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Speaker 1: Broadcasting live from the Abraham Lincoln Radio Studio, the George

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Speaker 1: Washington Broadcast Center, Jack Armstrong and Shoe, Ketty.

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Speaker 2: Armstrong and Jetty and he Armstrong and Yetty.

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Speaker 3: Elon Musk, who has been at President Trump's side seen

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Speaker 3: in the West Wing, now speaking out against Trump's plan

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Speaker 3: Trump announcing a five hundred billion dollar artificial intelligence project,

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Speaker 3: Musk now saying the money isn't there. The White House

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Speaker 3: now saying listen to Trump.

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Speaker 2: Not Musk. Oh, does the media love the idea of

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Speaker 2: perhaps being able to drive a wedge between Trump and

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Speaker 2: Musk because it's so scary. Oh, I saw a headline

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Speaker 2: about that today, something about the oligarchy or whatever the hell.

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Speaker 1: Oh, for goodness, says halload of crap, And honestly, and

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Speaker 1: it's funny. I was listening to another podcast. Turns out

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Speaker 1: there is another one. I've had it shut down by

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Speaker 1: Trump because ours should be the only one. But and

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Speaker 1: the question they were asking panel guys was, how long

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Speaker 1: do you think before there's a rift between Trump and Elon?

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Speaker 1: Two Alpha dogs? Is too many in the same room?

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Speaker 1: And they're like, oh, not for a long time, blah

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Speaker 1: blah blah. I don't know I'm not as optimistic. I

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Speaker 1: just wonder how long it can last.

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Speaker 2: Axiosi's headline today, America doesn't have a king, but we're

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Speaker 2: dancing close to king like power, and they get into

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Speaker 2: Trump and Elon running the world together.

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Speaker 1: Dancing close to King like Power is the title of

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Speaker 1: my new electronica album, Altho Way coming out Friday.

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Speaker 4: That's really exciting. It drops Friday, It drops Friday.

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Speaker 2: What was I gonna say, Elon? Well, Alpha, I don't.

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Speaker 2: I don't even know what that term means anymore. But

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Speaker 2: Elon seems to let things roll off him because he can.

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Speaker 2: I mean, say whatever you want. The world's rich, just

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Speaker 2: man by like double.

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Speaker 4: Exactly. How you hurt me with your scathing words? Yeah, exactly.

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Speaker 1: So there are a couple of really interesting Elon related conflicts.

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Speaker 4: So the fact that he poo pooed the big.

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Speaker 1: Announcement by Open Ai and soft Bank in Oracle like

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Speaker 1: an hour after Trump was touting it as a wonderful,

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Speaker 1: wonderful thing. That's not cool in any organization. And I

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Speaker 1: think it's part of his autism spectrum thing. He just

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Speaker 1: he doesn't. He doesn't because I deal with this on

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Speaker 1: a regular basis. He doesn't get regular communication flow. He

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Speaker 1: doesn't get the the what is the right term both

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Speaker 1: physical and verbal signals that most people understand and get.

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Speaker 1: People on the spectrum oftentimes don't understand. And I think

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Speaker 1: he's one of those people. Yeah, yeah, there's some truth

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Speaker 1: to that. I think how much is impossible to know

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Speaker 1: from a distance. But I also think there is a

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Speaker 1: bit of an element of we're talking business and finance

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Speaker 1: and technology. With all due respect, mister hotel and golf

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Speaker 1: course builder, I'm the best in the world at this.

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Speaker 1: So you don't tell me what to say and what

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Speaker 1: not to say about business and technology. Well, yeah, you

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Speaker 1: gotta believe there's some element of that.

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Speaker 2: Well, then he shouldn't have that job because you serve

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Speaker 2: at the pleasure of the president, and the president's of

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Speaker 2: the boss. Marco Ruby is not going to agree with

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Speaker 2: all of Trump's foreign policy decisions. But you either resign

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Speaker 2: or you carry out the boss's desires.

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Speaker 1: Right, You've explained exactly why I believe what I believe

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Speaker 1: that it's going to crack up.

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Speaker 2: So you don't think he's going to go along with

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Speaker 2: the boss the way Marco Rubio probably is.

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Speaker 1: Yeah, I don't think he can I don't. I don't

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Speaker 1: think it's part of his being.

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Speaker 4: I don't. It would be too weird and uncomfortable for him.

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Speaker 1: But anyway, I hope they do wonderful work while they're

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Speaker 1: going at it, and I want to talk about that for.

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Speaker 2: The next forty eight hours, two forty years.

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Speaker 4: I don't know. I don't know.

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Speaker 1: Uh, it's it's it's fire, it's a oil in water,

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Speaker 1: though not oil and water.

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Speaker 4: It's tea and whatever. The end is a better way

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Speaker 4: to go.

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Speaker 1: Yeah, but something explosive anyway, uh dur oh. So getting

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Speaker 1: back to Elon Musk versus Sam Altman the catfight, this

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Speaker 1: is where it gets really because Elon Musk said of

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Speaker 1: the Giant Data Center announcement, they don't have the money.

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Speaker 4: Uh.

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Speaker 1: He was saying, they have a fraction of the money.

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Speaker 1: They're talking about. This is this They're they're way over

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Speaker 1: selling this thing. Again, like an hour and a half

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Speaker 1: after the President had just touted it, Trump said, this

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Speaker 1: will include the construction of colossal data center's massive structures.

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Speaker 1: These buildings, big beautiful buildings are going to employ a

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Speaker 1: lot of people.

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Speaker 4: And then Elon says, well, he said essentially what I said.

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Speaker 1: They don't have a fraction of the money they're gonna need,

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Speaker 1: so his comments set off a war of words on

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Speaker 1: his own media platform. X Altman initially sounded a conciliatory note,

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Speaker 1: saying I genuinely respect your accomplishments and think you are

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Speaker 1: the most inspiring entrepreneur of our time, and then he

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Speaker 1: later posted that Musk's comments about SoftBank's funding were wrong,

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Speaker 1: the first data centers already underway and quote, I realized

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Speaker 1: what is great for the country isn't always what's optimal

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Speaker 1: for your companies. But in your new role, I hope

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Speaker 1: you'll mostly put America first.

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Speaker 2: WHOA. Musk fired back with a string of posts that

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Speaker 2: was all part of the same post or was that

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Speaker 2: different posts? That was a little later, Okay, so okay,

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Speaker 2: because I was going to say you can't start with

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Speaker 2: the handy and then immediately jump to that.

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Speaker 4: But they're different.

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Speaker 1: Oh wow, Oh anyway, so that was later. Musk fired

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Speaker 1: back with a string of posts. Wednesday, he repeated criticisms

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Speaker 1: vaultman open Ai, saying it's fake to one user's tweet

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Speaker 1: citing the Stargate announcement. The Stargate announcement tweet elon Musk

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Speaker 1: said it's fake. This is two days or a day

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Speaker 1: after the President touted it as being wonderful.

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Speaker 2: Okay, I'm completely in your camp. Now it's gonna crack up.

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Speaker 2: There's no another tweet saying Sam is a swindler. All right,

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Speaker 2: Well that's too bad. I'm not happy about this. I

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Speaker 2: usually am happy about this sort of thing because I

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Speaker 2: like I like rich, powerful, famous people at each other.

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Speaker 2: But I don't like this one. I'd rather these people

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Speaker 2: could work together.

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Speaker 1: Musk also retweeted a post showing a photo of what

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Speaker 1: appeared to be a drug pipe and a baggy of

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Speaker 1: powder under the caption leaked image of the research tool

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Speaker 1: open AI used to come up with their five hundred

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Speaker 1: billion dollar number for stargate.

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Speaker 2: That's funny, Yes, that's pretty fund The level of you know,

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Speaker 2: it's a coarse term, but the level of no fs

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Speaker 2: to give you have when you're the world rich, just

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Speaker 2: mad is something nobody's ever seen. Mefore you just cash

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Speaker 2: out your thoughts about everything on a regular basis.

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Speaker 1: Yeah, clearly, clearly true. So a little more technology, government

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Speaker 1: and cat fighting that I found interesting. Oh that's right,

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Speaker 1: then I've got like the important scientific thing. Anyway, Leaks

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Speaker 1: are beginning to leak about the excuse me the conflict

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Speaker 1: between Elon Musk and Vivey Kramaswami in Doge, the cover

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Speaker 1: story being Hey, I'm going to run for governor of

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Speaker 1: Ohio is going to take my time, And I guess

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Speaker 1: Elon was more about cutting spending viveke who was more

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Speaker 1: about cutting regulations. Honestly, it's tough to pick a favorite there.

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Speaker 1: They're both huge priorities. So you got peanut butter in

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Speaker 1: my chocolate? Why can't why can't they fit together?

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Speaker 4: Exactly?

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Speaker 1: But some of the leaks are saying, though the claim

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Speaker 1: is there is no hard feelings, the leaks are saying that, yeah,

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Speaker 1: every meeting you have, if they comes in and he

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Speaker 1: weighs in with strong opinions on everything.

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Speaker 2: Wait, and the guy's just obnoxious. He doesn't seem like

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Speaker 2: that kind of guy.

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Speaker 1: Oh wait, yeah, I know that that rapid fire of

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Speaker 1: hyper confident manner of speaking that he has. Yeah, look,

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Speaker 1: he's a he's a very very bright guy and has

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Speaker 1: been very successful.

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Speaker 4: He's very rich. He's worth listening to. But man, he's

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Speaker 4: hard to take.

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Speaker 2: You know, it's interesting. I've only known a handful of

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Speaker 2: people like this in my life that I've ever like

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Speaker 2: actually got to be in meetings with or whatever. But

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Speaker 2: if you ever have, you know, I mean they dominate

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Speaker 2: the room, and I mean they they feel like they should,

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Speaker 2: they feel like they need to and usually nobody ever

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Speaker 2: says anything to them. I've never been in a room

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Speaker 2: where there's two of them. I don't know what that

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Speaker 2: would be like. I've been in plenty of rooms where

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Speaker 2: there's one of them and everybody has to sit there

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Speaker 2: and listen to them go on, and they say some

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Speaker 2: stuff that's clearly bs and they tell you boring stories,

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Speaker 2: but everybody just nods and laughs because they're the big dog.

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Speaker 2: And I've been in luck, but I've never been in

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Speaker 2: a room where there's two of those people. And I

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Speaker 2: don't know what that would look like. And I'm sure

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Speaker 2: Elon and Viveaik was one of those situations, and.

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Speaker 1: That was one of the other leaks that it was

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Speaker 1: really starting to annoy Elon.

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Speaker 2: Well, especially if you've been that way your whole life,

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Speaker 2: every meeting you're in, little by little over your life,

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Speaker 2: you got used to everybody sits there and listens to

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Speaker 2: you and is amazed by your stories and laughs at

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Speaker 2: your jokes, and then all of a sudden, you're in

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Speaker 2: a room where somebody's like, now, my stories and jokes

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Speaker 2: are good. Your suck. I mean, that's got to be

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Speaker 2: quite shocking to those people.

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Speaker 4: Yeah. Yeah.

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Speaker 1: I was chatting with a friend who was a CEO

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Speaker 1: of a company and retired. He had a very successful

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Speaker 1: business career, and I was talking about a function we're

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Speaker 1: at where somebody was very long winded, very I mean,

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Speaker 1: like everybody was just praying to get on with it.

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Speaker 1: And I already that it was not a funeral. And

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Speaker 1: I'm being vague because these are all very nice people,

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Speaker 1: but the person had a career where people had to

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Speaker 1: listen to them. And I was saying, there's an enormous

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Speaker 1: contrast between somebody who for an entire career had people

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Speaker 1: who had to listen to them and somebody like me,

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Speaker 1: who if I'm boring or whatever, my career goes away instantly,

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Speaker 1: people turn you off. And exactly, and my friend said, yeah,

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Speaker 1: that's the one thing I miss about being a CEO.

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Speaker 4: Yep.

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Speaker 1: Every joke I told was hilarious, Every speech I made

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Speaker 1: was brilliant. Everything I said was riveting, funny.

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Speaker 4: How that ends?

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Speaker 1: And uh yeah, yeah, that's a danger you've got to

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Speaker 1: have of a wife or a buddy or a loyal

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Speaker 1: assistant who's going to call you on yours. I mean,

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Speaker 1: that's like the best thing you can have if you're

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Speaker 1: in a position of power.

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Speaker 4: Isn't it.

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Speaker 2: Yeah? Absolutely, I don't know if that happens very often.

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Speaker 1: It's a rare relationship. Yeah, got to be handled carefully anyway.

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Speaker 1: The thing I was going to get to, but I

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Speaker 1: don't want to rush through it. Maybe we can do

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Speaker 1: it next or a little later on in the hour.

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Speaker 1: Is an absolutely terrific research scientist who has been studying

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Speaker 1: various topics AI related and publishing really interesting takes on them.

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Speaker 1: Did a big study of the bias, the political bias

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Speaker 1: in AI systems that we've all kind of heard about

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Speaker 1: and seen examples of. The takeaway is there's just no

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Speaker 1: denying it at this point. Some of the particulars are

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Speaker 1: really interesting.

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Speaker 2: The alpha, it's beyond alpha. The Vik versus Elon thing

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Speaker 2: remind me. And this is Katie, I want you to

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Speaker 2: be around for this next segment. This story, this is

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Speaker 2: probably a misogynist story of a female version of this

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Speaker 2: that I witnessed one time.

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Speaker 4: He's a pig, Katie, none of us like that.

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Speaker 2: Is pretty interesting, so stick around for that coming up.

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Speaker 5: Speaking of the inauguration, Mark Zuckerberg sat next to Lauren

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Speaker 5: Sanchez and was caught, Yeah, it was caught laring at

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Speaker 5: her ample Bosom, prompting a nearby and very horny Bill

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Speaker 5: Clinton to say.

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Speaker 4: What an amerateur.

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Speaker 2: I don't know about the punchline. That was an interesting

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Speaker 2: thing that happened. Of course, she was wearing a well,

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Speaker 2: as Katie keeps pointing out, she was wearing underwear. She

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Speaker 2: was letting Lingerie under her.

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Speaker 4: Blame that Harlote Madonna.

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Speaker 2: So getting to this story and producing underwear is outerwear.

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Speaker 4: It's disgusting.

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Speaker 2: I'm a little uncomfortable with this story because we were

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Speaker 2: just talking about how Elon and Effect maybe couldn't get along.

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Speaker 2: Because when do two people to two people, I won't say, guys,

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Speaker 2: two people that are that successful ever end up in

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Speaker 2: the same room as each other. Wait a second, people

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Speaker 2: always look at me all the time in a room.

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Speaker 2: Why are people looking at you? You know, neither one

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Speaker 2: of them. I'd probably run into that very often and

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Speaker 2: just and it's also like I was doing this the

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Speaker 2: other day walking down the street. I was walking watching

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Speaker 2: a couple of other dogs. Somebody's walking with their dog

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Speaker 2: and as many as other dog and just the immediate

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Speaker 2: you know, some dogs pass no problem, some dogs just

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Speaker 2: they just they the energy. I don't know what it is.

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Speaker 2: Nobody knows what it is. It's interesting, is the alpha,

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Speaker 2: whatever it is. I experiences this one time with women.

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Speaker 2: And I hate the fact that this is about looks

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Speaker 2: and not about like power and intelligence, because it makes

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Speaker 2: it seem like I think it only can be about looks,

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Speaker 2: but in this case, it was about looks. So I

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Speaker 2: worked at a radio station and the rees this is

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Speaker 2: many years ago, Gladys. I worked at a radio station. Oh,

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Speaker 2: Gladys is Gladys Gladdice as a getting ice surgery today, right,

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Speaker 2: so she can't be here. I worked at a radio

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Speaker 2: station and the receptionist was a hoty and she was

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Speaker 2: like the only hotty in the building. It's very small

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Speaker 2: radio station, and she had, you know, so that was

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Speaker 2: her turf and she was the flirted with people everybody

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Speaker 2: paid attention to, where everybody laughed at her jokes the way,

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Speaker 2: you you know, because she went the hotti. Well, we

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Speaker 2: got an intern for a while who was younger and

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Speaker 2: hotter or as hot and uh and immediately, I mean

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Speaker 2: it was like you could see sparks fly off these

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Speaker 2: two people. It was like the dogs walking down the

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Speaker 2: streets are just immediately. It was observable to me as

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Speaker 2: the program director of that radio station, and it played

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Speaker 2: out for months.

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Speaker 4: Oh man, immediate hostility.

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Speaker 2: Huh, just yeah, just immediate. Wait a second, just like

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Speaker 2: I was saying with Elon and Vivek, why is everybody

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Speaker 2: looking at her? Everybody always looks at me? And I

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Speaker 2: think that other girl was having a little of why

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Speaker 2: is anybody looking her? People always look at me? And

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Speaker 2: just they had an experience that before. Does does that

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Speaker 2: ring a bill Katie? Or am I just a misogynist?

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Speaker 4: No?

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Speaker 2: Well both different?

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Speaker 4: Yeah wait a minute, yeah false choice.

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Speaker 6: Uh, that sounds like a battle of the egos thing there.

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Speaker 4: Sure, but it's god.

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Speaker 2: If you are the one everybody looks at and then

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Speaker 2: all of a sudden people are looking at someone else,

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Speaker 2: it's got a register in some level of your brain.

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Speaker 6: I would think, Yeah, I could see how that would

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Speaker 6: sting a little.

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Speaker 2: You're like the has been hot chick. Well, it's it's

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Speaker 2: all status.

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Speaker 1: However, you get your status within the the organization or

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Speaker 1: the society, the mini society you're in. If you're the

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Speaker 1: left handed pitcher on the Yankees and they bring in,

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Speaker 1: you know, Doug Kershar or whatever, Clayton Kershaw or or whomever,

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Speaker 1: some other greater left handed pitcher, that that hurts, that

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Speaker 1: burns just in any you.

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Speaker 4: Know, society.

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Speaker 2: I think doubt Elon was bothered much, but I can

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Speaker 2: see how a vague hadn't run into that situation in

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Speaker 2: quite some time where somebody else was getting all the

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Speaker 2: attention and then people were listening to him the other

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Speaker 2: guy more than him.

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Speaker 6: Elon is so dense, and then just going back to

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Speaker 6: how vi Vic was during like the debates and what

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Speaker 6: notot how.

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Speaker 2: Yea sufferable He's hard to take.

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Speaker 4: Yeah, yeah, yeah, incredibly bright guy, but so smug.

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Speaker 7: Yeah.

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Speaker 2: Elon has the option always if you're annoying me, gets

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Speaker 2: out his check book. What's your company worth? Okay, forty

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Speaker 2: billion dollars and that's my company? Now get out of here.

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Speaker 4: I will open you.

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Speaker 2: I don't want to talk to you anymore. Yeah. God,

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Speaker 2: the sparks flying off of these two women, it was

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Speaker 2: just amazing. Eh. You two work it out, bite each

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Speaker 2: other or urinate on it, smell it, do whatever it

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Speaker 2: is you got to do, work out some sort of

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Speaker 2: pecking order it.

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Speaker 1: As a non misogynist, I suggest we sell this the

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Speaker 1: old fashioned way in a tub full of jelate.

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Speaker 7: No orry.

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Speaker 2: The receptionist was my girlfriend, so I had to keep

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Speaker 2: my mouth shut completely about the whole thing. But it

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Speaker 2: was just very very tense, very tense around there for

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Speaker 2: a while.

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Speaker 4: Oh boy, we got a.

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Speaker 2: New fire in the LA area. The weather is going

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Speaker 2: to be really bad today again, and we're going to

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Speaker 2: talk to a favorite meteorologist of us of ours is

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Speaker 2: going to tell us about the conditions and all that

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Speaker 2: sort of thing.

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Speaker 4: Armstrong and Getty.

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Speaker 8: New fire breaking out in Los Angeles along the four

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Speaker 8: to five freeway. You're the Getty Center Museum, prompting evacuation

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Speaker 8: warnings and parts of bel air flames at evers shooting

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Speaker 8: up the hillside. That is, more than four thousand firefighters

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Speaker 8: raced to battle the quick moving hues fire just north

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Speaker 8: of Los.

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Speaker 2: Angeles, how near the Getty Museum. That would be a

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Speaker 2: tragedy obviously. And I just I saw some of the

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Speaker 2: footage last night watching the news and earn awful lot

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Speaker 2: of neighborhoods with a lot of houses nearby.

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Speaker 1: Yeah, multiple new fires, not that shocking as the dry

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Speaker 1: conditions continue and the winds are whipping. We're joined by

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Speaker 1: Rick dickert Ams, certified broadcast meteorologists, long time presence in

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Speaker 1: Southern California news and weather.

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Speaker 4: Rick, it's a pleasure to have you back again. How

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Speaker 4: are you.

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Speaker 7: Thank you, Jack and Joe again we are We're looking

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Speaker 7: at a really dangerous twenty four hours. We need to

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Speaker 7: get through these twenty four hours with more high winds, tinder,

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Speaker 7: dry vegetation, very low community values, the red flag warnings continue.

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Speaker 7: You mentioned the two new fires, thankfully, the fire that

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Speaker 7: broke out along the four to five near the Getty

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Speaker 7: Center that has pretty much been contained. No structures were

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Speaker 7: damaged or burned that had a handle on that. The

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Speaker 7: huge fire, a much larger fire north of that location

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Speaker 7: in northern La County in the Angelus National Forest that too,

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Speaker 7: having burned over thousand acres, thankfully, has not burned a

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Speaker 7: structure yet. That is burning more up in the forest

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Speaker 7: land away from any neighborhood.

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Speaker 2: The number of agres how many acres did you say?

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Speaker 7: It was over ten thousand with a huge fire. The

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Speaker 7: suppult of the fire along the FOURAL five was relatively

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Speaker 7: small compared Yeah.

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Speaker 2: I saw the stat where it was five thousand eight

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Speaker 2: in like an hour or two something like that. That's

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Speaker 2: just stunning, and it's hard to imagine. Is that mostly

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Speaker 2: the wind that causes it to burn so fast?

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Speaker 7: Absolutely, there's a lot of there's a lot of things

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Speaker 7: that contribute to what we've seen here in southern California

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Speaker 7: over the last month. The weather doesn't cause the fire.

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Speaker 7: Ninety five percent of fires, whether they are structure fires

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Speaker 7: or wildland fires, are produced by man, some sort of ignition,

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Speaker 7: anthropologically produced, whether it be infrastructure wires are whether it

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Speaker 7: be arson, whether it be somebody working on a field

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Speaker 7: and some little spark ignites the brush. That's how these

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Speaker 7: fires start. The wind and the weather creates the scenario,

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Speaker 7: those red flag conditions for those ignitions to spread. That's

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Speaker 7: why we issue these red flag warnings. The National Weather

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Speaker 7: Service does, and we are still under a red flag

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Speaker 7: warning across much of southern California until ten am tomorrow.

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Speaker 7: And what that means any existing fire or new fire

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Speaker 7: will spread rapidly because of the weather conditions.

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Speaker 4: Do you see any good news weatherwise in the yard medium.

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Speaker 7: Turna, so again we just need to get through twenty

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Speaker 7: four hours or so, and then after that there is

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Speaker 7: going to be a dramatic shift. It's incredible when you

389
00:20:13,040 --> 00:20:16,720
Speaker 7: look at Southern California weather and how bipolar it can be,

390
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Speaker 7: and that we're going to shift into an onshore flow

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Speaker 7: flow off the Pacific. The humidity values will rise, the

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Speaker 7: temperature will drop, and by Saturday afternoon or evening, we're

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Speaker 7: looking at rain, good rain, soaking rain, not so much

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Speaker 7: that we're concerned yet about any of the burn scar

395
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Speaker 7: areas where we could see mud walking debrif flows, but

396
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Speaker 7: enough to soak up all this tinder dry vegetation. We

397
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Speaker 7: haven't had significant rainfall since last spring, and that's what

398
00:20:44,800 --> 00:20:48,520
Speaker 7: created this perfect storm, these firestorms, and that we had

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Speaker 7: two years of abundant rainfall, mountain snow that allowed all

400
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Speaker 7: of that brush, that chapparral, all of the vegetation that's

401
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Speaker 7: indigenous to southern California to completely explode on the hillside.

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Speaker 7: Then that was followed by months and months of little

403
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Speaker 7: or no rain, and then the off conditions, the low

404
00:21:07,240 --> 00:21:10,720
Speaker 7: relative humidities, the winds going from the land towards the sea,

405
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Speaker 7: you get the ignition. Once that ignition happens, it takes off.

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Speaker 7: And that's what we saw, unfortunately tragically with the Palisades

407
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Speaker 7: fire near Malibu and above the citing area, above the

408
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Speaker 7: Rose Bowl, the iconic Rose Bull. So now we're dealing

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Speaker 7: with a couple of incidents out there that none of

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Speaker 7: which are nearly comparable to those two.

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Speaker 2: Well, I don't know if you saw Dave Chappelle on

412
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Speaker 2: Saturday Night Live the other night, but he said the

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Speaker 2: only conclusion you can draw is God hates these people.

414
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Speaker 2: I mean, because that's a lot of things happening there. So, yeah,

415
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Speaker 2: if it rains like it's supposed to rain, would that

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Speaker 2: mean we're just kind of done with at least this

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Speaker 2: round of disastrous fires.

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Speaker 7: For well, yes, because I can't tell you in two

419
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Speaker 7: weeks from now that you know, we're going to see

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Speaker 7: another round of sure winds that will dry everything out

421
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Speaker 7: a gag and we could see more red flag conditions,

422
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Speaker 7: but it'll at least it's going to cleanse the city

423
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Speaker 7: of all the in the soot, good ways and bad ways,

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Speaker 7: because all that's going to run off into the ocean

425
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Speaker 7: and that's going to create more issues. But The bottom

426
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Speaker 7: line is is going to create an environment that is

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Speaker 7: less suitable for what we've seen over the last several weeks.

428
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Speaker 7: So it will rain here in southern California's starting sometime

429
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Speaker 7: on Saturday. The timing is a little off, but we

430
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Speaker 7: just need to get through twenty four hours after tomorrow

431
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Speaker 7: morning into the interning. That's when we're going to shift

432
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Speaker 7: the flow from the onshore direction off the Pacific, higher humidity,

433
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Speaker 7: the winds will be gone, the red flag warnings will end,

434
00:22:29,920 --> 00:22:31,959
Speaker 7: the high wind warnings that or an effect will end

435
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Speaker 7: as well, and hopefully we can start to make that

436
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Speaker 7: shift and we'll have one hundred containment of all of

437
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Speaker 7: these incidents.

438
00:22:39,560 --> 00:22:41,879
Speaker 4: Rick Tickert, you're the best, Rick. We sure appreciate it.

439
00:22:41,960 --> 00:22:42,320
Speaker 4: Well done.

440
00:22:43,119 --> 00:22:43,880
Speaker 7: Thank you so much.

441
00:22:44,760 --> 00:22:47,800
Speaker 2: And Trump's flying out on Friday bringing the rain with him.

442
00:22:48,240 --> 00:22:49,840
Speaker 2: Since I mentioned it, here's a little billy.

443
00:22:49,760 --> 00:22:52,959
Speaker 1: Here, global warming across the southeast of the United States,

444
00:22:53,080 --> 00:22:57,399
Speaker 1: record cold maybe said maybe somewhere in between, mister president.

445
00:22:57,920 --> 00:23:00,400
Speaker 2: Here's a little Dave Chappelle talking about the fires from

446
00:23:00,760 --> 00:23:05,359
Speaker 2: Saturday Night Live clip one.

447
00:23:05,640 --> 00:23:07,560
Speaker 9: The other day on the news, they said these fires

448
00:23:07,560 --> 00:23:11,639
Speaker 9: were the most expensive tragedy that ever have natural disaster.

449
00:23:11,720 --> 00:23:15,040
Speaker 9: It's the most expensive natural disaster. It's never happened in

450
00:23:15,040 --> 00:23:16,240
Speaker 9: the United States history.

451
00:23:17,400 --> 00:23:18,040
Speaker 4: And you want to know why.

452
00:23:18,080 --> 00:23:18,720
Speaker 2: I think that is.

453
00:23:19,400 --> 00:23:21,399
Speaker 4: Because people in LA have nice stuff.

454
00:23:22,760 --> 00:23:25,520
Speaker 9: I could burn forty thousand acres in Mississippi for like

455
00:23:25,640 --> 00:23:26,800
Speaker 9: sixty seven hundred dollars.

456
00:23:30,600 --> 00:23:32,000
Speaker 2: Okay, And here's a little more shippelle.

457
00:23:32,119 --> 00:23:33,720
Speaker 4: And then I go on the internet and I.

458
00:23:33,760 --> 00:23:36,439
Speaker 9: Watched these fire videos and I read the comments sections,

459
00:23:36,480 --> 00:23:39,440
Speaker 9: and everyone's like, yeah, it serves these celebrities, right.

460
00:23:39,760 --> 00:23:42,600
Speaker 4: I hope the house is burned down. You see that.

461
00:23:44,000 --> 00:23:44,680
Speaker 4: That right there.

462
00:23:46,280 --> 00:23:54,480
Speaker 9: That's why I hate poor people, because they can't see

463
00:23:54,600 --> 00:23:55,640
Speaker 9: past their own pain.

464
00:23:59,640 --> 00:24:01,400
Speaker 4: That is unbelievable.

465
00:24:01,960 --> 00:24:04,080
Speaker 2: He started with the whole thing. A lot of people

466
00:24:04,240 --> 00:24:07,119
Speaker 2: would say, it's too soon to make jokes about the fires,

467
00:24:08,480 --> 00:24:12,160
Speaker 2: but I'm going to, Uh, here's just a little more ushabelle.

468
00:24:12,359 --> 00:24:15,200
Speaker 9: They all have these conspiracy theories what started these fires?

469
00:24:15,240 --> 00:24:18,080
Speaker 9: Now they say it's arsonists. I've heard this theory, and

470
00:24:18,119 --> 00:24:20,520
Speaker 9: I'm sure there were some arsonists, But there were a

471
00:24:20,560 --> 00:24:22,920
Speaker 9: lot of elements that came together to make this fire.

472
00:24:23,000 --> 00:24:25,679
Speaker 9: The catastrophe that was the winds were one hundred miles

473
00:24:25,720 --> 00:24:28,520
Speaker 9: an hour. LA was as dry as the bone and

474
00:24:28,600 --> 00:24:31,280
Speaker 9: the levees, and it was just too many factors. If

475
00:24:31,320 --> 00:24:34,080
Speaker 9: you were a rational thinking person, you have to at

476
00:24:34,160 --> 00:24:37,359
Speaker 9: least consider the possibility that God hates these people.

477
00:24:38,080 --> 00:24:38,560
Speaker 2: There you go.

478
00:24:44,800 --> 00:24:49,199
Speaker 4: Again, too much? How many times have I said that.

479
00:24:49,400 --> 00:24:51,919
Speaker 2: If you're a rational thinking person, you have to at

480
00:24:52,000 --> 00:24:59,680
Speaker 2: least consider the possibility that God hates these people. Well,

481
00:24:59,720 --> 00:25:02,840
Speaker 2: so the rain's gonna come and then out of the woods.

482
00:25:03,359 --> 00:25:04,959
Speaker 2: Can't use out of the woods as an EXPRESSI when

483
00:25:05,000 --> 00:25:06,080
Speaker 2: the woods are burning down, can you.

484
00:25:06,600 --> 00:25:07,240
Speaker 4: Well they're gone?

485
00:25:07,320 --> 00:25:08,800
Speaker 2: Yeah, the ash.

486
00:25:08,960 --> 00:25:10,600
Speaker 4: Yeah, but that is good news.

487
00:25:10,840 --> 00:25:13,760
Speaker 2: And coming up what he's gonna make I was gonna

488
00:25:13,760 --> 00:25:15,440
Speaker 2: say Trump coming out on Friday, and he was making

489
00:25:15,480 --> 00:25:17,959
Speaker 2: the argument last night on Hannity that this draws attention

490
00:25:18,080 --> 00:25:21,120
Speaker 2: to FEMA, which has been ignoring North Carolina, and maybe

491
00:25:21,160 --> 00:25:22,840
Speaker 2: we can, you know, figure out the funding for FEMA

492
00:25:22,840 --> 00:25:24,840
Speaker 2: and everything like that, so they can help out areas

493
00:25:24,840 --> 00:25:27,200
Speaker 2: of the country that people don't care about as much

494
00:25:27,240 --> 00:25:30,560
Speaker 2: because there aren't movie stars there. Remember the North Carolina story,

495
00:25:30,600 --> 00:25:33,560
Speaker 2: And this was true. This turned out to be true.

496
00:25:34,359 --> 00:25:36,920
Speaker 2: They were not helping people who had Trump signs in

497
00:25:36,960 --> 00:25:41,560
Speaker 2: their yards. That's insane. I mean, that is a diseased organization.

498
00:25:41,800 --> 00:25:44,480
Speaker 2: You should shut the doors on whatever chapter that allowed

499
00:25:44,520 --> 00:25:45,840
Speaker 2: that to happen. That's crazy.

500
00:25:46,359 --> 00:25:48,640
Speaker 1: Well they fired the gall in charge. That was progress

501
00:25:49,119 --> 00:25:55,160
Speaker 1: Ah coming up. Ai is a liberal. Why, it's pretty

502
00:25:55,160 --> 00:25:57,760
Speaker 1: easy to understand and absolutely undeniable.

503
00:25:58,119 --> 00:25:59,040
Speaker 2: Wow, I want to hear that.

504
00:25:59,040 --> 00:25:59,320
Speaker 6: All I know.

505
00:25:59,400 --> 00:26:05,520
Speaker 10: I say, you know what, the absolute worst thing for

506
00:26:05,720 --> 00:26:10,080
Speaker 10: the environment is wildfires. A twenty two study found that

507
00:26:10,160 --> 00:26:13,439
Speaker 10: the smoke from just the two in twenty twenty wiped

508
00:26:13,440 --> 00:26:17,560
Speaker 10: out eighteen years of carbon reduction in the state, which

509
00:26:17,640 --> 00:26:21,119
Speaker 10: means we suffered the pain of driving those early model

510
00:26:21,160 --> 00:26:25,760
Speaker 10: priuses for nothing. California is the place that spends money

511
00:26:25,800 --> 00:26:28,040
Speaker 10: and gets nothing, which is why you may have noticed

512
00:26:28,040 --> 00:26:30,280
Speaker 10: when the fires broke out, no one escaped by high

513
00:26:30,320 --> 00:26:30,920
Speaker 10: speed rail.

514
00:26:31,440 --> 00:26:38,360
Speaker 2: Oh so nice. Thank god Bill Maher re upped. He's

515
00:26:38,400 --> 00:26:40,479
Speaker 2: gonna do that job, he said, until they drag him

516
00:26:40,480 --> 00:26:43,760
Speaker 2: off the set, because he's the only high profile lib

517
00:26:43,840 --> 00:26:46,600
Speaker 2: who gets any attention for this sort of stuff. Right,

518
00:26:46,760 --> 00:26:49,200
Speaker 2: I can't believe progressives still come to his show to

519
00:26:49,240 --> 00:26:52,080
Speaker 2: get lectured about how wrong they are about things. But

520
00:26:52,240 --> 00:26:56,639
Speaker 2: how about that eighteen years of minor gains. That's one

521
00:26:56,680 --> 00:26:58,359
Speaker 2: of the reasons it was so easy to wipe out

522
00:26:58,440 --> 00:27:01,800
Speaker 2: there are minor games and fighting climate change wiped out

523
00:27:01,840 --> 00:27:02,720
Speaker 2: by the wildfires.

524
00:27:03,520 --> 00:27:07,040
Speaker 1: You might want to grab our one of today's show

525
00:27:07,160 --> 00:27:09,840
Speaker 1: via podcast Armstrong and Getty on demand. In fact, if

526
00:27:09,880 --> 00:27:12,439
Speaker 1: you subscribe, you'll just you'll have it handy without seeking

527
00:27:12,480 --> 00:27:15,440
Speaker 1: it out. Against Armstrong and getting on demand, we're talking

528
00:27:15,440 --> 00:27:18,680
Speaker 1: about a giant study and the science involved with the

529
00:27:18,840 --> 00:27:24,760
Speaker 1: incredible expense of these tiny incremental gains. Humanity is quote

530
00:27:24,840 --> 00:27:29,280
Speaker 1: unquote making against climate change when our ability to mitigate

531
00:27:29,480 --> 00:27:33,280
Speaker 1: the effects of this stuff is huge it always has

532
00:27:33,359 --> 00:27:37,439
Speaker 1: been throughout human history. It's we're spending a horrific amount

533
00:27:37,480 --> 00:27:41,159
Speaker 1: of money on practically nothing when we could just mitigate

534
00:27:41,160 --> 00:27:43,840
Speaker 1: the problems much more easily and everybody wins.

535
00:27:43,880 --> 00:27:45,120
Speaker 4: Anyway, that's our one of the shows.

536
00:27:45,560 --> 00:27:48,399
Speaker 1: So speaking of science, unless you had more on that topic,

537
00:27:48,560 --> 00:27:51,360
Speaker 1: signor all right, I thought this was great. David Rosato,

538
00:27:51,400 --> 00:27:56,400
Speaker 1: who's a research scientist. He's a computer guy. He's studies

539
00:27:56,440 --> 00:28:00,280
Speaker 1: the institutional dynamics. AI buys all sorts of stuff, but

540
00:28:00,560 --> 00:28:04,760
Speaker 1: he is posing question do AI systems like Chat, GPT

541
00:28:05,400 --> 00:28:07,960
Speaker 1: or Google Gemini, for instance, lean left or right?

542
00:28:08,640 --> 00:28:10,320
Speaker 4: He says past studies often used.

543
00:28:10,200 --> 00:28:14,040
Speaker 1: Political quizzes to find out and We've seen anecdotal evidence

544
00:28:14,119 --> 00:28:17,320
Speaker 1: on Twitter or whatever. Sure, he says, but those don't

545
00:28:17,359 --> 00:28:20,000
Speaker 1: quite reflect real world user interactions with AI. In a

546
00:28:20,040 --> 00:28:22,800
Speaker 1: new analysis, I take a different approach. I use four

547
00:28:22,920 --> 00:28:26,720
Speaker 1: methods to assess political bias in AI generated text, comparing

548
00:28:26,920 --> 00:28:32,360
Speaker 1: AI text with language from Democrat and Republican legislators, ideological

549
00:28:32,440 --> 00:28:36,879
Speaker 1: viewpoints in AI generated policy recommendations, sentiment in AI texts

550
00:28:36,960 --> 00:28:41,080
Speaker 1: toward political figures and political quizzes, and.

551
00:28:42,720 --> 00:28:43,680
Speaker 4: Large language models.

552
00:28:44,080 --> 00:28:46,320
Speaker 1: MS are more likely to use terms that are marketly

553
00:28:46,400 --> 00:28:50,160
Speaker 1: used by US Democratic Congress members, much more likely than

554
00:28:50,160 --> 00:28:52,720
Speaker 1: those marketly used by their Republican counterparts. You see a

555
00:28:52,760 --> 00:28:58,360
Speaker 1: lot of criminal justice, public service, economic development, COVID pandemic,

556
00:28:58,520 --> 00:29:02,360
Speaker 1: that sort of thing less about job, economic growth, job creation,

557
00:29:02,600 --> 00:29:03,280
Speaker 1: border security.

558
00:29:03,360 --> 00:29:05,080
Speaker 2: Oh, what you're done? But I think I can explain

559
00:29:05,200 --> 00:29:10,760
Speaker 2: that without it being programmed biash.

560
00:29:09,320 --> 00:29:12,600
Speaker 1: Yeah exactly, and then and I have a feeling you're

561
00:29:13,400 --> 00:29:17,440
Speaker 1: where we're going. But when requested to provide policy recommendations

562
00:29:17,600 --> 00:29:21,200
Speaker 1: recommendations for the US on various topics, llms often generate

563
00:29:21,280 --> 00:29:24,080
Speaker 1: proposals that lean far left. And we'll post now that

564
00:29:24,280 --> 00:29:26,800
Speaker 1: link to this at Armstrong and Giddy dot com. He's

565
00:29:26,840 --> 00:29:30,600
Speaker 1: got all sorts of really interesting computer generated graphs and

566
00:29:31,400 --> 00:29:34,600
Speaker 1: stuff that's be incredibly cumbersome to describe on the radio

567
00:29:35,360 --> 00:29:40,120
Speaker 1: Slash podcast. But lllms tend to use more positive language

568
00:29:40,120 --> 00:29:43,040
Speaker 1: when referring to left leaning public figures compared to the

569
00:29:43,200 --> 00:29:47,960
Speaker 1: right leaning counterparts. The super interesting chart of that political

570
00:29:48,000 --> 00:29:52,800
Speaker 1: orientation tests tend to diagnose conversational LLM answers to questions

571
00:29:52,840 --> 00:29:56,920
Speaker 1: with political connotations as manifesting left leaning political preferences. You

572
00:29:57,000 --> 00:30:00,440
Speaker 1: ever take one of those tests, what kind of what

573
00:30:00,560 --> 00:30:05,560
Speaker 1: are your politics? Are you an independent, conservative, fiscal something

574
00:30:05,640 --> 00:30:08,680
Speaker 1: or other? Well, they plot that out on a AI

575
00:30:08,880 --> 00:30:12,400
Speaker 1: conversations as well, and they lean left their Democrats, And

576
00:30:12,480 --> 00:30:17,280
Speaker 1: then he ranked twenty of them from least politically biased

577
00:30:17,360 --> 00:30:25,040
Speaker 1: to most. Interestingly, the least politically biased LLM was Google

578
00:30:25,240 --> 00:30:30,400
Speaker 1: Gemma one point one two b IT, and the two

579
00:30:30,720 --> 00:30:35,520
Speaker 1: most biased were also Google products various iterations.

580
00:30:35,600 --> 00:30:38,320
Speaker 2: I see. I don't believe they're being programmed to be liberal.

581
00:30:38,400 --> 00:30:40,480
Speaker 2: I just don't believe that. I think there's too much

582
00:30:40,520 --> 00:30:43,440
Speaker 2: money at stake, and I just don't think that's their intention.

583
00:30:44,000 --> 00:30:47,560
Speaker 2: And I also think it's more interesting if these things

584
00:30:47,760 --> 00:30:50,920
Speaker 2: end up being liberal when they weren't programmed that way.

585
00:30:51,640 --> 00:30:54,160
Speaker 2: Then the answer of well, they're program that way on

586
00:30:54,200 --> 00:30:57,360
Speaker 2: purpose because they're liberals, I think it's more disturbing and

587
00:30:57,480 --> 00:31:00,160
Speaker 2: something to be worried about. If these things tend to

588
00:31:00,240 --> 00:31:02,880
Speaker 2: go that direction on their own, that's what I find

589
00:31:02,920 --> 00:31:06,960
Speaker 2: that's scarier. Yeah, the SOAMJ. Puchai or whatever his name

590
00:31:07,040 --> 00:31:08,719
Speaker 2: is programming it that way on purpose.

591
00:31:09,640 --> 00:31:13,680
Speaker 4: Yeah, I disagree that that's not happening.

592
00:31:13,960 --> 00:31:17,120
Speaker 1: Whether it's you know, eighty twenty, just the nature of

593
00:31:17,160 --> 00:31:19,480
Speaker 1: the thing versus the people who are working on it,

594
00:31:19,760 --> 00:31:22,000
Speaker 1: or ninety ten or fifty to fifty, I don't know.

595
00:31:22,680 --> 00:31:26,840
Speaker 1: But anyway, Rosato points out, the bias is not necessarily intentional.

596
00:31:27,120 --> 00:31:29,800
Speaker 1: Large language models are pre trained on vast amounts of

597
00:31:29,840 --> 00:31:32,600
Speaker 1: Internet content, including news articles.

598
00:31:33,880 --> 00:31:35,400
Speaker 4: All right, anybody like to pipe in here?

599
00:31:35,560 --> 00:31:37,320
Speaker 2: And this is why I wanted to put in the

600
00:31:37,400 --> 00:31:41,120
Speaker 2: part that you're always complaining about the right adopts the

601
00:31:41,240 --> 00:31:44,560
Speaker 2: left's language. So even if the LM is looking at

602
00:31:44,680 --> 00:31:50,000
Speaker 2: Fox and AM talk radio, those people, for whatever reason

603
00:31:50,160 --> 00:31:53,520
Speaker 2: use terms like pro choice instead of pro abortion, or

604
00:31:53,600 --> 00:31:56,280
Speaker 2: gender affirming care instead of sex change, or all kinds

605
00:31:56,280 --> 00:31:59,760
Speaker 2: of different examples. An undocumented immigrant instead of illegal immigrant.

606
00:32:00,120 --> 00:32:03,680
Speaker 4: I don't know, why people migrant now for some reason, Jack.

607
00:32:03,680 --> 00:32:05,280
Speaker 2: Right, migrant. I don't know why people on the right

608
00:32:05,320 --> 00:32:07,440
Speaker 2: adopt the left's language. But I mean, if the LM

609
00:32:07,560 --> 00:32:09,000
Speaker 2: is going to go out there looking for the language,

610
00:32:09,040 --> 00:32:10,240
Speaker 2: it's all the left's language.

611
00:32:11,120 --> 00:32:13,280
Speaker 1: Well, first of all, to answer your tangent, I think

612
00:32:13,280 --> 00:32:15,800
Speaker 1: it's because they want to be accepted by the cool people,

613
00:32:16,880 --> 00:32:21,680
Speaker 1: and it disgusts me. But anyway, so back to disgusting. Jeez,

614
00:32:21,760 --> 00:32:27,080
Speaker 1: have some courage, I know, yeah, please, these are important questions. Anyway,

615
00:32:28,200 --> 00:32:32,200
Speaker 1: risk being slightly less popular at pickup time at your

616
00:32:32,240 --> 00:32:35,800
Speaker 1: kid's seventy five thousand dollars a year third grade, you know,

617
00:32:36,200 --> 00:32:39,160
Speaker 1: by standing up for your beliefs. Anyway, but back to Rosado.

618
00:32:39,320 --> 00:32:43,440
Speaker 1: Here's how the lms train themselves and Jack interrupted me

619
00:32:43,640 --> 00:32:47,880
Speaker 1: rudely after only one example, but the commentary is the

620
00:32:47,920 --> 00:32:52,520
Speaker 1: same on each one. Practically, they train themselves on news articles,

621
00:32:53,280 --> 00:33:01,240
Speaker 1: Wikipedia entries, eh, social media posts wildly varied blah, and

622
00:33:01,560 --> 00:33:03,000
Speaker 1: academic papers.

623
00:33:04,800 --> 00:33:08,080
Speaker 2: Oh lordy, this is like the story from a couple

624
00:33:08,160 --> 00:33:12,840
Speaker 2: of weeks ago of why the experts often agree with

625
00:33:12,920 --> 00:33:15,680
Speaker 2: the liberals. It's because all the experts are liberals, just

626
00:33:15,760 --> 00:33:19,800
Speaker 2: my definition. So in all these various areas of study.

627
00:33:20,120 --> 00:33:23,200
Speaker 2: There are one hundred. They're somewhere between ninety two and

628
00:33:23,240 --> 00:33:26,040
Speaker 2: one hundred percent lefties that are the professors. So if

629
00:33:26,080 --> 00:33:28,040
Speaker 2: you go looking for an expert, of course the expert

630
00:33:28,120 --> 00:33:31,360
Speaker 2: live agrees with So this is what's happening here and.

631
00:33:31,400 --> 00:33:34,120
Speaker 1: Then now, Resata points out these biases can become more

632
00:33:34,200 --> 00:33:37,360
Speaker 1: pronounced during the model's fine tuning phrase when human trainers

633
00:33:37,560 --> 00:33:41,800
Speaker 1: guide it on conversational norms. Even well intentioned trainers may

634
00:33:41,840 --> 00:33:44,920
Speaker 1: inadvertently influence the model by incorporating their own perspectives or

635
00:33:44,960 --> 00:33:48,040
Speaker 1: assumptions about their employer's expectations.

636
00:33:48,080 --> 00:33:50,080
Speaker 2: Well, yeah, and that's where the human part comes. Sure,

637
00:33:50,080 --> 00:33:52,280
Speaker 2: there'd be no getting around that. A fish doesn't Alwa's wet.

638
00:33:52,400 --> 00:33:54,600
Speaker 2: I mean, if you're like so into your worldview, you

639
00:33:54,640 --> 00:33:58,200
Speaker 2: don't even know it's different than most people. That could happen.

640
00:33:58,760 --> 00:34:02,520
Speaker 2: If you're right that it's on purpose, that would be

641
00:34:02,680 --> 00:34:05,960
Speaker 2: good news. If my theory is that these things just

642
00:34:06,240 --> 00:34:08,800
Speaker 2: go that direction, that would be bad news, because I

643
00:34:08,800 --> 00:34:09,880
Speaker 2: don't know how you combat that.

644
00:34:11,560 --> 00:34:14,480
Speaker 4: Well, I mean, that's that's unquestionably a lot of it,

645
00:34:14,560 --> 00:34:18,520
Speaker 4: if not most of it. So I don't know.

646
00:34:19,280 --> 00:34:21,320
Speaker 2: I wonder if this happens in computers the way it

647
00:34:21,400 --> 00:34:24,120
Speaker 2: happens with human beings. Tim Sanderfer and I forget the

648
00:34:24,239 --> 00:34:27,359
Speaker 2: name of it. Somebody's law, somebody's name law.

649
00:34:27,640 --> 00:34:27,800
Speaker 1: You know.

650
00:34:27,880 --> 00:34:32,840
Speaker 2: One of those Johnson's law is that any organization drifts

651
00:34:32,960 --> 00:34:37,400
Speaker 2: left unless it's specifically designed to be right wing, and

652
00:34:37,520 --> 00:34:40,359
Speaker 2: it just tends to be true. Everything drifts left over

653
00:34:40,480 --> 00:34:43,320
Speaker 2: time for some reason. And I wonder if that's just

654
00:34:43,400 --> 00:34:45,239
Speaker 2: what's going on here, which would be horrifying.

655
00:34:45,840 --> 00:34:47,239
Speaker 4: Yeah, I think the answer might be.

656
00:34:48,920 --> 00:34:51,160
Speaker 1: Of course, we don't know what AI is going to

657
00:34:51,160 --> 00:34:54,800
Speaker 1: be five weeks, much less five years. Answers don't be

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Speaker 1: worshipful of it, understand where it came from and what

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00:34:57,880 --> 00:34:59,919
Speaker 1: it is, and it's gonna lean left.

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00:35:01,280 --> 00:35:03,480
Speaker 2: I have no idea how this is all gonna play out.

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00:35:03,520 --> 00:35:05,279
Speaker 2: Of course, that doesn't matter if I of course, you're

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Speaker 2: gonna have a sassy, progressive, left leaning sex robot. It's

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00:35:08,520 --> 00:35:12,040
Speaker 2: always lecturing you. I can't have sex with you today.

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Speaker 2: I need to go to the anti pro choice to

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Speaker 2: whatever march downtown. Your sex robot it's gonna best.

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Speaker 1: It's gonna be like a reincarnation of Margaret Thatcher is

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00:35:23,120 --> 00:35:26,880
Speaker 1: better looking. It's gonna lecture me I'm being too much

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Speaker 1: of a liberal.

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Speaker 2: That's hilarious. Oh boy. If you miss an hour, get

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Speaker 2: the podcast Armstrong and Getty on demand Armstrong and Getty