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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.

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Speaker 1: I'm Jill Wisenthal and I'm Tracy Alloway.

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Speaker 2: Tracy, something I've thought for a long time. I think

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Speaker 2: you might feel the same way. I'm glad we're not

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Speaker 2: like politics reporters.

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Speaker 3: Absolutely, I cannot imagine what.

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Speaker 4: That job is.

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Speaker 2: I sometimes look at some of the stuff that are

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Speaker 2: colleagues in the political media have to report on and like,

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Speaker 2: look like a lot of it does touch what we do,

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Speaker 2: and they're obviously like there's no way to avoid it

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Speaker 2: all together. And obviously some of the stuff is big implications.

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Speaker 2: But by and large, you know, getting to look at

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Speaker 2: the numbers on the chart, or learning how you know

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Speaker 2: debt structures work, or learning how cardboard boxes work is

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Speaker 2: just much more, or is very satisfying to be relative too.

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Speaker 3: You prefer the lines over the lines coming from the

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Speaker 3: mouths of politicians, the lines over lines.

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Speaker 1: That's a good one, thank you.

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Speaker 2: But it's gonna fall apart, I think because I think

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Speaker 2: this AI story is such high stakes and there's so

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Speaker 2: many different policy questions, et cetera, whether it's related to

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Speaker 2: electricity prices or the possibility for labor to play displacement.

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Speaker 2: I think there's basically no way at this point that

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Speaker 2: twenty twenty eight, maybe even twenty twenty six AI somehow

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Speaker 2: is going to be a big political story in a

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Speaker 2: way that it was not really in twenty twenty four.

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Speaker 3: Well, I think you're already seeing it in twenty twenty five, right,

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Speaker 3: So we had Sarah Fryar came out and said she

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Speaker 3: was talking about maybe getting a government backstop for AI

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Speaker 3: kapec spend this is.

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Speaker 1: The open AICFI yes.

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Speaker 3: And of course she rolled it back a little bit,

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Speaker 3: but it got up really but not really, and it

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Speaker 3: got everyone talking about, you know, what is the government's

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Speaker 3: involvement or obligations here. We know that jd Vance of

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Speaker 3: course has been supported by Peter Teel, who has very

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Speaker 3: strong and possibly idiosyncratic opinions when it comes to AI,

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Speaker 3: and of course there's the labor market aspect. There's also

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Speaker 3: electricity prices. Inflation is still a big political challenge for

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Speaker 3: the administration, and having all these data centers consume energy

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Speaker 3: is pushing prices up. And so I expect you'll hear

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Speaker 3: a lot about AI.

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Speaker 2: I don't think you know, we're recording this November thirteen.

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Speaker 2: I don't think we're probably not currently in a recession,

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Speaker 2: that the economy is not booming, but who knows exactly.

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Speaker 2: But you know, if you imagine a recession is going on,

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Speaker 2: and here's this technology where many of its biggest backers

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Speaker 2: talk about the ability to replace a lot of labor,

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Speaker 2: and you know, there's some debate, but and there's this

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Speaker 2: talk about place in greater strand on electricity, and as

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Speaker 2: you mentioned, there's the possibility that oh and maybe the

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Speaker 2: government should back I mean, this is a lot, this

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Speaker 2: is a powder keg.

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Speaker 3: Absolutely, yeah, all right.

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Speaker 2: We have to learn more though about the different factions,

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Speaker 2: how it's going to play out politically, will AI have

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Speaker 2: any friends in Washington, DC? Whatsoever? How the different candidates

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Speaker 2: are positioning themselves Visa Vivas. So I wanted to talk

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Speaker 2: to one of the most plugged in guys I know

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Speaker 2: in DC, someone I've talked to for a very long time,

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Speaker 2: who's been talking about the politics of AI on his

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Speaker 2: own show for a long time. Literally the perfect guest

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Speaker 2: we're going to be speaking with Sager and Jetti. He

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Speaker 2: is the co host of Breaking Points, massive popular podcast,

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Speaker 2: knows a lot more about all the political dimensions than

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Speaker 2: we do so Sager, thank you so much for coming

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Speaker 2: on Odd Loaves.

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Speaker 1: Thank you for having me guys, longtime listener, and congratulations

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Speaker 1: on your ten year anniversary. Thank you so much.

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Speaker 2: You've been doing these segments on AI and they're blowing

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Speaker 2: up on YouTube.

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Speaker 1: Huh yeah, yeah, I mean, purely analytics wise, I have

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Speaker 1: a little bit of a test here for how the

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Speaker 1: people are feeling. And I think you summed it up

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Speaker 1: actually really well. Joe is in a time of economic procarity,

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Speaker 1: a very low consumer sentiment. When Dario and Elon and

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Speaker 1: Sam Altman just routinely go on podcasts in twenty twenty five, right,

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Speaker 1: and they're just like, yeah, we are going to replace you.

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Speaker 1: You don't have to work anymore. It's basically the embodiment

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Speaker 1: of that World Economic Forum meme about you will own

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Speaker 1: nothing and be happy. And so, I mean, I don't

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Speaker 1: think that they really understand the implications that a lot

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Speaker 1: of us are actually taking their words very seriously, and

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Speaker 1: I'm increasingly tapped into both, not only the data center backlash,

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Speaker 1: which is real. By the way, heat Map just did

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Speaker 1: a fantastic piece about the data center backlash, how it's

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Speaker 1: swallowing American politics. Georgia's power races very recently were massively

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Speaker 1: influenced by AI and data centers specifically. And I'm watching

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Speaker 1: this like fascinating. Horseshoe is really not the right word,

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Speaker 1: because horseshoe implies, you know, far right and far left.

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Speaker 1: I sent you a tweet earlier today, Joe, which kind

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Speaker 1: of brings it together, and it was like, if Tim

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Speaker 1: Miller of The Bulwark and Ryan Grimm, co host of Mine,

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Speaker 1: Far lefty, and then Matt Walsh are all agreeing on

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Speaker 1: attacking AI, with Jon Favreau of Pod Save America coming in,

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Speaker 1: We're not really a horseshoe, are we? Like we're the

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Speaker 1: entire spectrum of American politics all agreeing to stand in opposition.

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Speaker 1: I think to what fundamentally is about an issue where

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Speaker 1: we do not feel that we are in control of

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Speaker 1: this technology. We are not sold yet on the benefits.

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Speaker 1: And you guys talked about that recently in one of

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Speaker 1: your latest episodes about how AI is not even going

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Speaker 1: to make you rich, which is what was also one

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Speaker 1: of the promises that's kind of behind the entire pitch.

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Speaker 3: All right, So AI truly a bipartisan issue or bipartisan gripe.

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Speaker 3: But at the same time, we hear a lot of

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Speaker 3: politicians talk about the tech competition with China in particular, right,

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Speaker 3: And we have an episode that came out recently where

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Speaker 3: we talk about the existentialism around AI, this idea that

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Speaker 3: you have to win otherwise you're basically going to die

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Speaker 3: or fall into some sort of like modern dystopian poverty

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Speaker 3: forever and ever. Why is that rhetoric still out there

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Speaker 3: if at a local level everyone kind of agrees, like,

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Speaker 3: you know, this is bad for electricity prices, it's potentially

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Speaker 3: bad for the labor market.

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Speaker 1: Yeah, great question, And I think actually that might be

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Speaker 1: the politicians not really getting to the heart of the issue.

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Speaker 1: And we have to separate the AI technology and the

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Speaker 1: promises that were originally there, which we could probably strategically

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Speaker 1: agree like we need to maintain technological advancement, and then

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Speaker 1: the current way that it's structured, where it's basically in

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Speaker 1: the hands of three or four like super CEOs, and

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Speaker 1: everything's kind of seems rolled up fundamentally that you guys

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Speaker 1: are no going a lot more than I do. But

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Speaker 1: because of the cost of compute of data centers, specifically

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Speaker 1: like the Big tech companies are the ones that are

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Speaker 1: in control. So I think we should disaggregate like the

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Speaker 1: idea of AI itself as a technology potential, open source

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Speaker 1: and its usefulness from the way that it's actually being structured,

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Speaker 1: used and controlled. And I think that's really where a

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Speaker 1: lot of the pushback is. And also, though I would say,

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Speaker 1: you know, politicians, this may seem counterintuitive, but are often

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Speaker 1: the last people to know whenever something organically is pushing up.

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Speaker 1: You know, I'm I guess on the forefront of a

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Speaker 1: YouTube show. I mean in ways like I have real

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Speaker 1: time analytics day to day about how people are feeling.

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Speaker 1: You only get a democratic kind of feel about this

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Speaker 1: once every two to four years, so it can take

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Speaker 1: a long time for this to bubble up through the

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Speaker 1: halls of Washington and kind of get rid of some

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Speaker 1: of the twenty eighteen, twenty nineteen, you know more think

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Speaker 1: tank talking points that I think that you're describing their Tracy.

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Speaker 2: David Sachs, the co host of the All In podcast

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Speaker 2: and the White House's AI and cryptos are He says,

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Speaker 2: all the negativity is because of effective altruists and this

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Speaker 2: sort of movement in San Francisco that's pushing these doom scenarios.

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Speaker 2: Are all these people that you follow and talk to

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Speaker 2: or that follow you, are they all like reading EA substacks?

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Speaker 1: Is that where they're getting they have they have no

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Speaker 1: idea what EA even is. I've actually had to do

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Speaker 1: a few explainers on the show about effective altruism, you know,

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Speaker 1: and again the vast majority of my audience. I mean,

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Speaker 1: these are you know, Uber drivers and hotel these are

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Speaker 1: just normal people who are kind of like going about

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Speaker 1: their day to day life. David is absolutely wrong on

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Speaker 1: this issue. I mean, he may be structurally correct in

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Speaker 1: terms of the way that the rhetoric around AI that

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Speaker 1: causes a Dario and an Elon and others to like

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Speaker 1: go on shows and talk specifically about how you're not

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Speaker 1: gonna work, but you're gonna be okay, and this whole

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Speaker 1: idea that kind of backstops EA. But this is really

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Speaker 1: just like small d democratic like pushback, I really think,

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Speaker 1: and I also think an idea of really been rolling

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Speaker 1: around in my head. I'm curious for what you guys think.

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Speaker 1: Is that at the heart of the social media revolution,

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Speaker 1: we're some newer upstarts. You know, we never heard of

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Speaker 1: Mark Zuckerberg. We never heard of. You know, even if

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Speaker 1: we push back to Google's you know, these guys were

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Speaker 1: revolutionary entrepreneurs this time around. You know, they sold us

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Speaker 1: the promise of social media, and regardless of whether you

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Speaker 1: think it's good or not, we don't really have a

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Speaker 1: high level of societal trust in you know, the Arab

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Speaker 1: Spring and all that, And these are the very same

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Speaker 1: people now making these multi billion dollars in some cases

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Speaker 1: trillion dollar bets on AI and telling us to trust them.

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Speaker 1: So that's something that really belies that, you know, the

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Speaker 1: Mark Zuckerberg's of the AI space. Let's I don't know

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Speaker 1: Alexander Wang like he now works for Zuckerberg. Right, It's

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Speaker 1: just it's just fundamentally like a little bit different in

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Speaker 1: terms of who the key players are who are trying

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Speaker 1: to tell us about the beneficiary of the technology. And

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Speaker 1: I also think you know, they're no longer talking about

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Speaker 1: the things that they originally did, Like they were like,

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Speaker 1: we're going to cure cancer, and it's like, well, Sam,

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Speaker 1: why are you talking about erotica porn man, Because that's

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Speaker 1: that's a little bit different. It's like maybe the Internet

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Speaker 1: in chat ept is just going to be the exact

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Speaker 1: same as the Internet as it always has been, which

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Speaker 1: is basically increasing you know, user time in chat EPT.

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Speaker 1: Putting some ads in there just an a miserating experience,

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Speaker 1: which of course also has high levels of efficiency for

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Speaker 1: business use.

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Speaker 3: I was going to say exactly that. I'm not sure

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Speaker 3: it's that people don't trust the promise. It's that the

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Speaker 3: promise itself doesn't seem that enticing at the moment you

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Speaker 3: know you're going to lose your job and AI is

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Speaker 3: going to get to do all the fun stuff in

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Speaker 3: human life, like make pictures and movies.

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Speaker 2: And getting married to an AI model.

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Speaker 1: Yeah, exactly, exactly, Yeah, you're exactly right. I mean, I

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Speaker 1: thought the erotica thing from Sam was just the biggest tell.

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Speaker 1: I was like, wow, man, like, we're not talking about biomedical,

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Speaker 1: We're not talking about chat GPT curing cancer and all

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Speaker 1: of this. I mean, to be fair to him, he

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Speaker 1: has talked about the Open Eye Foundation, how they're going

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Speaker 1: to try and move things in that direction, but all

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Speaker 1: of the public consumption and everything being you know, Geared,

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Speaker 1: I don't know if either of you are NFL fans.

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Speaker 1: Have you noticed the number of chat GPT ads during

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Speaker 1: NFL game? Now, Oh, it's every game, and it's like

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Speaker 1: here chat gepts designing my workout, Chat gpt is designing

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Speaker 1: my vacation. Listen, no hate, but it's basically an extpedia ad. Right,

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Speaker 1: this is not cancer curing, it's Instagram for teens. It's

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Speaker 1: chat GPT, Microsoft Copilot. I believe ran an ad about

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Speaker 1: image generation. You know, the studio Ghibli thing. Yeah, I

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Speaker 1: mean that's that's not enterprise groundbreaking technology like this is

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Speaker 1: like you're burning data center or like you're increasing my

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Speaker 1: power bills so that people can do studio Ghibli recreates.

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Speaker 1: Like I'm out on this, you know, you know that

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Speaker 1: that's how I feel.

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Speaker 2: There are obviously a number of different concerns that people have,

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Speaker 2: electricity prices, labor, and we'll get into that. But you

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Speaker 2: said something interesting, which is that even you know, prior

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Speaker 2: to AI having come out, prior to late twenty twenty

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Speaker 2: two and chat shept burst on the scene, people were

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Speaker 2: already souring on these big techmo goals, you know, on

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Speaker 2: the right specifically, but also but really across you know,

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Speaker 2: there's concerned about the algorithm, what's it doing? Shadow banning?

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Speaker 2: Are certain things being labeled as fake news that aren't

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Speaker 2: fake news. You know, there was all the backlashing guys

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Speaker 2: YouTube for what videos they allowed during the pandemic, et cetera.

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Speaker 2: It feels like, just intuitively, there's no reason that all

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Speaker 2: of these concerns don't quickly map onto very cleanly, onto

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Speaker 2: AI concerns.

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Speaker 1: You're exactly right. I mean, it already has right and

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Speaker 1: in some of the early cases. You guys we've all

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Speaker 1: been around, We remember the early content about Facebook moderation,

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Speaker 1: remember all of that. I mean, look at what's happening

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Speaker 1: with chat, GPT, story after story about these suicides and

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Speaker 1: now erotica, and you know, they're saying the same level

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Speaker 1: of stuff that Facebook used to say, like oh, we have,

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Speaker 1: you know, controls in place. And look, I mean, at

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Speaker 1: some level, I do sympathize, like when a billion people

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Speaker 1: use your product, you know one percent of them might

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Speaker 1: be crazy and that's actually a really, really tough problem.

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Speaker 1: But also, you know, when your technology is being implicated

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Speaker 1: in all of these insane lawsuits and you have screenshots

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Speaker 1: and stuff that come out that make people go, WHOA,

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Speaker 1: I don't even know if I want my nineteen or

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Speaker 1: twenty year old like anywhere close to this. That really

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Speaker 1: because of the way that we all lived through those

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Speaker 1: past conversations show they track exactly onto Sam Altman and

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Speaker 1: Mark Zuckerberg. I mean, look at the stuff he's had

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Speaker 1: to deal with with the meta AI Instagram chatbots, you know,

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Speaker 1: for teenagers, and it's funny because they're trying to get

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Speaker 1: ahead of it to the NFL thing. I watch these

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Speaker 1: ads closely. Like one of the big ads right now

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Speaker 1: ad campaigns is Instagram for teens, which they're like, we

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Speaker 1: have safety stuff, you know, like that's kind of baked in,

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Speaker 1: and they're trying to get ahead of it because you know,

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Speaker 1: you don't run that ad if you don't have The

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Speaker 1: Anxious Generation as one of the top best selling books

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Speaker 1: in the United States, Like it's an upper middle class

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Speaker 1: like revolution now about people who are like against iPad

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Speaker 1: kiss right, And I think that's going to track very

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Speaker 1: very cleanly onto AI. There's no reason that I shouldn't.

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Speaker 3: How would you describe the relationship between you know, the

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Speaker 3: people running some of the biggest tech companies now versus

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Speaker 3: years past, because I remember, you know, we're talking a

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Speaker 3: little bit about some of the scandals and discussions about

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Speaker 3: political influence and what is actually shown on Facebook, and

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Speaker 3: I get the sense that once upon a time in

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Speaker 3: d C, people did kind of feel like big tech

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Speaker 3: was in control. Right, it was d C coming to big.

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Speaker 1: Tech for help.

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Speaker 3: And now I'm thinking back to the inauguration when we

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Speaker 3: had you know, people like Bezos and I think Zuckerberg

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Speaker 3: was there as well, Yes he was, and they're sort

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Speaker 3: of you know, I don't know how I saw one person.

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Speaker 3: I'm not going to describe it this way myself, but

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Speaker 3: I did see one person describe it as a hostage

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Speaker 3: situation where the big tech CEOs, you know, they're all

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Speaker 3: gathered in front of Trump and they seem kind of scared.

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Speaker 3: They're smiling, they're smiling, but not with their eyes. How

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Speaker 3: would you describe the relationship now between the administration and

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Speaker 3: you know, the big wigs of big tech.

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Speaker 1: Yeah, that's a great question, because you know what I'm

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Speaker 1: talking about a small d democratic politics. Now you're just

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Speaker 1: asking about power politics. Our politics here is very different.

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Speaker 1: I mean, you know, David Sachs has navigated his position incredibly. Well,

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Speaker 1: you know, Joe, there was a funny incident I think

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Speaker 1: I talked to you about this where I found a

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Speaker 1: snippet I think was a Financial Times piece and it

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Speaker 1: was just about it was like AI accounts for eighty

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Speaker 1: percent of stock gains and x percent of GDP in

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Speaker 1: terms of CAPEX, and David Sachs like retweeted it and

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Speaker 1: I was like, oh, so you think that's a good thing,

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Speaker 1: you know. I was like, I was like, oh, I

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Speaker 1: was like, okay, but you know that's actually if you're

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Speaker 1: in the Trump White House right now, you need this.

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Speaker 1: You know, you need the rally, you need the GDP

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Speaker 1: and the data center spend because that's what's propping up again.

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Speaker 1: You're the economist. Just from my listening to you and

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Speaker 1: reading Wall Street Journal, Bloomberg, Financial Times and others like,

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Speaker 1: that's a main reason why Joe you were like, well,

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Speaker 1: I don't think we're in a recession. Derek Thompson has

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Speaker 1: written very eloquently about this as well. Is just how

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Speaker 1: much of that spend currently accounts for why things seem okay.

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Speaker 1: So there is like a real alliance I think right

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Speaker 1: now between the Jensen's, David and even Sam Altman and others,

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Speaker 1: like they all come to the White House and announce

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Speaker 1: their crazy deals. For a reason, they get these awesome

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Speaker 1: press conferences from Trump. I mean there's the famous moment

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Speaker 1: where Zuck what he leads over to Trump, He's like,

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Speaker 1: I didn't know how much you wanted me to say.

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Speaker 1: I'm like, wow, okay, So you know you could I mean,

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Speaker 1: you could describe that as at heel. You could also

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Speaker 1: say he's got to bend over and kiss the ring

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Speaker 1: while also being able to basically just do whatever he wants.

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Speaker 1: So there is I think a very you know, alliance

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Speaker 1: of convenience right now, where the amount of money they're

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Speaker 1: pumping into the economy, the amount of stock value that

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Speaker 1: they're creating, is extremely beneficial to the Scott Bessens and

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Speaker 1: others of the world. Talking about MAGA economics.

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Speaker 2: Let's talk a little bit more about electoral politics specifically.

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Speaker 2: Ron DeSantis actually seems to be trying to make a

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Speaker 2: carveline for himself early on, even before the Sarah Friar comments,

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Speaker 2: actually saying no bailouts for AI companies, Like, what do

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Speaker 2: you see right now in terms of candidate positioning.

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Speaker 1: Yeah, that's it's all very early signaling. And that's actually

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Speaker 1: you know, I love this because usually one of the

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Speaker 1: things I hate most about politics recently it's all top down.

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Speaker 1: Do you have guys who are running for city council

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Speaker 1: talking about impeachment or something. It's like, dude, tell me

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Speaker 1: about the roads, you know, but actually this one is

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Speaker 1: really bottom up, you know, the data center stuff. It's Tucson.

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Speaker 1: Actually here in northern Virginia where I live, there's a

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Speaker 1: big comment and there's a lot of public controversy about

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Speaker 1: data centers. My state, Virginia, forty percent of our power

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Speaker 1: is consumed actually by data. Oregon is like thirty three percent,

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Speaker 1: I believe of power. There's been a lot of local

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Speaker 1: stuff and it hasn't really bubbled to the national level.

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Speaker 1: And I would say people here they care about money,

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Speaker 1: but they also care about electability. So there's an ocean

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Speaker 1: of money to be had if you're quote pro AI

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Speaker 1: or pro tech, but they'll always choose their own careers.

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Speaker 1: It went, and if you know, it does come down

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Speaker 1: to that, I haven't seen it bubble up yet. I

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Speaker 1: did see I'm not sure if you guys did that.

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Speaker 1: Bernie Sanders and a few of other progressives signed a

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Speaker 1: letter against data centers specifically, and that was the first

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Speaker 1: indication that I'm like, Ah, the staffers, they're paying attention, right,

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Speaker 1: they're listening to the pods. They're reading David Weigel's piece

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Speaker 1: about data center politics, and they're trying to operationalize that

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Speaker 1: into something at the national level. You're exactly right in

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Speaker 1: terms of dron desantists tried to pick that lane about bailout,

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Speaker 1: and that actually gets to something very recently where you

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Speaker 1: talked about Sarah's comments. I mean, we did a segment

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Speaker 1: about that, and it was huge about the open AI

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Speaker 1: bail out because people can feel it. They're like, I've

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Speaker 1: seen this movie before. They're you know, you know that. Yeah,

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Speaker 1: they make twenty billion, but they've got seventy five billion

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Speaker 1: in projected losses to twenty twenty eight. They've got a

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Speaker 1: trillion dollar in committed spend. You know, the pe ratios

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Speaker 1: and all this are so crazy, and if it doesn't

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Speaker 1: work out, they're going to say, oh, well, we need

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Speaker 1: you to bail us out, or we need you to

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Speaker 1: build the data centers for us and take all the

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Speaker 1: most expensive part of our business out. They are inherently

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Speaker 1: distrustful already of the numbers, and I think this is

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Speaker 1: very American, like you don't want to be controlled. And

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Speaker 1: the AI people tell us about how this is the

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Speaker 1: second Industrial Revolution and it's like, well, you know, then

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Speaker 1: we should probably look to the politics of the previous

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Speaker 1: industrial revolutions. You know, the railroad obviously, like had a

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Speaker 1: tremendous benefit. It doesn't take a week or it took

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Speaker 1: a week to go to California instead of three months.

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Speaker 1: That's awesome. But you know, very quickly in the eighteen

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Speaker 1: eighties and eighteen nineties Titanic, you know, uprisings in the

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Speaker 1: Midwest of farmers feeling like they're being controlled and anti

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Speaker 1: railroad politicians were some of the original like anti industrial

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Speaker 1: populace in Washington, even though originally everybody he was really sold.

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Speaker 2: I didn't know that the politicians.

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Speaker 1: Oh yeah, yeah, there definitely were. They were all throughout

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Speaker 1: the eighteen nineties, some of the original populist party. And

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Speaker 1: you look at what that all was. A lot of

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Speaker 1: it was about the railroad. And you know, I mean

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Speaker 1: there's legendary stories Texas where you and I are from,

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Speaker 1: Joe White, Patman, I'd have to write right back. Yeah.

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Speaker 1: There were like people like Sam Rayburn actually famously the

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Speaker 1: very anti railroad. He wouldn't take the free railroad passes.

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Speaker 1: These were like big, big stories all the way up

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Speaker 1: until the nineteen twenties, and so and so I see

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Speaker 1: a very similar kind of track, except though that I'm

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Speaker 1: not so sure that the socialized benefits will be as

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Speaker 1: good as the railroad, because the railroad, at the end

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Speaker 1: of the day, it was awesome. I could go to

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Speaker 1: California in a week. I don't have to take the

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Speaker 1: stagecoach or the Oregon Trail or whatever. This time around,

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Speaker 1: I'm like, listen, I don't know if you guys have

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Speaker 1: used Sora. Sorry, Like, it's not worth the power bills.

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Speaker 1: It's not worth it.

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Speaker 2: Tracy's insisting that I correct that I'm not really a

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Speaker 2: real Texan. I only went to college there.

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Speaker 1: He's a photo true. Okay, okay, all right.

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Speaker 3: This is important to me as someone with a dad

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Speaker 3: who was raised in Dallas and Lancaster. Anyway, I was

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Speaker 3: just thinking, you know, we were talking about populism in

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Speaker 3: the eighteen hundreds, and did you ever see the interpretations

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Speaker 3: of the Wizard of oz as.

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Speaker 4: Like, yeah, yeah, yeah, it was farmers who's like wanted

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Speaker 4: to move a silver backed currency, and a lot of

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Speaker 4: it supposedly was tied to populism.

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Speaker 3: Anyway, I wanted to go back to power politics for

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Speaker 3: one second, and ask what the heck JD. Evans is

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Speaker 3: going to do here, because it's not impossible that he

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Speaker 3: is the chosen successor to Trump in whatever timeframe, and

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Speaker 3: in that case, he's sort of stuck between I guess

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Speaker 3: two peas here, populism and Peter Teel.

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Speaker 1: Yeah, well, you know, it's a lot more peas than

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Speaker 1: that actually, because it's popularism, it's Trump, it's Tea. I mean,

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Speaker 1: you know, the Teal thing. I've always thought it was

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Speaker 1: frankly a little bit overstated terms of influence. But regardless,

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Speaker 1: I wouldn't say more like Teal sphere or Teal thought

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Speaker 1: if that makes sense. Like, as you guys know, probably

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Speaker 1: most of the people listening to this, like there's an

429
00:21:09,520 --> 00:21:12,840
Speaker 1: entire like ecosystem around like zero you know, zero to

430
00:21:12,880 --> 00:21:15,760
Speaker 1: one posting and all of that within tech anyway, So

431
00:21:16,000 --> 00:21:18,320
Speaker 1: that sphere, let's call it, right tech, I guess, you know,

432
00:21:18,359 --> 00:21:21,760
Speaker 1: to be extremely reductive, that sphere is really the one

433
00:21:22,000 --> 00:21:24,359
Speaker 1: which currently embodies, you know, really a lot of this

434
00:21:24,440 --> 00:21:28,679
Speaker 1: more pro AI direction. Let's say, Palenteer, Alex KRP and others.

435
00:21:28,680 --> 00:21:31,240
Speaker 1: But even he is fascinating, right because he's pro AI,

436
00:21:31,400 --> 00:21:33,440
Speaker 1: but only for Palenteer, and he thinks the rest of

437
00:21:33,480 --> 00:21:37,080
Speaker 1: this up. So like these things do break down. But Jad,

438
00:21:37,320 --> 00:21:39,200
Speaker 1: I mean, look, he's gonna be in a tough position

439
00:21:39,280 --> 00:21:42,159
Speaker 1: as well because he also it's not exactly like in

440
00:21:42,200 --> 00:21:45,480
Speaker 1: repudiate the Trump administration. I actually think it's one of

441
00:21:45,520 --> 00:21:48,359
Speaker 1: his biggest weaknesses going into twenty twenty eight. I mean,

442
00:21:48,440 --> 00:21:51,080
Speaker 1: you guys will remember they savaged Kamala for the what

443
00:21:51,160 --> 00:21:54,479
Speaker 1: would you do different conversation from Biden? I mean, how

444
00:21:54,520 --> 00:21:56,840
Speaker 1: do you answer that when Donald Trump is still alive

445
00:21:56,960 --> 00:21:59,120
Speaker 1: and you about to run. You know you're gonna get

446
00:21:59,119 --> 00:22:01,920
Speaker 1: destroyed if you say the truth. And I mean, look,

447
00:22:01,960 --> 00:22:04,159
Speaker 1: I don't know where things are gonna end up. Trump

448
00:22:04,200 --> 00:22:07,680
Speaker 1: is basically trending in Biden territory right now in terms

449
00:22:07,720 --> 00:22:09,960
Speaker 1: of his approval ratings. And so look, you know, we

450
00:22:10,000 --> 00:22:12,520
Speaker 1: saw that movie, and in my opinion, when you start

451
00:22:12,600 --> 00:22:16,080
Speaker 1: chart posting about how the economy is actually good, you're cooked.

452
00:22:16,600 --> 00:22:19,440
Speaker 1: You know, to borrow a phrase from the youth about politics,

453
00:22:19,440 --> 00:22:23,320
Speaker 1: but yeah, you're the power politics of it are really

454
00:22:23,359 --> 00:22:25,439
Speaker 1: I think trepidacious. So I think for a lot of

455
00:22:25,440 --> 00:22:28,280
Speaker 1: the right because they don't want to get away from

456
00:22:28,280 --> 00:22:30,840
Speaker 1: Elon and from his money. Right, Elon is very very

457
00:22:30,840 --> 00:22:34,920
Speaker 1: powerful center. I think in Republican politics. XAI. He's got

458
00:22:34,920 --> 00:22:37,240
Speaker 1: this huge new data center project. I'm sure you guys

459
00:22:37,480 --> 00:22:39,840
Speaker 1: have read or seen something about that. He's probably going

460
00:22:39,880 --> 00:22:43,359
Speaker 1: to demand, you know, certain things. And at the same time,

461
00:22:43,400 --> 00:22:45,520
Speaker 1: you're i mean a huge amount of the right wing

462
00:22:45,760 --> 00:22:50,040
Speaker 1: commentariot who would probably be more jd vance ideologically position

463
00:22:50,119 --> 00:22:52,200
Speaker 1: let's say, like the Matt Walsh's of the world, that

464
00:22:52,320 --> 00:22:54,479
Speaker 1: Tucker Carlson's of the world. These were were straight up

465
00:22:54,520 --> 00:22:57,439
Speaker 1: on record as this is, you know, demonic. We need

466
00:22:57,480 --> 00:23:00,640
Speaker 1: to stand for humanity, and it's going to be very tough.

467
00:23:00,680 --> 00:23:03,199
Speaker 1: I actually think it might be the single toughest position

468
00:23:03,440 --> 00:23:06,320
Speaker 1: that you'll have to navigate in the campaign. And I

469
00:23:06,320 --> 00:23:09,200
Speaker 1: think that the left, the political left in particular, they

470
00:23:09,240 --> 00:23:11,720
Speaker 1: are looking at this data center stuff. They are all

471
00:23:11,760 --> 00:23:13,880
Speaker 1: over it. More Perfect Union, which is one of those

472
00:23:14,119 --> 00:23:17,600
Speaker 1: it's like a Bernie aligned institution. They have been all

473
00:23:17,760 --> 00:23:20,720
Speaker 1: over this issue. So I'm watching the populace left really

474
00:23:20,760 --> 00:23:23,440
Speaker 1: really grab onto it. And I think a democratic position

475
00:23:23,520 --> 00:23:25,639
Speaker 1: in twenty twenty eight is going to come down hard

476
00:23:25,920 --> 00:23:29,399
Speaker 1: in terms of either regulation or protecting Americans, you know,

477
00:23:29,480 --> 00:23:32,680
Speaker 1: reforming power. However, it's going to be there. They are

478
00:23:32,680 --> 00:23:34,359
Speaker 1: going to be there and the right is going to

479
00:23:34,359 --> 00:23:35,200
Speaker 1: have a very tough time.

480
00:23:35,280 --> 00:23:38,040
Speaker 2: It's interesting because, like even ten years ago, we thought

481
00:23:38,040 --> 00:23:41,720
Speaker 2: of Silicon Valley as like basically being democratic aligned, right

482
00:23:41,800 --> 00:23:45,159
Speaker 2: like most big tech executives. And I still think, you know,

483
00:23:45,200 --> 00:23:48,080
Speaker 2: most people in San Francisco obviously vote Democrat, including a

484
00:23:48,080 --> 00:23:51,000
Speaker 2: lot of high people at these tech companies. But there's

485
00:23:51,040 --> 00:23:53,800
Speaker 2: this well known shift that everyone talks about and they've

486
00:23:53,800 --> 00:23:56,960
Speaker 2: got much more comfortable. But it feels like to me

487
00:23:57,560 --> 00:23:59,959
Speaker 2: they could you know, they've moved, they've switched asides to

488
00:24:00,680 --> 00:24:01,320
Speaker 2: like they could.

489
00:24:01,240 --> 00:24:03,959
Speaker 1: Kind of end up friendless and all this. Oh, I

490
00:24:03,960 --> 00:24:07,560
Speaker 1: mean I already I think that they massively overplayed their

491
00:24:07,600 --> 00:24:10,399
Speaker 1: hand with DOGE. And I mean, you know, look, I

492
00:24:10,560 --> 00:24:12,760
Speaker 1: for being honest, just look at the stats. Like it's

493
00:24:12,760 --> 00:24:15,320
Speaker 1: a total failure in terms of its stated project. You

494
00:24:15,320 --> 00:24:18,480
Speaker 1: could debate whether USA I D or whatever is bad.

495
00:24:18,560 --> 00:24:21,000
Speaker 1: I mean, Treasury data is public, guys, you can go

496
00:24:21,040 --> 00:24:23,880
Speaker 1: read it for yourself in terms of spending. So that's

497
00:24:23,920 --> 00:24:26,360
Speaker 1: one thing. But two, you're exactly right, Joe in terms

498
00:24:26,400 --> 00:24:28,760
Speaker 1: of the fair weather friend nature of all of this

499
00:24:28,840 --> 00:24:30,440
Speaker 1: is that you know, a in terms of the way

500
00:24:30,440 --> 00:24:32,920
Speaker 1: that a lot of the democratic base has been radicalized

501
00:24:32,920 --> 00:24:35,359
Speaker 1: at this point, they're never going back. And then in

502
00:24:35,400 --> 00:24:37,159
Speaker 1: the right wing coalition, you know, I don't want to

503
00:24:37,160 --> 00:24:42,120
Speaker 1: go too deep in the weeds here, but like, yeah,

504
00:24:42,119 --> 00:24:44,280
Speaker 1: I mean the tech the tech right. Let's take this

505
00:24:44,480 --> 00:24:47,200
Speaker 1: H one B thing for example. This is like a huge,

506
00:24:47,480 --> 00:24:50,520
Speaker 1: huge split right now in the political right because a

507
00:24:50,560 --> 00:24:52,959
Speaker 1: lot of these tech guys are very pro H one B.

508
00:24:53,320 --> 00:24:55,679
Speaker 1: You know, Elon famously said he would fight to the

509
00:24:55,760 --> 00:24:58,760
Speaker 1: death over the issue of H one B. This was

510
00:24:58,880 --> 00:25:01,560
Speaker 1: during the whole fight, you know, in December before Trump

511
00:25:01,680 --> 00:25:04,080
Speaker 1: took office, and Trump ultimately he just did an interview

512
00:25:04,119 --> 00:25:05,960
Speaker 1: a few days ago where he embraced H one B,

513
00:25:06,080 --> 00:25:08,600
Speaker 1: say actually, we need more talent. But there's a lot

514
00:25:08,680 --> 00:25:12,040
Speaker 1: of the activist political right which is vary against that

515
00:25:12,080 --> 00:25:15,600
Speaker 1: and increasingly are blaming a lot of these tech right

516
00:25:15,680 --> 00:25:19,800
Speaker 1: politicians for hijacking the administration. So that's why, you know,

517
00:25:19,880 --> 00:25:22,720
Speaker 1: for them, I would be very careful as to how

518
00:25:22,800 --> 00:25:25,600
Speaker 1: exactly you know they're going to navigate. There's also I mean,

519
00:25:25,600 --> 00:25:28,199
Speaker 1: this breaks on foreign policy lines as well, because a

520
00:25:28,280 --> 00:25:31,720
Speaker 1: subset of the tech right is also very pro Israel,

521
00:25:31,760 --> 00:25:34,200
Speaker 1: which is also a very divisive issue. I don't want

522
00:25:34,200 --> 00:25:36,359
Speaker 1: to drag that into this, but only to say, like

523
00:25:36,640 --> 00:25:39,919
Speaker 1: Joe Lonsdale, Sean Maguire, these are you know, very very

524
00:25:40,040 --> 00:25:43,680
Speaker 1: rich people, very activated in the current you know, cohort

525
00:25:43,680 --> 00:25:47,160
Speaker 1: of MAGA, but not exactly like beloved. Let's say amongst

526
00:25:47,200 --> 00:25:49,800
Speaker 1: a different segment you know, of the American right. And

527
00:25:49,880 --> 00:25:53,280
Speaker 1: so I could see a huge clash coming to the

528
00:25:53,320 --> 00:25:55,840
Speaker 1: front with that. And that's another issue I would say,

529
00:25:55,960 --> 00:25:57,720
Speaker 1: you know, for a JD. Vance is like, I'm sure

530
00:25:57,760 --> 00:25:59,960
Speaker 1: he's friendly with them, and that's easy when Trump is

531
00:26:00,040 --> 00:26:03,399
Speaker 1: papering over all these coalitional politics. But that's going to

532
00:26:03,440 --> 00:26:06,520
Speaker 1: break out into the open like nobody's business in a primary,

533
00:26:06,760 --> 00:26:08,719
Speaker 1: and I could see it. I could see them becoming

534
00:26:08,720 --> 00:26:12,080
Speaker 1: friendless quickly because again, I mean, look, I could be wrong.

535
00:26:12,480 --> 00:26:14,600
Speaker 1: I don't think that that's where the base is. I

536
00:26:14,680 --> 00:26:17,840
Speaker 1: really don't, just because if we look at the math

537
00:26:18,240 --> 00:26:21,199
Speaker 1: of who are the people increasingly identifying as MAGA or

538
00:26:21,440 --> 00:26:24,280
Speaker 1: Republican Like, people making less than one hundred thousand dollars

539
00:26:24,280 --> 00:26:26,840
Speaker 1: a year are not going to be cheering on, you know,

540
00:26:27,000 --> 00:26:29,160
Speaker 1: multi billionaires telling them that they don't have to work,

541
00:26:29,440 --> 00:26:31,359
Speaker 1: that they're not gonna work anymore, not that they don't

542
00:26:31,359 --> 00:26:34,440
Speaker 1: have to work anymore. They're not going to work anymore more,

543
00:26:34,480 --> 00:26:37,240
Speaker 1: blue collar types. I mean, you know the driverless you know,

544
00:26:37,280 --> 00:26:40,919
Speaker 1: the driverless trucks issue. Like the Teamsters president, I had

545
00:26:40,960 --> 00:26:44,480
Speaker 1: him on my show, just going hard, Sean O'Brien, famously

546
00:26:44,520 --> 00:26:47,879
Speaker 1: the only union president to not endorse in the election.

547
00:26:48,119 --> 00:26:50,840
Speaker 1: Much more Trump friendly, So that part of the coalition

548
00:26:50,920 --> 00:26:52,600
Speaker 1: is gonna have something to say, I think. Yeah.

549
00:26:52,640 --> 00:26:55,040
Speaker 3: I feel like we saw some of the sensitivity burst

550
00:26:55,040 --> 00:26:57,840
Speaker 3: into the open with the h one B Visa comments

551
00:26:58,240 --> 00:27:01,240
Speaker 3: when Trump was like, we don't have enough talent in America. Yeah,

552
00:27:01,440 --> 00:27:04,000
Speaker 3: I mean Twitter slash x just exploded.

553
00:27:04,240 --> 00:27:06,040
Speaker 2: I ask you to take all the jobs and the

554
00:27:06,119 --> 00:27:09,080
Speaker 2: jobs that will be left. We're also going to Yeah, yeah,

555
00:27:09,320 --> 00:27:11,479
Speaker 2: that's right. Whatever jobs are left, they'll be in India.

556
00:27:11,920 --> 00:27:13,639
Speaker 2: So you're like, I don't know about that.

557
00:27:13,960 --> 00:27:16,080
Speaker 3: Okay, So I feel like I know the answer to

558
00:27:16,119 --> 00:27:19,320
Speaker 3: this question. But if a lot of people are going

559
00:27:19,359 --> 00:27:23,119
Speaker 3: to be unable to work, is there anyone in DC

560
00:27:23,600 --> 00:27:26,919
Speaker 3: or elsewhere who is talking about universal basic income?

561
00:27:28,080 --> 00:27:30,760
Speaker 1: Andrew Yang I just Adam on the show. He's doing

562
00:27:30,800 --> 00:27:33,639
Speaker 1: the biggest victory lap over all of this. You know,

563
00:27:33,760 --> 00:27:37,080
Speaker 1: I have not seen it. I know even Bernie famously,

564
00:27:37,119 --> 00:27:39,320
Speaker 1: I think it's against UBI. I have to go back

565
00:27:39,320 --> 00:27:41,880
Speaker 1: and check. He's more of a federal jobs guarantee guy,

566
00:27:41,880 --> 00:27:44,880
Speaker 1: and that's the most most out there. Like I said,

567
00:27:44,920 --> 00:27:48,520
Speaker 1: I mean to be honest covering politics now, I've only

568
00:27:48,560 --> 00:27:51,360
Speaker 1: watched them play catch up. I mean famously, I've talked

569
00:27:51,400 --> 00:27:54,040
Speaker 1: with Jack Dorsey and others about this. But like those

570
00:27:54,080 --> 00:27:57,159
Speaker 1: famous hearings where they'd be like, so, how does Facebook,

571
00:27:57,240 --> 00:27:59,840
Speaker 1: you know, make money? And you're like, oh my god, man,

572
00:27:59,880 --> 00:28:02,280
Speaker 1: you know, in front of these hearings when the congressmen

573
00:28:02,480 --> 00:28:05,080
Speaker 1: you know, don't even know the most basic facets, or

574
00:28:05,119 --> 00:28:07,480
Speaker 1: they'll be asking stuff like why do my campaign emails

575
00:28:07,520 --> 00:28:11,359
Speaker 1: get stuck in gmails? Filters? Like these are real questions

576
00:28:11,400 --> 00:28:14,159
Speaker 1: that were asked in Friday, the United States senator in

577
00:28:14,200 --> 00:28:16,800
Speaker 1: the Congress. So I don't have a lot of faith

578
00:28:16,960 --> 00:28:20,760
Speaker 1: that there are a lot of very forward facing thinkers

579
00:28:21,040 --> 00:28:23,639
Speaker 1: around this issue, and in particular, I mean I know

580
00:28:23,760 --> 00:28:26,240
Speaker 1: that to be the case. It could change in twenty

581
00:28:26,280 --> 00:28:29,080
Speaker 1: twenty six. I think a dem you know, tea party

582
00:28:29,160 --> 00:28:31,879
Speaker 1: type wave is coming. There might be some interesting candidates

583
00:28:31,960 --> 00:28:34,040
Speaker 1: that start to say things like that, and maybe on

584
00:28:34,080 --> 00:28:35,760
Speaker 1: the Republican side as well. I'd like to see it.

585
00:28:51,440 --> 00:28:54,000
Speaker 2: We've been talking a lot about the right, the Republicans.

586
00:28:54,240 --> 00:28:56,160
Speaker 2: I actually really like a lot of these guys. But

587
00:28:56,200 --> 00:28:57,960
Speaker 2: when I think about the other side of the aisle

588
00:28:58,640 --> 00:29:00,560
Speaker 2: and I think about some of these tensions, as you mentioned,

589
00:29:00,560 --> 00:29:02,560
Speaker 2: more perfect union and some of the more you know,

590
00:29:02,640 --> 00:29:06,360
Speaker 2: kind of populous left things, I'm not very bullish on

591
00:29:06,800 --> 00:29:11,480
Speaker 2: the abundance Dems right now, and how that sort of

592
00:29:11,560 --> 00:29:14,360
Speaker 2: faction that would like to sort of take the party

593
00:29:14,400 --> 00:29:17,240
Speaker 2: in a sort of more you know, I guess, I

594
00:29:17,240 --> 00:29:21,520
Speaker 2: don't know, centrist, but a centrist moderate liberal realm. Yeah,

595
00:29:21,560 --> 00:29:24,000
Speaker 2: whatever it is. I don't know how you feel. Give

596
00:29:24,040 --> 00:29:26,000
Speaker 2: us your sort of your view on the other side

597
00:29:26,000 --> 00:29:27,160
Speaker 2: of the aisle, definitely.

598
00:29:27,240 --> 00:29:30,360
Speaker 1: I mean the interesting thing about the whole abundance thing

599
00:29:30,640 --> 00:29:33,800
Speaker 1: and all of that is, I think, you know what

600
00:29:33,840 --> 00:29:37,240
Speaker 1: it misses is the theory of control. And you know,

601
00:29:37,280 --> 00:29:39,680
Speaker 1: it's like more housing awesome, you know, but it doesn't

602
00:29:39,680 --> 00:29:42,239
Speaker 1: get to ownership, I guess, and the feeling of like

603
00:29:42,560 --> 00:29:45,400
Speaker 1: the ability to control your own destiny, which I think

604
00:29:45,480 --> 00:29:49,240
Speaker 1: is really baked into the American spirit and the American project.

605
00:29:49,480 --> 00:29:51,440
Speaker 1: And you know, you see stuff like this about debates

606
00:29:51,480 --> 00:29:54,240
Speaker 1: around housing and you know, they'll kind of ridicule this

607
00:29:54,360 --> 00:29:57,360
Speaker 1: idea about corporate ownership, and you know, I'm not going

608
00:29:57,440 --> 00:29:59,720
Speaker 1: to get into the weeds about whether that's even true

609
00:29:59,800 --> 00:30:02,040
Speaker 1: or not. I know there's a lot of controversy, but

610
00:30:02,400 --> 00:30:05,800
Speaker 1: the idea is definitely very popular, right, you know, for

611
00:30:06,040 --> 00:30:08,560
Speaker 1: a lot of people. And the reason why is fundamentally,

612
00:30:08,600 --> 00:30:11,640
Speaker 1: they're like, I'm being priced out of my ability to

613
00:30:11,840 --> 00:30:14,160
Speaker 1: buy a home. Like it really, what it is about

614
00:30:14,400 --> 00:30:18,720
Speaker 1: is about you know, the individuality of reclaiming your own destiny.

615
00:30:18,760 --> 00:30:21,760
Speaker 1: And that's why that's why those two things are very linked.

616
00:30:21,800 --> 00:30:24,120
Speaker 1: You know, it's about power. You know, to kind of

617
00:30:24,120 --> 00:30:27,000
Speaker 1: bring back to Tracy's question earlier about you know, beating China,

618
00:30:27,360 --> 00:30:30,680
Speaker 1: et cetera, it's the technology is not the same as

619
00:30:30,720 --> 00:30:33,240
Speaker 1: the companies that are in charge. And when you see

620
00:30:33,280 --> 00:30:38,400
Speaker 1: these swelling valuations and talks of bailouts and the encroachment

621
00:30:38,480 --> 00:30:40,520
Speaker 1: into your daily life or your office, and you see

622
00:30:40,520 --> 00:30:44,880
Speaker 1: headlines about firing workers, you know, on the promise of AI.

623
00:30:45,320 --> 00:30:48,640
Speaker 1: None of this goes to the heart about democratic input.

624
00:30:48,720 --> 00:30:50,680
Speaker 1: You know, I was thinking about that in terms of

625
00:30:50,720 --> 00:30:53,680
Speaker 1: the chatchypt suicide question, which I know is fringe from

626
00:30:53,720 --> 00:30:55,680
Speaker 1: their point of view, but I think it's important if

627
00:30:55,680 --> 00:30:58,560
Speaker 1: billions of people are going to use your technology. And

628
00:30:58,640 --> 00:31:01,320
Speaker 1: Sam famously in a Tucker car else an interview, was like, well,

629
00:31:01,560 --> 00:31:04,320
Speaker 1: in a country where assistant suicide is legal, like, would

630
00:31:04,360 --> 00:31:08,120
Speaker 1: you direct people to those resources? He effectively said yes,

631
00:31:08,160 --> 00:31:10,120
Speaker 1: And I was like, oh my god, I mean, then

632
00:31:10,280 --> 00:31:14,000
Speaker 1: you know chat ept's mental health standards. In my opinion,

633
00:31:14,080 --> 00:31:16,840
Speaker 1: like there needs to be some like there needs to

634
00:31:16,840 --> 00:31:18,920
Speaker 1: be stuff hashed out in the US Congress and at

635
00:31:18,920 --> 00:31:21,640
Speaker 1: the state and the local level where like we all

636
00:31:21,680 --> 00:31:25,680
Speaker 1: agree on what so called guardrails and acceptable norms are.

637
00:31:25,960 --> 00:31:27,720
Speaker 1: And I can't just be leaving it to Sam Altman.

638
00:31:27,760 --> 00:31:30,520
Speaker 1: I'm sorry, I think you know, no offense to that guy.

639
00:31:30,640 --> 00:31:32,680
Speaker 1: It's not even about him, it's about everybody. No, but

640
00:31:32,840 --> 00:31:35,360
Speaker 1: no one person should have that immense amount of power

641
00:31:35,520 --> 00:31:38,760
Speaker 1: which is going to affect millions and millions amow. Yeah.

642
00:31:38,760 --> 00:31:41,640
Speaker 3: And it just seems incredibly difficult to have any democratic

643
00:31:41,680 --> 00:31:44,480
Speaker 3: input into what are effectively black boxes, right and have

644
00:31:45,320 --> 00:31:48,280
Speaker 3: always been black boxes. I have a very basic question,

645
00:31:48,600 --> 00:31:54,000
Speaker 3: but is anyone in DC using AI in an interesting way?

646
00:31:54,560 --> 00:31:57,719
Speaker 3: I imagine you know, people are probably editing policy papers

647
00:31:57,800 --> 00:32:00,320
Speaker 3: or whatever with it and doing some research, but is

648
00:32:00,360 --> 00:32:04,600
Speaker 3: anyone I don't know creating lots of AI driven bots

649
00:32:04,640 --> 00:32:07,760
Speaker 3: to sway conversations, or I don't know, come up with

650
00:32:07,800 --> 00:32:10,320
Speaker 3: some new system of campaign finance. Who knows.

651
00:32:10,400 --> 00:32:13,280
Speaker 1: I'm not privy to any of this secret stuff like you.

652
00:32:13,440 --> 00:32:16,600
Speaker 1: I mean that kind of either, Just to be clear,

653
00:32:17,240 --> 00:32:19,400
Speaker 1: I mean it kind of begs the question is is

654
00:32:19,440 --> 00:32:21,920
Speaker 1: anything interesting being done with A right? You know what

655
00:32:21,960 --> 00:32:24,800
Speaker 1: you just said of editing papers? Everybody's editing papers great,

656
00:32:24,960 --> 00:32:27,160
Speaker 1: you know, awesome. A friend of mine, John Coogan, I'm

657
00:32:27,160 --> 00:32:29,840
Speaker 1: sure you guys know, Yeah, yeah, I think John had

658
00:32:29,880 --> 00:32:32,680
Speaker 1: a great analogy to AI. He was like, I kind

659
00:32:32,680 --> 00:32:34,400
Speaker 1: of think it'll be like the credit card, you know,

660
00:32:34,440 --> 00:32:38,400
Speaker 1: like frictionless payments. It enabled an entire new ecosystem. We

661
00:32:38,440 --> 00:32:40,360
Speaker 1: could build wealth. It'll make your life like a little

662
00:32:40,360 --> 00:32:42,840
Speaker 1: bit better, you know, and all that. I think that's

663
00:32:42,920 --> 00:32:46,520
Speaker 1: basically the technology, right, you know, summarizing editing. It's a

664
00:32:46,560 --> 00:32:48,840
Speaker 1: better Google. That's cool and all that. But I haven't

665
00:32:48,920 --> 00:32:52,040
Speaker 1: yet seen like some mass sophisticated thing. I mean, to

666
00:32:52,080 --> 00:32:54,000
Speaker 1: be honest, you know, most people in DC are probably

667
00:32:54,080 --> 00:32:59,320
Speaker 1: using AI for editing purposes, research, to check things, what else?

668
00:32:59,360 --> 00:33:02,680
Speaker 1: I mean, image generation to slander your political opponents. That's

669
00:33:02,680 --> 00:33:04,600
Speaker 1: a pretty big one. We use it for thumbnails for

670
00:33:04,640 --> 00:33:07,360
Speaker 1: our YouTube videos. You know, Like, I guess that's cool,

671
00:33:07,720 --> 00:33:09,880
Speaker 1: you know, I mean, yeah, it's nice. It's definitely nice.

672
00:33:10,160 --> 00:33:11,920
Speaker 1: It's not that I don't have. I also have photoshop

673
00:33:11,960 --> 00:33:13,840
Speaker 1: guys who worked for me, so you know, they use

674
00:33:13,920 --> 00:33:15,520
Speaker 1: it a little bit every once in a while. But

675
00:33:15,560 --> 00:33:17,880
Speaker 1: I haven't seen anything truly novel. I really have it.

676
00:33:18,160 --> 00:33:21,560
Speaker 2: You know, in terms of elected officials who maybe a

677
00:33:21,600 --> 00:33:23,800
Speaker 2: few years ago this would have been a surprise, but

678
00:33:23,920 --> 00:33:29,320
Speaker 2: who seemed to have an very good intuitions about things mergery,

679
00:33:29,360 --> 00:33:32,080
Speaker 2: Taylor Green, one of these names. Was like, Oh, I'm like,

680
00:33:32,280 --> 00:33:34,880
Speaker 2: she seems savvy in a way that maybe people wouldn't

681
00:33:34,880 --> 00:33:36,640
Speaker 2: have guessed. Has she been talking about AI at all?

682
00:33:37,480 --> 00:33:40,840
Speaker 1: You know, I haven't looked. Definitely, I haven't seen anything

683
00:33:40,920 --> 00:33:44,440
Speaker 1: in particular. I can almost guess where she would land them. Yeah,

684
00:33:44,520 --> 00:33:47,400
Speaker 1: especially look, her own home state is now ground zero

685
00:33:47,680 --> 00:33:49,960
Speaker 1: for a statewide referendum. Again, talk to us about that.

686
00:33:50,000 --> 00:33:51,719
Speaker 1: We haven't actually told us. We haven't.

687
00:33:51,720 --> 00:33:54,880
Speaker 2: This Georgia election, I know, it was like kind of powers,

688
00:33:54,960 --> 00:33:57,680
Speaker 2: elect empowers and electricity stuff. It's kind of directly on

689
00:33:57,720 --> 00:33:58,360
Speaker 2: the ballot.

690
00:33:58,160 --> 00:34:02,760
Speaker 1: Right, Yeah, two races literally about power. Both made Democratic candidates,

691
00:34:02,760 --> 00:34:05,760
Speaker 1: they made data centers power usage like a central part

692
00:34:05,840 --> 00:34:08,840
Speaker 1: of their campaign, and they won a huge victory. I

693
00:34:08,840 --> 00:34:11,280
Speaker 1: don't want to ascribe the entire thing. Let's be honest.

694
00:34:11,280 --> 00:34:14,719
Speaker 1: In a national environment, a lot of people are decrets, right,

695
00:34:14,760 --> 00:34:16,680
Speaker 1: but it was it was an awesome night for Democrats. Right.

696
00:34:16,960 --> 00:34:19,560
Speaker 1: But look, I mean in a certain way, like those

697
00:34:19,800 --> 00:34:24,560
Speaker 1: arguments still are really finding a lot of salience because

698
00:34:24,920 --> 00:34:28,560
Speaker 1: you know these stories about power going up, and look,

699
00:34:28,640 --> 00:34:32,200
Speaker 1: I mean everybody remembers, you know, the walmartization conversation, the

700
00:34:32,200 --> 00:34:36,240
Speaker 1: Amazon warehouse conversation. Nobody loves this idea of these big

701
00:34:36,280 --> 00:34:40,440
Speaker 1: companies coming in buying land, using a ton of resources,

702
00:34:40,600 --> 00:34:44,040
Speaker 1: driving prices up. Nobody buys the BS anymore. About how

703
00:34:44,040 --> 00:34:46,239
Speaker 1: this is going to create a ton of jobs. I mean,

704
00:34:46,280 --> 00:34:49,600
Speaker 1: definitely some construction jobs. But nobody's really under the illusion.

705
00:34:49,719 --> 00:34:52,160
Speaker 1: Everybody knows tech is a power law business. You know,

706
00:34:52,200 --> 00:34:53,960
Speaker 1: the vast majority of the wealth is going to flow

707
00:34:54,080 --> 00:34:56,120
Speaker 1: up to the top of the CEO and the networks

708
00:34:56,160 --> 00:34:58,640
Speaker 1: of Zuck and Elon and the rest of these people.

709
00:34:58,719 --> 00:35:02,560
Speaker 1: So that's where politics of this really matter. And you know,

710
00:35:02,600 --> 00:35:04,480
Speaker 1: heat map I shouted them out They wrote a great

711
00:35:04,480 --> 00:35:08,120
Speaker 1: piece about the data center backlashes swallowing American politics, and

712
00:35:08,160 --> 00:35:10,000
Speaker 1: you should go read it just to even look at polling,

713
00:35:10,120 --> 00:35:13,040
Speaker 1: not just about Georgia. I mean, this is bipartisan at

714
00:35:13,080 --> 00:35:16,240
Speaker 1: every level. And younger generations are the most anti data center,

715
00:35:16,560 --> 00:35:20,160
Speaker 1: anti AI kind of politics. And I mean, look, they're suffering.

716
00:35:20,239 --> 00:35:22,399
Speaker 1: I mean, they have a high unemployment rate right now.

717
00:35:22,480 --> 00:35:24,520
Speaker 1: A lot of them either blame AI or look at

718
00:35:24,520 --> 00:35:26,560
Speaker 1: AI is one of the reasons why they're having a

719
00:35:26,600 --> 00:35:28,680
Speaker 1: tough time in the labor market. And you know, it

720
00:35:28,719 --> 00:35:31,759
Speaker 1: doesn't help. Whenever you see Wall Street Journal articles just

721
00:35:32,040 --> 00:35:35,399
Speaker 1: about white collar companies increasingly bet on more productivity out

722
00:35:35,400 --> 00:35:38,960
Speaker 1: of existing workforce, like this stuff confirmed. It both confirms

723
00:35:39,000 --> 00:35:41,960
Speaker 1: biases and it's real, so it's not even particularly a bias.

724
00:35:42,960 --> 00:35:45,560
Speaker 3: What is the state of antitrust at the moment, because

725
00:35:45,640 --> 00:35:48,440
Speaker 3: I remember at the beginning of this year, it seemed

726
00:35:48,520 --> 00:35:51,680
Speaker 3: like it was emerging as a possibly bipartisan issue or

727
00:35:51,680 --> 00:35:54,120
Speaker 3: at least a direct link between the Biden administration and

728
00:35:54,160 --> 00:35:57,439
Speaker 3: the Trump administration. Everyone wanted to go after the big

729
00:35:57,480 --> 00:36:00,400
Speaker 3: tech companies, possibly for different reasons, but you know, the

730
00:36:00,440 --> 00:36:03,360
Speaker 3: outcomes the same. Is that still a live issue?

731
00:36:03,400 --> 00:36:07,719
Speaker 1: In DC. Yeah. I mean, look, speaking quite candidly, like

732
00:36:08,160 --> 00:36:10,480
Speaker 1: the state of the government is like large portions of

733
00:36:10,520 --> 00:36:13,640
Speaker 1: it for sale and for you know, large portions of

734
00:36:13,680 --> 00:36:17,880
Speaker 1: it are basically you know, they are very beholden to

735
00:36:18,080 --> 00:36:20,680
Speaker 1: financial interests. I'll put that very politely, shall I at

736
00:36:20,760 --> 00:36:23,960
Speaker 1: least about who can get to Trump's ear, who can

737
00:36:24,320 --> 00:36:27,040
Speaker 1: get and have some influence with the administration. I don't

738
00:36:27,080 --> 00:36:29,479
Speaker 1: want to poop poo it entirely, you know, when big

739
00:36:29,520 --> 00:36:32,000
Speaker 1: money or a donor or something like that is not involved.

740
00:36:32,000 --> 00:36:34,120
Speaker 1: The FTC has done some good work and has been

741
00:36:34,200 --> 00:36:37,040
Speaker 1: looking to try and confirmed, but they've also quite honestly,

742
00:36:37,120 --> 00:36:40,400
Speaker 1: you know, moved bass some of the antitrust cases and

743
00:36:40,440 --> 00:36:43,000
Speaker 1: others that Lena Khan put forward. So I would say

744
00:36:43,360 --> 00:36:45,920
Speaker 1: it's mixed. I mean, I think that the look, this

745
00:36:45,960 --> 00:36:47,440
Speaker 1: stuff is happening on in the open, so I don't

746
00:36:47,440 --> 00:36:49,560
Speaker 1: even know, I'm afraid of saying it. Like he convenes

747
00:36:49,560 --> 00:36:52,359
Speaker 1: the tech CEOs and they are like, how much money

748
00:36:52,400 --> 00:36:54,200
Speaker 1: do you want me to announce? And they'll just do it,

749
00:36:54,280 --> 00:36:56,560
Speaker 1: you know. I Mean, that's pretty much is the whole

750
00:36:56,600 --> 00:36:58,719
Speaker 1: ball game. And I think that gets to the kind

751
00:36:58,760 --> 00:37:01,520
Speaker 1: of power alliance I talked about earlier, like the Trump

752
00:37:01,560 --> 00:37:05,399
Speaker 1: administration really needs these companies for headlines and because they're

753
00:37:05,400 --> 00:37:07,200
Speaker 1: propping up the economy in their stock marketing.

754
00:37:07,239 --> 00:37:09,359
Speaker 2: So so this is exactly where I wanted to ask

755
00:37:09,400 --> 00:37:12,120
Speaker 2: you about next, which is if there's one tension here,

756
00:37:12,560 --> 00:37:15,680
Speaker 2: which is the stock market itself has sort of become

757
00:37:15,719 --> 00:37:18,840
Speaker 2: a populist issue. Right, This seem is very strange to

758
00:37:18,880 --> 00:37:21,640
Speaker 2: talk about the stock market and populism in the same sentence,

759
00:37:21,880 --> 00:37:24,560
Speaker 2: But you see all of the different people you know

760
00:37:24,600 --> 00:37:29,280
Speaker 2: who will randomly pull up on their phone various stock positions.

761
00:37:29,280 --> 00:37:32,360
Speaker 3: Well, Trump himself also benchmarks and Trump.

762
00:37:32,239 --> 00:37:34,120
Speaker 2: Talks about the stock market, so you know, this whole

763
00:37:34,120 --> 00:37:36,279
Speaker 2: time is like, oh, it's you know, it's main Street's turn,

764
00:37:36,320 --> 00:37:39,160
Speaker 2: et cetera. But a lot of young people, people who

765
00:37:39,200 --> 00:37:43,000
Speaker 2: are middle class or lower middle class feel either directly

766
00:37:43,200 --> 00:37:45,720
Speaker 2: either very directly in the stock market where they feel

767
00:37:45,760 --> 00:37:48,719
Speaker 2: like they have some very interest in it. How does

768
00:37:48,760 --> 00:37:50,760
Speaker 2: that intersect with this? What do you see like among

769
00:37:50,840 --> 00:37:53,480
Speaker 2: like the commenters on your show in terms of like

770
00:37:53,680 --> 00:37:55,359
Speaker 2: how they think about you know, just the sort of

771
00:37:55,760 --> 00:37:58,399
Speaker 2: betting and everything and investing in trading stuff.

772
00:37:58,560 --> 00:38:00,279
Speaker 1: That's a great question. I mean, I don't know, there's

773
00:38:00,320 --> 00:38:02,400
Speaker 1: like a couple of different angles to it, right, because

774
00:38:03,239 --> 00:38:07,200
Speaker 1: the Trump people ridiculed Biden for saying the economy was good,

775
00:38:07,320 --> 00:38:09,719
Speaker 1: but now they're doing the exact same thing. You guys know,

776
00:38:09,880 --> 00:38:11,799
Speaker 1: the S and P did pretty well under Biden. Right,

777
00:38:11,920 --> 00:38:14,440
Speaker 1: let's be honest, start benchmarketing from the day took office

778
00:38:14,560 --> 00:38:16,200
Speaker 1: to the day off. I forget the exact number, it's

779
00:38:16,239 --> 00:38:19,080
Speaker 1: like sixty eighty percent. Like these are decent returns over

780
00:38:19,360 --> 00:38:21,440
Speaker 1: the four year period. But it's not like anybody at

781
00:38:21,440 --> 00:38:23,480
Speaker 1: home was like, oh, the economy is so much better.

782
00:38:23,640 --> 00:38:26,480
Speaker 1: But of course politicians do that, you know, whenever they

783
00:38:26,520 --> 00:38:29,160
Speaker 1: come into office. But Joe, I mean, you're not wrong,

784
00:38:29,200 --> 00:38:31,280
Speaker 1: And I guess this is a much more meta conversation

785
00:38:31,360 --> 00:38:33,040
Speaker 1: where I mean you and you and I have talked

786
00:38:33,040 --> 00:38:35,480
Speaker 1: about this privately. The numbers got to go up, right,

787
00:38:35,600 --> 00:38:38,560
Speaker 1: because all of our futures are in the number. Like,

788
00:38:38,600 --> 00:38:40,200
Speaker 1: we don't have pensions in this country, We don't have

789
00:38:40,200 --> 00:38:43,319
Speaker 1: a robust social safety net until we turn sixty five,

790
00:38:43,440 --> 00:38:46,479
Speaker 1: So in the interim robust, Yeah, and then it gets

791
00:38:46,560 --> 00:38:49,160
Speaker 1: very robust. And that's a separate combo. But yeah, I

792
00:38:49,160 --> 00:38:51,520
Speaker 1: mean in the interim, like you know, your hopes of

793
00:38:51,640 --> 00:38:56,160
Speaker 1: retirement your hopes of buying something or asset appreciation, like

794
00:38:56,239 --> 00:38:59,200
Speaker 1: that's all we got. And I think that the increasing

795
00:38:59,520 --> 00:39:03,160
Speaker 1: financialization hope is exactly behind crypto sports betting issue and

796
00:39:03,239 --> 00:39:06,320
Speaker 1: very passionately against this is exactly why is I think,

797
00:39:06,520 --> 00:39:08,600
Speaker 1: you know, it removed a lot of the hope kind

798
00:39:08,600 --> 00:39:12,760
Speaker 1: of undergirding the stability of the American foundation, and within

799
00:39:12,840 --> 00:39:16,279
Speaker 1: that speculation and fast riches and plus, I mean, the

800
00:39:16,320 --> 00:39:18,360
Speaker 1: tech is so good. I mean, have you guys placed

801
00:39:18,400 --> 00:39:21,400
Speaker 1: trades on robin Hood, you know, with the and everything,

802
00:39:21,880 --> 00:39:23,400
Speaker 1: you gotta go do it. You know, you got to

803
00:39:23,440 --> 00:39:26,160
Speaker 1: feel it, put a little bit, put a position behind it,

804
00:39:26,200 --> 00:39:31,279
Speaker 1: and you'll see exactly that's the responsible thing to do.

805
00:39:31,640 --> 00:39:34,160
Speaker 1: Go put a couple of hundred into the spy and

806
00:39:34,440 --> 00:39:35,040
Speaker 1: but you'll just.

807
00:39:34,960 --> 00:39:39,319
Speaker 3: See like like celebratory Oh yeah, animations when you place

808
00:39:39,360 --> 00:39:39,840
Speaker 3: the trade.

809
00:39:40,320 --> 00:39:45,560
Speaker 1: It's happened to me recently, Yeah exactly. But I mean

810
00:39:45,640 --> 00:39:49,600
Speaker 1: I can see how you know, addictive that this can

811
00:39:49,640 --> 00:39:53,319
Speaker 1: all be. And they send you a million push notifications

812
00:39:53,360 --> 00:39:55,239
Speaker 1: a day, you know, the opposite of what a good

813
00:39:55,280 --> 00:39:57,640
Speaker 1: investor should do, Like, oh, stock is down five percent,

814
00:39:57,640 --> 00:39:59,640
Speaker 1: stock is up two percent. You know, they want you

815
00:39:59,719 --> 00:40:02,160
Speaker 1: click and staying in the app and the charts, everything

816
00:40:02,239 --> 00:40:04,160
Speaker 1: is designed for you to kind of be in there.

817
00:40:04,200 --> 00:40:06,160
Speaker 1: So these are big tech companies which they make a

818
00:40:06,200 --> 00:40:08,720
Speaker 1: lot of money doing this stuff. And you can see

819
00:40:08,760 --> 00:40:13,880
Speaker 1: why societally, structurally and technologically why that this became kind

820
00:40:13,920 --> 00:40:15,200
Speaker 1: of like a democratic issue.

821
00:40:15,320 --> 00:40:17,960
Speaker 2: By the way, while we're recording this, just breaking the

822
00:40:18,080 --> 00:40:22,280
Speaker 2: breaking news from Bloomberg Thinking Machines Lab and artificial intelligence

823
00:40:22,280 --> 00:40:25,800
Speaker 2: startup funded by former Open ai executive Mirror Murati early

824
00:40:25,840 --> 00:40:28,000
Speaker 2: talks to raise a new round of funding a roughly

825
00:40:28,040 --> 00:40:29,759
Speaker 2: fifty billion dollars valuation.

826
00:40:29,960 --> 00:40:33,000
Speaker 3: So every time we record an episode about AI, a

827
00:40:33,040 --> 00:40:35,440
Speaker 3: headline comes out recording.

828
00:40:35,640 --> 00:40:37,960
Speaker 1: Yeah, it helps us benchmark where we are. That is

829
00:40:38,080 --> 00:40:41,360
Speaker 1: so crazy, It's so didn't core there was something I

830
00:40:41,360 --> 00:40:44,160
Speaker 1: saw research every day every day every day was one

831
00:40:44,239 --> 00:40:49,680
Speaker 1: yesterday and a new one hundred billion anthropic anthrop data

832
00:40:49,719 --> 00:40:52,759
Speaker 1: center exactly. It's just I mean, I don't know, I

833
00:40:53,120 --> 00:40:55,920
Speaker 1: really That's why I love your show. Thank you. Just

834
00:40:55,960 --> 00:40:58,480
Speaker 1: thinking about you know, I've been doing a lot of

835
00:40:58,560 --> 00:41:01,640
Speaker 1: John Cassidy dot com dot com reading, you know, back

836
00:41:01,640 --> 00:41:05,680
Speaker 1: from two thousand and vendor finance and just thinking really

837
00:41:05,680 --> 00:41:08,359
Speaker 1: deeply about it because I do think, you know, if

838
00:41:08,360 --> 00:41:10,440
Speaker 1: that look, you know, I don't know today, tomorrow, a

839
00:41:10,480 --> 00:41:13,480
Speaker 1: few years, who knows, you know, where exactly it comes.

840
00:41:13,680 --> 00:41:16,960
Speaker 1: But some sort of downturn is probably inevitable, and the

841
00:41:17,000 --> 00:41:20,640
Speaker 1: political ramifications of that are just so immense, you guys, know,

842
00:41:20,719 --> 00:41:23,719
Speaker 1: with the downturn in the stock market, interest rates, reduction

843
00:41:24,040 --> 00:41:26,200
Speaker 1: in workforce. I'm really afraid.

844
00:41:26,160 --> 00:41:28,440
Speaker 2: This is the thing, which is that there's already this

845
00:41:29,000 --> 00:41:31,560
Speaker 2: backlash and the stock market is in your record highs

846
00:41:31,560 --> 00:41:34,680
Speaker 2: and unemployment is near fifty year lows or whatever, so

847
00:41:34,719 --> 00:41:37,520
Speaker 2: we haven't actually seen any of like really bad stuff.

848
00:41:37,640 --> 00:41:40,040
Speaker 2: Soccer and Jetty, thank you so much for coming on

849
00:41:40,080 --> 00:41:40,640
Speaker 2: to oddlogs.

850
00:41:40,760 --> 00:41:42,160
Speaker 1: That was a love for you guys for having me.

851
00:41:42,239 --> 00:41:56,600
Speaker 2: I love it, Tracy. I thought that was a lot

852
00:41:56,640 --> 00:41:58,960
Speaker 2: of fun. You know, this idea that like there's already

853
00:41:59,000 --> 00:42:03,279
Speaker 2: this backlash that brewing, and we actually don't have widespread

854
00:42:03,360 --> 00:42:06,399
Speaker 2: unemployment yet. We haven't had a stock market crash yet

855
00:42:06,520 --> 00:42:08,640
Speaker 2: or anything like that. You know, three years of a

856
00:42:08,640 --> 00:42:10,560
Speaker 2: long time. We have no idea what the economy is

857
00:42:10,560 --> 00:42:12,160
Speaker 2: going to look like in twenty twenty eight or really

858
00:42:12,200 --> 00:42:16,520
Speaker 2: even twenty twenty six. But if there's so much negativity

859
00:42:16,560 --> 00:42:19,880
Speaker 2: already building, right, that sort of tells you tells you

860
00:42:19,920 --> 00:42:20,320
Speaker 2: a lot.

861
00:42:20,480 --> 00:42:23,239
Speaker 3: Right, If people are angry with the SMP at like

862
00:42:23,400 --> 00:42:26,120
Speaker 3: six thousand, seven hundred, how are they going to feel

863
00:42:26,840 --> 00:42:30,400
Speaker 3: when it's like below six thousand. Yeah, I mean I

864
00:42:30,440 --> 00:42:33,040
Speaker 3: think everyone is agreed at this point that this is

865
00:42:33,120 --> 00:42:35,439
Speaker 3: going to become a political issue, right, and the only

866
00:42:35,520 --> 00:42:38,360
Speaker 3: question is how big and then what the response actually

867
00:42:38,480 --> 00:42:41,080
Speaker 3: is to it. The other thing I'm thinking is because

868
00:42:41,440 --> 00:42:46,400
Speaker 3: relationships both within the government and between the government and

869
00:42:46,480 --> 00:42:49,799
Speaker 3: private entities seem so I'm just going to say, complicated

870
00:42:49,840 --> 00:42:53,239
Speaker 3: and fluid at the moment, it seems really hard to

871
00:42:53,360 --> 00:42:55,640
Speaker 3: guess like which direction this is going to go in.

872
00:42:55,920 --> 00:42:58,319
Speaker 2: Totally, it seems really hard to guess. I thought your

873
00:42:58,400 --> 00:43:00,480
Speaker 2: question about is anyone talking about you ubi?

874
00:43:00,560 --> 00:43:01,640
Speaker 1: And this is really interesting.

875
00:43:01,760 --> 00:43:06,319
Speaker 2: It's grim like how there seems to be almost no

876
00:43:07,200 --> 00:43:10,680
Speaker 2: truly forward thinking politics. I mean, really, this conversation should

877
00:43:10,719 --> 00:43:13,879
Speaker 2: have been happening throughout the twenty twenty four election, right,

878
00:43:14,000 --> 00:43:16,320
Speaker 2: like this was like obviously going to be a massive

879
00:43:16,360 --> 00:43:19,160
Speaker 2: thing and the stakes would be incredibly high. It was

880
00:43:19,239 --> 00:43:20,440
Speaker 2: virtually non existent.

881
00:43:20,480 --> 00:43:21,520
Speaker 1: That was just a year ago.

882
00:43:21,600 --> 00:43:23,520
Speaker 2: Yeah, So I think that really tells you something. And

883
00:43:23,560 --> 00:43:25,920
Speaker 2: the fact that by and large, you know there's a

884
00:43:25,960 --> 00:43:28,600
Speaker 2: couple of people tweeting about it here and there, like

885
00:43:28,600 --> 00:43:32,200
Speaker 2: maybe Aron DeSantis, but by and large, this still doesn't

886
00:43:32,200 --> 00:43:35,560
Speaker 2: seem to be something that any elected official like wants

887
00:43:35,600 --> 00:43:38,040
Speaker 2: to sort of carve their land out.

888
00:43:38,239 --> 00:43:41,239
Speaker 3: Yeah, and it's probably not until unemployment actually picks up

889
00:43:41,360 --> 00:43:43,799
Speaker 3: that you're going to see like people actually start to

890
00:43:43,800 --> 00:43:47,480
Speaker 3: talk about some possible solutions, but again it's hard to

891
00:43:47,480 --> 00:43:48,160
Speaker 3: see what those are.

892
00:43:48,200 --> 00:43:50,640
Speaker 2: We're just like, yeah, thinking and advance any of this stuff.

893
00:43:50,680 --> 00:43:52,319
Speaker 3: Yeah, all right, shall we leave it there?

894
00:43:52,360 --> 00:43:53,239
Speaker 2: Sure, let's leave it there.

895
00:43:53,360 --> 00:43:55,680
Speaker 3: This has been another episode of the All Thoughts podcast.

896
00:43:55,800 --> 00:43:58,600
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.

897
00:43:58,680 --> 00:44:01,360
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.

898
00:44:01,560 --> 00:44:04,400
Speaker 2: Follow our guests Sager and Jetty. He's at e Sager.

899
00:44:04,600 --> 00:44:07,680
Speaker 2: Follow our producers Carmen Rodriguez at Kerman Armann, dash Ol

900
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901
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904
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905
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912
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