaker 3: Twenty five hundred years later, we finally banished Socrates.

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Speaker 1: I want to ask you one last question. Besides the

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Speaker 1: dead ceiling, what are the things keeping you up at night?

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Speaker 3: It's that constant drip drip erosion of the democracy. It's

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Speaker 3: the obvious return of Trump, these routine assaults on the

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Speaker 3: Justice Department, on the FBI, and you know what will

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Speaker 3: happen at the next stage as they start to decide

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Speaker 3: who's going to bear the brunt of these funding cuts.

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Speaker 3: We don't know the top line number yet, but we

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Speaker 3: do know that basically all of these cuts are going

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Speaker 3: to be focused on the fifteen to twenty percent of

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Speaker 3: the government is non defense, you know, discretionary spending, and

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Speaker 3: that's going to wipe out a lot of people.

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Speaker 1: Yeah, thank you, Dana. Congresswoman Summerly represents Pennsylvania's thirty fourth district.

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Speaker 1: Welcome to Fast Politics. Congresswoman Summerly, thank you for having me.

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Speaker 1: We're delighted to have you. Tell us a little bit

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Speaker 1: about your story, how you got to Congress and you know,

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Speaker 1: sort of your backstory.

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Speaker 4: Well, goodness, how I got to Congress.

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Speaker 5: You know, I started as truly an organizer trying to

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Speaker 5: find my way, and believe it or not, I was

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Speaker 5: just have this idea that more people would vote, and

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Speaker 5: then that line, more people would vote if they had

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Speaker 5: people who look like them and responded to them and

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Speaker 5: sounded like them running for offits, right. Yeah, really, really

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Speaker 5: a simple notion. In western Pennsylvania, we have a lot

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Speaker 5: of municipalities, you know, a lot, one hundreds even. Each

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Speaker 5: of them has you know, their own school districts, their

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Speaker 5: own borough councils, their own mayors, you know, their own

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Speaker 5: township commissioners, and so many of these offices. And I

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Speaker 5: knew so many people, or should I say I knew

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Speaker 5: very few people who actually knew a single elected official,

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Speaker 5: right washingn Pennsylvania had this old boys network and it

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Speaker 5: worked for them, and it was almost impossible for any

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Speaker 5: of us to break into this to ever, you know,

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Speaker 5: just stare an agenda that actually matched with the people

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Speaker 5: in any of these communities really really caring about. And

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Speaker 5: we were in the midst of figuring out how can

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Speaker 5: we use and how could I use the organizing skills

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Speaker 5: that I just you know, encountered on my stint being

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Speaker 5: an organizer, you know, in a twenty sixteen campaign, how

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Speaker 5: do we translate that? And we started to, you know,

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Speaker 5: talk to candidates and talk to community members and organize

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Speaker 5: these communities around these races.

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Speaker 4: And that's when someone came to me and said you

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Speaker 4: should run.

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Speaker 5: And I never thought about running for elected office before then,

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Speaker 5: never not once, And that's how I ended up running

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Speaker 5: in twenty eight team for the state House and the

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Speaker 5: urgency that carried me there.

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Speaker 4: You know, it hasn't gone away with the same issues.

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Speaker 5: Right if you're black, or you're brown, or you're poor,

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Speaker 5: you are more likely to live in these communities with

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Speaker 5: environmental pollutants, these communities that have a dearth of jobs.

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Speaker 5: We have inequitable funding schemes for our schools, so our

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Speaker 5: kids are basically running through a cycle every generation that

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Speaker 5: is just almost impossible to break. And we had so

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Speaker 5: few politicians who were talking about it, acknowledging it, working

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Speaker 5: through it. And when we couple that with the rising tide,

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Speaker 5: trump Ism, of white supremacy, of anti blackness, and so

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Speaker 5: many other attacks, our attacks to democracy, it was just

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Speaker 5: clear to me that we deserve and we need people

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Speaker 5: in office who are going to respond and be responsive

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Speaker 5: to the actual electorate who's going to expand it and

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Speaker 5: care about it and center it.

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Speaker 4: And that's how I got here.

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Speaker 1: What you're saying is so relevant and important and also

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Speaker 1: important to the Biden administration in twenty four and for

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Speaker 1: winning elections, but also more importantly for serving the people

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Speaker 1: that Democrats, you know, say they want to serve. I'm

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Speaker 1: thinking of this, you know, a blue Sky tweet. I

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Speaker 1: guess it's not a tweet that I saw the other day,

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Speaker 1: which was that, you know, feminism that isn't intersectional is

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Speaker 1: swam supremacy? Yeah it is, And so I wanted to

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Speaker 1: talk to you about this idea. And then also, you

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Speaker 1: come from a battleground state. It's not like you're from Connecticut.

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Speaker 1: I mean, you're from like a state that Democrats must

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Speaker 1: win and you're progressive. So explain to us a little

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Speaker 1: bit about the area you come from and what it

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Speaker 1: looks like and how you built a grassroots campaign there.

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Speaker 5: When I first got into politics, when I first ran

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Speaker 5: for office in our state house, we had one woman

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Speaker 5: in a delegation.

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Speaker 4: She was a tea choice.

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Speaker 5: We had two black folks in a delegation, both from

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Speaker 5: the city of Pittsburgh, from western Pennsylvania. We had never

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Speaker 5: had a black woman serve. We had never had a

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Speaker 5: black person serve from western Pennsylvania. Outside of the city limits.

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Speaker 5: We had almost no people who identified explicitly as we

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Speaker 5: are progressive leftists, however you want, you know, to categorize that.

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Speaker 5: When I ran, I ran, you know, really closely with

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Speaker 5: my good sister who just got elected to county executive,

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Speaker 5: Sarah and Morado. We were two women who were like,

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Speaker 5: we can't win the same way that other people do this.

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Speaker 4: We have to do this together. We have to take

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Speaker 4: it differently. We have to show people a different way

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Speaker 4: to do politics.

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Speaker 5: We doubled the number of women after that with a

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Speaker 5: group of people we found this pack because we needed

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Speaker 5: to build infrastructure so that we can get more people

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Speaker 5: in right. Me and Sarah saw that we were supported,

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Speaker 5: We were well resourced for fay House candidates, and we

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Speaker 5: get sparked and energy that you just typically don't see

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Speaker 5: around local elections.

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Speaker 4: But what we know is is that it wasn't unintentional.

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Speaker 5: It takes a lot for candidates to be able to

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Speaker 5: start on day one with the organizing capacity, with the

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Speaker 5: volunteer base, with the money, to be able to gain

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Speaker 5: the traction, to even be considered viable. We lose so

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Speaker 5: many good people who are more representative of our lived experiences,

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Speaker 5: more representative of our of our ethnic, racial, gender backgrounds,

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Speaker 5: and they never make it to the starting line because

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Speaker 5: we have an outdated political system that tells us that,

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Speaker 5: you know, you can't be in here unless you can

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Speaker 5: raise a lot of money yourself. You can be in

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Speaker 5: here unless you can go through your own rolodex and

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Speaker 5: find one hundred thousand, five hundred thousand a million dollars.

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Speaker 4: And those same people who have access.

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Speaker 5: To that money have access to even more because of

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Speaker 5: the corporate money flooding into our politics.

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Speaker 4: So we had to start to address that. So we

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Speaker 4: chipped away.

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Speaker 5: We started to we use our pack to start to

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Speaker 5: help other candidates, progressives, people color who were running not

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Speaker 5: just for state House, but for our magistrates and our

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Speaker 5: quar to common please DA county counts, so city council's,

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Speaker 5: borough councils, school boards, and we ran Canadas every single cycle.

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Speaker 4: Yeah, we never were lented.

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Speaker 5: And you know, the people who were accustomed to Begionaire

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Speaker 5: are just every cycle they just thought we'd go away.

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Speaker 4: And we never ever did. And you never found a

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Speaker 4: way of get us.

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Speaker 1: The thing that I can never get my head around

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Speaker 1: is how few black senators there have been.

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Speaker 4: Yeah, I'm not shocked.

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Speaker 1: It's so fucked up. I can say this because we're

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Speaker 1: not on cable news. I can curse.

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Speaker 4: We love that.

438
00:22:24,960 --> 00:22:27,640
Speaker 1: Yeah, Like, oh my god, Like what did we even

439
00:22:27,720 --> 00:22:28,040
Speaker 1: do it?

440
00:22:28,040 --> 00:22:28,920
Speaker 4: I'm not shocked at all.

441
00:22:29,680 --> 00:22:30,399
Speaker 2: How would they get in?

442
00:22:30,920 --> 00:22:32,600
Speaker 5: Like when you think about like again, like going by

443
00:22:32,640 --> 00:22:35,399
Speaker 5: just the western Pennsylvania, Right, you're supposed to be white male,

444
00:22:35,800 --> 00:22:37,320
Speaker 5: blue collar labor, or.

445
00:22:37,720 --> 00:22:39,200
Speaker 4: At least you're supposed to look like.

446
00:22:39,160 --> 00:22:41,480
Speaker 5: You care about labor. Right, those are people we talk

447
00:22:41,520 --> 00:22:45,119
Speaker 5: about Central Cassie. If you're black, weren't gonna get millions

448
00:22:45,119 --> 00:22:48,439
Speaker 5: of dollars to listen in Pennsylvania. When I was organized

449
00:22:48,480 --> 00:22:50,840
Speaker 5: in twenty sixteen, we broke a rock frund in Pennsylvania.

450
00:22:50,840 --> 00:22:53,080
Speaker 5: I think it was something like thirty four million dollars

451
00:22:53,359 --> 00:22:56,760
Speaker 5: was spent on the Senate race. This year, John Fetterman

452
00:22:56,840 --> 00:22:59,480
Speaker 5: and doctor Au spent two hundred and thirty million dollars.

453
00:22:59,680 --> 00:23:04,400
Speaker 5: That's the difference in what six years? Two hundred million dollars.

454
00:23:04,720 --> 00:23:07,080
Speaker 5: How can you if you're a block, if you're working class,

455
00:23:07,119 --> 00:23:09,960
Speaker 5: if you're brown, you're coming from an immigrant background, if

456
00:23:10,000 --> 00:23:12,800
Speaker 5: you're poorer trends, if you're working class in a union,

457
00:23:12,880 --> 00:23:16,240
Speaker 5: where are you getting from? Where are you getting it from?

458
00:23:16,480 --> 00:23:19,639
Speaker 5: And both parties rely on this flood of dark money.

459
00:23:19,680 --> 00:23:22,800
Speaker 5: Both parties do because it's the way that we stop progressives,

460
00:23:23,240 --> 00:23:25,600
Speaker 5: and they are just able to flood us.

461
00:23:25,680 --> 00:23:27,000
Speaker 4: But it doesn't just stop progressives.

462
00:23:27,040 --> 00:23:29,880
Speaker 5: It keeps us from having a representative democracy, which then

463
00:23:30,040 --> 00:23:32,960
Speaker 5: keeps us from winning races. It keeps us from expanding

464
00:23:32,960 --> 00:23:35,800
Speaker 5: the electorate. And it's a cycle that we cannot bring.

465
00:23:35,960 --> 00:23:36,920
Speaker 1: So how did you win?

466
00:23:37,200 --> 00:23:39,680
Speaker 4: We raise money? Honestly, no, you know, but it didn't

467
00:23:39,720 --> 00:23:40,200
Speaker 4: come early.

468
00:23:40,480 --> 00:23:40,800
Speaker 2: First.

469
00:23:40,800 --> 00:23:42,879
Speaker 4: We won by energy, We won by momentum.

470
00:23:43,040 --> 00:23:45,800
Speaker 5: We won by tapping into people that the parties had

471
00:23:45,920 --> 00:23:48,399
Speaker 5: said were not worth wanting to get. So you know

472
00:23:48,440 --> 00:23:50,400
Speaker 5: where typically you get the parties who say we're only

473
00:23:50,440 --> 00:23:53,119
Speaker 5: going to send a mailer to super voters. I said,

474
00:23:53,480 --> 00:23:55,280
Speaker 5: you know, why won't we send If you're a black

475
00:23:55,359 --> 00:23:57,280
Speaker 5: voter and you voted for Obama, we're going knock on

476
00:23:57,320 --> 00:24:00,000
Speaker 5: your door. If you're a woman, we're coming to find you.

477
00:24:00,240 --> 00:24:03,000
Speaker 5: If you are young, if you are in college, we're

478
00:24:03,000 --> 00:24:05,520
Speaker 5: coming and talking to you because I know that I

479
00:24:05,520 --> 00:24:08,600
Speaker 5: can speak your language. So by doing that, we were

480
00:24:08,640 --> 00:24:11,480
Speaker 5: able to bring new people into the political process who

481
00:24:11,520 --> 00:24:14,880
Speaker 5: had just been discarded right, they had been ignored. We

482
00:24:14,880 --> 00:24:16,960
Speaker 5: were able to double voter turnout my first race in

483
00:24:17,000 --> 00:24:20,400
Speaker 5: twenty eighteen. In twenty twenty, in a pandemic year, we

484
00:24:20,400 --> 00:24:23,840
Speaker 5: were able to build even on that. We had almost

485
00:24:23,920 --> 00:24:26,879
Speaker 5: over fifty percent voter turnout in our primary. By still

486
00:24:26,920 --> 00:24:29,080
Speaker 5: being committed to going back and getting those people. We

487
00:24:29,240 --> 00:24:31,040
Speaker 5: just go and talk to people about the things they

488
00:24:31,119 --> 00:24:34,720
Speaker 5: care about about here and housing and racial inequity and

489
00:24:34,800 --> 00:24:38,879
Speaker 5: covid or unions and workers' rights and just things that

490
00:24:38,960 --> 00:24:40,359
Speaker 5: working class people care about.

491
00:24:40,560 --> 00:24:41,280
Speaker 4: They believed.

492
00:24:41,440 --> 00:24:43,639
Speaker 5: They believed it because they knew that I came from that,

493
00:24:44,080 --> 00:24:46,560
Speaker 5: and because in my two years I never folded. And

494
00:24:46,600 --> 00:24:48,480
Speaker 5: we just keep building and we keep listening, and we

495
00:24:48,560 --> 00:24:50,360
Speaker 5: keep centering. That's all these folks want. They just want

496
00:24:50,359 --> 00:24:51,440
Speaker 5: to know someone's fighting for them.

497
00:24:51,840 --> 00:24:55,800
Speaker 1: I also think you may not realize this, but I

498
00:24:55,880 --> 00:24:58,520
Speaker 1: hope you do because you are a woman, and women

499
00:24:58,640 --> 00:25:01,600
Speaker 1: politicians always you know, it's there. I don't know if

500
00:25:01,640 --> 00:25:05,320
Speaker 1: you remember there's a statistic that women politicians, right they

501
00:25:05,320 --> 00:25:08,600
Speaker 1: need to be asked to run, you know, numerous times

502
00:25:08,800 --> 00:25:13,000
Speaker 1: multiples more than male politicians do. But also you're just

503
00:25:13,000 --> 00:25:15,119
Speaker 1: a very gifted communicator.

504
00:25:16,480 --> 00:25:18,000
Speaker 4: I didn't know that when I was well.

505
00:25:18,000 --> 00:25:19,600
Speaker 1: I mean, I'm sure you didn't start out knowing that,

506
00:25:19,680 --> 00:25:20,280
Speaker 1: but you must.

507
00:25:20,200 --> 00:25:22,320
Speaker 4: Know that I didn't know that that was it was truly,

508
00:25:22,560 --> 00:25:23,560
Speaker 4: you know, I had heard that.

509
00:25:23,840 --> 00:25:25,960
Speaker 5: I heard that statistic a lot when when when me

510
00:25:26,000 --> 00:25:29,560
Speaker 5: and Sarah and Mrodo were running twenty eighteen because Pennsylvania

511
00:25:29,680 --> 00:25:32,000
Speaker 5: was wearing forty ninth out of fifty states for women

512
00:25:32,000 --> 00:25:36,200
Speaker 5: in elected office, forty nine. This is Pennsylvania with Pittsburgh

513
00:25:36,240 --> 00:25:38,560
Speaker 5: and Philly. That's how bad it was.

514
00:25:38,840 --> 00:25:40,560
Speaker 4: So we heard it a lot. I think I was

515
00:25:40,640 --> 00:25:44,520
Speaker 4: ex once and I don't know. I don't know. I've

516
00:25:44,560 --> 00:25:47,080
Speaker 4: taken leave of my senses when I did this.

517
00:25:48,119 --> 00:25:50,679
Speaker 1: So what's next for you? I mean, I feel like,

518
00:25:51,000 --> 00:25:54,400
Speaker 1: I mean, Pennsylvania is so important in this map. It's

519
00:25:54,680 --> 00:25:57,240
Speaker 1: such an important state. I also feel like it's important

520
00:25:57,320 --> 00:25:59,479
Speaker 1: a because it's a swing state, but really it's important

521
00:25:59,480 --> 00:26:01,879
Speaker 1: because it's just state that is is transitioning.

522
00:26:02,240 --> 00:26:04,240
Speaker 5: Yes, and that think that's precisely what we show in

523
00:26:04,280 --> 00:26:07,879
Speaker 5: Allegheny County. Right, Allegheny County is this sleeper area that

524
00:26:08,080 --> 00:26:11,080
Speaker 5: people when they think about Western Pennsylvania, they're still thinking

525
00:26:11,119 --> 00:26:14,560
Speaker 5: about the nineteen you know, seventies, eighties or even fifties.

526
00:26:15,000 --> 00:26:18,560
Speaker 5: We're thinking blue collar, we're thinking large white male, we're

527
00:26:18,600 --> 00:26:21,520
Speaker 5: thinking blue dog Dems. But what we shown in Western

528
00:26:21,520 --> 00:26:24,880
Speaker 5: Pennsylvania is that actually a progressive movement was was not

529
00:26:24,960 --> 00:26:27,800
Speaker 5: just you know, it wasn't just timely. It was able

530
00:26:27,840 --> 00:26:31,320
Speaker 5: to have the impact. It was because we leaned in,

531
00:26:31,720 --> 00:26:35,520
Speaker 5: because we were uniquely able to build coalitions that other

532
00:26:35,600 --> 00:26:38,919
Speaker 5: cities just hadn't been able to replicate. And because of that,

533
00:26:38,960 --> 00:26:42,040
Speaker 5: when we had a progressive and Congress with two progressives

534
00:26:42,080 --> 00:26:45,159
Speaker 5: in Congress and our county executive seat which is our

535
00:26:45,160 --> 00:26:50,760
Speaker 5: biggest office, mayor school boards, Senate, judgeships, Senate and those

536
00:26:50,800 --> 00:26:54,080
Speaker 5: all came from Western Pennsylvania. And I think our organizing

537
00:26:54,119 --> 00:26:55,960
Speaker 5: model is what we're going to need to apply a

538
00:26:56,280 --> 00:26:58,560
Speaker 5: because in Pennsylvania we do have to run the tab

539
00:26:59,640 --> 00:27:01,600
Speaker 5: some area where we're going to have to mitigate losses

540
00:27:01,680 --> 00:27:04,320
Speaker 5: for our twenty twenty four candidates. You know, we just

541
00:27:04,320 --> 00:27:06,480
Speaker 5: don't want to get crushed, you know, in the middle

542
00:27:06,520 --> 00:27:09,040
Speaker 5: of the city. But Pittsburgh and Philly and Weshtern, Pennsylvania,

543
00:27:09,160 --> 00:27:11,280
Speaker 5: we have to bring out voters. We have to bring

544
00:27:11,359 --> 00:27:13,840
Speaker 5: out new voters. We got to get young people, and

545
00:27:13,920 --> 00:27:16,879
Speaker 5: we in our movement, has shown that we can attract

546
00:27:16,880 --> 00:27:18,919
Speaker 5: young voters, not just as voters. We can get them

547
00:27:18,960 --> 00:27:21,080
Speaker 5: to volunteer, we can get them to participate, we can

548
00:27:21,119 --> 00:27:23,240
Speaker 5: get them to lead. And we did that by not

549
00:27:23,280 --> 00:27:26,200
Speaker 5: ignoring them, by not being condescending to them, right, by

550
00:27:26,200 --> 00:27:27,200
Speaker 5: not patronizing them.

551
00:27:27,640 --> 00:27:30,400
Speaker 1: What can President Biden do to make your job easier?

552
00:27:30,560 --> 00:27:33,200
Speaker 5: He can continue to do the things that our base

553
00:27:33,400 --> 00:27:35,040
Speaker 5: is asking him and put him in office to do.

554
00:27:35,359 --> 00:27:40,200
Speaker 5: Young people came out and historic numbers around climate justice. Yes,

555
00:27:40,359 --> 00:27:43,320
Speaker 5: around you know, making sure that kids are no longer

556
00:27:43,359 --> 00:27:46,800
Speaker 5: being killed in their classrooms, black and brown folks, around

557
00:27:46,800 --> 00:27:50,919
Speaker 5: housing justice and racial justice, students, around student loan debt.

558
00:27:51,160 --> 00:27:55,160
Speaker 4: These are the issues that galvanize the nation when we pull.

559
00:27:55,480 --> 00:27:57,560
Speaker 4: They are popular everywhere.

560
00:27:57,720 --> 00:28:00,840
Speaker 5: They're popular in the cities and urban communities, rural communities,

561
00:28:00,880 --> 00:28:04,560
Speaker 5: since exerman communities, all across the country. Right, we have

562
00:28:04,760 --> 00:28:08,760
Speaker 5: to resist the urge to lean into a nostalgia that

563
00:28:08,880 --> 00:28:10,000
Speaker 5: just doesn't exist anymore.

564
00:28:10,119 --> 00:28:13,040
Speaker 4: That was a time period where it was easier to

565
00:28:13,040 --> 00:28:13,800
Speaker 4: cod to work with one.

566
00:28:14,280 --> 00:28:16,879
Speaker 1: I have to tell you that nostalgia, I mean it

567
00:28:17,000 --> 00:28:21,000
Speaker 1: seems like so stupid to me, Like it's a terrible

568
00:28:21,040 --> 00:28:23,359
Speaker 1: time for everyone. I mean, I don't know, it was

569
00:28:23,440 --> 00:28:24,280
Speaker 1: nostalgic for.

570
00:28:24,240 --> 00:28:26,640
Speaker 5: That except for the people who wasn't a terrible time

571
00:28:26,680 --> 00:28:28,320
Speaker 5: for and for those people was really good.

572
00:28:28,680 --> 00:28:31,840
Speaker 1: I mean, I guess. But yes, I agree, the nostalgia

573
00:28:31,880 --> 00:28:35,639
Speaker 1: will kill us. I mean, that is just the stupidest American.

574
00:28:36,280 --> 00:28:38,960
Speaker 5: It is a fatal mistakes. I believe it is a

575
00:28:38,960 --> 00:28:41,040
Speaker 5: fatal flaw. If we don't get it together, it ain't

576
00:28:41,080 --> 00:28:42,720
Speaker 5: just going to be a fatal fall for the Democrats.

577
00:28:42,880 --> 00:28:44,720
Speaker 5: It's going to be a fatal fall for democracy.

578
00:28:45,240 --> 00:28:49,120
Speaker 1: Yeah, the stakes are so incredibly high. I can't believe

579
00:28:49,120 --> 00:28:51,080
Speaker 1: how the stakes have stayed so high.

580
00:28:51,320 --> 00:28:54,920
Speaker 4: Well again, it's because the Republicans are up in the ante.

581
00:28:55,080 --> 00:28:59,640
Speaker 5: Yeah, every cycle, every you know, every project, every bail

582
00:28:59,680 --> 00:29:03,200
Speaker 5: they r in a state, every takeover they do, they

583
00:29:03,400 --> 00:29:06,200
Speaker 5: empower and umvote in themselves and their bases, and now

584
00:29:06,200 --> 00:29:09,400
Speaker 5: they have to and they yearn for more. When they

585
00:29:09,440 --> 00:29:12,120
Speaker 5: get away with one thing, they're able to then tiptoe

586
00:29:12,160 --> 00:29:14,480
Speaker 5: into the next thing. Right, they got away with stealing

587
00:29:14,520 --> 00:29:16,600
Speaker 5: a Supreme Court seat, so then they can get away

588
00:29:16,640 --> 00:29:17,440
Speaker 5: with it with the next thing.

589
00:29:17,480 --> 00:29:18,720
Speaker 4: They got away with an insurrection.

590
00:29:19,040 --> 00:29:21,240
Speaker 5: So now they can take the state of Florida and

591
00:29:21,320 --> 00:29:24,280
Speaker 5: they can turn it into a fascist state. They got

592
00:29:24,320 --> 00:29:27,280
Speaker 5: away with racial justice against black people, so now they

593
00:29:27,280 --> 00:29:30,000
Speaker 5: can move on to our Jewish siblings and our trans siblings. Right,

594
00:29:30,360 --> 00:29:33,120
Speaker 5: every time we allow them to get off with something

595
00:29:33,120 --> 00:29:36,680
Speaker 5: with impunity, we give them permission to go harder, to

596
00:29:36,720 --> 00:29:37,760
Speaker 5: go deeper, to go further.

597
00:29:38,080 --> 00:29:40,160
Speaker 1: I totally agree. And in fact, I don't know if

598
00:29:40,160 --> 00:29:44,800
Speaker 1: you read this really scary academic paper about how when

599
00:29:45,000 --> 00:29:49,160
Speaker 1: police shoot black people, then in that area black people

600
00:29:49,240 --> 00:29:49,880
Speaker 1: vote less.

601
00:29:50,360 --> 00:29:51,400
Speaker 4: Oh I've not seen that.

602
00:29:51,760 --> 00:29:54,080
Speaker 5: I haven't see that, but I would imagine it's because

603
00:29:54,400 --> 00:29:57,440
Speaker 5: it's another reminder that no one is coming to save

604
00:29:57,520 --> 00:30:01,280
Speaker 5: and protect us. Yeah, we're looking towards our Democratic leaders

605
00:30:01,400 --> 00:30:04,280
Speaker 5: to take our side, to not throw us under the bus.

606
00:30:04,440 --> 00:30:06,520
Speaker 5: But what we see is when times get tough every

607
00:30:06,560 --> 00:30:09,680
Speaker 5: election cycle, the first thing that we throw away is

608
00:30:09,800 --> 00:30:13,280
Speaker 5: our you know, criminal justice lens. We always throw away

609
00:30:13,280 --> 00:30:14,600
Speaker 5: that racial justice lens.

610
00:30:14,920 --> 00:30:18,960
Speaker 4: With policing with George Floyd, we always just go back to, oh, well.

611
00:30:18,840 --> 00:30:20,720
Speaker 5: Now we have to keep walking people up, you know,

612
00:30:20,800 --> 00:30:22,400
Speaker 5: we have to keep criminalizing people.

613
00:30:22,400 --> 00:30:23,800
Speaker 4: We know that it's going to have an impact on

614
00:30:23,800 --> 00:30:24,240
Speaker 4: black people.

615
00:30:24,240 --> 00:30:26,920
Speaker 5: So when a police officer kills a black person and

616
00:30:27,040 --> 00:30:29,040
Speaker 5: no one gets indicted and no one goes to jail,

617
00:30:29,080 --> 00:30:31,480
Speaker 5: and no one needed gets fired, we're reminded that that

618
00:30:31,720 --> 00:30:33,960
Speaker 5: is the state. The state did this to us.

619
00:30:34,240 --> 00:30:37,240
Speaker 1: And the thing I'm struck by is like, the message

620
00:30:37,240 --> 00:30:40,480
Speaker 1: should be these police need to be trained or they

621
00:30:40,480 --> 00:30:42,760
Speaker 1: need to be fired. There are complaints against them, they

622
00:30:42,800 --> 00:30:44,680
Speaker 1: need to be fired. And that can I go a.

623
00:30:44,680 --> 00:30:45,440
Speaker 4: Little bit deeper?

624
00:30:45,800 --> 00:30:46,640
Speaker 1: Yes? Please?

625
00:30:46,920 --> 00:30:51,320
Speaker 5: Police officers are American citizens. They reflect our societal conditions.

626
00:30:51,760 --> 00:30:54,520
Speaker 5: So the real thing is is that implicit biases are

627
00:30:54,640 --> 00:30:59,160
Speaker 5: rampant and pervasive in American society, implicit biases against black men,

628
00:30:59,320 --> 00:31:01,000
Speaker 5: especially black women, black children.

629
00:31:01,240 --> 00:31:04,320
Speaker 4: It proliferates throughout every aspect of our society.

630
00:31:04,320 --> 00:31:08,880
Speaker 5: Police officers get that same conditioning, They taken that same

631
00:31:08,920 --> 00:31:10,040
Speaker 5: messaging as everyone else.

632
00:31:10,520 --> 00:31:14,000
Speaker 4: Training that we need needs to happen from K to twelve.

633
00:31:14,240 --> 00:31:16,040
Speaker 5: It needs to happen in our jobs, It needs to

634
00:31:16,040 --> 00:31:17,520
Speaker 5: happen in our schools.

635
00:31:17,720 --> 00:31:17,880
Speaker 4: Right.

636
00:31:17,960 --> 00:31:22,240
Speaker 5: We have to realize that when children are born, they

637
00:31:22,360 --> 00:31:24,760
Speaker 5: learn and they internalize that black people are dangerous.

638
00:31:24,880 --> 00:31:27,880
Speaker 4: Black kids are different than white kids. You learned that early.

639
00:31:28,000 --> 00:31:29,040
Speaker 4: So how are you going to thenk.

640
00:31:28,880 --> 00:31:31,080
Speaker 5: Get to police, to the police academy and have to

641
00:31:31,160 --> 00:31:33,680
Speaker 5: unlearn that what training can help you learn something that

642
00:31:33,720 --> 00:31:36,800
Speaker 5: you that you have implicitly internalized your entire li likes,

643
00:31:36,840 --> 00:31:38,320
Speaker 5: not just police, right.

644
00:31:38,400 --> 00:31:41,040
Speaker 1: I mean, we also had a reporter on this podcast

645
00:31:41,080 --> 00:31:44,080
Speaker 1: you talked about there's a policy. I'm sure you know this,

646
00:31:44,200 --> 00:31:46,560
Speaker 1: but I was shocked by this that there are like

647
00:31:46,720 --> 00:31:51,600
Speaker 1: police gangs in Los Angeles. They are gangs of police officers.

648
00:31:52,040 --> 00:31:53,520
Speaker 4: Yeah, the nineties happened.

649
00:31:53,800 --> 00:31:56,680
Speaker 5: That's always said when this movement around police battalion and

650
00:31:56,680 --> 00:31:59,000
Speaker 5: police bolalci. This isn't a new new movement. This movement

651
00:31:59,080 --> 00:32:01,280
Speaker 5: wasn't sparked with Tamir Rice or with my Gone or

652
00:32:01,320 --> 00:32:04,440
Speaker 5: none of these. This movement has been happening since black

653
00:32:04,440 --> 00:32:07,400
Speaker 5: people have existed in this country. That's why it's so

654
00:32:07,480 --> 00:32:10,040
Speaker 5: disheartening when people say that, oh, we just don't care

655
00:32:10,040 --> 00:32:12,280
Speaker 5: about public safety, or we don't care about police, or

656
00:32:12,280 --> 00:32:15,720
Speaker 5: we hate police. No, we love ourselves, we love our communities.

657
00:32:15,960 --> 00:32:18,400
Speaker 5: We recognize more than anyone that when black people are

658
00:32:18,440 --> 00:32:21,000
Speaker 5: in danger, so too is the entire country. That's how

659
00:32:21,040 --> 00:32:23,680
Speaker 5: we got here. Now, that's why democracy is at risk.

660
00:32:24,080 --> 00:32:25,920
Speaker 5: We understand this, right.

661
00:32:26,160 --> 00:32:28,920
Speaker 1: Right, this is all connected to democracy, right, Like, if

662
00:32:29,000 --> 00:32:32,920
Speaker 1: you disenfranchise a part of your country, you are not

663
00:32:33,000 --> 00:32:36,360
Speaker 1: practicing democracy. And that's how we got here. Tell me

664
00:32:36,440 --> 00:32:39,320
Speaker 1: something that makes you a little bit hopeful, like Maxwell

665
00:32:39,400 --> 00:32:42,400
Speaker 1: Frost makes me hopeful. I mean, you make me hopeful.

666
00:32:42,520 --> 00:32:44,040
Speaker 1: Tell me what makes you hopeful.

667
00:32:44,320 --> 00:32:48,120
Speaker 5: The People's movement that continues to grow right western Pennsylvania

668
00:32:48,120 --> 00:32:50,800
Speaker 5: makes me hopeful. We shouldn't have won there. If conventional

669
00:32:50,840 --> 00:32:53,320
Speaker 5: reason had prevailed, we would not have even run in

670
00:32:53,320 --> 00:32:54,360
Speaker 5: the first place.

671
00:32:54,120 --> 00:32:55,600
Speaker 4: Let alone been in the position to win.

672
00:32:56,400 --> 00:32:59,400
Speaker 5: Young people who are walking out of schools for their

673
00:32:59,400 --> 00:33:03,080
Speaker 5: own safety, who are protests, and environmental and climate justice

674
00:33:03,200 --> 00:33:07,000
Speaker 5: reinforcements are coming. Reinforcements are on the way, and we

675
00:33:07,080 --> 00:33:09,120
Speaker 5: will outnumber them, we will outlast them.

676
00:33:09,320 --> 00:33:11,360
Speaker 4: Now, the problem is is what happens in the interim.

677
00:33:11,480 --> 00:33:13,240
Speaker 5: We're going to fight to make sure that we mitigate

678
00:33:13,280 --> 00:33:15,600
Speaker 5: as much harm and damage as we can. But those

679
00:33:15,640 --> 00:33:19,000
Speaker 5: reinforcements are on the way, and it's powerful, and it's strong,

680
00:33:19,280 --> 00:33:21,720
Speaker 5: and it's accountable, and it's intersectional.

681
00:33:22,240 --> 00:33:23,960
Speaker 4: That's a precise movement that we need.

682
00:33:24,240 --> 00:33:26,560
Speaker 1: Now I'm going to cry, and I'm not allowed to cry,

683
00:33:26,600 --> 00:33:30,720
Speaker 1: because I cried when I was interviewing State Senator Megan

684
00:33:30,760 --> 00:33:33,320
Speaker 1: from Oklahoma. So I'm not going to cry, but I

685
00:33:33,400 --> 00:33:35,960
Speaker 1: do feel like very moved by you, and just I

686
00:33:36,040 --> 00:33:38,760
Speaker 1: appreciate you and thank you. You know, it gives me

687
00:33:38,800 --> 00:33:39,480
Speaker 1: a lot of hope.

688
00:33:39,600 --> 00:33:42,760
Speaker 4: So thank you, thank you. I appreciate that, I really do.

689
00:33:43,160 --> 00:33:44,840
Speaker 5: I think that The other thing that we need to know, though,

690
00:33:44,880 --> 00:33:48,840
Speaker 5: is that listen, again, these movements don't just happen. We

691
00:33:48,960 --> 00:33:52,080
Speaker 5: have people who are making them happen. They are under resourced,

692
00:33:52,480 --> 00:33:55,880
Speaker 5: they are undersupported, and we see black women candidates who

693
00:33:55,920 --> 00:33:58,960
Speaker 5: are coming with fire and with energy. No, we are

694
00:33:59,000 --> 00:34:03,080
Speaker 5: the least fundac CAUs candidates that exists, the least funded politicians,

695
00:34:03,400 --> 00:34:05,600
Speaker 5: the most attacked, and we are also.

696
00:34:05,400 --> 00:34:06,920
Speaker 4: The ones who lay ourselves on the line.

697
00:34:07,000 --> 00:34:09,960
Speaker 5: When we see people who are trying to break into politics,

698
00:34:10,000 --> 00:34:10,799
Speaker 5: we need to support them.

699
00:34:10,840 --> 00:34:12,520
Speaker 4: We need to trust them. And we can't say we

700
00:34:12,600 --> 00:34:13,279
Speaker 4: trust black women.

701
00:34:13,360 --> 00:34:15,680
Speaker 5: We can't say we trust young candidates and then not

702
00:34:15,960 --> 00:34:18,640
Speaker 5: make sure that they go into these fights well resourced.

703
00:34:18,680 --> 00:34:20,600
Speaker 4: That's the same for organizers and activists.

704
00:34:20,800 --> 00:34:22,520
Speaker 5: You know, those are things that can help us to

705
00:34:22,600 --> 00:34:24,520
Speaker 5: continue to stay in the fight.

706
00:34:24,719 --> 00:34:27,080
Speaker 4: Our biggest threat is not being able to stay in.

707
00:34:27,040 --> 00:34:30,640
Speaker 1: The fight Congresswoman Summerly, thank you so much for joining us.

708
00:34:30,760 --> 00:34:32,279
Speaker 4: Thank you so much for having me.

709
00:34:33,239 --> 00:34:38,120
Speaker 1: Hi. It's Molly and I am wildly excited that for

710
00:34:38,160 --> 00:34:42,719
Speaker 1: the first time, Fast Politics, the show you're listening to

711
00:34:43,120 --> 00:34:46,000
Speaker 1: right now, is going to have merch for sale over

712
00:34:46,200 --> 00:34:55,840
Speaker 1: at shop dot fastpoliticspod dot com. You can now buy shirts, hats, hoodies,

713
00:34:55,920 --> 00:34:59,719
Speaker 1: and toe bags with our incredible designs. We've heard your

714
00:34:59,760 --> 00:35:03,200
Speaker 1: c guy to spread the word about our podcast and

715
00:35:03,280 --> 00:35:08,440
Speaker 1: get a toe bag with my adorable Leo the Rescue

716
00:35:08,480 --> 00:35:13,080
Speaker 1: Puppy on it. And now you can grab this merchandise

717
00:35:13,320 --> 00:35:20,560
Speaker 1: only at shop dot fastpoliticspod dot com. Thanks for your support.

718
00:35:23,880 --> 00:35:28,040
Speaker 1: Doug Rushkoff is the author of Survival of the Riches.

719
00:35:29,400 --> 00:35:36,640
Speaker 1: Welcome back to Fast Politics fan favorite It's true, Dog Rushcoff.

720
00:35:37,080 --> 00:35:40,160
Speaker 2: I'm both a fan and fan favorite. I'm glad. Oh

721
00:35:40,200 --> 00:35:42,560
Speaker 2: my gosh, yes exactly.

722
00:35:43,120 --> 00:35:46,960
Speaker 1: Let us talk to you and I about AI. In

723
00:35:47,160 --> 00:35:50,800
Speaker 1: five months, Will this entire podcast just be a bot

724
00:35:50,960 --> 00:35:53,520
Speaker 1: talking to another bot discuss huh.

725
00:35:54,040 --> 00:35:56,720
Speaker 2: No, First, I don't even think that there is AI.

726
00:35:57,000 --> 00:36:00,560
Speaker 6: There's these sort of language models, right, and the idea

727
00:36:00,560 --> 00:36:03,480
Speaker 6: of these language models someday becoming AI feels to me

728
00:36:03,560 --> 00:36:06,920
Speaker 6: a bit like maybe search engines becoming AI. I mean,

729
00:36:06,960 --> 00:36:09,840
Speaker 6: they looked really cool at the beginning, but they're just

730
00:36:10,040 --> 00:36:13,239
Speaker 6: it's just basically Wikipedia automated.

731
00:36:13,520 --> 00:36:14,600
Speaker 2: What we're looking at.

732
00:36:14,640 --> 00:36:19,200
Speaker 6: This AI panic for me is just AI hype, like

733
00:36:19,400 --> 00:36:23,399
Speaker 6: Crypto flopped. AI is the next thing, and put out

734
00:36:23,400 --> 00:36:25,440
Speaker 6: these letters and these calls for regulation.

735
00:36:25,840 --> 00:36:27,439
Speaker 2: Let's just pause here a minute.

736
00:36:27,560 --> 00:36:30,719
Speaker 6: Whenever big business is asked for a big pause, it's

737
00:36:30,760 --> 00:36:34,520
Speaker 6: to try to entrench their positions. It's to preserve their monopoly.

738
00:36:34,600 --> 00:36:38,319
Speaker 6: Everybody's stopped right in the game. Okay, pause, time out.

739
00:36:38,719 --> 00:36:43,120
Speaker 6: Concerned tech pros are in a coordinated money grab to

740
00:36:43,200 --> 00:36:47,000
Speaker 6: get funding for their their either their little tech pro orgs.

741
00:36:47,160 --> 00:36:50,439
Speaker 6: You know, remember Obama did that Brain Project and all.

742
00:36:50,719 --> 00:36:53,120
Speaker 6: It's all these little orgs, Future of Live, Center for

743
00:36:53,200 --> 00:36:56,520
Speaker 6: Humane Technology, Future of Humanity. You know, they want to

744
00:36:56,560 --> 00:36:59,760
Speaker 6: make another Netflix movie like Social Dilemma and get famous.

745
00:37:00,320 --> 00:37:06,080
Speaker 6: But the worst case apocalypse existential scenarios for me pale

746
00:37:06,080 --> 00:37:10,160
Speaker 6: in comparison to the real problems right now that we know.

747
00:37:10,520 --> 00:37:16,280
Speaker 6: Algorithmic injustice, prison sentences, police work, health insurance, mortgages, actual

748
00:37:16,440 --> 00:37:22,799
Speaker 6: real world injustice, incarceration, economic disenfranchisement, it's all real, and

749
00:37:23,320 --> 00:37:28,320
Speaker 6: I have a really hard time imagining these language models

750
00:37:28,600 --> 00:37:32,840
Speaker 6: threatening anyone because, honestly, what they don't admit, these AI things,

751
00:37:32,920 --> 00:37:38,239
Speaker 6: they are so labor intensive. They look free and profitable,

752
00:37:38,360 --> 00:37:42,319
Speaker 6: but they are anything but. Machine learning involves thousands of

753
00:37:42,400 --> 00:37:46,719
Speaker 6: human beings tagging all this data and preparing it. It's

754
00:37:46,719 --> 00:37:50,160
Speaker 6: not machines trolling the net like Google did. There's thousands

755
00:37:50,200 --> 00:37:53,600
Speaker 6: of minimum wage workers. The chips for this stuff require

756
00:37:53,760 --> 00:37:58,320
Speaker 6: zillions of tons of cobalt and rare earth metals, electricity

757
00:37:58,360 --> 00:38:02,000
Speaker 6: and minding. Its billions and billions of dollars of investment,

758
00:38:02,320 --> 00:38:06,239
Speaker 6: and the result so far, it's not profitable. They just

759
00:38:06,360 --> 00:38:09,319
Speaker 6: commodify all the businesses that use them. So we're looking

760
00:38:09,360 --> 00:38:12,719
Speaker 6: at an intensive amount of capital expenditure for something that's

761
00:38:12,800 --> 00:38:16,839
Speaker 6: not actually going to produce that much, right, right, It's

762
00:38:16,840 --> 00:38:18,520
Speaker 6: that thing, you know. It's like, did you ever see

763
00:38:18,560 --> 00:38:20,520
Speaker 6: in the streets of New York when somebody's like, hold

764
00:38:20,560 --> 00:38:23,479
Speaker 6: me back, man, hold me back, right, like they're gonna fight.

765
00:38:23,880 --> 00:38:26,439
Speaker 6: It's like that's what they're doing. That's sim hold me back.

766
00:38:26,680 --> 00:38:29,319
Speaker 6: My AI is so big, baby, Oh hold me back,

767
00:38:29,480 --> 00:38:30,680
Speaker 6: or I'm gonna unleash it.

768
00:38:30,680 --> 00:38:32,520
Speaker 2: It's going to take over the finger planet.

769
00:38:32,760 --> 00:38:32,960
Speaker 3: Right.

770
00:38:33,200 --> 00:38:37,120
Speaker 1: It's very interesting to me, and I think that's right.

771
00:38:37,200 --> 00:38:40,279
Speaker 1: And I think we see that again with every new technology, right,

772
00:38:40,320 --> 00:38:43,319
Speaker 1: there's a sort of panic. Right, Yeah, And so I

773
00:38:43,400 --> 00:38:45,920
Speaker 1: get what you're saying here, and it's quite interesting. So

774
00:38:45,960 --> 00:38:48,960
Speaker 1: we're going to talk about failure to launch the Rond

775
00:38:48,960 --> 00:38:54,719
Speaker 1: De Santis spaces again. I love Ronda Santis because he's

776
00:38:54,760 --> 00:38:58,600
Speaker 1: a terrible candidate with zero with negative charisma, who is

777
00:38:58,640 --> 00:39:01,800
Speaker 1: completely not ready for time time, and yet being treated

778
00:39:01,960 --> 00:39:05,799
Speaker 1: like he's going to bring down this wildly popular, if

779
00:39:05,840 --> 00:39:10,280
Speaker 1: not demented. Other presidential candidate, Donald Trump discussed.

780
00:39:12,120 --> 00:39:14,560
Speaker 2: Yeah, it is funny, isn't it. I mean, it's it

781
00:39:14,680 --> 00:39:15,080
Speaker 2: is weird.

782
00:39:15,120 --> 00:39:19,280
Speaker 6: It's almost like, you know, DeSantis is to Trump as

783
00:39:19,640 --> 00:39:23,799
Speaker 6: like Alon Musk is to what Zuckerberg.

784
00:39:23,920 --> 00:39:24,359
Speaker 2: I don't know.

785
00:39:25,600 --> 00:39:28,879
Speaker 1: In my mind, it's Jeb, except without Like with Jeb,

786
00:39:29,040 --> 00:39:31,000
Speaker 1: you felt like the guy was sort of a good

787
00:39:31,040 --> 00:39:33,600
Speaker 1: guy deep down. Maybe you didn't believe in what he

788
00:39:33,680 --> 00:39:36,360
Speaker 1: believed in, but he seemed like he was sort of

789
00:39:36,360 --> 00:39:38,719
Speaker 1: a good guy, whereas with Desanta's I have none of that.

790
00:39:38,920 --> 00:39:41,200
Speaker 1: I just think he just as completely craven.

791
00:39:41,600 --> 00:39:45,080
Speaker 6: Right, Jeb had some sort of romniesque something going for him.

792
00:39:45,280 --> 00:39:47,320
Speaker 1: Right, at least it's in our minds.

793
00:39:47,440 --> 00:39:50,720
Speaker 6: Yeah, DeSantis is like you a Roman roy or something

794
00:39:50,760 --> 00:39:51,240
Speaker 6: at best.

795
00:39:52,400 --> 00:39:56,120
Speaker 1: Right, But here's my question to you. You are a

796
00:39:56,200 --> 00:40:01,040
Speaker 1: tech guy. You know what's going on. Did Santus's wild

797
00:40:01,080 --> 00:40:04,720
Speaker 1: popularity break Twitter spaces or was the fact that Elon

798
00:40:04,880 --> 00:40:07,880
Speaker 1: fired half of the people work their relative?

799
00:40:08,160 --> 00:40:08,359
Speaker 2: Oh?

800
00:40:08,560 --> 00:40:11,759
Speaker 6: Twitter spaces is the old uh what was it called

801
00:40:11,800 --> 00:40:15,919
Speaker 6: periscope code? So it's code from ten or twelve years

802
00:40:15,960 --> 00:40:19,280
Speaker 6: ago that was never quite finished because you know, Twitter

803
00:40:19,320 --> 00:40:22,080
Speaker 6: decided not to go into video. They brought it out

804
00:40:22,120 --> 00:40:24,680
Speaker 6: of the dust bin and they're running something that was

805
00:40:24,719 --> 00:40:29,839
Speaker 6: written for like you know, Netscape three point six right

806
00:40:30,000 --> 00:40:34,040
Speaker 6: right right, your iPhone three out of a modern thing.

807
00:40:34,200 --> 00:40:36,080
Speaker 6: Of course, it doesn't work, it doesn't scale. They have

808
00:40:36,120 --> 00:40:38,359
Speaker 6: no people, it turns out, And this is the thing

809
00:40:38,360 --> 00:40:41,880
Speaker 6: people don't get. Technology requires lots of lots of people

810
00:40:41,920 --> 00:40:44,600
Speaker 6: to make it work. The tech pro dream is to

811
00:40:44,640 --> 00:40:47,200
Speaker 6: get rid of people. That's what they've wanted. Well, that's

812
00:40:47,239 --> 00:40:50,000
Speaker 6: what industry has wanted since the eleventh or twelfth century. Right.

813
00:40:50,080 --> 00:40:52,319
Speaker 6: The assembly lines were about getting rid of me, get

814
00:40:52,400 --> 00:40:55,200
Speaker 6: rid of human laborers. You know, they need insurance, they

815
00:40:55,239 --> 00:40:57,640
Speaker 6: need money, they need whatever. And the tech bro dream

816
00:40:57,680 --> 00:40:59,040
Speaker 6: has always been to get rid of people.

817
00:40:59,280 --> 00:41:00,960
Speaker 2: You know. The reason why the tech brows.

818
00:41:00,800 --> 00:41:05,400
Speaker 6: Are panicking now is because the ais are modeled on themselves, right,

819
00:41:05,440 --> 00:41:07,760
Speaker 6: And if they've spent their lives getting rid of humans

820
00:41:07,920 --> 00:41:10,960
Speaker 6: and now they created these super machines, they were like, oh, shoot,

821
00:41:11,000 --> 00:41:12,760
Speaker 6: they're going to try to get rid of us now.

822
00:41:12,920 --> 00:41:16,040
Speaker 6: The create is something that colonizes themselves. But yeah, so

823
00:41:16,280 --> 00:41:18,319
Speaker 6: when Musk goes into Twitter and gets her to half

824
00:41:18,320 --> 00:41:20,480
Speaker 6: the people and then tries to take something out of

825
00:41:20,520 --> 00:41:23,080
Speaker 6: a box and run it without any of the legacy

826
00:41:23,120 --> 00:41:24,839
Speaker 6: people who even worked on the project and go, oh,

827
00:41:24,840 --> 00:41:26,440
Speaker 6: here we're going to go and this is going to

828
00:41:26,520 --> 00:41:28,840
Speaker 6: launch what this is? This what Tucker Carson Show is

829
00:41:28,840 --> 00:41:31,440
Speaker 6: going to be on that scene, same platform.

830
00:41:31,680 --> 00:41:34,319
Speaker 1: If you're Tucker Carlson right now and you saw that

831
00:41:34,480 --> 00:41:36,160
Speaker 1: space as you're feeling pretty.

832
00:41:35,880 --> 00:41:38,960
Speaker 2: Nervous, I would think so like you're like, I.

833
00:41:39,080 --> 00:41:41,520
Speaker 1: Just went into business with Amateur Hour.

834
00:41:41,880 --> 00:41:44,600
Speaker 6: Yeah, although in theory they have enough money to buy

835
00:41:44,680 --> 00:41:48,520
Speaker 6: any of a number of other streaming streaming video is

836
00:41:48,560 --> 00:41:49,880
Speaker 6: not that hard anymore.

837
00:41:49,960 --> 00:41:52,160
Speaker 2: I mean back in the days of see U see Me,

838
00:41:52,440 --> 00:41:52,719
Speaker 2: you know.

839
00:41:53,040 --> 00:41:55,560
Speaker 1: So, why did the Twitter space die of streaming video?

840
00:41:55,600 --> 00:41:56,600
Speaker 1: Is not that hard.

841
00:41:56,520 --> 00:42:00,439
Speaker 6: Because they are utterly incompetent. I mean, they and they've

842
00:42:00,480 --> 00:42:02,680
Speaker 6: got a guy who's like, you know, the what's kind

843
00:42:02,680 --> 00:42:03,640
Speaker 6: of boss that musk is?

844
00:42:03,719 --> 00:42:06,120
Speaker 2: I want this now? I'm sorry, Boss is not going

845
00:42:06,160 --> 00:42:09,000
Speaker 2: to be possible. You're fired next. Oh we can do it.

846
00:42:09,280 --> 00:42:11,080
Speaker 6: You know it's going to be like I'm sure the

847
00:42:11,160 --> 00:42:13,520
Speaker 6: yes people around tossanti sir Trump. You know, it's like

848
00:42:13,520 --> 00:42:16,040
Speaker 6: Trump's lawyers, you know, you keep firing them until you

849
00:42:16,120 --> 00:42:17,880
Speaker 6: find one that will say that the law says what

850
00:42:17,920 --> 00:42:20,279
Speaker 6: you think it does. But code doesn't work that way.

851
00:42:20,320 --> 00:42:23,320
Speaker 6: It's like, you can you know, code as hard rules

852
00:42:23,320 --> 00:42:27,040
Speaker 6: to it. Finally it yes, the server will crash. Sorry,

853
00:42:29,520 --> 00:42:30,759
Speaker 6: I mean that is I.

854
00:42:30,719 --> 00:42:33,480
Speaker 1: Think such an important point what you're talking about there

855
00:42:33,520 --> 00:42:35,240
Speaker 1: that you can't bully code.

856
00:42:35,640 --> 00:42:37,400
Speaker 2: You can't. And these are the guys.

857
00:42:37,560 --> 00:42:39,080
Speaker 6: These are the guys that are going to make the

858
00:42:39,120 --> 00:42:41,760
Speaker 6: AIS that are going to be sending you your ambulances

859
00:42:41,880 --> 00:42:44,720
Speaker 6: or doing the analysis on your radiology.

860
00:42:45,000 --> 00:42:48,320
Speaker 2: It's like, what possible task is? Like, the only task.

861
00:42:48,080 --> 00:42:53,160
Speaker 6: I'm comfortable with AIS doing is like writing game scripts

862
00:42:53,200 --> 00:42:55,799
Speaker 6: or you know, scripts for video games or something. And

863
00:42:55,840 --> 00:42:58,000
Speaker 6: even then, it's like, what's the point of that like,

864
00:42:58,040 --> 00:43:00,680
Speaker 6: are you would you watch the last episode to Succession

865
00:43:00,719 --> 00:43:02,759
Speaker 6: if it was written by an AI? It's like I

866
00:43:02,760 --> 00:43:06,120
Speaker 6: wouldn't care well that, I wonder.

867
00:43:05,960 --> 00:43:09,000
Speaker 2: No, because where we care? What are they thinking?

868
00:43:09,080 --> 00:43:10,840
Speaker 6: What are they wondering when you look at a van go,

869
00:43:11,080 --> 00:43:13,160
Speaker 6: are you thinking what's the impact on my retina of

870
00:43:13,200 --> 00:43:14,080
Speaker 6: this palette?

871
00:43:14,239 --> 00:43:16,359
Speaker 2: No? You're thinking who was that crazy dude who made

872
00:43:16,360 --> 00:43:16,799
Speaker 2: this thing?

873
00:43:17,120 --> 00:43:17,480
Speaker 1: Right? Right?

874
00:43:17,560 --> 00:43:17,840
Speaker 2: Right?

875
00:43:17,920 --> 00:43:20,799
Speaker 1: I think that's a really important point. I do want

876
00:43:20,840 --> 00:43:23,200
Speaker 1: to get back to this for a second though, this idea,

877
00:43:23,320 --> 00:43:27,439
Speaker 1: because I think a lot of times, because a lot

878
00:43:27,480 --> 00:43:30,000
Speaker 1: of us don't have like a working knowledge of code.

879
00:43:30,080 --> 00:43:32,719
Speaker 1: I'm going to shock you here, a lot of times

880
00:43:32,719 --> 00:43:36,719
Speaker 1: when we're talking about tech, we defer to sort of

881
00:43:36,920 --> 00:43:42,400
Speaker 1: people who you know, Like one of Elon's huge advantages

882
00:43:42,480 --> 00:43:45,040
Speaker 1: when he's lying about stuff is that he is lying

883
00:43:45,040 --> 00:43:47,520
Speaker 1: about something that most people don't know about, so you

884
00:43:47,600 --> 00:43:49,719
Speaker 1: have to sort of agree with him, right, Like he's

885
00:43:49,760 --> 00:43:52,000
Speaker 1: tweeted a number of times like we're going to put

886
00:43:52,040 --> 00:43:58,440
Speaker 1: our algorithm online for greater transparency. Did he actually do that? No?

887
00:43:58,600 --> 00:43:59,279
Speaker 2: I don't even think.

888
00:43:59,440 --> 00:44:02,040
Speaker 6: I don't think they can even find their algorithm at

889
00:44:02,040 --> 00:44:05,840
Speaker 6: this point. You know, it's like running explain the dudes,

890
00:44:06,040 --> 00:44:08,359
Speaker 6: the dudes that wrote it, know where it is and

891
00:44:08,400 --> 00:44:10,680
Speaker 6: how to work it, or understand the code. I mean

892
00:44:10,719 --> 00:44:13,280
Speaker 6: code as code doesn't really mean anything to most people.

893
00:44:13,480 --> 00:44:15,600
Speaker 6: You have to document the code. You have to write

894
00:44:15,640 --> 00:44:18,719
Speaker 6: notes along the code, saying here's what this line of

895
00:44:18,760 --> 00:44:21,880
Speaker 6: code is trying to do. If you just look at code,

896
00:44:21,880 --> 00:44:24,680
Speaker 6: it's really really hard to know kind of what it

897
00:44:24,760 --> 00:44:27,080
Speaker 6: is and what it's for, because it's all mixed up.

898
00:44:27,160 --> 00:44:29,480
Speaker 6: It's not in order. Things are going to other things.

899
00:44:29,600 --> 00:44:33,480
Speaker 6: I mean, no single person even understands Microsoft Windows, much

900
00:44:33,560 --> 00:44:36,920
Speaker 6: less you know Twitters. It's true, and half of it's

901
00:44:36,920 --> 00:44:39,760
Speaker 6: written by a half of that's written by by bots anyway,

902
00:44:40,160 --> 00:44:41,239
Speaker 6: so you can't really know.

903
00:44:41,280 --> 00:44:44,080
Speaker 2: You have to document your code to really understand what

904
00:44:44,200 --> 00:44:45,560
Speaker 2: it is. But I do.

905
00:44:45,920 --> 00:44:50,480
Speaker 6: It's not necessarily a problem not to understand the layer

906
00:44:50,520 --> 00:44:52,560
Speaker 6: of code beyond you, because even if you understood the

907
00:44:52,600 --> 00:44:55,319
Speaker 6: code of Twitter, say, then you might not understand the

908
00:44:55,320 --> 00:44:58,040
Speaker 6: code of Browser. You might not understand the operating system

909
00:44:58,200 --> 00:45:00,920
Speaker 6: or the machine language under that. But what these guys

910
00:45:00,920 --> 00:45:05,719
Speaker 6: don't understand is basic capitalism. They accept capitalism as this

911
00:45:05,960 --> 00:45:11,000
Speaker 6: underlying operating system of humanity. They don't realize that what

912
00:45:11,040 --> 00:45:15,320
Speaker 6: they're doing is coding for capitalism. They're coding to extract

913
00:45:15,480 --> 00:45:19,040
Speaker 6: value from people in places and convert it into you know,

914
00:45:19,120 --> 00:45:22,600
Speaker 6: share value. That's what all of their companies, that's what

915
00:45:22,640 --> 00:45:25,799
Speaker 6: they're really really doing, you know. And that's that's where

916
00:45:25,800 --> 00:45:28,239
Speaker 6: it's a little bit dangerous. It's like, if the tech

917
00:45:28,280 --> 00:45:31,319
Speaker 6: pros are kind of like the equivalent of the of

918
00:45:31,360 --> 00:45:36,880
Speaker 6: the Renaissance monarchs, then social media. Social media was like

919
00:45:36,920 --> 00:45:40,279
Speaker 6: the missionaries that they sent out to South America to

920
00:45:40,440 --> 00:45:43,520
Speaker 6: like learn about the people and convert them to Christianity.

921
00:45:43,960 --> 00:45:47,719
Speaker 2: Ais. That's the conquistadors, right, They're.

922
00:45:47,440 --> 00:45:50,919
Speaker 6: The ones who come in using everything that missionaries learn

923
00:45:51,200 --> 00:45:54,560
Speaker 6: and finally take over the people. That's their intent. But

924
00:45:54,680 --> 00:45:57,759
Speaker 6: I don't think I don't think it can actually do it.

925
00:45:57,880 --> 00:45:59,719
Speaker 6: I really don't think it would work. You know, if

926
00:45:59,800 --> 00:46:03,319
Speaker 6: it did really work, it could be great. I mean,

927
00:46:03,440 --> 00:46:05,759
Speaker 6: I've got no problem with robots doing all the work

928
00:46:05,800 --> 00:46:07,600
Speaker 6: if I get to do all the play, you know,

929
00:46:07,680 --> 00:46:10,680
Speaker 6: But that's not the intent here. That the intent here

930
00:46:10,760 --> 00:46:14,200
Speaker 6: is not to relieve human labor to make humans have

931
00:46:14,239 --> 00:46:18,399
Speaker 6: a better time. The intent is to extract and monopolize resources.

932
00:46:18,680 --> 00:46:21,839
Speaker 6: So when you hear the guys building these things saying, oh,

933
00:46:21,960 --> 00:46:25,480
Speaker 6: everybody slow down a second, let's freeze on development and

934
00:46:25,560 --> 00:46:27,680
Speaker 6: have a bunch of meetings at the White House. They

935
00:46:27,719 --> 00:46:30,920
Speaker 6: want to create regulations that make it that their companies

936
00:46:30,920 --> 00:46:32,760
Speaker 6: are the only ones that will be in a position

937
00:46:32,800 --> 00:46:36,800
Speaker 6: to monopolize this stuff, because in truth, AI is really

938
00:46:36,880 --> 00:46:40,400
Speaker 6: easy to do. The code is easy once you actually

939
00:46:40,440 --> 00:46:42,480
Speaker 6: see how it is. The code is easy. The only

940
00:46:42,520 --> 00:46:47,160
Speaker 6: thing that's expensive is gathering all the data. And that's

941
00:46:47,200 --> 00:46:49,160
Speaker 6: why they've got to regulate. That's why they want to

942
00:46:49,160 --> 00:46:51,520
Speaker 6: lock it down because they're going to lose their competitive

943
00:46:51,520 --> 00:46:55,040
Speaker 6: advantage really fast, right, And that I think.

944
00:46:54,920 --> 00:46:57,799
Speaker 1: Is really important. And again that there are shades of

945
00:46:57,800 --> 00:47:01,879
Speaker 1: Mark Zuckerberg going to Congress being like, please regulate us.

946
00:47:02,000 --> 00:47:04,920
Speaker 1: We must be regulated, We have to be Can you

947
00:47:05,040 --> 00:47:08,239
Speaker 1: regulate us? And then you know, of course, I mean,

948
00:47:08,239 --> 00:47:11,200
Speaker 1: the good news is Congress cannot figure out how to

949
00:47:11,239 --> 00:47:14,160
Speaker 1: do that. I did think I watched the congressional hearing

950
00:47:14,360 --> 00:47:18,800
Speaker 1: about AI. I actually thought these guys are actually seem

951
00:47:18,840 --> 00:47:21,560
Speaker 1: a little bit better off and seem a little bit

952
00:47:21,920 --> 00:47:26,080
Speaker 1: more better briefed on what's going on than previous ones

953
00:47:26,080 --> 00:47:28,759
Speaker 1: of these hearings. Can you explain to us sort of?

954
00:47:29,160 --> 00:47:31,880
Speaker 1: I mean what is the hope. I mean Elon wants

955
00:47:32,239 --> 00:47:34,840
Speaker 1: Congress to stop it so they that he can quietly

956
00:47:34,880 --> 00:47:37,360
Speaker 1: do it on his own. I mean explain well.

957
00:47:37,880 --> 00:47:38,920
Speaker 2: I mean there's two things.

958
00:47:39,000 --> 00:47:42,840
Speaker 6: One, you know Elon and you know there's this crowd,

959
00:47:42,960 --> 00:47:47,759
Speaker 6: the Sam Bankman freed Elon Musk. Dudes, these these effective

960
00:47:47,920 --> 00:47:51,839
Speaker 6: altruists as they call themselves, really do think that they

961
00:47:52,080 --> 00:47:57,240
Speaker 6: are creating a post human creature. They're building the humans

962
00:47:57,640 --> 00:48:00,160
Speaker 6: or human robot AI things that are going to go

963
00:48:00,239 --> 00:48:03,200
Speaker 6: off to other planets and star seed the universe with

964
00:48:03,239 --> 00:48:06,719
Speaker 6: what's left of humanity as our planet goes away. You know,

965
00:48:06,880 --> 00:48:10,080
Speaker 6: we the eight billion, we're the larvae, you know, the

966
00:48:10,120 --> 00:48:12,840
Speaker 6: little maggots on the planet, and they're the flies that

967
00:48:13,200 --> 00:48:17,560
Speaker 6: somehow spawn and get on. So that when that's their

968
00:48:17,800 --> 00:48:21,560
Speaker 6: model when they're going to Congress, it's kind of I mean,

969
00:48:21,640 --> 00:48:23,719
Speaker 6: they scared themselves a little bit.

970
00:48:23,920 --> 00:48:25,479
Speaker 2: I think that they built something.

971
00:48:25,560 --> 00:48:28,160
Speaker 6: You know, they believe in this idea that you know,

972
00:48:28,239 --> 00:48:31,120
Speaker 6: if you don't do things that are friendly to AIS

973
00:48:31,200 --> 00:48:34,120
Speaker 6: now that when the Ais are in charge, they're gonna

974
00:48:34,160 --> 00:48:38,799
Speaker 6: make you suffer later. So they they're afraid to do

975
00:48:38,920 --> 00:48:42,239
Speaker 6: anything that looks anti So it's like if they could

976
00:48:42,280 --> 00:48:45,080
Speaker 6: get the government to do the stuff that's anti.

977
00:48:45,239 --> 00:48:48,399
Speaker 2: Then later the oh I was good. I you know,

978
00:48:48,680 --> 00:48:49,839
Speaker 2: I tried to create a.

979
00:48:49,760 --> 00:48:53,480
Speaker 6: Repuschement between us in government. But you guys, come on,

980
00:48:53,920 --> 00:48:57,960
Speaker 6: somebody stand up to this because we were kind of

981
00:48:58,000 --> 00:49:01,680
Speaker 6: afraid to. So there's that. But it's also it's about

982
00:49:01,719 --> 00:49:04,320
Speaker 6: really locking down things as they are. It was really

983
00:49:04,360 --> 00:49:07,200
Speaker 6: telling that in this open letter they wrote, they said,

984
00:49:07,320 --> 00:49:11,640
Speaker 6: and let's stop right now where chat GPT four is.

985
00:49:12,360 --> 00:49:15,080
Speaker 2: You know, let's let's stop here.

986
00:49:15,080 --> 00:49:18,000
Speaker 6: It's like you imagine Zuckerberg going, let's stop social media

987
00:49:18,160 --> 00:49:21,320
Speaker 6: at the exact place that Facebook is at right now.

988
00:49:21,440 --> 00:49:26,319
Speaker 6: It's like, what's that. Everybody stop teaching your AIS everybody.

989
00:49:25,800 --> 00:49:27,120
Speaker 4: You know, right right?

990
00:49:28,080 --> 00:49:30,359
Speaker 6: Let us get ahead, right, let us get ahead, and

991
00:49:30,480 --> 00:49:34,239
Speaker 6: let these little non so called nonprofits of fallen tech

992
00:49:34,280 --> 00:49:36,880
Speaker 6: pros get a ton of funding from the government to

993
00:49:36,920 --> 00:49:40,400
Speaker 6: come up with nice ways of regulating AI that are

994
00:49:40,480 --> 00:49:41,880
Speaker 6: still friendly to our industry.

995
00:49:42,040 --> 00:49:45,960
Speaker 1: So let me ask you about where we are right now.

996
00:49:46,160 --> 00:49:47,240
Speaker 1: Where are we right now?

997
00:49:47,480 --> 00:49:50,440
Speaker 6: I think we're at a much much earlier stage of

998
00:49:50,680 --> 00:49:52,800
Speaker 6: AI than people think.

999
00:49:53,080 --> 00:49:55,000
Speaker 2: I think this stuff, this stuff is.

1000
00:49:55,160 --> 00:49:58,840
Speaker 6: Harder than it looks. You know, right now, we have

1001
00:49:59,719 --> 00:50:03,400
Speaker 6: some thing that threatens search engines. Right for people that

1002
00:50:03,520 --> 00:50:06,400
Speaker 6: don't really care if the results are true or not.

1003
00:50:06,920 --> 00:50:10,160
Speaker 6: You know, you can you can use an AI and

1004
00:50:10,239 --> 00:50:11,600
Speaker 6: get something digested.

1005
00:50:11,760 --> 00:50:12,520
Speaker 2: But it's really bad.

1006
00:50:12,520 --> 00:50:14,600
Speaker 6: I mean, you go to chat GPT whatever it is

1007
00:50:14,680 --> 00:50:18,560
Speaker 6: right now, and you type in which is heavier a

1008
00:50:18,760 --> 00:50:21,520
Speaker 6: pound of feathers or five pounds of lead?

1009
00:50:21,800 --> 00:50:23,880
Speaker 2: And it will say, oh, they weigh.

1010
00:50:23,800 --> 00:50:28,239
Speaker 6: The same because because it's not thinking, and you know,

1011
00:50:28,480 --> 00:50:32,480
Speaker 6: and so where we are now basically we're training a parrot,

1012
00:50:33,080 --> 00:50:35,719
Speaker 6: you know. And the parrot's listening to a whole bunch

1013
00:50:35,719 --> 00:50:40,040
Speaker 6: of stuff and will parrot back whenever it thinks it heard.

1014
00:50:40,719 --> 00:50:44,439
Speaker 6: And that's a really dumb thing, right, that's a dumb

1015
00:50:44,440 --> 00:50:47,280
Speaker 6: thing to depend on in any way. Where the danger

1016
00:50:47,400 --> 00:50:50,759
Speaker 6: is is think of it this way. If what you

1017
00:50:50,920 --> 00:50:54,799
Speaker 6: tell AIS doesn't matter, right, they're not doing what you're

1018
00:50:54,840 --> 00:50:56,920
Speaker 6: telling them. They're like kids in the backseat of the car.

1019
00:50:57,000 --> 00:50:59,360
Speaker 6: You could tell them they be nice to other people

1020
00:50:59,480 --> 00:51:01,000
Speaker 6: and then someone cuts you off and you go, hey,

1021
00:51:01,120 --> 00:51:05,279
Speaker 6: fuck you. The kid is watching what you actually do, right,

1022
00:51:05,320 --> 00:51:10,719
Speaker 6: So AIS are modeling based on what we've done online

1023
00:51:10,760 --> 00:51:13,680
Speaker 6: for the last ten years. That's what they're looking at.

1024
00:51:13,800 --> 00:51:16,680
Speaker 6: So if you're training the things that are going to

1025
00:51:16,719 --> 00:51:21,400
Speaker 6: make our decisions based on the aggregate behavior of human

1026
00:51:21,480 --> 00:51:24,200
Speaker 6: beings on the Internet for the last decade. You know,

1027
00:51:24,600 --> 00:51:25,279
Speaker 6: watch out.

1028
00:51:25,400 --> 00:51:27,279
Speaker 2: You know. This is like that the Jim Morrison thing.

1029
00:51:27,320 --> 00:51:30,839
Speaker 6: You know, father, I'm going to kill you, right, That's

1030
00:51:31,280 --> 00:51:34,800
Speaker 6: what the a That's what the AI AI guys are thinking.

1031
00:51:34,840 --> 00:51:37,960
Speaker 6: It's like, oh shit, these are little AI children have

1032
00:51:38,040 --> 00:51:40,879
Speaker 6: been trained on the worst of the worst, and they

1033
00:51:40,920 --> 00:51:42,920
Speaker 6: are going to come for us in our sleep.

1034
00:51:43,000 --> 00:51:43,959
Speaker 1: Right right right.

1035
00:51:44,000 --> 00:51:46,680
Speaker 2: But they're not. These are just like lava lamps. You know.

1036
00:51:46,800 --> 00:51:49,640
Speaker 6: They're like lava lamps, and it's like, oh, look, you know,

1037
00:51:49,680 --> 00:51:51,359
Speaker 6: and it's not even interesting. Do you go to someone

1038
00:51:51,440 --> 00:51:53,439
Speaker 6: the next day, Oh my LoVa lamp did a really

1039
00:51:53,440 --> 00:51:55,239
Speaker 6: interesting combination of stuff last night.

1040
00:51:56,920 --> 00:51:59,439
Speaker 2: I don't care about your chet Gypt results. I don't

1041
00:51:59,480 --> 00:52:00,280
Speaker 2: care about you.

1042
00:52:00,280 --> 00:52:03,560
Speaker 6: You're whatever, you're the picture that you made on whichever

1043
00:52:03,640 --> 00:52:06,560
Speaker 6: one that is your generated AI picture. It's like I

1044
00:52:06,680 --> 00:52:09,640
Speaker 6: really hearing about someone's AI generated picture that oh it's

1045
00:52:09,640 --> 00:52:11,640
Speaker 6: a wolf and a breast and a thing. It's like

1046
00:52:11,960 --> 00:52:14,239
Speaker 6: listening to someone's acid trip. It was like it was

1047
00:52:14,280 --> 00:52:16,880
Speaker 6: good for you, but I really couldn't care less.

1048
00:52:17,239 --> 00:52:19,520
Speaker 1: Yeah, I think that's right. I think we got to

1049
00:52:19,640 --> 00:52:21,600
Speaker 1: end it on that. That was such a good line.

1050
00:52:22,120 --> 00:52:23,960
Speaker 1: Thank you, Doug Rushoff.

1051
00:52:24,239 --> 00:52:26,279
Speaker 2: Oh thank you. I love you. I love what you're doing.

1052
00:52:28,320 --> 00:52:35,560
Speaker 7: The no no, Jesse Cannon, Maley, John Fast you know,

1053
00:52:35,920 --> 00:52:39,960
Speaker 7: seems like match Slap. Like many MAGA Republicans, the troubles

1054
00:52:40,000 --> 00:52:42,480
Speaker 7: catching up with them and they're acting accordingly.

1055
00:52:42,760 --> 00:52:46,279
Speaker 1: Matt Slap, the prominent Trump ally who led the influential

1056
00:52:46,360 --> 00:52:50,800
Speaker 1: conservative political Action Conference which has most recently been in Hungary,

1057
00:52:51,360 --> 00:52:54,520
Speaker 1: has been accused of mismanaging money and staff in a

1058
00:52:54,800 --> 00:53:00,720
Speaker 1: scathing resignation letter from the parent organization's treasurer. Who could

1059
00:53:00,719 --> 00:53:04,360
Speaker 1: have seen it coming. But everyone, you will remember that

1060
00:53:04,520 --> 00:53:09,200
Speaker 1: earlier this year we had a Matchlap accuser on this

1061
00:53:09,320 --> 00:53:13,319
Speaker 1: podcast that has been going on match Lap not a

1062
00:53:13,400 --> 00:53:19,560
Speaker 1: huge shock, but for that he and his many mouthfeasances

1063
00:53:20,000 --> 00:53:24,160
Speaker 1: are our moment of fuck Ray. That's it for this

1064
00:53:24,239 --> 00:53:27,880
Speaker 1: episode of Fast Politics. Tune in every Monday, Wednesday and

1065
00:53:27,920 --> 00:53:31,000
Speaker 1: Friday to hear the best minds in politics makes sense

1066
00:53:31,040 --> 00:53:34,080
Speaker 1: of all this chaos. If you enjoyed what you've heard,

1067
00:53:34,440 --> 00:53:37,400
Speaker 1: please send it to a friend and keep the conversation going.

1068
00:53:37,760 --> 00:53:39,440
Speaker 1: And again, thanks for listening.