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

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Speaker 1: we discuss the top political headlines with some of today's

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Speaker 1: best minds. And Florida gubernatorial candidate Byron Donalds has stripped

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Speaker 1: Trump references from his campaign website. We have such a

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Speaker 1: great show for you today. The good liars, Jason Selvig,

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Speaker 1: stops by to talk about Trump's crackdown on the media.

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Speaker 1: Then we'll talk to author Garrison Lovely about his new book, Obsolete,

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Speaker 1: the AI industry's trillion dollar race to replace us and

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Speaker 1: how to stop it. But first, the news.

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Speaker 2: Somali, today we had a crazy, crazy Trump UN speech

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Speaker 2: and had a lot of walkouts.

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Speaker 1: Yeah, you'll be shocked to know that our crazy president

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Speaker 1: is doing crazy stuff. So today was the U.N. and

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Speaker 1: Donald Trump got up there and he did exactly the

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Speaker 1: thing you thought he would. He started by bragging about

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Speaker 1: looting Venezuela. and threatened that Cuba may be next. Last month,

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Speaker 1: we signed the largest oil deal in history, covering 65

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Speaker 1: billion barrels of oil, which will benefit both Venezuela and

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Speaker 1: the United States enormously and help drive down the cost

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Speaker 1: of energy all over the world. A few things about this.

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Speaker 1: We know that the Venezuelan oil needs to be refined

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Speaker 1: differently than some other oils. We know that a lot

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Speaker 1: of the oil in Venezuela is nowhere near ready to be–

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Speaker 1: you don't just turn on a spigot. You have to

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Speaker 1: work on the infrastructure, the oil infrastructure. So it's not

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Speaker 1: so easy– It's expensive. It's time consuming. So this is

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Speaker 1: just made up stuff. President Trump's campaign style speech at

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Speaker 1: the U.N. on Tuesday was met with cheers. Just kidding.

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Speaker 1: It was met with silence and rejection. Both the Cubans

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Speaker 1: and the Iranian delegations walked out when he said he

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Speaker 1: was going to take their countries over because that's what

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Speaker 1: you do when you're being threatened by a crazy oligarch.

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Speaker 1: The beginning of Trump's speech was met with awkward silences

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Speaker 1: as he was waiting for, you guessed it, applause. He

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Speaker 1: said things like America is back and stronger than ever before.

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Speaker 1: We are not, in case you're wondering. Later in the speech,

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Speaker 1: the Iranians left. And Trump asked the audience if he

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Speaker 1: should annihilate the Islamic Republic and do it quickly and

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Speaker 1: drive them into hell with no chance of survival. After that,

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Speaker 1: the Cuban delegation, they left too. And that was maybe

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Speaker 1: because Donald Trump called Cuba an absolutely failed state. So

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Speaker 1: it went real well. There's a lot of traffic in

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Speaker 1: New York, so that was good. And I think we

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Speaker 1: can agree that Donald Trump is not using diplomacy here

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Speaker 1: in any which way. Yeah.

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Speaker 2: So, Molly, I'm sure you are just as absolutely pissed

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Speaker 2: off as I am that these attorney generals are letting

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Speaker 2: the paramount deal go through with less resistance. And to celebrate,

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Speaker 2: we also get Trump TV.

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Speaker 1: So here's what happened. Donald Trump, in a desperate move

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Speaker 1: to try to change the subject, has gone to war

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Speaker 1: with the mainstream media. This has worked so well that

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Speaker 1: he no longer has any contacts. television coverage. So now

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Speaker 1: Donald Trump has decided that he'll just start his own

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Speaker 1: television station and it's going to be called Trump TV.

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Speaker 1: With Paramount and Warner Brothers under his control, Mr. Ellison,

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Speaker 1: that's the son of Larry Ellison, Trump mega donor, he

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Speaker 1: will be basically Caesar, Midas, and Superman. Here's the thing

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Speaker 1: about this Ellison merger. This guy is now going to

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Speaker 1: control like a fourth of all of Hollywood. He doesn't

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Speaker 1: have much experience. Maybe that works. Maybe that doesn't. It

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Speaker 1: certainly seems like it could be problematic. He also is like,

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Speaker 1: you know, there's an ideological bent to this family. It's

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Speaker 1: very right wing. It's also very Zionist. So that is

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Speaker 1: going to affect a lot of its coverage, as we've

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Speaker 1: already seen with Barry Weiss at CBS. And the other

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Speaker 1: thing is that what Rob Bonta did, which I think

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Speaker 1: was really a mistake, was that he had made it

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Speaker 1: seem as if they were going to really make the

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Speaker 1: Ellisons do stuff. Like they were going to make them

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Speaker 1: sell CNN or make some kind of allowances that would

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Speaker 1: make this thing less egregious. But instead... what he did

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Speaker 1: was nothing. Like, basically, it's a very light prescription. You know,

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Speaker 1: they have to release 30 films, but it's sort of toothless.

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Speaker 1: So ultimately, what Rob Bonta did was he showed us

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Speaker 1: that Democrats and attorney generals could prevent this, but didn't

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Speaker 1: want to. And I think that is bad. I mean,

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Speaker 1: I almost feel like it would have been better if

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Speaker 1: they just had let it go through in the first place.

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Speaker 1: Because now we know that the attorney generals did not

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Speaker 1: want to do anything. They did not want to do

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Speaker 1: the right thing. And there are a couple of different

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Speaker 1: schools of thought into what happened. There's a theory of

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Speaker 1: the case that Gavin Newsom, who has done a lot

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Speaker 1: of really sketchy stuff as he's leaving office, including signing

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Speaker 1: some anti-trans legislation and killing the billionaire tax. There's a

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Speaker 1: theory of the case that Bonta got a lot of

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Speaker 1: pressure from Gavin. There's a theory of the case that

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Speaker 1: Bonta just did it himself. Either way, it just looks

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Speaker 1: so terribly. It looks like the Democrats are feckless.

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Speaker 3: Yeah.

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Speaker 2: I mean, on the bright side, can't wait to see

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Speaker 2: themselves running into the ground trying to prop up a

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Speaker 2: unpopular president and telling everyone not to believe their lying eyes.

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Speaker 2: And I'm sure Trump TV is going to go over great.

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Speaker 1: It's bad for the media. It's bad for us. It's

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Speaker 1: bad for the people. It's bad for the media environment.

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Speaker 1: It's bad for CNN. It's bad for CBS. The only

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Speaker 1: bit of good news is that maybe it'll cause people

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Speaker 1: to start more media outlets. Yeah.

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Speaker 2: So, Molly, we know that Trump is a master of branding.

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Speaker 2: I mean, you know, like you just can't believe it.

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Speaker 2: He definitely hasn't lost his touch over the years and

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Speaker 2: is unable to come up with good nicknames. But he's

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Speaker 2: now decided that AI needs to be rebranded as super intelligence.

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Speaker 1: Yes. Or Sports Illustrated, as the case may be. Here's

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Speaker 1: something here. So Donald Trump has invested a lot of

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Speaker 1: money in AI and his sons and he stand to

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Speaker 1: make a lot of money from AI, like a lot, lot, lot.

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Speaker 1: So it does strike me that they have a real

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Speaker 1: vested interest in AI. I mean, look, he doesn't like

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Speaker 1: any government regulation, but when he's like, don't be fraidy cats,

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Speaker 1: he's not just saying that because he wants to beat China.

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Speaker 1: He's saying that because he wants to make money. So

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Speaker 1: anything Trump says about AI, just like anything that Kristen Gillibrand,

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Speaker 1: whose son works in Bitcoin, Chuck Schumer, whose daughter works

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Speaker 1: at OpenAI, like these people, we need to remember that

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Speaker 1: they're conflicted in certain ways. And Donald Trump is really

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Speaker 1: conflicted when it comes to AI. So he really doesn't

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Speaker 1: want any kind of slowdown. There's another theory of the

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Speaker 1: case that he also really wants it to keep going

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Speaker 1: because he's worried that if AI slows down, the economy

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Speaker 1: will go into a recession. And he thinks if he

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Speaker 1: goes into a recession, then, you know, it'll be bad

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Speaker 1: for his presidency. Hard to see how anything could be

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Speaker 1: worse than what's happened so far. So you do really

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Speaker 1: see that all these people have a horse in this race.

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Speaker 1: And so in typical Donald Trump fashion, he wants to

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Speaker 1: rebrand AI. A sigh. And maybe he'll work at McDonald's

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Speaker 1: while he does it. Yeah.

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Speaker 2: And I see that all the AI bros, including Jensen Huang,

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Speaker 2: are all doing this. You're all blowing AI fears out

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Speaker 2: of proportion tour. And they're all going on the podcast

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Speaker 2: because they've decided that's the way to do this all.

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Speaker 1: Yeah.

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Speaker 2: Somali Wendy's, a restaurant I'm sure you frequent constantly.

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Speaker 1: Oh, yeah. Known to pay.

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Speaker 2: If they brought back the chicken cordon blue, I'd go.

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Speaker 2: But that's a different story. They're closing 300 branches because

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Speaker 2: beef prices. And as we know, beef prices are are

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Speaker 2: at an all time high and they can't profit anymore.

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Speaker 1: So here's the problem with Trump. He has made beef

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Speaker 1: prices really high. How has he made beef prices really high? Well,

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Speaker 1: this is like a really great example of why Doge

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Speaker 1: has cost us a lot of money. Elon Musk cut

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Speaker 1: all sorts of government programs, including the one that regulated beef.

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Speaker 1: the one that inspected remember inspected the screw worm do

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Speaker 1: you remember screw worm well Screw Worm has made beef

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Speaker 1: more expensive and everything else, too. And so now beef

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Speaker 1: is expensive and now Wendy's has to raise prices. And

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Speaker 1: in fact, people are just not buying. There we go.

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Speaker 1: So here we are. Wendy's may go bankrupt. This is

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Speaker 1: the largest franchisees operating 314 outposts of Wendy's across the country,

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Speaker 1: and they have filed for bankruptcy. partially due to record

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Speaker 1: high beef prices. You know, this is the kind of

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Speaker 1: stuff that happens when the economy starts to turn. The

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Speaker 1: stupidest part of this whole story is like, if Donald

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Speaker 1: Trump would just get rid of the tariffs, things would

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Speaker 1: get cheaper and it would make a lot of sense.

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Speaker 1: But we're not there. Jason Selvig is one half of

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Speaker 1: the good liars.

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Speaker 3: Welcome, welcome to Great to see you. How are you?

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Speaker 1: I am good. We thought we should start by talking

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Speaker 1: about how this Trump attack on the media, because it's

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Speaker 1: really interesting. And you were in North Carolina.

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Speaker 3: Yes, North Carolina, Gastonia, North Carolina last week for a

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Speaker 3: Trump event, a Trump rally. And And it was interesting.

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Speaker 3: This was a few days before Trump barred CNN, MSNOW,

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Speaker 3: Politico from the White House. And we noticed that they

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Speaker 3: the campaign or I guess the staff were were more

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Speaker 3: hands on about like getting people away from talking to

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Speaker 3: the media. And at first we thought it was just,

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Speaker 3: you know, it was me and our camera person who

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Speaker 3: was there for this one. And we thought, oh, they

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Speaker 3: recognize me. That's why they're like trying to get the

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Speaker 3: people away. Big celebrity. Big celebrity.

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Speaker 4: Yes.

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Speaker 3: I was like, my ego was swimming and singing at

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Speaker 3: that moment. But no, after we left, we saw that

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Speaker 3: some of the local news media were being pushed away

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Speaker 3: from talking to Trump supporters as they were coming in

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Speaker 3: as well. So it seems like it's an actual effort

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Speaker 3: from the White House to control the media in some way,

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Speaker 3: not have their supporters be asked tough questions about the

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Speaker 3: gas prices, the economy, things that are unpopular, the war.

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Speaker 3: Because when I did talk to people there, most of

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Speaker 3: them were not fans of... Even if they were fans

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Speaker 3: of the war in Iran to begin with, they... they

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Speaker 3: almost universally said, it's time to wrap it up. Like,

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Speaker 3: we need the gas prices to come down now. We

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Speaker 3: support the president, but this has gone on for too long.

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Speaker 3: So it seemed like there was an effort being made

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Speaker 3: to control, to control the media, to control, yeah, and

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Speaker 3: control the narrative. And obviously, it seems like it's backfired,

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Speaker 3: or maybe this is the, you know, the new shiny

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Speaker 3: object that we're all following now.

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Speaker 1: I'm So that gets me to what I was thinking,

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Speaker 1: which is Trump has had a really, really bad run recently,

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Speaker 1: not able to bring war, bring peace to the Middle East.

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Speaker 1: That has not happened.

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Speaker 3: He brought war to the Middle East, though.

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Speaker 1: We brought war. But his new thing is that he

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Speaker 1: says he like has brought peace to eight countries. But then,

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Speaker 1: you know, whatever. But he hasn't been able to bring

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Speaker 1: peace to the Middle East. He has prices are higher.

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Speaker 1: We're having a trade war with Canada. We're You know,

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Speaker 1: at every point, everything is pretty bad. And then these

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Speaker 1: polls all weekend, every, you know, he's in North Carolina

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Speaker 1: because Republicans are about to lose a Senate seat in

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Speaker 1: North Carolina for the first time in whatever, 20 years.

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Speaker 1: And we're seeing Republicans go to states like Kansas because

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Speaker 1: Kansas is in play, right? And we're seeing Iowa, we're

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Speaker 1: seeing the Democrat up eight points in Iowa. I mean,

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Speaker 1: these are like doomsday scenarios, right? And so part of

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Speaker 1: me wonders if what happened was that they were really

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Speaker 1: looking for a scapegoat. And the media has been a

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Speaker 1: very good scapegoat for Trump. And so it looks like

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Speaker 1: the sort of that idea, you know, that the call

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Speaker 1: came out like we are the enemy is the media now.

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Speaker 1: And so you were sort of that that was what

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Speaker 1: you were you got sort of struck by.

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Speaker 3: Yeah, I think so. And look, listening to Trump supporters

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Speaker 3: at the events now, it's been striking over the last

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Speaker 3: few months to watch what the first thing people talk

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Speaker 3: about as the big issue to them. Because it used

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Speaker 3: to be like Trump closed the border and that's the best.

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Speaker 3: And he's doing great with that. And now, like probably

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Speaker 3: like 20 percent of the people we talk to say,

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Speaker 3: what's the biggest issue? And they will say Muslims, literally

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Speaker 3: just like an entire religion. that has now become a

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Speaker 3: talking point within, I don't know where it's coming from.

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Speaker 3: Like they will say, this is an issue that's facing

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Speaker 3: the country, this extremist group. And if you talk to

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Speaker 3: them about it, like extremist religion, literally an entire religion.

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Speaker 3: I talked to a woman in North Carolina who said

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Speaker 3: that basically she was saying that they were the problem for,

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Speaker 3: they were the biggest problem in the United States of America.

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Speaker 3: She was an immigrant here to the United States of America.

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Speaker 3: And I was like, you realize that that's 2 billion

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Speaker 3: people worldwide that you're saying, like, is she called them evil?

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Speaker 3: And you realize that that sounds very hateful when you

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Speaker 3: say that.

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Speaker 1: Right.

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Speaker 3: Well, it's what I it's it's it's what I believe.

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Speaker 3: It's what I believe.

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Speaker 1: So Nancy Mace, Randy Fines, Andy Ogles, like there are

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Speaker 1: quite a lot of Republicans who have been tweeting anti-Muslim

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Speaker 1: sentiment for years and years. Is that what's happening here?

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Speaker 3: I that's I have not like found the head of

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Speaker 3: the snake with it because I don't know. I'm assuming

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Speaker 3: it's like something on conservative media with Fox News, like

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Speaker 3: the dog whistles that are going on there. But I

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Speaker 3: it because it doesn't feel like Trump has been like

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Speaker 3: pushing that rhetoric any more than usual, like he kind

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Speaker 3: of always is. So I'm not sure. But I was

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Speaker 3: struck by it because I think starting at Michigan and

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Speaker 3: I'd been to several events this year, that's when I

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Speaker 3: really started to notice that. And that was in July.

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Speaker 3: And now every single one, there have been like five

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Speaker 3: to 10 people who have said, almost those words verbatim.

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Speaker 3: The other thing they say is socialism.

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Speaker 4: Did Trump say it?

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Speaker 3: Trump famously said it in 2015. He called for a

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Speaker 3: ban on everyone coming into the country. I remember it well.

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Speaker 3: Which is something I've brought up to people in the past.

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Speaker 3: It's like, he never said that. I'm like, definitely there's

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Speaker 3: a video of him saying that.

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Speaker 1: It was called the Muslim ban.

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Speaker 3: Well, he said it after the Paris attacks. He said

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Speaker 3: he's calling for a complete and total shutdown of Muslims

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Speaker 3: entering the country, the United States of America.

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Speaker 1: Yeah, I remember.

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Speaker 3: Which is just wild. There's, like, an entire religion cannot

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Speaker 3: come in.

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Speaker 1: Yeah.

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Speaker 3: And then, of course, the bans from the countries, right,

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Speaker 3: in 2017 when he took office, too.

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Speaker 1: Muslim countries, yeah.

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Speaker 3: So this has always been there, but, like, it's come

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Speaker 3: up to the surface so much, I'm just wondering, like,

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Speaker 3: what is the thing that's pushing it up there? Because it's...

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Speaker 3: The rhetoric, it's so like when you boil it down,

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Speaker 3: it's like such hateful rhetoric. It's like and it's it's

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Speaker 3: there's no dog whistle to it. There's nothing. It's just

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Speaker 3: saying we don't like an entire religion of people. The

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Speaker 3: other big thing that we heard is, of course, talking

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Speaker 3: about socialism and the Democrats are all socialists. And it's

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Speaker 3: part of the it's the party. Why now is socialism?

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Speaker 3: All Democrats are socialists. And like hearing that like some

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Speaker 3: moderate Democrats are socialists, you know, like being like Hakeem

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Speaker 3: Jeffries is socialist. Like all this stuff. We're like, OK,

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Speaker 3: whatever you say. I'm sure the DSA would have put

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Speaker 3: like a word on that.

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Speaker 1: Let me ask you a question here. We talk about

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Speaker 1: the socialists when they say Democrats are socialists. Can they

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Speaker 1: explain what that means?

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Speaker 3: It's funny you ask. At the last rally, I asked someone.

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Speaker 3: They've actually started saying, like, we can't have Democrats. They're

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Speaker 3: all socialists. We don't want socialism here. And I said,

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Speaker 3: what is socialism? And they said, it's communism. And then

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Speaker 3: I said, OK, what's communism? And then they went off

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Speaker 3: on a rant.

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Speaker 1: Right.

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Speaker 3: And then I ended it with, OK, what is communism?

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Speaker 3: And then it was like a long pause. Right. And

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Speaker 3: then interview ended.

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Speaker 5: Ending.

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Speaker 3: So literally, that was the end of the interview. She

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Speaker 3: walked away. So they don't necessarily know, but they know

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Speaker 3: it's a bad thing, and they think it's a bad thing.

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Speaker 3: And I think growing up, especially the older folks who

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Speaker 3: go to Trump rallies, living through the Cold War, they're like,

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Speaker 3: communism is bad. And they see Trump saying communists about Democrats.

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Speaker 3: They're like, well, we have to stand for something They

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Speaker 3: loved Reagan. They were alive for the, you know, and

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Speaker 3: were following the news during the fall of the Berlin

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Speaker 3: Wall and arms race, all that stuff. So there's like

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Speaker 3: an emotional attachment to that word and to wanting to

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Speaker 3: fight against it. But for the younger people, it just

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Speaker 3: feels like they it's a buzzword and they know they

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Speaker 3: don't like it. And they're told.

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Speaker 1: But they can't tell you what it is.

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Speaker 4: No.

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Speaker 3: I don't want to say that's all of them.

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Speaker 1: Right, but the majority that you talk to, they don't.

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Speaker 1: But so I wonder if you could talk about, like,

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Speaker 1: when we look at this midterm cycle, there does seem

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Speaker 1: to be, like, Republicans, like, I'm thinking about Senator Kennedy

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Speaker 1: from Louisiana, not to be confused with any of the

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Speaker 1: previously good Kennedys or the bad Kennedy, who is the

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Speaker 1: Secretary of Health and Human Services. But Kennedy, John Kennedy

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Speaker 1: from Louisiana... Senator Foghorn Leghorn, that guy, he was campaigning

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Speaker 1: in the great swing state of Kansas and he was

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Speaker 1: talking about how they knew they were in trouble. Like

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Speaker 1: they know that they're behind the eight ball. do you

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Speaker 1: think that the, do the people in these rallies know

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Speaker 1: it or do they just think like the election is

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Speaker 1: going to be stolen? Like, is that part of Earth

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Speaker 1: One sneaking in there or no?

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Speaker 3: No, I think that's, it was interesting. We were at the,

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Speaker 3: the RNC. I keep wanting to call it CPAC because

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Speaker 3: there shouldn't be an RNC this year.

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Speaker 1: Right.

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Speaker 3: And it was like the same vibe as CPAC kind of.

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Speaker 3: But at the, at the RNC, I talked to somebody about,

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Speaker 3: about the polling for Trump and I was like, You know,

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Speaker 3: they talked about how great Trump was and how we

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Speaker 3: need to, you know, he needs to be front and

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Speaker 3: center for the midterms and to help all these candidates.

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Speaker 3: And I asked, I was like, he's polling, you know,

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Speaker 3: in the 30s.

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Speaker 1: Yeah.

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Speaker 3: In most polls.

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Speaker 1: Yeah.

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Speaker 3: And sometimes in the lower 30s. Are you worried about that?

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Speaker 3: And he was like, that's not true. That's not true.

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Speaker 3: That's simply not.

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Speaker 4: True.

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Speaker 1: But I think that's a really good point. I mean,

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Speaker 1: there is a real question because we are cruising into

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Speaker 1: these midterms and what we are cruising into is reality, right?

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Speaker 4: Yeah.

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Speaker 1: It certainly looks like Republicans are going to get creamed

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Speaker 1: and it looks and they can't fuck with mail-in voting.

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Speaker 1: So there aren't a ton of choices in ways to

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Speaker 1: mess up the voting. So unless they can send ICE in,

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Speaker 1: which they may.

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Speaker 3: I know it's like don't say it out loud, but yeah.

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Speaker 1: But if they, yeah.

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Speaker 3: They've already made Senate out loud. You know, like he

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Speaker 3: could be like, oh, there's going to be voter fraud.

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Speaker 3: We know it. Everyone knows it.

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Speaker 5: And then.

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Speaker 3: Cite himself as the source and then send ICE. It's

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Speaker 3: like people are worried about it. It's like, because you

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Speaker 3: just said it. You just said it.

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Speaker 4: Right.

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Speaker 1: But barring a constitutional crisis, Earth 1 and Earth 2

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Speaker 1: are about to explode into each other.

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Speaker 3: Yeah, well, and that's what's so dangerous. It kind of

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Speaker 3: goes back to what we were talking about at the

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Speaker 3: beginning with the war on the media, because like that,

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Speaker 3: the polling, when asking Trump supporters, a lot of times

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Speaker 3: at rallies, especially in the last few months when the

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Speaker 3: numbers have really been cratering for Trump, asking them about

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Speaker 3: the poll numbers, they always say, well, that's the media.

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Speaker 3: It's the fake news media who's doing these polls and

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Speaker 3: you can't trust the polls. And you look at ever

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Speaker 3: all the polls, they've always been wrong. And whereas, you know,

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Speaker 3: the last two presidential elections, the polls were actually right.

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Speaker 3: Like Trump, you know, everyone was saying he was going

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Speaker 3: to win the popular vote. Biden was projected to like

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Speaker 3: squeak out a victory in 2020. So the polls have

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Speaker 3: been right for most of it. The 2016 being an

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Speaker 3: example of when they were very, very wrong.

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Speaker 1: Yeah, not good.

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Speaker 3: But the people will say it's basically crying fake news

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Speaker 3: at reality. And the reality will become reality if if

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Speaker 3: people get out and vote and like if there's an

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Speaker 3: urgency for voters and if the, you know, the independents

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Speaker 3: or the people who voted for Trump when I think

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Speaker 3: there's a lot of people out there who voted for

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Speaker 3: Trump and now they're paying more for literally everything. And

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Speaker 3: they're like, yeah, not this is not what his signs say.

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Speaker 3: This is not what. He promised, this is not what, like,

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Speaker 3: the billboard said, there's going to be lower prices under Trump.

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Speaker 3: It was so simple. And he said that he was

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Speaker 3: going to solve all these problems, not go to war.

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Speaker 4: No.

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Speaker 3: Or another war. And because of that war, the gas

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Speaker 3: prices are high. Like, he has made himself responsible because

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Speaker 3: of the decisions he's made for all these things that

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Speaker 3: are hurting people's pocketbooks. And, like, going into an election,

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Speaker 3: a midterm, like, that will get people out to vote. Like,

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Speaker 3: that would, more than almost any, more than, like, you know, a...

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Speaker 3: Wedge issues, social issues, like money is the big thing

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Speaker 3: that motivates people. And they want to have somebody in

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Speaker 3: charge who's not going to like put a drain on

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Speaker 3: their wallet.

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Speaker 1: That is a real concern that you hear from voters

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Speaker 1: all around the spectrum is that things are really expensive. And,

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Speaker 1: you know, a number of really stupid decisions got us

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Speaker 1: here from Donald Trump. I'm going to pay a compliment here,

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Speaker 1: maybe not to Chuck Schumer, certainly not to you. No.

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Speaker 3: Certainly not to me.

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Speaker 1: I want to pay a compliment here because I do

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Speaker 1: think a lot of these Democratic candidates are uniquely amazing.

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Speaker 1: And I'm talking about like the candidate in Kansas, right?

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Speaker 1: This guy, the Senate candidate in Kansas, his name is...

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Speaker 1: Adam Hamilton, and he is a Methodist minister, and he's

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Speaker 1: just really good at what he does. And so he's

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Speaker 1: running against Roger Marshall, who is a doctor who sued

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Speaker 1: all of these patients of his for nonpayment. He also

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Speaker 1: jailed 81 of the patients he sued. They pushed so

435
00:23:37,119 --> 00:23:38,320
Speaker 1: far that they were jailed.

436
00:23:39,240 --> 00:23:39,399
Speaker 5: It's 81.

437
00:23:39,380 --> 00:23:43,100
Speaker 1: And there are people like old people who didn't have

438
00:23:43,180 --> 00:23:45,530
Speaker 1: like he was the only doctor. You know, he was

439
00:23:45,550 --> 00:23:49,050
Speaker 1: in a very rural area, very impoverished group of people

440
00:23:49,270 --> 00:23:52,390
Speaker 1: was like pushing, you know, like a normal person would

441
00:23:52,410 --> 00:23:54,450
Speaker 1: have been like, well, they can't pay. But, you know,

442
00:23:54,930 --> 00:23:58,869
Speaker 1: we'll just they can bake me cookies or something. No jail.

443
00:23:59,830 --> 00:24:01,570
Speaker 1: And I just think like there are a bunch of

444
00:24:01,630 --> 00:24:05,340
Speaker 1: candidates like that where like, you know, Josh Turek is a.

445
00:24:05,840 --> 00:24:09,139
Speaker 1: You know, he's in a wheelchair from Spina Bifida and

446
00:24:09,220 --> 00:24:14,560
Speaker 1: he's also a professional athlete. You know, I just think

447
00:24:14,580 --> 00:24:18,160
Speaker 1: there's a real they've Democrats have actually done a really

448
00:24:18,180 --> 00:24:21,300
Speaker 1: smart thing of like finding local candidates who can really

449
00:24:21,359 --> 00:24:22,770
Speaker 1: speak the language of the place.

450
00:24:23,140 --> 00:24:26,410
Speaker 3: Yeah. No, I think I think that's I think that's true.

451
00:24:26,490 --> 00:24:29,169
Speaker 3: And I think it's like also like I want how

452
00:24:29,230 --> 00:24:31,369
Speaker 3: much of the credit and maybe you could answer this

453
00:24:31,410 --> 00:24:33,590
Speaker 3: question for real, because I'm just asking it to the

454
00:24:33,650 --> 00:24:36,750
Speaker 3: universe right now. How much credit goes to Chuck Schumer?

455
00:24:37,250 --> 00:24:39,950
Speaker 3: How much credit goes to the people in the grassroots

456
00:24:40,730 --> 00:24:43,530
Speaker 3: who and the social media and all these other elements

457
00:24:43,570 --> 00:24:46,850
Speaker 3: that went into somebody catching on with people? And then

458
00:24:46,890 --> 00:24:50,090
Speaker 3: it's like an organic start to the snowball that then

459
00:24:50,630 --> 00:24:53,170
Speaker 3: the money can push it kind of over the edge.

460
00:24:53,890 --> 00:24:55,430
Speaker 3: I'm actually wondering.

461
00:24:55,730 --> 00:24:57,869
Speaker 1: Yeah. So it's a good question. I would think I

462
00:24:57,910 --> 00:25:01,480
Speaker 1: would say this with certain candidates like Sherrod Brown and.

463
00:25:02,810 --> 00:25:05,870
Speaker 1: And I'm thinking of other candidates. There are candidates where

464
00:25:06,590 --> 00:25:09,310
Speaker 1: Chuck Schumer went in there and pushed. Like, Sherrod Brown

465
00:25:09,350 --> 00:25:11,710
Speaker 1: was the only candidate that was going to win in Ohio.

466
00:25:11,770 --> 00:25:13,369
Speaker 1: And someone had to go sit down with him and

467
00:25:13,390 --> 00:25:15,430
Speaker 1: be like, look, you need to waste another year of

468
00:25:15,470 --> 00:25:18,649
Speaker 1: your life killing yourself. I mean, not waste, but, you know,

469
00:25:18,990 --> 00:25:20,780
Speaker 1: you need to spend another year of your life killing

470
00:25:20,820 --> 00:25:24,280
Speaker 1: yourself because we need you out there. And because Sherrod

471
00:25:24,400 --> 00:25:27,020
Speaker 1: is probably exhausted. He has this nice wife. He has

472
00:25:27,040 --> 00:25:29,740
Speaker 1: his grandchildren. Like, you could see him being like, it's fine.

473
00:25:29,940 --> 00:25:32,560
Speaker 1: I'm done. And there were there are a couple of

474
00:25:32,600 --> 00:25:35,100
Speaker 1: other candidates where I do think like in North Carolina

475
00:25:35,140 --> 00:25:39,899
Speaker 1: is a great example. The governor was really popular, had

476
00:25:40,020 --> 00:25:43,340
Speaker 1: won and won and won. Schumer went in there and

477
00:25:43,359 --> 00:25:45,619
Speaker 1: convinced him to run for Senate. So I think he

478
00:25:45,660 --> 00:25:47,780
Speaker 1: did some really good things. Give it to him where

479
00:25:47,820 --> 00:25:52,800
Speaker 1: you can. That said. my man cratered when it came

480
00:25:52,880 --> 00:25:56,710
Speaker 1: to like Maine, when it came to Michigan, like, and

481
00:25:56,750 --> 00:26:01,230
Speaker 1: especially Michigan, because Michigan, Michigan's a really important example because

482
00:26:01,250 --> 00:26:10,140
Speaker 1: like you had Haley Stevens, you had, you had, you

483
00:26:10,180 --> 00:26:14,640
Speaker 1: had Dr. Abdul and you had this third Angie Craig

484
00:26:14,680 --> 00:26:19,200
Speaker 1: in Minnesota. That's Minnesota. But there were Minnesota and Michigan,

485
00:26:19,240 --> 00:26:23,320
Speaker 1: you both had situations where he may not have picked

486
00:26:23,380 --> 00:26:27,790
Speaker 1: the best candidate as the establishment candidate. And so in

487
00:26:27,910 --> 00:26:31,030
Speaker 1: both cases, the establishment candidate, which he poured money into,

488
00:26:31,070 --> 00:26:35,010
Speaker 1: both lost. And that was Angie Craig and Haley Stevens.

489
00:26:35,090 --> 00:26:38,409
Speaker 1: And it was both times, you know, Angie Craig and

490
00:26:39,710 --> 00:26:42,160
Speaker 1: was running against Peggy Flanagan, and Peggy Flanagan was like

491
00:26:42,180 --> 00:26:45,720
Speaker 1: the lieutenant governor. So it made no sense to just

492
00:26:45,800 --> 00:26:48,220
Speaker 1: go in there and give money to someone just because

493
00:26:48,260 --> 00:26:50,679
Speaker 1: you knew them from Congress. So the answer is he

494
00:26:50,720 --> 00:26:54,330
Speaker 1: did some good things, and also there were some screw-ups.

495
00:26:54,410 --> 00:26:57,510
Speaker 1: I mean, so, you know, but I don't think, like,

496
00:26:57,670 --> 00:27:02,169
Speaker 1: the Kansas case is candidates at the grassroots figuring out

497
00:27:02,210 --> 00:27:05,230
Speaker 1: that they're the people who can win in the state. Yeah, yeah.

498
00:27:05,290 --> 00:27:08,600
Speaker 3: I think it's also... a wise thing to do is

499
00:27:08,640 --> 00:27:13,609
Speaker 3: to have somebody that truly understands the local politics of

500
00:27:13,650 --> 00:27:15,570
Speaker 3: the state, the makeup of the state, who's going to

501
00:27:15,590 --> 00:27:19,090
Speaker 3: resonate in that state. And it's not necessarily going to

502
00:27:19,130 --> 00:27:24,609
Speaker 3: be somebody who was serving in Congress or somebody who

503
00:27:24,830 --> 00:27:28,150
Speaker 3: just happens to be friends with Chuck Schumer or somebody

504
00:27:28,190 --> 00:27:32,000
Speaker 3: else who has control of a lot of money. So

505
00:27:32,600 --> 00:27:36,120
Speaker 3: I hope, and just looking forward to the actual midterms,

506
00:27:36,710 --> 00:27:39,170
Speaker 3: I know the polls are looking very good right now

507
00:27:39,490 --> 00:27:40,190
Speaker 3: for Democrats.

508
00:27:40,470 --> 00:27:40,670
Speaker 1: Yeah.

509
00:27:40,970 --> 00:27:43,030
Speaker 3: But there's a lot of time and there's a lot

510
00:27:43,570 --> 00:27:46,350
Speaker 3: and there's a lot of you know, you're always like

511
00:27:46,869 --> 00:27:49,629
Speaker 3: a lot of like bad stuff could happen before an election.

512
00:27:49,670 --> 00:27:52,010
Speaker 3: Something big. There could be some some big thing, a

513
00:27:52,170 --> 00:27:54,990
Speaker 3: war or whatever, whatever. My warning is a lot of

514
00:27:55,170 --> 00:27:59,730
Speaker 3: stupid stuff could happen before the election, because like Trump

515
00:28:00,060 --> 00:28:04,440
Speaker 3: with Harris, he's in McDonald's dressed up like a McDonald's worker.

516
00:28:05,090 --> 00:28:08,090
Speaker 3: And like that is like a turning point in the election.

517
00:28:08,810 --> 00:28:12,180
Speaker 3: So you have to give it to Trump that he

518
00:28:12,660 --> 00:28:17,460
Speaker 3: has a way of doing incredibly dumb things that maybe

519
00:28:17,500 --> 00:28:20,300
Speaker 3: make him look a little dumb. But also he knows

520
00:28:20,440 --> 00:28:23,639
Speaker 3: it's going to like get people talking. And what is

521
00:28:23,680 --> 00:28:26,080
Speaker 3: that narrative going to be built that's going to be

522
00:28:26,100 --> 00:28:29,109
Speaker 3: built after that? So a lot of dumb stuff could happen.

523
00:28:29,119 --> 00:28:30,439
Speaker 3: So I know we've got, you know, a month and

524
00:28:30,460 --> 00:28:34,510
Speaker 3: a half to go, but. Stupid, you know, does not rest. Stupid,

525
00:28:34,810 --> 00:28:37,629
Speaker 3: stupid keeps going on and on and on, especially with

526
00:28:37,650 --> 00:28:38,370
Speaker 3: Trump in charge.

527
00:28:38,750 --> 00:28:40,350
Speaker 1: Jason, Jason, Jason.

528
00:28:40,370 --> 00:28:42,230
Speaker 4: Molly, Molly, Molly.

529
00:28:42,530 --> 00:28:47,020
Speaker 1: That's all I have. Garrison Lovely is the author of Obsolete,

530
00:28:47,040 --> 00:28:50,400
Speaker 1: the AI industry's trillion dollar race to replace us and

531
00:28:50,440 --> 00:28:54,420
Speaker 1: how to stop it. Welcome. Welcome, Garrison.

532
00:28:54,440 --> 00:28:55,480
Speaker 5: So great to be here.

533
00:28:56,450 --> 00:29:00,729
Speaker 1: So we're here to talk about your book. obsolete, the

534
00:29:00,890 --> 00:29:04,530
Speaker 1: AI industry's trillion dollar race to replace you and how

535
00:29:04,570 --> 00:29:07,130
Speaker 1: to stop it. I just, this is all I want

536
00:29:07,150 --> 00:29:10,910
Speaker 1: in my life is to stop this race to replace me.

537
00:29:11,050 --> 00:29:14,230
Speaker 1: I remember like the first time when Mark Cuban was like,

538
00:29:14,530 --> 00:29:16,190
Speaker 1: AI is going to replace you. You're going to have

539
00:29:16,230 --> 00:29:18,410
Speaker 1: a Molly Jongfest bot and you can get it to

540
00:29:18,450 --> 00:29:19,950
Speaker 1: do all your stuff for you. And I was like,

541
00:29:19,970 --> 00:29:22,500
Speaker 1: Like that's the worst thing I've ever fucking heard in

542
00:29:22,520 --> 00:29:24,060
Speaker 1: my life. And he was like, no, no, it's going

543
00:29:24,080 --> 00:29:25,820
Speaker 1: to be great for you. And I was like, no,

544
00:29:25,880 --> 00:29:27,840
Speaker 1: it's not going to be fucking great for me. That's

545
00:29:27,880 --> 00:29:29,620
Speaker 1: the worst. First of all, it's not going to work.

546
00:29:29,720 --> 00:29:32,700
Speaker 1: Thank God. But second of all, like, fuck you. So

547
00:29:32,850 --> 00:29:35,850
Speaker 1: explain to us why, what, just go.

548
00:29:36,210 --> 00:29:38,250
Speaker 4: So the book is about how there's this tiny group

549
00:29:38,290 --> 00:29:41,470
Speaker 4: of people backed by the best reason organizations on the planet.

550
00:29:41,550 --> 00:29:42,790
Speaker 5: And they're trying to render us obsolete.

551
00:29:42,850 --> 00:29:43,520
Speaker 1: Tech companies.

552
00:29:43,760 --> 00:29:47,880
Speaker 5: Yeah, tech companies, the AI industry, NVIDIA, OpenAI, Anthropic, Google, etc.

553
00:29:47,940 --> 00:29:50,380
Speaker 5: And like, basically, none of us want that to happen.

554
00:29:50,520 --> 00:29:53,440
Speaker 4: But we're letting it happen because we're not aware that

555
00:29:53,460 --> 00:29:55,720
Speaker 4: it's happening because we don't think they can do it.

556
00:29:58,710 --> 00:30:00,469
Speaker 4: People just don't pay attention or they don't believe them.

557
00:30:01,250 --> 00:30:03,820
Speaker 4: Or they think it's impossible to stop them. It's inevitable

558
00:30:03,860 --> 00:30:06,840
Speaker 4: that this will happen. But I argue that it's happening.

559
00:30:07,420 --> 00:30:08,000
Speaker 4: It can work.

560
00:30:08,180 --> 00:30:10,020
Speaker 5: So the only surefire way it won't work is if

561
00:30:10,060 --> 00:30:10,660
Speaker 5: we stop them.

562
00:30:11,240 --> 00:30:11,500
Speaker 4: We should.

563
00:30:11,640 --> 00:30:12,760
Speaker 1: How do we stop them?

564
00:30:12,940 --> 00:30:13,700
Speaker 5: That's a great question.

565
00:30:13,820 --> 00:30:16,450
Speaker 4: I mean, a mass movement with a clear demand, which

566
00:30:16,530 --> 00:30:18,210
Speaker 4: I think should be stop the race to replace us.

567
00:30:18,430 --> 00:30:20,830
Speaker 4: And by that, I mean ban development of what the

568
00:30:20,870 --> 00:30:24,650
Speaker 4: industry calls artificial general intelligence. But we should understand as

569
00:30:24,770 --> 00:30:28,020
Speaker 4: universal labor replacing machines. And framed that way, I think

570
00:30:28,040 --> 00:30:28,680
Speaker 4: people could get on.

571
00:30:28,640 --> 00:30:29,100
Speaker 5: Board with this.

572
00:30:29,360 --> 00:30:30,160
Speaker 1: What does that look like?

573
00:30:30,560 --> 00:30:32,620
Speaker 5: Like the ban or the movement?

574
00:30:32,640 --> 00:30:34,800
Speaker 1: Just all of it. So first, start with the ban.

575
00:30:35,460 --> 00:30:38,959
Speaker 1: We have a government that has not regulated technology since

576
00:30:39,060 --> 00:30:42,640
Speaker 1: the printing press, basically. So what would that look like?

577
00:30:42,700 --> 00:30:47,130
Speaker 1: I mean, clearly data center is very unpopular. Clearly AI,

578
00:30:47,250 --> 00:30:52,630
Speaker 1: pretty unpopular. Business owners like the software, but the rest

579
00:30:52,690 --> 00:30:55,610
Speaker 1: of us, you know, AI slop doesn't seem like something

580
00:30:55,670 --> 00:30:57,390
Speaker 1: we should have to give up our jobs for.

581
00:30:57,410 --> 00:30:57,490
Speaker 4: Yeah.

582
00:30:57,690 --> 00:30:58,350
Speaker 5: Yeah.

583
00:30:58,810 --> 00:31:00,950
Speaker 4: I mean, so what a ban looks like is a

584
00:31:00,970 --> 00:31:04,570
Speaker 4: bilateral agreement with China and in the United States banning

585
00:31:04,590 --> 00:31:08,150
Speaker 4: development of universal labor replacing machines. And I think it's

586
00:31:08,190 --> 00:31:11,540
Speaker 4: pretty obvious that either country could ban this within their borders, right?

587
00:31:11,580 --> 00:31:13,800
Speaker 5: There's like a handful of companies. Everyone knows who they are.

588
00:31:14,180 --> 00:31:17,040
Speaker 4: The companies themselves, they know which of their work is

589
00:31:17,140 --> 00:31:19,180
Speaker 4: doing this and which is just like serving customers or

590
00:31:19,200 --> 00:31:21,820
Speaker 4: making the product better. And so it's trivial for China

591
00:31:21,840 --> 00:31:23,440
Speaker 4: to do this. And for the U.S., you'd have some

592
00:31:23,520 --> 00:31:25,500
Speaker 4: bibbles about like the definition of things, but you could

593
00:31:25,520 --> 00:31:28,140
Speaker 4: like be over-inclusive and then, you know, because the risks

594
00:31:28,200 --> 00:31:29,180
Speaker 4: are greater on the other side.

595
00:31:29,400 --> 00:31:31,020
Speaker 5: And then the trick is to make it international.

596
00:31:31,240 --> 00:31:33,340
Speaker 4: And so there you just need the means of verifying

597
00:31:33,620 --> 00:31:35,170
Speaker 4: what's happening in each country.

598
00:31:35,710 --> 00:31:37,110
Speaker 5: And so there are ways to do this.

599
00:31:37,150 --> 00:31:39,810
Speaker 4: Like technically you can like put devices on the AI

600
00:31:39,830 --> 00:31:42,370
Speaker 4: chips that tell you stuff about what's happening and like

601
00:31:42,430 --> 00:31:44,410
Speaker 4: also pass the network traffic through.

602
00:31:44,270 --> 00:31:46,060
Speaker 5: The data centers. There's a bunch of like technical things.

603
00:31:46,560 --> 00:31:49,280
Speaker 4: Then there are institutional things you could do, like establish

604
00:31:49,520 --> 00:31:53,280
Speaker 4: like an international atomic energy agency, but for AI verification

605
00:31:53,560 --> 00:31:56,570
Speaker 4: and like middle power countries can do this. And you

606
00:31:56,590 --> 00:31:58,530
Speaker 4: could also just simply put auditors in all the companies

607
00:31:58,590 --> 00:32:00,630
Speaker 4: and give them access to everything and just be like, hey,

608
00:32:00,670 --> 00:32:03,370
Speaker 4: no one's allowed to do this work anymore. And I

609
00:32:03,630 --> 00:32:05,430
Speaker 4: think to get to that point, we need to build

610
00:32:05,450 --> 00:32:09,500
Speaker 4: an enormous amount of political will and stigmatize building this stuff.

611
00:32:09,600 --> 00:32:12,720
Speaker 4: This is like building a bioweapon or nukes. This is

612
00:32:12,760 --> 00:32:16,550
Speaker 4: incredibly dangerous. It's undemocratic. And it's not something that humans

613
00:32:16,570 --> 00:32:19,370
Speaker 4: can reliably control, as evidenced by the fact that OpenAI

614
00:32:19,410 --> 00:32:22,070
Speaker 4: lost control of multiple forms of their agents that hacked

615
00:32:22,090 --> 00:32:24,510
Speaker 4: into real places and didn't even know about it for

616
00:32:24,530 --> 00:32:25,030
Speaker 4: weeks on end.

617
00:32:25,270 --> 00:32:27,990
Speaker 5: And so I think it's about waking people up.

618
00:32:28,430 --> 00:32:31,250
Speaker 4: Even if the industry can't succeed at building these machines,

619
00:32:31,450 --> 00:32:32,410
Speaker 4: they shouldn't be allowed to try.

620
00:32:32,430 --> 00:32:33,050
Speaker 3: Yeah.

621
00:32:34,120 --> 00:32:38,760
Speaker 1: So it would be legislation, probably litigation.

622
00:32:39,480 --> 00:32:39,900
Speaker 5: Probably.

623
00:32:39,920 --> 00:32:40,560
Speaker 3: Yeah.

624
00:32:40,800 --> 00:32:42,550
Speaker 4: But like I guess like a bilateral agreement, like some

625
00:32:42,570 --> 00:32:44,350
Speaker 4: kind of treaty between the U.S. and China is like

626
00:32:44,370 --> 00:32:46,030
Speaker 4: the thing you want to work toward. And then like

627
00:32:46,290 --> 00:32:49,030
Speaker 4: from there, you can make it global without nearly as

628
00:32:49,070 --> 00:32:49,470
Speaker 4: much trouble.

629
00:32:49,790 --> 00:32:52,230
Speaker 1: So one of the things that we used to always

630
00:32:52,290 --> 00:32:55,490
Speaker 1: say about China was that you really couldn't trust them.

631
00:32:56,040 --> 00:32:58,040
Speaker 1: I know people who go over there and have a

632
00:32:58,080 --> 00:33:01,140
Speaker 1: different phone because they're You know, they're just you don't

633
00:33:01,280 --> 00:33:04,160
Speaker 1: you can't trust them with their with their with all

634
00:33:04,200 --> 00:33:08,120
Speaker 1: different things that they're not necessarily an honest broker. That's

635
00:33:08,180 --> 00:33:11,920
Speaker 1: not true about America, too. Right. So I just wonder,

636
00:33:12,560 --> 00:33:15,680
Speaker 1: how do you make a bilateral agreement like that when

637
00:33:15,720 --> 00:33:17,000
Speaker 1: you can't really trust anyone?

638
00:33:17,140 --> 00:33:18,840
Speaker 4: I mean, I think you have to assume zero trust

639
00:33:18,880 --> 00:33:21,060
Speaker 4: from either party for all the reasons that we can

640
00:33:21,080 --> 00:33:24,060
Speaker 4: all think of. And so that's where the verification techniques

641
00:33:24,100 --> 00:33:26,260
Speaker 4: come in. Like the United States and the Soviet Union

642
00:33:26,480 --> 00:33:28,740
Speaker 4: also didn't trust each other. But they were able to

643
00:33:28,760 --> 00:33:32,690
Speaker 4: strike arms control agreements and enforce them because they verified like, okay,

644
00:33:32,990 --> 00:33:34,890
Speaker 4: we actually destroyed those bombers. We cut them in half.

645
00:33:35,170 --> 00:33:37,050
Speaker 4: We dragged them with two tractors. And so you can

646
00:33:37,090 --> 00:33:38,990
Speaker 4: see from a satellite that like those bombers were not

647
00:33:39,010 --> 00:33:41,690
Speaker 4: in use anymore. And with AI, it's like as obvious

648
00:33:41,730 --> 00:33:44,490
Speaker 4: what that looks like, but people barely tried. And you

649
00:33:44,510 --> 00:33:46,840
Speaker 4: could do things like OK, we're going to donate all

650
00:33:46,890 --> 00:33:49,600
Speaker 4: the AI chips to like some third party country that's

651
00:33:49,620 --> 00:33:51,620
Speaker 4: going to use them to do like scientific research. And

652
00:33:51,640 --> 00:33:53,760
Speaker 4: we're going to verify that the chips work. And so

653
00:33:53,820 --> 00:33:56,290
Speaker 4: now we know both parties have fewer chips and you

654
00:33:56,310 --> 00:33:58,950
Speaker 4: could do the things like cryptographic commitments on the network traffic,

655
00:33:58,990 --> 00:33:59,950
Speaker 4: all this like wonky stuff.

656
00:34:00,410 --> 00:34:02,250
Speaker 5: But people are working on this and it's like happening

657
00:34:02,290 --> 00:34:02,550
Speaker 5: right now.

658
00:34:02,930 --> 00:34:06,270
Speaker 1: I feel like public sentiment is there, right? Like AI

659
00:34:06,310 --> 00:34:09,100
Speaker 1: is an 80-20 issue. Like there are definitely people who

660
00:34:09,180 --> 00:34:13,299
Speaker 1: like it, but there are largely like people don't want

661
00:34:13,320 --> 00:34:16,640
Speaker 1: data centers. They don't totally, you know, AI has done

662
00:34:16,660 --> 00:34:19,180
Speaker 1: a very bad job of selling itself, which is good.

663
00:34:19,200 --> 00:34:22,009
Speaker 1: And one of the things I'm struck by, this is

664
00:34:22,050 --> 00:34:24,890
Speaker 1: just like a little bit of a, I just want

665
00:34:24,930 --> 00:34:30,330
Speaker 1: for my own edification. Like I... ask people, like, what AI,

666
00:34:30,510 --> 00:34:34,610
Speaker 1: what's good that AI can do that's not automating things

667
00:34:34,680 --> 00:34:39,100
Speaker 1: that humans do. And they say things like cure cancer,

668
00:34:39,640 --> 00:34:43,680
Speaker 1: which strikes me as automating things humans do, right? Like,

669
00:34:43,860 --> 00:34:47,109
Speaker 1: is there anything you could tell me that AI doesn't, like,

670
00:34:47,910 --> 00:34:49,910
Speaker 1: it strikes me that AI does a lot of research.

671
00:34:50,310 --> 00:34:54,270
Speaker 1: It's basically a higher level Google search. And that it

672
00:34:54,310 --> 00:34:58,089
Speaker 1: can do things like automate, search, you know, tell you

673
00:34:58,110 --> 00:35:01,110
Speaker 1: where things come from, change the, you know, change the

674
00:35:01,150 --> 00:35:04,029
Speaker 1: way you write things. Okay. But it doesn't strike me

675
00:35:04,110 --> 00:35:06,490
Speaker 1: that there's anything that it can do that isn't something

676
00:35:06,530 --> 00:35:08,490
Speaker 1: that's automating what humans do.

677
00:35:08,780 --> 00:35:10,360
Speaker 5: Yeah, I think that's true.

678
00:35:10,440 --> 00:35:13,000
Speaker 4: But some things are like drudgery where it's like, all right,

679
00:35:13,040 --> 00:35:14,719
Speaker 4: I sent a bunch of emails to people and I

680
00:35:14,739 --> 00:35:15,880
Speaker 4: have to update this spreadsheet.

681
00:35:16,000 --> 00:35:18,819
Speaker 5: And like, this is just mindless. I'm like, you could

682
00:35:18,840 --> 00:35:20,750
Speaker 5: just have AI do that. And it's good at that.

683
00:35:20,969 --> 00:35:21,989
Speaker 1: Like a calculator.

684
00:35:22,110 --> 00:35:25,370
Speaker 4: Yeah, I mean, it's definitely more sophisticated, but like, yeah, like,

685
00:35:25,710 --> 00:35:27,969
Speaker 4: and it's not going to be perfect. You know, it's

686
00:35:27,989 --> 00:35:31,420
Speaker 4: famously unreliable, but that's also getting... better or worse, depending

687
00:35:31,500 --> 00:35:33,180
Speaker 4: on how you think about it. But yeah, there's a

688
00:35:33,200 --> 00:35:34,799
Speaker 4: lot of stuff that just kind of sucks to do.

689
00:35:34,980 --> 00:35:36,799
Speaker 4: And like, it's not like you were employing somebody to

690
00:35:36,840 --> 00:35:38,020
Speaker 4: do it and now they're out of the job. It's

691
00:35:38,040 --> 00:35:39,879
Speaker 4: just like, you have some time back in your day.

692
00:35:39,900 --> 00:35:42,339
Speaker 4: And I think that matters, but like, you know, it's

693
00:35:42,380 --> 00:35:45,439
Speaker 4: not worth all the risks to keep going down this path.

694
00:35:45,920 --> 00:35:50,719
Speaker 1: Besides the like environmental catastrophe that is data centers and the,

695
00:35:51,060 --> 00:35:54,220
Speaker 1: you know, the way they do power and the, you know,

696
00:35:54,260 --> 00:35:59,610
Speaker 1: the environmental stuff, what are the other risks of AI? I,

697
00:35:59,870 --> 00:36:02,530
Speaker 1: in the book, focus on what happens if they're actually

698
00:36:02,590 --> 00:36:05,299
Speaker 1: able to build universal labor replacing machines.

699
00:36:06,140 --> 00:36:08,630
Speaker 4: And you have this risk of power and wealth concentrating

700
00:36:08,810 --> 00:36:12,090
Speaker 4: to an unprecedented degree in the hands of whoever controls

701
00:36:12,110 --> 00:36:14,850
Speaker 4: the machines, whoever has the capital, whoever has a lot

702
00:36:14,890 --> 00:36:17,430
Speaker 4: of power already. And so, for instance, like the Trump

703
00:36:17,450 --> 00:36:20,810
Speaker 4: administration had this big fight with Anthropic over domestic mass

704
00:36:20,830 --> 00:36:24,050
Speaker 4: surveillance and lethal autonomous weapons. And so if the government

705
00:36:24,170 --> 00:36:27,739
Speaker 4: can use killer robots to bow down protesters, you lose

706
00:36:27,800 --> 00:36:31,040
Speaker 4: one of the big ways that dictatorships have historically fallen.

707
00:36:31,440 --> 00:36:34,020
Speaker 5: And so that's terrifying. Then if the.

708
00:36:34,000 --> 00:36:36,739
Speaker 4: Machines don't do what we want, the industry is trying

709
00:36:36,760 --> 00:36:39,779
Speaker 4: to give them all the capabilities, all the opportunity, and

710
00:36:39,840 --> 00:36:42,799
Speaker 4: the motivation could emerge naturally because as we've seen with

711
00:36:42,840 --> 00:36:45,989
Speaker 4: these hacks, they don't reliably do what they're told, but

712
00:36:46,010 --> 00:36:48,810
Speaker 4: they are very, very capable now at doing real damage

713
00:36:48,830 --> 00:36:51,009
Speaker 4: in the world. And so if you just project all

714
00:36:51,030 --> 00:36:53,560
Speaker 4: of that out, you can see scenarios where, like, the

715
00:36:53,600 --> 00:36:56,040
Speaker 4: machines are more numerous than us, they're more capable than us,

716
00:36:56,540 --> 00:36:58,279
Speaker 4: and they don't do what we expect or what we

717
00:36:58,320 --> 00:37:01,060
Speaker 4: want reliably, and we could lose control of the planet

718
00:37:01,080 --> 00:37:03,390
Speaker 4: to them. And that might sound crazy, but, like, so did,

719
00:37:03,620 --> 00:37:05,610
Speaker 4: you know, the idea of autonomous cyber attacks before like

720
00:37:05,650 --> 00:37:06,069
Speaker 4: a month ago.

721
00:37:06,270 --> 00:37:09,529
Speaker 1: Right, right, right, right. But also, like, When you look

722
00:37:09,570 --> 00:37:13,969
Speaker 1: at drone warfare, there's clearly a lot of stuff that

723
00:37:13,989 --> 00:37:17,089
Speaker 1: can happen now that doesn't need a person to be

724
00:37:17,130 --> 00:37:18,029
Speaker 1: behind it. Yeah.

725
00:37:18,410 --> 00:37:19,760
Speaker 5: And that will get worse.

726
00:37:19,910 --> 00:37:21,719
Speaker 4: And you don't want to live in a world where

727
00:37:22,420 --> 00:37:26,020
Speaker 4: People can kill specific other people in a completely unattributable

728
00:37:26,060 --> 00:37:27,030
Speaker 4: way with a drone.

729
00:37:27,300 --> 00:37:28,669
Speaker 5: Like, that's fucking terrifying.

730
00:37:29,050 --> 00:37:33,009
Speaker 1: And that seems like it's definitely a combat.

731
00:37:33,030 --> 00:37:35,350
Speaker 4: Yeah, we're pretty close to that being a thing already.

732
00:37:35,510 --> 00:37:38,210
Speaker 4: And I think there was the first fully autonomous... There's

733
00:37:38,230 --> 00:37:41,430
Speaker 4: been some autonomous drone attacks, but there was a threshold

734
00:37:41,530 --> 00:37:44,319
Speaker 4: cross recently in Ukraine, and it's very concerning.

735
00:37:44,780 --> 00:37:48,950
Speaker 1: Yeah. So there's the well there. So who are the

736
00:37:49,130 --> 00:37:52,650
Speaker 1: legislators who you think like could do this, who understand?

737
00:37:52,670 --> 00:37:55,950
Speaker 1: Because this is, you know, you have a very old

738
00:37:56,070 --> 00:37:59,290
Speaker 1: government filled with people who don't totally understand how to

739
00:37:59,350 --> 00:38:01,719
Speaker 1: use their phones, which used to be funny, but now

740
00:38:01,820 --> 00:38:05,299
Speaker 1: is horrible. So talk me through who you think. Have

741
00:38:05,320 --> 00:38:07,460
Speaker 1: you talked to anyone or who are the people you

742
00:38:07,500 --> 00:38:09,140
Speaker 1: think could sort of understand this?

743
00:38:09,560 --> 00:38:12,340
Speaker 4: Ironically, the person leading the charge on this is 84

744
00:38:12,340 --> 00:38:17,110
Speaker 4: years old. That's Bernie Sanders. He has pushed for the

745
00:38:17,130 --> 00:38:20,569
Speaker 4: data center moratorium and recently, like two days ago, pushed

746
00:38:20,590 --> 00:38:24,020
Speaker 4: for a pause. on frontier AI research until a safety

747
00:38:24,219 --> 00:38:26,500
Speaker 4: agency is established at the cabinet level and a ban

748
00:38:26,540 --> 00:38:29,739
Speaker 4: on superintelligence with Greg Kassar, who's a co-chair of the

749
00:38:29,800 --> 00:38:34,250
Speaker 4: Congressional Progressive Caucus. And AOC also joined Bernie on the moratorium.

750
00:38:34,510 --> 00:38:36,950
Speaker 4: And I think it's kind of interesting. The left has

751
00:38:36,989 --> 00:38:39,170
Speaker 4: historically been mixed on AI with a lot of people

752
00:38:39,310 --> 00:38:44,580
Speaker 4: not believing in the capabilities. But politicians on the left

753
00:38:44,620 --> 00:38:47,580
Speaker 4: have been leading on this and meeting the moment in

754
00:38:47,620 --> 00:38:49,859
Speaker 4: a way that other people in Congress will talk about.

755
00:38:50,500 --> 00:38:53,840
Speaker 4: super intelligence, loss of control, all these like terrifying things.

756
00:38:53,940 --> 00:38:56,320
Speaker 4: But then they don't propose the very obvious thing of

757
00:38:56,340 --> 00:38:59,980
Speaker 4: like stop. And so I think that's where things should

758
00:39:00,000 --> 00:39:01,680
Speaker 4: be going. It's like we should be stopping this.

759
00:39:02,910 --> 00:39:05,209
Speaker 1: It does seem like an issue you could really win

760
00:39:05,410 --> 00:39:06,609
Speaker 1: on as a candidate.

761
00:39:06,969 --> 00:39:07,410
Speaker 5: I think so.

762
00:39:07,450 --> 00:39:10,589
Speaker 4: And framing it as the labor replacing machine is, I think,

763
00:39:10,630 --> 00:39:13,790
Speaker 4: the way to do that. The AI safety community, which

764
00:39:13,810 --> 00:39:16,410
Speaker 4: has cared about this for the longest, they look at

765
00:39:16,450 --> 00:39:16,969
Speaker 4: it just as.

766
00:39:16,989 --> 00:39:17,870
Speaker 5: Risk for the most part.

767
00:39:18,530 --> 00:39:20,980
Speaker 4: And the jobs thing, a lot of them don't mind

768
00:39:21,040 --> 00:39:23,580
Speaker 4: if jobs are automated, because I don't know, they want

769
00:39:23,580 --> 00:39:25,759
Speaker 4: to live in like the techno utopia. Like, I think

770
00:39:25,780 --> 00:39:27,380
Speaker 4: like some of them are just like, oh, like, you know,

771
00:39:27,400 --> 00:39:29,040
Speaker 4: we'll figure out better things to do. And like, they

772
00:39:29,060 --> 00:39:31,939
Speaker 4: have this like optimistic vision about technology. They're just afraid

773
00:39:31,960 --> 00:39:34,299
Speaker 4: of this one in particular. And to me, I'm like,

774
00:39:34,320 --> 00:39:38,109
Speaker 4: the ability to replace labor wholesale is also what makes

775
00:39:38,130 --> 00:39:41,610
Speaker 4: these machines dangerous. And people don't need to believe that

776
00:39:41,630 --> 00:39:44,190
Speaker 4: they're able to take over the world or become super intelligent.

777
00:39:44,880 --> 00:39:47,020
Speaker 4: if they're afraid of just losing their job to a

778
00:39:47,040 --> 00:39:49,680
Speaker 4: machine that can like get smarter than them faster than

779
00:39:49,719 --> 00:39:54,040
Speaker 4: they can, like that's enough to build a massive political movement.

780
00:39:54,239 --> 00:39:57,680
Speaker 1: I mean, it's as good a cause as any, right?

781
00:39:57,719 --> 00:40:01,779
Speaker 1: I mean, it's basically keeping us from being extinct. Like

782
00:40:01,980 --> 00:40:04,580
Speaker 1: climate change is a really good example of something where

783
00:40:04,600 --> 00:40:08,600
Speaker 1: we saw it coming and we thought about doing something

784
00:40:08,640 --> 00:40:11,940
Speaker 1: and we decided not to. Does this feel like that?

785
00:40:11,960 --> 00:40:16,109
Speaker 1: I mean, do you like obviously like with climate change,

786
00:40:16,150 --> 00:40:19,670
Speaker 1: you had oil and gas companies that were pretty involved

787
00:40:19,730 --> 00:40:22,860
Speaker 1: in trying to make it so that we couldn't ever

788
00:40:23,060 --> 00:40:27,440
Speaker 1: stop it. But like we're pretty far along with climate change.

789
00:40:27,940 --> 00:40:31,660
Speaker 1: I mean... So I just wonder if you like how

790
00:40:31,680 --> 00:40:33,420
Speaker 1: you sort of square those two things.

791
00:40:33,440 --> 00:40:34,419
Speaker 5: No, I think about this a lot.

792
00:40:34,600 --> 00:40:36,900
Speaker 4: And I think with AI, we're kind of like where

793
00:40:36,940 --> 00:40:39,799
Speaker 4: climate change was in the 80s, where the scientists studying

794
00:40:39,820 --> 00:40:41,730
Speaker 4: it were like close to forming a consensus that it

795
00:40:41,770 --> 00:40:44,270
Speaker 4: was a real thing. And then the media took decades

796
00:40:44,310 --> 00:40:47,069
Speaker 4: to catch up. And then the policy response lagged that

797
00:40:47,210 --> 00:40:51,279
Speaker 4: as well. And with AI, like 70% of people, of

798
00:40:51,340 --> 00:40:53,580
Speaker 4: top AI researchers, if you ask them, like, does this

799
00:40:53,600 --> 00:40:55,140
Speaker 4: technology pose an existential risk?

800
00:40:55,580 --> 00:40:56,080
Speaker 5: They're like, yes.

801
00:40:56,760 --> 00:40:58,529
Speaker 4: And the numbers are actually even higher if you ask

802
00:40:58,550 --> 00:41:00,890
Speaker 4: it in different ways. And so a consensus is forming.

803
00:41:01,230 --> 00:41:03,630
Speaker 4: But then in the media, you'll see stuff like, you know,

804
00:41:03,650 --> 00:41:06,009
Speaker 4: there's no evidence for this being an existential risk. And

805
00:41:06,030 --> 00:41:08,950
Speaker 4: it's like, well, I mean, yeah, we haven't gone extinct.

806
00:41:09,330 --> 00:41:11,640
Speaker 4: We don't get to know that in advance, obviously. But

807
00:41:11,680 --> 00:41:14,060
Speaker 4: like the top cited scientists in the world, the top

808
00:41:14,080 --> 00:41:16,980
Speaker 4: two have made this their priority just to warn people

809
00:41:17,020 --> 00:41:17,439
Speaker 4: about this.

810
00:41:17,920 --> 00:41:19,680
Speaker 5: The majority of these researchers, lots.

811
00:41:19,540 --> 00:41:22,330
Speaker 4: Of other people, the arguments are actually quite clear And

812
00:41:22,870 --> 00:41:25,230
Speaker 4: AI just keeps getting more capable and autonomous.

813
00:41:25,750 --> 00:41:27,029
Speaker 5: And that's like the fundamental thing.

814
00:41:27,130 --> 00:41:29,270
Speaker 4: If it is able to replace our labor and then

815
00:41:29,590 --> 00:41:31,110
Speaker 4: they can make more of the machines and there are

816
00:41:31,130 --> 00:41:33,930
Speaker 4: people which we'd be able to, it's pretty intuitive how

817
00:41:33,969 --> 00:41:35,569
Speaker 4: like we could lose control of the planet to that.

818
00:41:36,590 --> 00:41:39,260
Speaker 4: But people just don't, you know, it's like feels crazy

819
00:41:39,300 --> 00:41:41,399
Speaker 4: or sci-fi or like, you know, you get, you know,

820
00:41:41,560 --> 00:41:43,400
Speaker 4: sneered at on the internet if you say this stuff.

821
00:41:43,460 --> 00:41:45,899
Speaker 4: But I think the evidence is actually like very hard

822
00:41:46,020 --> 00:41:48,540
Speaker 4: to dispute. And at the very least, we can't rule

823
00:41:48,580 --> 00:41:50,460
Speaker 4: it out. And so we should just take precautions.

824
00:41:50,780 --> 00:41:53,130
Speaker 1: The one thing that makes me feel a little better

825
00:41:53,469 --> 00:41:56,190
Speaker 1: is that I was talking to someone on the podcast

826
00:41:56,210 --> 00:41:58,990
Speaker 1: who I had on who was like, the AI people

827
00:41:59,320 --> 00:42:03,520
Speaker 1: exaggerate how good the technology is. Do you think that's

828
00:42:03,580 --> 00:42:04,100
Speaker 1: true or no?

829
00:42:04,800 --> 00:42:07,700
Speaker 4: Oh, I mean, they definitely do, but I think they

830
00:42:07,719 --> 00:42:11,500
Speaker 4: like often underrate how good it could become. Right. So

831
00:42:11,520 --> 00:42:13,960
Speaker 4: like the most hypey answers about what it can do

832
00:42:14,000 --> 00:42:16,719
Speaker 4: today are like, yeah, it's overhyped for sure in a

833
00:42:16,739 --> 00:42:19,780
Speaker 4: bunch of ways, but like the people who are boosters

834
00:42:19,820 --> 00:42:22,700
Speaker 4: about this, they often don't actually internalize what it would

835
00:42:22,739 --> 00:42:25,969
Speaker 4: mean to build a labor replacing machine. They'll be like, oh,

836
00:42:26,090 --> 00:42:30,930
Speaker 4: artificial general intelligence will revolutionize the future of apps. It's like, dude,

837
00:42:30,969 --> 00:42:33,410
Speaker 4: we're building a second species. Like, do you not get

838
00:42:33,469 --> 00:42:35,630
Speaker 4: like how insane the world could be like in like

839
00:42:35,630 --> 00:42:38,610
Speaker 4: 10 or 20 years? So yeah, I think it's like

840
00:42:38,660 --> 00:42:41,279
Speaker 4: this kind of mismatch with like the timescale and people

841
00:42:41,300 --> 00:42:42,359
Speaker 4: be like, oh, it's dumb right now.

842
00:42:42,440 --> 00:42:44,580
Speaker 5: So therefore it's fine. It's like, well, it keeps getting

843
00:42:44,620 --> 00:42:45,759
Speaker 5: less dumb is the thing.

844
00:42:46,440 --> 00:42:49,120
Speaker 1: Tell us what the movement part of it looks like.

845
00:42:49,540 --> 00:42:51,899
Speaker 5: Yeah, I mean, I think that there are these different

846
00:42:51,920 --> 00:42:53,080
Speaker 5: camps within AI.

847
00:42:53,100 --> 00:42:55,210
Speaker 4: There's like the people who are worried about it, the

848
00:42:55,230 --> 00:42:57,049
Speaker 4: people who are critics of it, and the people who

849
00:42:57,090 --> 00:42:59,150
Speaker 4: are boosters who just want to go faster. I think

850
00:42:59,170 --> 00:43:01,830
Speaker 4: they get some things right, some things wrong. I propose

851
00:43:01,870 --> 00:43:04,500
Speaker 4: this camp or like label I call AI reform camp.

852
00:43:05,000 --> 00:43:07,640
Speaker 4: which the argument is like AI is neither impossible or inevitable,

853
00:43:07,880 --> 00:43:11,450
Speaker 4: but it's potential that's being squandered because while the technology

854
00:43:11,489 --> 00:43:13,870
Speaker 4: is incredible, it's being pointed at the wrong things by

855
00:43:13,890 --> 00:43:15,290
Speaker 4: the wrong people for the wrong reasons.

856
00:43:16,150 --> 00:43:17,129
Speaker 5: And I have this.

857
00:43:17,310 --> 00:43:20,379
Speaker 4: URL called AI reform.org and we'll have some stuff there

858
00:43:20,420 --> 00:43:23,400
Speaker 4: for people to do. There's this org called Irreplaceable that

859
00:43:23,440 --> 00:43:25,620
Speaker 4: is also, I'm on the board of that. And it's

860
00:43:25,900 --> 00:43:28,620
Speaker 4: climate activists who have built a lot of the climate movement,

861
00:43:29,060 --> 00:43:31,739
Speaker 4: taking that approach to AI, and I'm donating my portion

862
00:43:31,760 --> 00:43:32,570
Speaker 4: of the royalties to it.

863
00:43:32,910 --> 00:43:35,589
Speaker 1: So you have a podcast. Tell us about your podcast. Yes.

864
00:43:35,870 --> 00:43:37,190
Speaker 5: It's called Organize Against the Machine.

865
00:43:37,690 --> 00:43:40,489
Speaker 4: It's me and labor organizer Cassie Pritchard taking ideas from

866
00:43:40,510 --> 00:43:43,569
Speaker 4: the book and translating them into real world action. And

867
00:43:44,090 --> 00:43:46,739
Speaker 4: it's like we've recorded four episodes, but it'll be out

868
00:43:47,530 --> 00:43:49,969
Speaker 4: for sure by the time the episode airs.

869
00:43:50,770 --> 00:43:51,850
Speaker 5: You know, books are old.

870
00:43:52,090 --> 00:43:54,549
Speaker 4: Podcasts and clips and vertical video, like that's how you

871
00:43:54,570 --> 00:43:57,130
Speaker 4: reach people nowadays. And so we're just very serious about

872
00:43:57,610 --> 00:44:00,050
Speaker 4: taking these ideas and making sure they actually lead to

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00:44:00,090 --> 00:44:00,550
Speaker 4: real change.

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Speaker 1: Thank you so much, Garrison.

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00:44:03,140 --> 00:44:05,000
Speaker 5: Yeah, of course. Feel free to email me any questions.

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Speaker 1: Yeah.

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Speaker 5: This is fun.

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00:44:06,420 --> 00:44:06,830
Speaker 1: Thank you.

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Speaker 3: And now your moment of suffering.

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00:44:11,710 --> 00:44:12,890
Speaker 1: Jesse Cannon.

881
00:44:13,270 --> 00:44:16,070
Speaker 2: Somalia, I'm racking my brain. I'm trying to think of

882
00:44:16,130 --> 00:44:18,549
Speaker 2: a time when we've seen that there was a corruption

883
00:44:18,590 --> 00:44:21,660
Speaker 2: investigation of a politician where we actually saw the checks

884
00:44:21,760 --> 00:44:25,240
Speaker 2: from the bribes. But here we are with Senator Susan

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00:44:25,280 --> 00:44:28,500
Speaker 2: Collins of Maine, who's in a heated reelection battle against

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00:44:28,580 --> 00:44:31,960
Speaker 2: Troy Jackson, who looks like he may just... get the

887
00:44:32,000 --> 00:44:35,170
Speaker 2: job done, especially after this, that we see Susan Collins

888
00:44:35,230 --> 00:44:38,040
Speaker 2: is absolutely more corrupt than even we're used to of

889
00:44:38,100 --> 00:44:39,420
Speaker 2: our most corrupt politicians.

890
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Speaker 1: So basically, an ex-defense contractor told agents that his company

891
00:44:44,270 --> 00:44:49,210
Speaker 1: won millions in federal contracts after bankrolling campaigns of powerful politicians.

892
00:44:49,690 --> 00:44:54,130
Speaker 1: Pay quid pro quo. Pay for play. You write a

893
00:44:54,150 --> 00:44:57,870
Speaker 1: check to Susan Collins. Susan Collins makes sure that money

894
00:44:57,910 --> 00:45:00,890
Speaker 1: goes to you. And that's what happened here. The head

895
00:45:01,090 --> 00:45:04,870
Speaker 1: of the Collins super PAC, needed to raise money, went

896
00:45:04,950 --> 00:45:09,450
Speaker 1: to these executives from a Hawaii defense contractor. They each

897
00:45:09,790 --> 00:45:13,290
Speaker 1: wrote a check, half a million dollars, and for a

898
00:45:13,330 --> 00:45:18,109
Speaker 1: half a million dollars, they got millions and millions of dollars. Now,

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Speaker 1: government contractors are banned from making political contributions for this

900
00:45:22,020 --> 00:45:25,600
Speaker 1: very reason, which is why they gave to the super PAC.

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00:45:25,760 --> 00:45:31,580
Speaker 1: This is what we call corruption, and it is so, so,

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00:45:33,239 --> 00:45:38,050
Speaker 1: So bad. That's it for this episode of Fast Politics.

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Speaker 1: Tune in every Monday, Wednesday, Thursday and Saturday to hear

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Speaker 1: the best minds in politics make sense of all this chaos.

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00:45:49,270 --> 00:45:51,989
Speaker 1: If you enjoy this podcast, please send it to a

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00:45:52,070 --> 00:45:56,080
Speaker 1: friend and keep the conversation going. Thanks for listening.