WEBVTT - Did America Just Vote Down AI? - Week in Tech

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<v Speaker 1>Kyle, Reid, Julia, good to see you all. As some

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<v Speaker 1>of you know, we've been releasing this series at Kaleidoscope

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<v Speaker 1>called The Builders with Walter Isaacs, and we've had all

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<v Speaker 1>kinds of guests from Elon to the Amodes. This week

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<v Speaker 1>was Gary Tan, and he said something that made me

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<v Speaker 1>think of you, Reid.

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<v Speaker 2>In about 15 minutes right before this talk, it was

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<v Speaker 2>able to download and transcribe your top 10 podcasts you've

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<v Speaker 2>ever done. And then it actually diarized them. So it

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<v Speaker 2>had an LLM go line by line, timestamp by timestamp,

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<v Speaker 2>to pull out major themes. But to be able to

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<v Speaker 2>do that for almost anything that you do in your

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<v Speaker 2>day-to-day life, in order to have this experience, I spend

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<v Speaker 2>about maybe $ 100, 000 to $ 200, 000 a year on tokens. And

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<v Speaker 2>so I realized that's a crazy privilege that not everyone

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<v Speaker 2>can do that. A lot of CEOs... that I know

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<v Speaker 2>are doing it, and they have wings.

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<v Speaker 3>Love that. Someone had a tweet this week where they're like,

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<v Speaker 3>give a man a fish and he eats for a day.

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<v Speaker 3>Give a man $ 200 a month Claude subscription and he'll

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<v Speaker 3>waste away his entire life doing side projects on Claude.

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<v Speaker 1>Welcome to Tech Stuff. I'm Osvald Oschin, and this is

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<v Speaker 1>The Week in Tech, where I'm joined by the world's

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<v Speaker 1>most plugged-in reporters, to break down what's really happening in

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<v Speaker 1>tech right now. Today, we're joined by Reid Albergotti, tech

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<v Speaker 1>editor for Semaphore, Kyle Chaker, staff reporter at The New Yorker,

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<v Speaker 1>and Julia Black, the tech and money correspondent at Vanity Fair.

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<v Speaker 1>Welcome back, everyone.

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<v Speaker 3>Great to be here.

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<v Speaker 4>Thanks for having us.

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<v Speaker 5>What a week to be here.

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<v Speaker 1>The midterms, that's right. Julia, how is the tech world

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<v Speaker 1>feeling about the blue wave?

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<v Speaker 6>I believe the word is reeling, at least in my

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<v Speaker 6>corner of tech, which for the last few years, I've

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<v Speaker 6>covered a lot of the tech right and a group

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<v Speaker 6>that has spent a lot of the last few years

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<v Speaker 6>crowing with confidence.

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<v Speaker 7>And yeah, they're scuttling away into increasingly dark corners now,

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<v Speaker 7>feeling pretty freaked out, I would say.

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<v Speaker 3>Reid? Yeah, I think that this has been a week

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<v Speaker 3>of people who who play the long game, feeling really

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<v Speaker 3>good about themselves. There's like a lot of people who

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<v Speaker 3>were like, oh, this is forever. This is going to

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<v Speaker 3>be forever. And it was for two years and they've

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<v Speaker 3>all learned a lesson.

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<v Speaker 1>Talk about the long game. There was a piece in

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<v Speaker 1>the Times about how basically Schumer had been kind of

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<v Speaker 1>running around and courting all these candidates and over the

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<v Speaker 1>last two years. And I think also in terms of

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<v Speaker 1>how the spending on political ads was happening and when

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<v Speaker 1>the Democrat versus the Trump PACs decided to enter races.

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<v Speaker 1>And I think that they were outspending for the same

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<v Speaker 1>minute of airtime in Alaska. I think the Dems had

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<v Speaker 1>paid like $ 2, 700 for it a year ago, and the

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<v Speaker 1>Republicans had to pay $ 30, 000 for the same minute.

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<v Speaker 4>This is the real winner, like the tech winner here

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<v Speaker 4>at the TV stations.

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<v Speaker 3>That is totally true. I talked to an executive this morning, actually,

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<v Speaker 3>who I think made this really salient point, which is like,

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<v Speaker 3>you know, this is what's so funny is like the

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<v Speaker 3>tech right, especially, like painted this, this whole political picture

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<v Speaker 3>in the US is like a battle of extremes. And

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<v Speaker 3>the Democrats have become the radical left. If you look

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<v Speaker 3>at the results, though, this is really kind of like

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<v Speaker 3>a rebuke of the fringes of like the freak shows

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<v Speaker 3>in the political world. You saw a lot of these

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<v Speaker 3>like moderate Democrats, that's who really like took back the

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<v Speaker 3>House and the Senate. So it kind of like burst

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<v Speaker 3>that whole narrative as well. Like, It's almost like there's

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<v Speaker 3>not really like the people who sort of went right

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<v Speaker 3>in the tech industry. They can't really sit here and go, oh, no, like,

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<v Speaker 3>you know, the radical left is taking over again. We

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<v Speaker 3>must stop them. It's like, no, these are like the

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<v Speaker 3>middle of the road people. Like, you know, I think

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<v Speaker 3>there's going to be no choice but to work with them.

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<v Speaker 3>And I thought that was a really good point.

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<v Speaker 4>They're all really anti data center, right? Like almost every

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<v Speaker 4>data center, anti data center measure passed. So it's like

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<v Speaker 4>the normie politics people, but they're still not feeling great

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<v Speaker 4>about tech at the moment.

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<v Speaker 7>Yeah, that said, I do think a lot of AI

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<v Speaker 7>safety types are eager to project this idea that, you know,

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<v Speaker 7>this was the AI election.

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<v Speaker 5>This was the most important issue.

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<v Speaker 7>At the end of the day, I think most people

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<v Speaker 7>were voting on gas prices and grocery prices. That said,

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<v Speaker 7>I do think it is significant that there were these,

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<v Speaker 7>I think, at least 38 bans or slowdowns of data centers.

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<v Speaker 5>Passed across the state.

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<v Speaker 7>So it's not that anyone's pro data centers at this point,

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<v Speaker 7>but yeah, I think some people are over-indexing a little

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<v Speaker 7>bit on the significance of AI in this election, as

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<v Speaker 7>much as I would love to see that be the truth.

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<v Speaker 1>Julia, you mentioned you're The tech right reeling, have you

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<v Speaker 1>spoken to any sources?

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<v Speaker 3>Yeah.

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<v Speaker 7>I mean, the big concern is data centers because that

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<v Speaker 7>is the lifeblood of this industry right now. And there's

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<v Speaker 7>just this sentiment like, well, I guess we got to

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<v Speaker 7>go to space because America is no longer.

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<v Speaker 1>That's what Elon tweeted, right?

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<v Speaker 5>A lot of them have been tweeting and saying that.

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<v Speaker 5>But I often.

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<v Speaker 7>enjoy reporting on vibes and the vibes, the vibe shift

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<v Speaker 7>is significant. I know we've gone through vibe shift after

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<v Speaker 7>vibe shift, but like I said, there was just this

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<v Speaker 7>feeling amongst Silicon Valley types that this was this period

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<v Speaker 7>where they were going to enjoy completely unfettered impunity.

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<v Speaker 5>They could do whatever they want.

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<v Speaker 7>And they were so unashamed for so long, not so long,

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<v Speaker 7>but a few years, a few tough years. And there

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<v Speaker 7>is now this feeling that Congress may be coming for us.

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<v Speaker 7>There's going to be some subpoenas handed out. The age

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<v Speaker 7>of impunity has come to an end.

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<v Speaker 3>I think there's a lot of catastrophizing on Twitter about

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<v Speaker 3>the data center thing. But I think if you talk

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<v Speaker 3>to people who are on the ground actually doing this stuff,

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<v Speaker 3>it's not that they're so much concerned that this means

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<v Speaker 3>they're not going to build any more data centers. I

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<v Speaker 3>think it's more like, okay, we need to reset our

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<v Speaker 3>public relations strategy here. The tech industry always had a

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<v Speaker 3>PR strategy around data centers, which was like, come in,

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<v Speaker 3>you know, with these NDAs, like sneak into these towns

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<v Speaker 3>and we're going to like, you know, we'll get the

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<v Speaker 3>deal almost there and then we'll throw in some sweeteners

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<v Speaker 3>and we'll fund the public schools and everybody will be

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<v Speaker 3>happy and we'll move on. That kind of worked in

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<v Speaker 3>the cloud era. It just, it was never going to

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<v Speaker 3>work in the AI era where you're talking about scales

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<v Speaker 3>that are so much larger. And so I think that

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<v Speaker 3>this just means like there's going to have to be

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<v Speaker 3>a lot more just like upfront PR slash deal sweeteners

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<v Speaker 3>right off the bat. Like we're going to lower your

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<v Speaker 3>energy costs. We're going to, we're not going to affect

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<v Speaker 3>your water bill or your water usage. We're going to

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<v Speaker 3>use our, you know, generate our own energy. And then

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<v Speaker 3>I think these deals will get done. And I don't

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<v Speaker 3>think it's actually as big of a deal as like,

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<v Speaker 3>I think people on X, but also the markets are,

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<v Speaker 3>I think, overreacting a little bit to this as well. So,

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<v Speaker 3>you know, people tend to get excited after these things,

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<v Speaker 3>but I think it's not, this is not the catastrophe

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<v Speaker 3>that some think it is.

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<v Speaker 1>On the topic of PR, I mean, we were talking

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<v Speaker 1>before we started recording about MTS, the Anderson-backed media company,

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<v Speaker 1>and TBPN and the Sequoia, BC-backed media company who nobody

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<v Speaker 1>knows the name of. Do you think this maybe signals

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<v Speaker 1>a little bit of a reconsideration of the go-direct strategy

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<v Speaker 1>for the tech sector?

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<v Speaker 4>They have to go direct to some different people. They

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<v Speaker 4>were going real direct to the hardcore ex-users and the

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<v Speaker 4>people who were already bought into their messaging, I think.

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<v Speaker 4>And so it's like, okay, sure, you've saturated that market

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<v Speaker 4>for sure. Now maybe you actually have to speak to

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<v Speaker 4>some people who don't share your exact political beliefs and

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<v Speaker 4>who aren't just like doubling down on the same ideology.

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<v Speaker 4>And that's like, I mean, as I feel like reads

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<v Speaker 4>on this tack as well, but it's like, I would

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<v Speaker 4>like to see more open conversation among the left and

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<v Speaker 4>tech companies and just like a more even discourse between

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<v Speaker 4>people than we have had for the past few years.

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<v Speaker 7>Yeah, I mean, purely anecdotally, I will say I started

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<v Speaker 7>to notice this happen in the spring. That's when the

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<v Speaker 7>mood started to shift from my point of view. I

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<v Speaker 7>found the tech world to be pretty hostile to me personally.

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<v Speaker 5>In the last few years.

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<v Speaker 7>And in the spring, maybe it's because I started at

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<v Speaker 7>Vanity Fair and everyone wants, you know, their glossy photo shoot.

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<v Speaker 7>But I was getting a lot more calls from the

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<v Speaker 7>types of comms teams who like previously only sent me

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<v Speaker 7>nasty emails. Like it started to feel like people were

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<v Speaker 7>preparing for this reconciliation.

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<v Speaker 3>Yeah, that's true. I agree. Like, yeah, before the midterms, like,

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<v Speaker 3>there were people who were, like, sort of regretting their

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<v Speaker 3>decisions and, like, blaming it on their comms team, which

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<v Speaker 3>was probably, like, trying to stop them from making those

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<v Speaker 3>decisions the whole time.

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<v Speaker 7>Whoever can get the definitive Chris Lehane story, who, of course,

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<v Speaker 7>is at OpenAI, managing a lot of their Washington relationships

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<v Speaker 7>and their communication around data centers, which they're now trying

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<v Speaker 7>to rebrand to compute campuses.

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<v Speaker 5>Whoever can really dig into.

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<v Speaker 7>what he's been doing for the last year, which has

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<v Speaker 7>been just such a baffling, frenetic strategy that I can't

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<v Speaker 7>make head or tail of, that will be a great

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<v Speaker 7>story to read.

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<v Speaker 3>Chris Lehane, though, okay, this is the thing about him.

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<v Speaker 3>So he was at Airbnb. He basically won the regulatory

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<v Speaker 3>battle at Airbnb. Then he went to the crypto industry,

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<v Speaker 3>won that battle for the crypto industry. You could argue

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<v Speaker 3>it maybe got a little bad for a while there.

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<v Speaker 3>But I view Lehane as a guy who wins. But

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<v Speaker 3>he doesn't always win the hearts and minds of the

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<v Speaker 3>public in doing so. And I feel like that was

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<v Speaker 3>never his MO. This election showed that the hearts and

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<v Speaker 3>minds have not been won But I think in the end,

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<v Speaker 3>these data centers are going to get built. And that's

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<v Speaker 3>what they actually care about. Or what he cares about.

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<v Speaker 3>So I think I would separate him from the PR

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<v Speaker 3>problem that tech has, if that makes sense. That's how

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<v Speaker 3>I view Lehane.

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<v Speaker 7>I don't totally doubt that the long-term data center strategy

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<v Speaker 7>could work out. But this week, to say that Chris

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<v Speaker 7>Lehane won on data centers feels.

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<v Speaker 3>This week was not a win, but I think in

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<v Speaker 3>the long run, this is, again, people who play the

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<v Speaker 3>long game. In the long run, these data centers are

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<v Speaker 3>going to get built. It's going to happen.

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<v Speaker 1>You opened your newsletter this week, essentially saying, how does

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<v Speaker 1>America ensure that in its, probably, I'm putting words into

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<v Speaker 1>your mouth, but perhaps justified desire to prosecute and redistribute

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<v Speaker 1>the wealth of the new oligarchy at the same time,

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<v Speaker 1>not throw the baby out with the bathwater and kind

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<v Speaker 1>of go after tech innovation writ large. Is that a

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<v Speaker 1>kind of fair? Yeah.

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<v Speaker 3>I mean, I think it's, I think the problem is like,

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<v Speaker 3>you know, that people aren't sharing in the wealth. Like

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<v Speaker 3>you look at the tech industry and it's like people

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<v Speaker 3>hate AI or whatever. like you're not really going to

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<v Speaker 3>stop technology. Like if you do stop technology, that's really bad.

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<v Speaker 3>Like if you look at societies in history that have

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<v Speaker 3>tried to stop technology, like things don't go very well.

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<v Speaker 3>But like you can ensure that people share in the wealth.

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<v Speaker 3>And I think that's actually what I think would be

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<v Speaker 3>really interesting to see if like these data center deals

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<v Speaker 3>come not with necessarily like just energy and school funding,

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<v Speaker 3>but like putting shares of these companies in the retirement

0:12:02.600 --> 0:12:04.660
<v Speaker 3>accounts of the kids in town, you know, like that

0:12:04.700 --> 0:12:08.550
<v Speaker 3>kind of stuff. Like the Trump accounts kind of does that.

0:12:08.890 --> 0:12:10.850
<v Speaker 3>I hate that it's called Trump accounts now because it's

0:12:10.910 --> 0:12:14.270
<v Speaker 3>not his idea, but it is a good idea. And,

0:12:14.770 --> 0:12:15.950
<v Speaker 3>you know, I think stuff like that.

0:12:15.970 --> 0:12:18.520
<v Speaker 4>This is what Elon pledged too, right? The SpaceX shares

0:12:18.540 --> 0:12:19.339
<v Speaker 4>into the Trump accounts.

0:12:19.380 --> 0:12:21.840
<v Speaker 3>Elon pledged to like give, yeah, like give his, well, yeah,

0:12:21.940 --> 0:12:24.110
<v Speaker 3>I was... I'm not sure it was clear.

0:12:24.170 --> 0:12:26.069
<v Speaker 1>And the Amadeus didn't pledge any shares, but they did

0:12:26.090 --> 0:12:28.590
<v Speaker 1>say in their prospectus that a share in Anthropic would

0:12:28.610 --> 0:12:31.189
<v Speaker 1>be a share in the future. So there's definitely some.

0:12:31.610 --> 0:12:33.650
<v Speaker 3>I saw that and I did, I did a little

0:12:33.690 --> 0:12:35.990
<v Speaker 3>math and like, if you had given every, like in

0:12:36.010 --> 0:12:39.700
<v Speaker 3>the nineties or something, if you'd given like every, every kid,

0:12:39.980 --> 0:12:42.480
<v Speaker 3>one share of Apple, it would have been worth by

0:12:42.520 --> 0:12:44.880
<v Speaker 3>the time they, you know, they were 40, it would

0:12:44.900 --> 0:12:46.980
<v Speaker 3>be worth like, I can't remember the exact number now,

0:12:47.020 --> 0:12:50.040
<v Speaker 3>but it was like, tens of thousands of dollars, right?

0:12:50.600 --> 0:12:54.540
<v Speaker 3>And that's a lot of money, just one share. Of course,

0:12:54.580 --> 0:12:57.970
<v Speaker 3>you can't predict the returns on all of these shares,

0:12:58.040 --> 0:13:01.030
<v Speaker 3>but I would think it's a pretty good guess that

0:13:01.050 --> 0:13:03.010
<v Speaker 3>it's going to be a lot. And then the important

0:13:03.030 --> 0:13:05.849
<v Speaker 3>thing is people are like, there's some upside for people,

0:13:05.970 --> 0:13:07.989
<v Speaker 3>even if they don't work for these companies, even if

0:13:08.010 --> 0:13:10.929
<v Speaker 3>they don't like the technology, which many people don't.

0:13:10.950 --> 0:13:13.790
<v Speaker 4>Do the companies have a plan for that, though? I

0:13:13.809 --> 0:13:17.870
<v Speaker 4>feel like I hear no one really Discussing, I don't know, some,

0:13:18.050 --> 0:13:22.050
<v Speaker 4>some large percentage of well-sharing and like a collective way.

0:13:22.870 --> 0:13:25.730
<v Speaker 3>They've talked about it. I mean, the hands actually put

0:13:25.770 --> 0:13:29.110
<v Speaker 3>forth some of these ideas at open AI, but to.

0:13:29.010 --> 0:13:31.719
<v Speaker 4>Me, the messaging still feels like, like no bless oblige

0:13:31.770 --> 0:13:35.100
<v Speaker 4>or something. It's like, oh, we will mint a thousand

0:13:35.140 --> 0:13:37.280
<v Speaker 4>people who have a hundred million dollars out of this

0:13:37.420 --> 0:13:40.319
<v Speaker 4>IPO and they will donate all of their money to

0:13:40.460 --> 0:13:44.540
<v Speaker 4>shrimp protection or whatever, effective altruist causes.

0:13:45.380 --> 0:13:46.900
<v Speaker 3>Usually outside the country, right?

0:13:47.220 --> 0:13:51.510
<v Speaker 4>Yeah, it's a top-down thing. There's so little messaging of

0:13:51.610 --> 0:13:56.930
<v Speaker 4>bottom-up policies or anything democratic. It feels so, so elitist

0:13:57.010 --> 0:13:57.569
<v Speaker 4>at this point.

0:13:58.230 --> 0:14:01.600
<v Speaker 1>On the topic of Reid's idea of spreading the wealth,

0:14:02.130 --> 0:14:05.720
<v Speaker 1>Julia had an exceptional story in Vanity Fair about spreading

0:14:05.740 --> 0:14:09.320
<v Speaker 1>the wealth. This is from OpenAI co-founder Greg Brockman and

0:14:09.360 --> 0:14:10.920
<v Speaker 1>his wife to Donald Trump.

0:14:10.940 --> 0:14:11.640
<v Speaker 4>So.

0:14:12.679 --> 0:14:14.870
<v Speaker 1>Julia, can you tell us a little bit about this

0:14:14.970 --> 0:14:18.219
<v Speaker 1>slice of the tech right and their desire for political

0:14:18.240 --> 0:14:19.280
<v Speaker 1>influence through donations?

0:14:19.380 --> 0:14:20.600
<v Speaker 3>Yeah. I mean.

0:14:21.280 --> 0:14:24.880
<v Speaker 7>I've always been intrigued by Anna Brockman. Married to Greg,

0:14:24.940 --> 0:14:28.680
<v Speaker 7>the co-founder of OpenAI. She's kind of just been hovering

0:14:29.000 --> 0:14:31.640
<v Speaker 7>in the background of so many scenes in tech history,

0:14:31.660 --> 0:14:34.010
<v Speaker 7>and no one knew anything about her. So I got

0:14:34.030 --> 0:14:36.530
<v Speaker 7>a little obsessive and went down the rabbit hole. Found

0:14:36.610 --> 0:14:38.890
<v Speaker 7>out her past. Very surprising. I learned I went to

0:14:38.930 --> 0:14:42.630
<v Speaker 7>college with her, which I went to Wesleyan. I don't

0:14:42.650 --> 0:14:44.010
<v Speaker 7>know if you're familiar with that school.

0:14:44.210 --> 0:14:45.450
<v Speaker 1>There's too many members of.

0:14:45.410 --> 0:14:46.390
<v Speaker 3>The tech right. Yeah.

0:14:46.960 --> 0:14:49.200
<v Speaker 5>Yeah, it's basically one person and that's her.

0:14:49.220 --> 0:14:52.010
<v Speaker 7>But what was interesting and why I decided to lean

0:14:52.070 --> 0:14:55.340
<v Speaker 7>into this story is that just a couple of weeks ago,

0:14:55.520 --> 0:14:58.760
<v Speaker 7>she and Greg were at Mar-a-Lago sitting at the head

0:14:59.120 --> 0:15:02.180
<v Speaker 7>of the table with Sergey Brin and Donald Trump.

0:15:02.260 --> 0:15:03.640
<v Speaker 5>It was four people wide.

0:15:04.470 --> 0:15:06.830
<v Speaker 7>And, you know, this was a period where tech already

0:15:06.870 --> 0:15:11.470
<v Speaker 7>appeared to be distancing itself from MAGA. Sam Altman has, well,

0:15:11.510 --> 0:15:14.110
<v Speaker 7>I've actually heard he's continued to appear at the White House,

0:15:14.190 --> 0:15:17.930
<v Speaker 7>but not as publicly. But Greg was the one who

0:15:17.970 --> 0:15:21.290
<v Speaker 7>was sent to sign the accord on superintelligence. And there's

0:15:21.330 --> 0:15:23.900
<v Speaker 7>Greg at Mar-a-Lago just weeks before this election when the

0:15:23.940 --> 0:15:27.400
<v Speaker 7>polls are showing that MAGA is not popular.

0:15:28.100 --> 0:15:30.740
<v Speaker 5>So it started to kind of feel like they were

0:15:31.320 --> 0:15:32.880
<v Speaker 5>being thrown under the bus a little bit.

0:15:32.940 --> 0:15:37.350
<v Speaker 7>Like there's this understanding that, yeah, I mean, and this

0:15:37.410 --> 0:15:39.450
<v Speaker 7>is the question of the story and what no one

0:15:39.490 --> 0:15:41.850
<v Speaker 7>seems to have a definitive answer on is, are they

0:15:41.890 --> 0:15:44.930
<v Speaker 7>just this passionate about MAGA, which I don't think they are.

0:15:44.990 --> 0:15:46.570
<v Speaker 5>They call themselves single-issue donors.

0:15:46.590 --> 0:15:47.430
<v Speaker 3>Yeah.

0:15:48.350 --> 0:15:50.670
<v Speaker 7>Or is this like this open AI strategy where they

0:15:50.730 --> 0:15:54.420
<v Speaker 7>know they need to retain the goodwill of the executive branch?

0:15:54.880 --> 0:15:57.300
<v Speaker 7>You know, there's still two years left. Trump is not

0:15:57.340 --> 0:16:00.200
<v Speaker 7>someone who you want to have on your bad side.

0:16:01.100 --> 0:16:04.060
<v Speaker 7>But they also don't want to have Sam Altman, CEO,

0:16:04.220 --> 0:16:06.660
<v Speaker 7>out as the face. You know, people are starting to

0:16:06.700 --> 0:16:10.750
<v Speaker 7>distance themselves from this MAGA persona. So, yeah, just asking

0:16:10.770 --> 0:16:14.250
<v Speaker 7>this question, like, are they really these loyal diehards or

0:16:14.370 --> 0:16:16.210
<v Speaker 7>did they kind of get the short end of the

0:16:16.230 --> 0:16:17.420
<v Speaker 7>stick here and end up?

0:16:17.910 --> 0:16:22.250
<v Speaker 5>As the scapegoats and the face of the maggot commitment.

0:16:22.390 --> 0:16:24.270
<v Speaker 1>And what was your conclusion?

0:16:24.910 --> 0:16:26.250
<v Speaker 5>Probably more of the latter.

0:16:26.270 --> 0:16:29.390
<v Speaker 7>Again, to get back to my kind of Chris Lehane obsession,

0:16:30.010 --> 0:16:34.050
<v Speaker 7>I think probably he was, I should be careful about

0:16:34.090 --> 0:16:36.550
<v Speaker 7>overstating this, but yeah, it seemed like there was opening

0:16:36.600 --> 0:16:39.240
<v Speaker 7>eye strategy. They wanted to distance themselves from these donations

0:16:39.290 --> 0:16:42.480
<v Speaker 7>and claim that they were purely personal, which is absurd.

0:16:42.640 --> 0:16:44.980
<v Speaker 7>It's the co-founder and the president of the company. This

0:16:45.020 --> 0:16:48.250
<v Speaker 7>is not some extracurricular activity. he's doing this on behalf

0:16:48.590 --> 0:16:49.190
<v Speaker 7>of his company.

0:16:50.290 --> 0:16:52.700
<v Speaker 1>There's also a great line in your piece about the

0:16:52.760 --> 0:16:55.760
<v Speaker 1>partners of billionaires, how they've long been appointed to roles

0:16:55.800 --> 0:16:59.780
<v Speaker 1>distributing their spouse's wealth through philanthropic and political giving. These women,

0:16:59.820 --> 0:17:02.320
<v Speaker 1>often brilliant in their own right, become trusted confidants and

0:17:02.340 --> 0:17:05.880
<v Speaker 1>strategic advisors, but they also find themselves in paradoxical positions,

0:17:05.960 --> 0:17:09.270
<v Speaker 1>often wishing for the personal privacy of a private citizen

0:17:09.650 --> 0:17:12.669
<v Speaker 1>while wielding immense power through their wealth and stature. So

0:17:13.050 --> 0:17:14.790
<v Speaker 1>I want to hear more about Anna Brockman, but of course,

0:17:15.369 --> 0:17:19.130
<v Speaker 1>Isn't Sergey Brin's girlfriend famously his entree to the MAGA

0:17:19.150 --> 0:17:19.770
<v Speaker 1>world as well?

0:17:20.490 --> 0:17:24.840
<v Speaker 7>Yeah, Trump calls her his MAGA girlfriend. But also I

0:17:24.859 --> 0:17:27.740
<v Speaker 7>was reminded of Camilla Clark, who we only learned about

0:17:27.780 --> 0:17:32.230
<v Speaker 7>this summer. She's married to Dario Amadei. And I get it.

0:17:32.280 --> 0:17:35.100
<v Speaker 7>Everyone wants to protect their family, protect their spouse from

0:17:35.119 --> 0:17:39.379
<v Speaker 7>the public eye and scrutiny. But if you're Married to

0:17:39.440 --> 0:17:41.040
<v Speaker 7>one of the most powerful people on earth, you become

0:17:41.080 --> 0:17:42.980
<v Speaker 7>one of the most powerful people on earth. So my

0:17:43.020 --> 0:17:45.480
<v Speaker 7>point of view is the public will always have the

0:17:45.500 --> 0:17:48.160
<v Speaker 7>right to know who you are, what you want, how

0:17:48.200 --> 0:17:50.639
<v Speaker 7>you're going to wield your power. So learning a bit

0:17:50.660 --> 0:17:52.840
<v Speaker 7>more about Anna and the fact that, by the way,

0:17:53.100 --> 0:17:55.040
<v Speaker 7>people slept on this part of the piece. The couple

0:17:55.100 --> 0:17:58.470
<v Speaker 7>allegedly owns over 20 animals in their home. You know,

0:17:58.490 --> 0:17:59.770
<v Speaker 7>these are interesting people.

0:17:59.790 --> 0:18:01.209
<v Speaker 4>Which animals?

0:18:02.070 --> 0:18:04.560
<v Speaker 5>Could not get her team.

0:18:04.600 --> 0:18:05.220
<v Speaker 3>But Kyle, you.

0:18:05.160 --> 0:18:07.260
<v Speaker 1>Were close with the shrimp. Didn't they give five and

0:18:07.300 --> 0:18:10.699
<v Speaker 1>a half million dollars to a pair of eagles? They did.

0:18:10.900 --> 0:18:11.359
<v Speaker 4>They did.

0:18:11.420 --> 0:18:13.140
<v Speaker 5>I mean, she loves animals.

0:18:13.859 --> 0:18:14.320
<v Speaker 3>And it's funny.

0:18:14.340 --> 0:18:16.659
<v Speaker 1>To be fair, they're American heroes, those eagles.

0:18:17.440 --> 0:18:19.609
<v Speaker 3>These people are just, this is like to my earlier

0:18:19.670 --> 0:18:21.970
<v Speaker 3>point about like the people on the fringes, like these

0:18:22.010 --> 0:18:24.270
<v Speaker 3>people are like, they're kind of on the fringes, you know?

0:18:24.330 --> 0:18:27.300
<v Speaker 3>And I think, It's like they haven't learned that to

0:18:27.340 --> 0:18:30.240
<v Speaker 3>be savvy yet and to be like sort of polished

0:18:30.340 --> 0:18:34.020
<v Speaker 3>public figures. And it's like the same thing happened like

0:18:34.060 --> 0:18:36.020
<v Speaker 3>in the in the Obama years, right? Like the tech

0:18:36.060 --> 0:18:40.020
<v Speaker 3>industry went way, way, way overboard to the Obama side

0:18:40.160 --> 0:18:43.310
<v Speaker 3>and like had no concept of the cost of that, right?

0:18:43.350 --> 0:18:46.710
<v Speaker 3>Like completely forgot that there was a conservative side of

0:18:46.750 --> 0:18:49.210
<v Speaker 3>the aisle. And it's like, you know, like if you're

0:18:49.230 --> 0:18:51.280
<v Speaker 3>going to do this, you've got to be smart about it.

0:18:51.310 --> 0:18:53.889
<v Speaker 3>I think like Microsoft is a good example. Like, they

0:18:53.910 --> 0:18:56.940
<v Speaker 3>have battle scars, right, for this. And they've learned, like,

0:18:56.980 --> 0:18:59.300
<v Speaker 3>you try to get Satya Nadella to say something, like,

0:18:59.460 --> 0:19:03.000
<v Speaker 3>out of the mainstream, and it's, like, it's very difficult, right? It's, they're,

0:19:03.140 --> 0:19:05.980
<v Speaker 3>they carefully, like, what they should be doing is just, like,

0:19:06.420 --> 0:19:10.030
<v Speaker 3>carefully planning out their strategy so they do as little

0:19:10.070 --> 0:19:13.910
<v Speaker 3>as possible to offend either side. Like, that's the, that's

0:19:13.950 --> 0:19:16.129
<v Speaker 3>the game. And I think these people haven't quite learned it.

0:19:16.780 --> 0:19:18.790
<v Speaker 4>It does seem like they have to play both sides now.

0:19:18.869 --> 0:19:22.350
<v Speaker 4>I mean, particularly now after the midterms, like you have

0:19:22.390 --> 0:19:24.290
<v Speaker 4>to court the left as much as you're courting the

0:19:24.330 --> 0:19:27.850
<v Speaker 4>right and not lean quite so hard into the like

0:19:28.130 --> 0:19:32.230
<v Speaker 4>super mega, super online vocabulary as people have been doing.

0:19:33.030 --> 0:19:35.310
<v Speaker 4>But like, I don't know, to Julia and Julia's point,

0:19:35.350 --> 0:19:38.710
<v Speaker 4>like in their personal lives, these people are very strange

0:19:38.790 --> 0:19:42.490
<v Speaker 4>and like not, not particularly relatable people. And I don't know,

0:19:42.490 --> 0:19:45.150
<v Speaker 4>it kind of strikes me even less relatable than like

0:19:45.150 --> 0:19:48.190
<v Speaker 4>the last generation of of tech oligarchs the the mark

0:19:48.210 --> 0:19:52.260
<v Speaker 4>zuckerberg's the social media era like those people i don't

0:19:52.300 --> 0:19:56.280
<v Speaker 4>know sean parker yeah yeah if they were so weird

0:19:56.300 --> 0:19:59.000
<v Speaker 4>they at least kept out of the public eye a

0:19:59.020 --> 0:20:03.040
<v Speaker 4>little more whereas now it's i don't know maybe it's

0:20:03.060 --> 0:20:04.810
<v Speaker 4>harder Julia.

0:20:04.830 --> 0:20:07.290
<v Speaker 1>For those who didn't read that Wall Street Journal story

0:20:07.930 --> 0:20:12.139
<v Speaker 1>about Cammy Clark, Dario's wife, can you give us the key?

0:20:12.540 --> 0:20:13.180
<v Speaker 1>What do we need to know?

0:20:13.859 --> 0:20:18.760
<v Speaker 7>Gosh, well, there's the Jeffrey Epstein connection probably being the

0:20:18.800 --> 0:20:19.680
<v Speaker 7>most prominent.

0:20:19.780 --> 0:20:24.340
<v Speaker 1>This was the fundraising for the luxury porn website.

0:20:24.520 --> 0:20:24.960
<v Speaker 7>Yes.

0:20:25.300 --> 0:20:29.730
<v Speaker 5>Yeah. Which, interestingly, that also comes up in Elon Musk's family.

0:20:29.810 --> 0:20:33.090
<v Speaker 5>His sister has a luxury porn site, so I don't

0:20:33.130 --> 0:20:34.450
<v Speaker 5>know what... Cat tells you.

0:20:34.910 --> 0:20:36.310
<v Speaker 7>But yeah, I mean, I just thought it was really

0:20:36.330 --> 0:20:40.950
<v Speaker 7>striking that she has accompanied him to events and somehow

0:20:40.970 --> 0:20:42.010
<v Speaker 7>just no one knew she existed.

0:20:42.310 --> 0:20:42.810
<v Speaker 1>Even Claude.

0:20:43.190 --> 0:20:44.870
<v Speaker 5>Even Claude. Well, especially Claude.

0:20:44.890 --> 0:20:47.429
<v Speaker 7>I think you will find that any time you try

0:20:47.450 --> 0:20:50.459
<v Speaker 7>to ask Claude about the inner workings of Anthropic, you

0:20:50.480 --> 0:20:51.139
<v Speaker 7>will be stymied.

0:20:51.160 --> 0:20:55.859
<v Speaker 1>Well, I guess we have to talk about Elon. Was

0:20:55.920 --> 0:20:57.760
<v Speaker 1>it $ 430 odd million, almost $ 300 of which in Texas?

0:21:03.050 --> 0:21:04.570
<v Speaker 1>how do you think he's feeling after these midterms?

0:21:05.109 --> 0:21:08.230
<v Speaker 3>I mean, yeah, I think it was closer to 450 million.

0:21:08.490 --> 0:21:10.149
<v Speaker 3>What is that for me? That'd be like, if I

0:21:10.190 --> 0:21:12.909
<v Speaker 3>spent a hundred dollars or something, I'd be like, damn,

0:21:12.930 --> 0:21:16.470
<v Speaker 3>that was a waste of a hundred dollars. But I, yeah,

0:21:16.490 --> 0:21:18.389
<v Speaker 3>I mean, I think it just shows like you, we

0:21:18.410 --> 0:21:21.010
<v Speaker 3>talk a lot about political spending, but like ultimately it

0:21:21.609 --> 0:21:23.530
<v Speaker 3>can only get you so far. Like it's not going to,

0:21:23.990 --> 0:21:26.330
<v Speaker 3>it's not going to shift public opinion, really. It's going

0:21:26.350 --> 0:21:28.430
<v Speaker 3>to maybe help you in the margins. And you know,

0:21:28.730 --> 0:21:31.030
<v Speaker 3>if you're fighting a losing battle, you're still going to lose.

0:21:31.109 --> 0:21:33.660
<v Speaker 3>And no matter how much you spend. So, I mean,

0:21:33.740 --> 0:21:35.880
<v Speaker 3>I don't know, at least that seems like a bright

0:21:36.500 --> 0:21:39.180
<v Speaker 3>spot for me. It's like, you can't just like change

0:21:39.240 --> 0:21:41.439
<v Speaker 3>everybody's mind with political spending.

0:21:41.880 --> 0:21:43.830
<v Speaker 4>And was it too far gone? Like it came so

0:21:43.850 --> 0:21:46.850
<v Speaker 4>late in the game to try and shift so much

0:21:46.910 --> 0:21:49.810
<v Speaker 4>public opinion and there was already so much negative sentiment

0:21:50.230 --> 0:21:54.580
<v Speaker 4>against data centers, against Elon Musk individually. I don't know,

0:21:54.630 --> 0:21:57.120
<v Speaker 4>that felt like too much of a Hail Mary in

0:21:57.200 --> 0:21:57.540
<v Speaker 4>any way.

0:21:57.560 --> 0:22:01.220
<v Speaker 3>Yeah, Musk is used to this, right? Like giving people

0:22:01.280 --> 0:22:06.090
<v Speaker 3>just like, unrealistic deadlines and, you know, hoping they get

0:22:06.150 --> 0:22:09.429
<v Speaker 3>somewhere close to it. Like, that doesn't really work in politics. Like,

0:22:09.470 --> 0:22:13.630
<v Speaker 3>it takes decades to build a really powerful political system, right,

0:22:13.650 --> 0:22:16.210
<v Speaker 3>or political machine. And it's, you know, look at the

0:22:16.250 --> 0:22:18.370
<v Speaker 3>Koch brothers, right? I mean, talk about the long game.

0:22:18.430 --> 0:22:20.350
<v Speaker 3>I mean, it just took them a very long time. And,

0:22:21.010 --> 0:22:23.210
<v Speaker 3>you know, that's, I think that's how you have to, he,

0:22:23.619 --> 0:22:26.300
<v Speaker 3>Musk's talent just is not in politics. Like, he just

0:22:26.320 --> 0:22:29.479
<v Speaker 3>should not, like, he went to Washington, completely flamed out, like,

0:22:29.980 --> 0:22:33.169
<v Speaker 3>That's just not his skill set, and I think he

0:22:33.230 --> 0:22:34.050
<v Speaker 3>needs to accept that.

0:22:34.710 --> 0:22:38.950
<v Speaker 1>Reid, what about the California billionaires? They survived the billionaire tax,

0:22:39.130 --> 0:22:40.230
<v Speaker 1>which I'm quite surprised about.

0:22:40.250 --> 0:22:44.760
<v Speaker 3>They not only beat the tax, did not pass this

0:22:44.800 --> 0:22:48.119
<v Speaker 3>billionaire tax. It was like a 5% one-time wealth tax,

0:22:48.180 --> 0:22:50.300
<v Speaker 3>which we can get into why that's kind of a

0:22:50.320 --> 0:22:53.040
<v Speaker 3>crazy idea in the first place. But they also got

0:22:53.140 --> 0:22:57.280
<v Speaker 3>another proposition passed. where they basically, there's like a moratorium

0:22:57.340 --> 0:22:59.629
<v Speaker 3>on any kind of wealth tax like this passing in

0:22:59.650 --> 0:23:00.070
<v Speaker 3>the future.

0:23:00.090 --> 0:23:01.170
<v Speaker 1>How did this happen?

0:23:02.109 --> 0:23:05.790
<v Speaker 3>Well, I mean, I don't know. I think California, so

0:23:05.830 --> 0:23:08.970
<v Speaker 3>California is a really interesting place. Like the state is

0:23:09.690 --> 0:23:13.110
<v Speaker 3>so mismanaged and like even Democrats, you talk to Democrats,

0:23:13.130 --> 0:23:16.180
<v Speaker 3>like I don't know, like I don't know any Republicans essentially.

0:23:16.340 --> 0:23:19.060
<v Speaker 3>Like I know some, but like no one admits to

0:23:19.100 --> 0:23:22.360
<v Speaker 3>voting Republican here. They're still just like, everyone's like fed

0:23:22.380 --> 0:23:24.900
<v Speaker 3>up with California. It's like the highest taxes in the country.

0:23:25.490 --> 0:23:27.750
<v Speaker 3>And I think the billionaire tax people were trying to

0:23:27.790 --> 0:23:30.630
<v Speaker 3>blame this on political spending. But as we just discussed,

0:23:31.190 --> 0:23:34.230
<v Speaker 3>you could spend all you want on an unpopular thing.

0:23:34.270 --> 0:23:36.889
<v Speaker 3>And it doesn't necessarily mean you're going to change people's minds.

0:23:37.369 --> 0:23:39.389
<v Speaker 3>And people are just like, okay, like, here we go,

0:23:39.430 --> 0:23:43.170
<v Speaker 3>like a one time tax, which is kind of insane. Like,

0:23:43.690 --> 0:23:45.859
<v Speaker 3>you know, a wealth tax, which is also kind of

0:23:45.900 --> 0:23:48.460
<v Speaker 3>like a new idea that's, I think, sort of weird

0:23:48.500 --> 0:23:50.859
<v Speaker 3>and frightening to a lot of people. And then spending

0:23:50.900 --> 0:23:53.439
<v Speaker 3>it on what? On like healthcare that really should be

0:23:53.480 --> 0:23:57.440
<v Speaker 3>coming from the federal government. California sends $ 80 billion a

0:23:57.500 --> 0:24:00.929
<v Speaker 3>year like net to the federal government because, you know,

0:24:00.990 --> 0:24:03.770
<v Speaker 3>you pay your state taxes, but then you're also paying

0:24:03.790 --> 0:24:06.369
<v Speaker 3>your federal income taxes and California like doesn't get any

0:24:06.390 --> 0:24:09.730
<v Speaker 3>of that money back. And then who knows where the

0:24:09.770 --> 0:24:11.710
<v Speaker 3>money goes? Like the highest tax rate in the country,

0:24:11.820 --> 0:24:13.900
<v Speaker 3>like no one can figure out like what the California

0:24:13.940 --> 0:24:16.500
<v Speaker 3>budget even is. I think people are just kind of

0:24:16.560 --> 0:24:19.200
<v Speaker 3>fed up with this in California. Like that's my sort

0:24:19.220 --> 0:24:23.100
<v Speaker 3>of like my own just talking to people gut feeling

0:24:23.180 --> 0:24:26.199
<v Speaker 3>on this. But then like, this is how insane California is.

0:24:26.660 --> 0:24:29.879
<v Speaker 3>So this other bill passed that gives like $ 8 billion

0:24:30.020 --> 0:24:33.320
<v Speaker 3>to immunology research, which is a lot of money for

0:24:33.340 --> 0:24:36.800
<v Speaker 3>a state to put into research that was put on

0:24:36.820 --> 0:24:40.810
<v Speaker 3>the ballot by one guy who like runs a three

0:24:40.869 --> 0:24:45.030
<v Speaker 3>person nonprofit that Sean Parker's involved in, where like half

0:24:45.090 --> 0:24:46.990
<v Speaker 3>the money is going to go to him. So basically,

0:24:47.010 --> 0:24:51.250
<v Speaker 3>like one guy got a bill passed in California, not bill,

0:24:51.430 --> 0:24:54.889
<v Speaker 3>a proposition passed in California, to give himself $ 4 billion

0:24:54.930 --> 0:24:58.710
<v Speaker 3>for his nonprofit to do immunology research, which like, I'm,

0:24:58.810 --> 0:25:00.990
<v Speaker 3>I am a huge supporter of like the federal government

0:25:01.040 --> 0:25:05.020
<v Speaker 3>funding basic research. But like, this is not what states do,

0:25:05.100 --> 0:25:07.540
<v Speaker 3>like the California does not put anywhere near that money

0:25:07.600 --> 0:25:10.619
<v Speaker 3>into that much money into like basic research. So, so

0:25:10.640 --> 0:25:13.520
<v Speaker 3>it just shows how like this whole proposition system, it

0:25:13.540 --> 0:25:17.560
<v Speaker 3>can just lead to these crazy results. And I think

0:25:17.600 --> 0:25:19.139
<v Speaker 3>that's what it's all about. People are just fed up

0:25:19.180 --> 0:25:22.560
<v Speaker 3>with it and probably didn't realize what they were doing

0:25:22.580 --> 0:25:25.639
<v Speaker 3>when they voted for this immunology proposition.

0:25:26.420 --> 0:25:30.350
<v Speaker 1>Sergey Brin did put over $ 100 billion into fighting the

0:25:30.410 --> 0:25:34.129
<v Speaker 1>proposed billionaire tax. $ 100 million. $ 100 billion.

0:25:34.150 --> 0:25:35.550
<v Speaker 3>That would be cost inefficient. Money has ceased to have

0:25:35.570 --> 0:25:35.950
<v Speaker 3>any meaning.

0:25:35.970 --> 0:25:39.060
<v Speaker 1>I mean, these numbers have become so ridiculous that it's

0:25:39.100 --> 0:25:39.470
<v Speaker 1>easy to do that.

0:25:46.990 --> 0:25:49.129
<v Speaker 7>I was talking to a Democratic strategist the other day

0:25:49.390 --> 0:25:51.670
<v Speaker 7>who works a lot of these like big tech.

0:25:51.670 --> 0:25:52.850
<v Speaker 5>Donors on the left.

0:25:53.570 --> 0:25:58.130
<v Speaker 7>And he was saying something interesting, which is that we're

0:25:58.190 --> 0:26:02.580
<v Speaker 7>reaching this like near French Revolution point of populist anger

0:26:02.710 --> 0:26:07.139
<v Speaker 7>against the super wealthy and putting all this money into

0:26:07.180 --> 0:26:11.240
<v Speaker 7>defeating the billionaire tax, whether or not that's what actually

0:26:11.320 --> 0:26:15.710
<v Speaker 7>did it. is going to backfire in terms of political badwill,

0:26:16.170 --> 0:26:18.889
<v Speaker 7>even more political badwill towards these guys. And he was like,

0:26:18.910 --> 0:26:21.149
<v Speaker 7>you know, this would have been actually an easy chance

0:26:21.210 --> 0:26:22.090
<v Speaker 7>to release some.

0:26:21.990 --> 0:26:24.050
<v Speaker 5>Pressure in this pressure cooker environment.

0:26:24.350 --> 0:26:27.470
<v Speaker 7>You know, these people are literally building bunkers, not because

0:26:27.510 --> 0:26:29.649
<v Speaker 7>I think they're afraid of like nuclear war or even AI.

0:26:29.670 --> 0:26:32.970
<v Speaker 7>I think what they're most afraid of is populist uprising.

0:26:33.109 --> 0:26:35.210
<v Speaker 7>So like he was kind of saying, that'll be interesting

0:26:35.230 --> 0:26:37.090
<v Speaker 7>to see how that plays out when there are easy

0:26:37.790 --> 0:26:40.959
<v Speaker 7>targets to point to like Sergey Brin. And interestingly, you know,

0:26:41.020 --> 0:26:44.179
<v Speaker 7>it's like I think Vinod Khosla, who is a Democratic donor,

0:26:44.580 --> 0:26:46.340
<v Speaker 7>put several million into this effort.

0:26:46.800 --> 0:26:48.410
<v Speaker 5>Patrick Collison, always.

0:26:48.240 --> 0:26:52.710
<v Speaker 1>An interesting figure. Million into preventing the billionaire tax. The

0:26:52.750 --> 0:26:55.189
<v Speaker 1>Washington Post had a headline that California's mega rich have

0:26:55.230 --> 0:26:58.690
<v Speaker 1>outspent foes 250 to 1 to sink the billionaire tax.

0:26:59.340 --> 0:27:01.940
<v Speaker 3>I think in this one, this is rare, but in

0:27:01.960 --> 0:27:04.480
<v Speaker 3>this one particular instance, I think voters were like too

0:27:04.560 --> 0:27:08.180
<v Speaker 3>smart to say, Let's have like really bad policy just

0:27:08.220 --> 0:27:10.460
<v Speaker 3>because it feels good. I think people kind of came

0:27:10.500 --> 0:27:11.700
<v Speaker 3>to their senses.

0:27:12.600 --> 0:27:13.420
<v Speaker 4>Well, it is.

0:27:13.359 --> 0:27:15.659
<v Speaker 7>Interesting because, you know, I feel like every election we

0:27:15.720 --> 0:27:20.379
<v Speaker 7>actually relearn how low information most voters are and how

0:27:20.440 --> 0:27:22.560
<v Speaker 7>little nuance there is. And so it is kind of

0:27:22.580 --> 0:27:26.200
<v Speaker 7>surprising that this was perceived as a more nuanced question

0:27:26.280 --> 0:27:27.900
<v Speaker 7>than just like billionaires are bad.

0:27:28.280 --> 0:27:30.879
<v Speaker 4>There's just that like axiom or whatever that everyone in

0:27:30.940 --> 0:27:35.490
<v Speaker 4>America thinks they're a temporarily embarrassed millionaire and now billionaire probably.

0:27:35.510 --> 0:27:38.010
<v Speaker 4>So it's like, well, no, I wouldn't want to tax

0:27:38.130 --> 0:27:39.590
<v Speaker 4>on my hypothetical wealth.

0:27:40.990 --> 0:27:44.030
<v Speaker 5>It's making me curious what you're hearing about the new governor.

0:27:44.830 --> 0:27:47.190
<v Speaker 7>Cause I don't, I confess I haven't followed that closely.

0:27:47.210 --> 0:27:48.350
<v Speaker 5>How's tech feeling about him?

0:27:48.670 --> 0:27:51.130
<v Speaker 3>You know, that's an interesting one. Like it's not. So

0:27:51.150 --> 0:27:54.800
<v Speaker 3>if you look at like the, the top politicians in California,

0:27:54.820 --> 0:27:59.109
<v Speaker 3>like they've really come from San Francisco and, And this election,

0:27:59.170 --> 0:28:02.109
<v Speaker 3>it's like this power center has like moved to LA.

0:28:02.350 --> 0:28:05.629
<v Speaker 3>And so I think the tech industry is like, I

0:28:05.670 --> 0:28:09.020
<v Speaker 3>think actually very wary. Like there's, it's like unsure, like what's,

0:28:09.580 --> 0:28:12.180
<v Speaker 3>what's this guy going to do? Like Newsome, he was

0:28:12.420 --> 0:28:14.340
<v Speaker 3>a good friend of the tech industry. Remember he like

0:28:14.420 --> 0:28:17.800
<v Speaker 3>vetoed the AI bill. The first one 10 was 1047.

0:28:17.800 --> 0:28:21.459
<v Speaker 3>The safety bill. He was, he, he, he was against

0:28:21.930 --> 0:28:24.649
<v Speaker 3>the billionaire tax. Like he's been sort of a, a

0:28:25.510 --> 0:28:28.869
<v Speaker 3>good tech, governor? And I think there are questions like, is,

0:28:29.510 --> 0:28:31.790
<v Speaker 3>is Becerra going to be good for the tech industry?

0:28:31.890 --> 0:28:34.729
<v Speaker 3>So I don't know. The answer is like, I don't know.

0:28:34.770 --> 0:28:37.560
<v Speaker 3>People are kind of like a little worried, I would say.

0:28:38.230 --> 0:28:38.820
<v Speaker 5>Well, it's funny.

0:28:38.860 --> 0:28:41.400
<v Speaker 7>You got like Jason Calacanis and Elon Musk being like,

0:28:41.420 --> 0:28:43.840
<v Speaker 7>you know, we came to Texas because California got so

0:28:43.860 --> 0:28:45.660
<v Speaker 7>blue and ridiculous. So they're going to bounce back to

0:28:45.680 --> 0:28:47.100
<v Speaker 7>California and then they're just going to have to bounce

0:28:47.120 --> 0:28:49.020
<v Speaker 7>back to Texas. It seems like they'll never find a home.

0:28:49.880 --> 0:28:50.600
<v Speaker 4>Only on Mars.

0:28:55.070 --> 0:28:57.940
<v Speaker 1>When we come back, The role of AI in the

0:28:58.010 --> 0:28:59.690
<v Speaker 1>political ads this election cycle.

0:29:00.050 --> 0:29:00.469
<v Speaker 3>Stay with us.

0:29:07.690 --> 0:29:09.830
<v Speaker 1>Welcome back. Kyle, you've wrote in The New Yorker about

0:29:09.870 --> 0:29:13.930
<v Speaker 1>a slew of AI-generated political ads that ran in the midterms.

0:29:14.710 --> 0:29:16.870
<v Speaker 1>What did you find out and how consequential were they?

0:29:17.360 --> 0:29:19.160
<v Speaker 4>Yeah, I mean, it really felt like this was the

0:29:19.240 --> 0:29:23.420
<v Speaker 4>first election year with generative video, like with the super

0:29:23.460 --> 0:29:27.590
<v Speaker 4>accessible generative AI tools and just making it really easy

0:29:27.670 --> 0:29:31.910
<v Speaker 4>to satirize your opponents and envision them doing things that

0:29:31.930 --> 0:29:34.350
<v Speaker 4>they never would do and saying things that they wouldn't say.

0:29:34.370 --> 0:29:40.150
<v Speaker 4>So I was trawling through this hash of 58 different

0:29:40.350 --> 0:29:45.040
<v Speaker 4>political AI attack ads that this organization, Ad Impact, had collected.

0:29:45.060 --> 0:29:48.340
<v Speaker 4>And I think they said over $ 10 million was spent

0:29:48.660 --> 0:29:51.440
<v Speaker 4>promoting them, which is not a huge amount compared to

0:29:51.500 --> 0:29:54.700
<v Speaker 4>Elon Musk, but still, when you think of $ 10 million

0:29:54.760 --> 0:29:58.200
<v Speaker 4>going to deepfake ads... And the one that stuck out

0:29:58.240 --> 0:30:01.140
<v Speaker 4>to me was this one against Jessica Bickler, who was

0:30:01.300 --> 0:30:05.130
<v Speaker 4>running for North Carolina State Senate. Not a huge race,

0:30:05.430 --> 0:30:09.050
<v Speaker 4>not super dramatic, not maybe important on a national scheme,

0:30:09.350 --> 0:30:13.229
<v Speaker 4>but her opponent, Michael Lee, made this deepfake AI out

0:30:13.290 --> 0:30:17.180
<v Speaker 4>of her, twisted into this grotesque yoga pose with a

0:30:17.300 --> 0:30:21.760
<v Speaker 4>vacant smile on her face. It was totally photorealistic. Bickler

0:30:21.900 --> 0:30:25.260
<v Speaker 4>owns a yoga studio. This is her whole brand. And

0:30:25.320 --> 0:30:30.820
<v Speaker 4>so I just... the AI ad was really gross. Almost

0:30:30.860 --> 0:30:34.780
<v Speaker 4>like a voodoo doll attack on someone to put their

0:30:34.840 --> 0:30:38.180
<v Speaker 4>body in such a vicious position. And speaking to her,

0:30:38.260 --> 0:30:43.130
<v Speaker 4>she felt very attacked by this and it manipulated her image.

0:30:43.150 --> 0:30:46.090
<v Speaker 4>It kind of even cast doubt in the minds of

0:30:46.130 --> 0:30:49.530
<v Speaker 4>some voters that why had you let this guy into

0:30:49.570 --> 0:30:52.910
<v Speaker 4>your yoga studio? So I think maybe AI ads aren't

0:30:52.930 --> 0:30:56.630
<v Speaker 4>having this huge national impact yet, but on a you

0:30:56.650 --> 0:30:59.750
<v Speaker 4>know case by case basis they're actually making people feel

0:30:59.810 --> 0:31:04.190
<v Speaker 4>things and think things and that's not what was happening before.

0:31:04.070 --> 0:31:05.630
<v Speaker 1>And when you talk about that made me think of

0:31:05.690 --> 0:31:08.150
<v Speaker 1>the all the deep fake porn stuff frankly i mean

0:31:08.210 --> 0:31:11.430
<v Speaker 1>this idea of like taking control of somebody else's body

0:31:11.470 --> 0:31:14.430
<v Speaker 1>and manipulating it in a way which uh is upsetting

0:31:14.470 --> 0:31:16.430
<v Speaker 1>like it's sort of and.

0:31:16.370 --> 0:31:20.530
<v Speaker 4>That's that's illegal like there are laws against that against

0:31:20.570 --> 0:31:23.150
<v Speaker 4>like deep fake porn and and fake nudes and stuff

0:31:23.800 --> 0:31:26.500
<v Speaker 4>And digital platforms are actually much more likely to take

0:31:26.540 --> 0:31:31.060
<v Speaker 4>care of those issues and respond to takedown requests. But

0:31:31.100 --> 0:31:36.210
<v Speaker 4>these political ads are covered under free speech, under satire,

0:31:36.530 --> 0:31:39.650
<v Speaker 4>or similar kinds of laws. And so you just see

0:31:39.710 --> 0:31:43.110
<v Speaker 4>these kind of dramatic cases where there's an ad having

0:31:43.190 --> 0:31:47.090
<v Speaker 4>Josh Shapiro talking about how he supports data centers.

0:31:47.470 --> 0:31:51.250
<v Speaker 3>And they just label it as AI, so that's okay? Like, basically,

0:31:51.270 --> 0:31:52.190
<v Speaker 3>that's how you get around it.

0:31:52.210 --> 0:31:54.590
<v Speaker 4>I think that's the excuse, yeah. So the Bickler one

0:31:54.650 --> 0:31:57.550
<v Speaker 4>had a little tag that says, oh, these silly videos

0:31:57.610 --> 0:32:01.260
<v Speaker 4>were generated with AI. And the Shapiro one, Shapiro says,

0:32:02.100 --> 0:32:04.580
<v Speaker 4>I support data centers, like, just like the ones used

0:32:04.620 --> 0:32:07.200
<v Speaker 4>to make this ad. So this is kind of fig leaf,

0:32:07.480 --> 0:32:07.880
<v Speaker 4>I think.

0:32:08.540 --> 0:32:11.380
<v Speaker 3>Julia? Do you think we should just ban this stuff? Sorry,

0:32:11.400 --> 0:32:11.680
<v Speaker 3>go ahead.

0:32:12.180 --> 0:32:13.310
<v Speaker 4>Yeah, they should ban it.

0:32:14.240 --> 0:32:15.080
<v Speaker 3>Maybe, yeah.

0:32:15.160 --> 0:32:17.420
<v Speaker 7>I mean, I just want to take the opportunity to

0:32:17.480 --> 0:32:22.040
<v Speaker 7>promote my colleague, Jose Crealis Insueta, who's our fashion correspondent

0:32:22.220 --> 0:32:24.760
<v Speaker 7>here at B &amp; A Fair. And yesterday there was

0:32:24.800 --> 0:32:26.400
<v Speaker 7>all this buzz around his desk and I was like,

0:32:26.480 --> 0:32:28.600
<v Speaker 7>what is going on over there? And everyone was pouring

0:32:28.740 --> 0:32:32.720
<v Speaker 7>over this photo that Olivia Rheingold at the Free Press

0:32:32.880 --> 0:32:34.960
<v Speaker 7>had posted. I don't know if you saw this. She said,

0:32:34.980 --> 0:32:38.420
<v Speaker 7>you know, I'm at the Abdul El-Sayed rally, like the

0:32:38.460 --> 0:32:42.260
<v Speaker 7>victory party, and I'm spotting Chanel and Valentino and like,

0:32:42.540 --> 0:32:47.080
<v Speaker 7>You know, basically implying like, oh, these socialists and designer fashion, hypocrites, whatever.

0:32:47.100 --> 0:32:50.240
<v Speaker 7>And everyone was looking and they were like, wait a second,

0:32:50.740 --> 0:32:53.520
<v Speaker 7>the holes in the Valentino shoe, the studs, that's off.

0:32:53.540 --> 0:32:55.620
<v Speaker 5>That's not how a Chanel bag looks. And it's an

0:32:55.820 --> 0:32:57.030
<v Speaker 5>AI generated photo.

0:32:57.310 --> 0:33:00.510
<v Speaker 7>We ran it through an image processor and every single

0:33:00.590 --> 0:33:01.990
<v Speaker 7>one came back AI generated.

0:33:02.940 --> 0:33:04.560
<v Speaker 5>Which, I mean, check my Twitter.

0:33:04.600 --> 0:33:06.880
<v Speaker 7>It's blowing up on Twitter because I think people have

0:33:06.900 --> 0:33:09.140
<v Speaker 7>a lot of strong feelings about Olivia as a reporter.

0:33:09.160 --> 0:33:13.090
<v Speaker 7>And like, it's this small thing, but it does clearly,

0:33:13.310 --> 0:33:17.910
<v Speaker 7>it's striking a nerve because there was no disclosure. I actually,

0:33:18.110 --> 0:33:21.330
<v Speaker 7>in her defense, I have no doubt she saw designer fashion.

0:33:21.570 --> 0:33:23.770
<v Speaker 7>And I guess maybe she took a photo of someone

0:33:24.030 --> 0:33:25.850
<v Speaker 7>and she said, oh, it'd be better if they had

0:33:25.870 --> 0:33:27.350
<v Speaker 7>the shoes and the Chanel bag.

0:33:27.610 --> 0:33:28.830
<v Speaker 1>That's in her defense?

0:33:28.930 --> 0:33:31.470
<v Speaker 3>I mean, it's bad. It's bad.

0:33:31.550 --> 0:33:34.320
<v Speaker 7>And she has not responded to a request for comment.

0:33:34.710 --> 0:33:37.040
<v Speaker 7>But like, I think how people justify it in their

0:33:37.080 --> 0:33:39.240
<v Speaker 7>mind is if they think, you know, there's a basic

0:33:39.400 --> 0:33:42.380
<v Speaker 7>truth here that I'm just using AI as a tool

0:33:42.400 --> 0:33:44.540
<v Speaker 7>to get across and to demonstrate. It's almost like a

0:33:44.600 --> 0:33:47.860
<v Speaker 7>documentary that like reconstructs a scene. And it's like, well,

0:33:47.880 --> 0:33:49.600
<v Speaker 7>it really happened, but we're having actors.

0:33:50.280 --> 0:33:52.560
<v Speaker 1>That is such a good point. I was watching the

0:33:53.300 --> 0:33:57.140
<v Speaker 1>person who created, I guess, Josh Shapiro's opponent, whoever it was.

0:33:57.160 --> 0:33:59.340
<v Speaker 1>There was a woman in Pennsylvania who created these ads

0:33:59.740 --> 0:34:02.710
<v Speaker 1>and she was interviewed on local TV. And the journalist

0:34:02.750 --> 0:34:05.240
<v Speaker 1>said to her, well, you know, would you mind if

0:34:05.320 --> 0:34:08.160
<v Speaker 1>someone did this to you? And she was like, well, no,

0:34:08.239 --> 0:34:10.219
<v Speaker 1>be fine, so long as everything they put in my

0:34:10.260 --> 0:34:10.980
<v Speaker 1>mouth was true.

0:34:11.000 --> 0:34:14.980
<v Speaker 4>And that's the problem. I mean, so many of these

0:34:15.160 --> 0:34:18.900
<v Speaker 4>ads have put false statements in their faces and voices,

0:34:19.040 --> 0:34:20.640
<v Speaker 4>and that is not legal.

0:34:21.020 --> 0:34:23.020
<v Speaker 1>But also, I think it's what Julia is getting at,

0:34:23.020 --> 0:34:25.540
<v Speaker 1>this idea that there's kind of like almost like a

0:34:25.600 --> 0:34:29.060
<v Speaker 1>spiritual truth, which people think is okay to create a

0:34:29.200 --> 0:34:31.739
<v Speaker 1>fake representation of, so long as in their view, it

0:34:32.239 --> 0:34:33.620
<v Speaker 1>actually represents the truth.

0:34:33.980 --> 0:34:34.760
<v Speaker 3>It's a very strange.

0:34:35.560 --> 0:34:38.670
<v Speaker 7>Certainly for a journalist, it should call your credibility into question.

0:34:38.930 --> 0:34:42.290
<v Speaker 3>I think that's... I think that's a fireable offense. I mean,

0:34:42.350 --> 0:34:42.830
<v Speaker 3>for sure.

0:34:42.850 --> 0:34:46.390
<v Speaker 1>Olivia Rheingold, as of this taping, has put out a

0:34:46.410 --> 0:34:49.330
<v Speaker 1>statement on her ex. Hello, my name is Olivia Rheingold,

0:34:49.390 --> 0:34:52.009
<v Speaker 1>and I use ChatGPT to brighten and sharpen an image

0:34:52.050 --> 0:34:54.300
<v Speaker 1>of a woman in Valentino shoes and a Chanel bag.

0:34:54.880 --> 0:34:58.339
<v Speaker 1>After processing, it still shows these same things. A woman

0:34:58.380 --> 0:35:01.130
<v Speaker 1>wearing Valentino shoes and a Chanel bag. She is a

0:35:01.219 --> 0:35:04.930
<v Speaker 1>real and chic woman really wearing those items. The observation holds.

0:35:05.390 --> 0:35:06.070
<v Speaker 3>That's ridiculous.

0:35:06.090 --> 0:35:08.069
<v Speaker 7>I'm glad she at least responded because she did not

0:35:08.090 --> 0:35:09.250
<v Speaker 7>respond to Jose yesterday.

0:35:10.010 --> 0:35:12.330
<v Speaker 3>But okay, this is also a good example of how

0:35:12.590 --> 0:35:15.529
<v Speaker 3>people have been worried about deep fakes in politics for

0:35:15.630 --> 0:35:18.370
<v Speaker 3>so long, like long before the technology was even there.

0:35:18.390 --> 0:35:21.430
<v Speaker 3>And it was like, the worry was so different than

0:35:21.450 --> 0:35:24.029
<v Speaker 3>the actual reality. The worry was like, people would just

0:35:24.070 --> 0:35:28.270
<v Speaker 3>create completely fabricated, you know, social media, whatever, posts and

0:35:28.290 --> 0:35:31.609
<v Speaker 3>videos of people And they would give people completely like

0:35:31.830 --> 0:35:34.890
<v Speaker 3>false information. And it's like, that wasn't the risk at all.

0:35:34.950 --> 0:35:38.060
<v Speaker 3>Like the risk is what Julia is describing, which is like, no,

0:35:38.100 --> 0:35:41.220
<v Speaker 3>like you can make an AI generated ad or whatever,

0:35:41.420 --> 0:35:44.900
<v Speaker 3>and sort of like say it's AI and you're not, nobody,

0:35:45.000 --> 0:35:47.900
<v Speaker 3>everybody knows it's fake and it's still like hugely damaging.

0:35:48.120 --> 0:35:48.319
<v Speaker 1>Right.

0:35:48.380 --> 0:35:50.910
<v Speaker 3>And so it just kind of shows how like the

0:35:51.030 --> 0:35:53.549
<v Speaker 3>risks of this technology are always a little different than

0:35:54.190 --> 0:35:57.969
<v Speaker 3>what they, you know, what we actually predicted. And, you know,

0:35:58.110 --> 0:36:01.260
<v Speaker 3>here it's like, I think, we maybe should talk about like,

0:36:01.340 --> 0:36:04.040
<v Speaker 3>let's have a ban on just using deep fakes at

0:36:04.200 --> 0:36:07.500
<v Speaker 3>all for political ads. Like that would, I think probably

0:36:07.600 --> 0:36:09.899
<v Speaker 3>solve this problem. Like sure, people are going to post

0:36:09.920 --> 0:36:12.609
<v Speaker 3>stuff on social media, but like you can get rid

0:36:12.620 --> 0:36:14.250
<v Speaker 3>of a lot of this by just saying like, you're

0:36:14.270 --> 0:36:18.350
<v Speaker 3>not allowed to take someone's identity and make them do something.

0:36:18.770 --> 0:36:21.550
<v Speaker 3>Even if you, even if you say it's actually AI.

0:36:22.070 --> 0:36:23.840
<v Speaker 1>But that's what's kind of interesting. I mean, Kyle, that

0:36:23.950 --> 0:36:27.440
<v Speaker 1>people thought that the Jessica Bickler AI ads were actually

0:36:27.780 --> 0:36:29.420
<v Speaker 1>I mean, that question arose, why did you let him

0:36:29.460 --> 0:36:33.420
<v Speaker 1>into the yoga studio? So, like, people, even despite the disclaimer,

0:36:33.460 --> 0:36:34.279
<v Speaker 1>believe these were real.

0:36:34.840 --> 0:36:38.100
<v Speaker 4>Yeah, there's, like, an emotional truth, as we were talking about.

0:36:38.300 --> 0:36:41.700
<v Speaker 4>And it would, like, the FCC regulates this stuff really heavily.

0:36:41.900 --> 0:36:44.080
<v Speaker 4>There was an incident in 2024 when there was a

0:36:44.120 --> 0:36:48.219
<v Speaker 4>Joe Biden deepfake voice, and that person got fined, and

0:36:48.260 --> 0:36:51.779
<v Speaker 4>all of this stuff happened. So I think, I don't know,

0:36:52.239 --> 0:36:55.140
<v Speaker 4>as Reid was saying, the consequence or after effect is

0:36:55.180 --> 0:36:57.250
<v Speaker 4>not what you think it is. I think as long

0:36:57.350 --> 0:37:00.570
<v Speaker 4>as this is possible, as long as it's possible to

0:37:00.590 --> 0:37:04.060
<v Speaker 4>envision your opponent doing something fake, then it's going to

0:37:04.160 --> 0:37:07.660
<v Speaker 4>impact voters. There was another ad actually of Jon Ossoff

0:37:08.360 --> 0:37:11.820
<v Speaker 4>showing him in like a salon getting his nails filed

0:37:11.920 --> 0:37:14.840
<v Speaker 4>and makeup done and also it doesn't say anything in

0:37:14.880 --> 0:37:17.430
<v Speaker 4>the ad you know if you look really closely you

0:37:17.450 --> 0:37:19.930
<v Speaker 4>can see that the nail techs are like filing his

0:37:20.010 --> 0:37:23.830
<v Speaker 4>fingers not his nails but it really looks like he's

0:37:23.930 --> 0:37:26.609
<v Speaker 4>in this ad like it looks like real footage and

0:37:26.650 --> 0:37:29.850
<v Speaker 4>it that's just so that's more frightening in a way

0:37:29.890 --> 0:37:33.160
<v Speaker 4>of something that's trying to to be fake like it

0:37:33.520 --> 0:37:35.799
<v Speaker 4>I don't know. It's very uncanny and disturbing to me.

0:37:36.060 --> 0:37:38.770
<v Speaker 3>It's really wrong. It's like morally wrong to do. Yeah. Yeah.

0:37:38.850 --> 0:37:39.709
<v Speaker 4>It's morally wrong.

0:37:39.790 --> 0:37:41.830
<v Speaker 3>It's like, you know what you're doing, but then like

0:37:41.870 --> 0:37:44.670
<v Speaker 3>drawing the line within it, it's like, you're never going

0:37:44.690 --> 0:37:47.529
<v Speaker 3>to be able to draw a clear line. You're just,

0:37:47.630 --> 0:37:51.110
<v Speaker 3>it has to just be a blanket ban because otherwise

0:37:51.230 --> 0:37:54.259
<v Speaker 3>it's like impossible to draw the line. Like, where is it? Okay. Like,

0:37:54.280 --> 0:37:55.920
<v Speaker 3>where is it? Okay. To have a deep fake. And

0:37:55.940 --> 0:37:56.400
<v Speaker 3>where is it not?

0:37:56.700 --> 0:37:59.480
<v Speaker 1>I know, but then you get into the hermeneutics of

0:37:59.560 --> 0:38:02.779
<v Speaker 1>what constitutes something made by AI, right? Like are you

0:38:02.820 --> 0:38:05.819
<v Speaker 1>allowed to like, to Olivia Rheingold's point, like are you

0:38:05.860 --> 0:38:09.740
<v Speaker 1>allowed to do like, are you allowed to generate some interstitials?

0:38:09.780 --> 0:38:13.589
<v Speaker 1>Like the truth that like, how do you testify that

0:38:13.620 --> 0:38:15.290
<v Speaker 1>something has no AI in it?

0:38:15.370 --> 0:38:16.969
<v Speaker 3>I mean, it's not so easy. I think it's not

0:38:17.010 --> 0:38:19.070
<v Speaker 3>about AI. It's just like when you show an image

0:38:19.110 --> 0:38:23.529
<v Speaker 3>of a political candidate in an ad or whatever, you know,

0:38:23.570 --> 0:38:25.629
<v Speaker 3>maybe ad is too narrow, but you know, that kind

0:38:25.670 --> 0:38:28.520
<v Speaker 3>of a thing, it should have to represent reality. Like

0:38:28.560 --> 0:38:31.500
<v Speaker 3>it can't be doctored in any way. I think that's the,

0:38:32.280 --> 0:38:34.700
<v Speaker 3>I think that to me would make a lot of sense. Like,

0:38:34.860 --> 0:38:36.899
<v Speaker 3>I don't think it's like you could, I'd say like

0:38:37.420 --> 0:38:39.610
<v Speaker 3>you can use AI to make political ads, but you

0:38:39.640 --> 0:38:43.089
<v Speaker 3>just can't take like a real person and doctor them

0:38:43.130 --> 0:38:46.350
<v Speaker 3>in any way or fabricate that, you know, completely from scratch.

0:38:46.810 --> 0:38:48.810
<v Speaker 1>Kyle, one detail of your piece I was quite interested

0:38:48.850 --> 0:38:52.390
<v Speaker 1>by was that two thirds of the AI ads identified

0:38:52.430 --> 0:38:56.310
<v Speaker 1>by that organization you mentioned, Ad Impact, came from Republicans.

0:38:57.810 --> 0:39:00.430
<v Speaker 4>So it seems like Republicans are much more likely to

0:39:00.570 --> 0:39:04.430
<v Speaker 4>use AI in their attack ads. And another quality that

0:39:04.469 --> 0:39:07.720
<v Speaker 4>I also found to them was that they often targeted identity,

0:39:08.260 --> 0:39:12.520
<v Speaker 4>like they're targeting someone's gender or race or their religion

0:39:12.540 --> 0:39:16.680
<v Speaker 4>and kind of illustrating that with the AI imagery. And

0:39:16.719 --> 0:39:19.060
<v Speaker 4>I think, I mean, this is something I discussed with

0:39:19.080 --> 0:39:22.069
<v Speaker 4>the New Yorker fact checkers and my editor, but that

0:39:22.130 --> 0:39:25.819
<v Speaker 4>is a Republican tendency lately to make an issue out

0:39:25.840 --> 0:39:29.049
<v Speaker 4>of someone's identity and kind of lampoon them for it.

0:39:29.190 --> 0:39:33.520
<v Speaker 4>And so I think that's the capacity of generative AI,

0:39:33.820 --> 0:39:38.020
<v Speaker 4>to fake someone's identity and make fun of them for it.

0:39:38.660 --> 0:39:41.040
<v Speaker 7>Well, of course, Trump has set the tone on, I mean,

0:39:41.320 --> 0:39:44.960
<v Speaker 7>no one loves AI-generated content more than him. And that's

0:39:45.020 --> 0:39:47.440
<v Speaker 7>actually an interesting scene from Greg Brockman's life, as he

0:39:47.460 --> 0:39:51.540
<v Speaker 7>was the one who originally showed Trump Sora. And that

0:39:51.700 --> 0:39:53.920
<v Speaker 7>apparently was a very pivotal moment in terms of Trump's

0:39:54.000 --> 0:39:55.520
<v Speaker 7>personal embrace of the technology.

0:39:55.640 --> 0:39:56.960
<v Speaker 5>He just thought that was the coolest thing.

0:39:57.219 --> 0:39:58.960
<v Speaker 3>What was it like Lee Atwater, who's sort of the

0:39:59.020 --> 0:40:02.719
<v Speaker 3>father of Republican disingenuous political attack ads? Like he didn't

0:40:02.739 --> 0:40:05.399
<v Speaker 3>have AI. Like you can do this without AI, right?

0:40:05.580 --> 0:40:06.860
<v Speaker 4>You don't need AI.

0:40:07.300 --> 0:40:12.080
<v Speaker 3>It's like robbing these Republican attack ad people of their creativity.

0:40:12.160 --> 0:40:14.780
<v Speaker 3>Like they should have to do it without AI. What

0:40:14.800 --> 0:40:17.040
<v Speaker 3>was the best ad was actually, it was a Democrat,

0:40:17.060 --> 0:40:20.500
<v Speaker 3>but it was the Scott Cullum Pick Another Protein ad.

0:40:20.900 --> 0:40:22.420
<v Speaker 5>Oh, Pick Another Protein was good.

0:40:22.460 --> 0:40:24.580
<v Speaker 3>Wasn't that the best ad? I don't know. That was

0:40:24.640 --> 0:40:25.360
<v Speaker 3>so funny.

0:40:25.920 --> 0:40:26.620
<v Speaker 1>Describe it, read.

0:40:27.180 --> 0:40:31.260
<v Speaker 3>It was Cindy Hyde-Smith. They were talking about beef prices.

0:40:31.320 --> 0:40:33.680
<v Speaker 3>And she said on some interview, well, you can pick

0:40:33.719 --> 0:40:37.630
<v Speaker 3>another protein. And it was like a layup. And then

0:40:37.670 --> 0:40:40.370
<v Speaker 3>there's an ad where they just showed people's reactions like,

0:40:40.820 --> 0:40:44.100
<v Speaker 3>pick another protein. Are you crazy? Like what's wrong with you?

0:40:44.120 --> 0:40:47.080
<v Speaker 3>And it was just like, it was a great ad.

0:40:47.100 --> 0:40:48.940
<v Speaker 4>Then it's like, why don't you pick another?

0:40:48.960 --> 0:40:49.040
<v Speaker 6>Yeah.

0:40:49.340 --> 0:40:51.080
<v Speaker 3>Why don't you pick another senator?

0:40:51.640 --> 0:40:54.180
<v Speaker 7>But I also like the sinners ad that he did

0:40:54.400 --> 0:40:58.319
<v Speaker 7>where he represented her as the vampire, but like just

0:40:58.340 --> 0:41:00.500
<v Speaker 7>showed a blonde woman from behind. Like it was very

0:41:00.580 --> 0:41:04.089
<v Speaker 7>obviously an actress, but it's like, yeah, you can, there's

0:41:04.110 --> 0:41:07.609
<v Speaker 7>a lot of poetic license in these things without using AI.

0:41:07.710 --> 0:41:08.210
<v Speaker 1>Yeah.

0:41:08.430 --> 0:41:08.649
<v Speaker 3>Yeah.

0:41:09.070 --> 0:41:10.989
<v Speaker 1>That's all we have time for today. Thank you all

0:41:11.010 --> 0:41:11.730
<v Speaker 1>so much for joining us.

0:41:12.070 --> 0:41:13.109
<v Speaker 4>Great to discuss it all.

0:41:13.250 --> 0:41:13.730
<v Speaker 6>Thank you.

0:41:13.950 --> 0:41:14.569
<v Speaker 3>Thanks for having me.

0:41:26.180 --> 0:41:29.380
<v Speaker 1>For Tech Stuff, I'm Oz Veloshin. This episode was produced

0:41:29.420 --> 0:41:32.319
<v Speaker 1>by Eliza Dennis. It was executive produced by me and

0:41:32.360 --> 0:41:35.940
<v Speaker 1>Julian Nutter for Kaleidoscope and Katrina Norvell for iHeart Podcast.

0:41:36.730 --> 0:41:39.569
<v Speaker 1>Jack Inslee mixed this episode and Kyle Murdoch wrote our

0:41:39.590 --> 0:41:42.969
<v Speaker 1>theme song. Thank you to Rita Albergotti, Kyle Chaker and

0:41:43.050 --> 0:41:46.049
<v Speaker 1>Julia Black. Check out all the work they put into

0:41:46.070 --> 0:41:48.759
<v Speaker 1>the world. We're lucky to call them Friends of the Pod.