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Speaker 1: There are No Girls on the Internet. As a production

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Speaker 1: of iHeartRadio and Unbossed Creative, I'm Bridget Todd, and this

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Speaker 1: is there are no girls on the Internet. I've brought

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Speaker 1: it up on the show.

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Speaker 2: Before, but I feel like you and I Mike live

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Speaker 2: in and.

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Speaker 1: Occupy very different social media internet spheres. Oftentimes I'll ask

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Speaker 1: you did you see so and so or did you

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Speaker 1: see such and such online? And I'm asking rhetorically because

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Speaker 1: I already know the answer is no, you definitely didn't

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Speaker 1: see that. But I will ask you this rhetorically even

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Speaker 1: though I feel I know the answer. Have you seen

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Speaker 1: the latest Drew Ski skit?

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Speaker 3: I think this is more evidence that you're right we

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Speaker 3: occupy different internets because I have not seen this skit.

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Speaker 3: Familiar with Erica Kirk, famed widow and alleged girlfriend of

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Speaker 3: Jade Vance, but I can't say that I'm familiar with

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Speaker 3: Drew Ski or his her there skipping.

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Speaker 1: Yes, So, Drewski is this comedian YouTuber He's known for

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Speaker 1: doing skits. He had this skit recently where he is

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Speaker 1: a black megachurch pastor who comes down from the sky

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Speaker 1: in church and as somebody who grew up in the

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Speaker 1: Black Church. It's sometimes his stuff is like barely even

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Speaker 1: parody because it's so spot on. He's also known for

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Speaker 1: doing like extreme makeup to really dramatically become other people.

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Speaker 1: So Drewski's a black dude, and he went viral a

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Speaker 1: while back for doing what I can only describe as

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Speaker 1: full body, head to toe makeup to become a very

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Speaker 1: sunburnt white guy at a NASCAR race. And I know

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Speaker 1: this is all very visual. We'll add links to the

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Speaker 1: show notes because you obvious he can't see what I'm describing.

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Speaker 1: But when I say that Drewski became a sunburnt white

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Speaker 1: guy at a NASCAR at he's They even put makeup

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Speaker 1: on his chest and he's wearing denim overalls with one

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Speaker 1: of the strats down. He his manner is of like

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Speaker 1: he just really he becomes the pupil that he is

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Speaker 1: that he is trying to become the makeup. So his

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Speaker 1: latest video, it's called How Conservative Women in America Act,

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Speaker 1: he wears that extreme makeup to become a white woman

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Speaker 1: with blonde, wavy hair who wears a pink pants suit

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Speaker 1: and dances around on stage with pyrotechnics going and has

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Speaker 1: this very weird pointed way of speaking, where everything she

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Speaker 1: says is punctuited by intensely staring into the camera for

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Speaker 1: some reason, I'm dancing around it. But it's obviously supposed

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Speaker 1: to be Erica Kirk. I can't even really describe it

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Speaker 1: because he really does look like her. Like it's uncanny,

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Speaker 1: even the hands, Like he's got makeup on his hands

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Speaker 1: and a manicure, and he's wearing you know how she

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Speaker 1: does the Super Bowl rings, Erica Kirk, Like, even down

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Speaker 1: to the most minute detail, he has become her.

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Speaker 3: So he's been studying Erica Kirk.

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Speaker 1: This is what I'm saying. It is I've never seen

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Speaker 1: anybody commit to a bit more it is. It is

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Speaker 1: like you have really committed to the bit.

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Speaker 3: That's great. That's what we need more of. You know,

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Speaker 3: there's in a world where you can just have AI

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Speaker 3: generate videos. We need creative humans really committing to the bit.

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Speaker 1: You get what I'm saying When you're like, it's it's

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Speaker 1: so both uncanny and also like it's spot on, but

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Speaker 1: so disturbing. He's done something to his face to make

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Speaker 1: it look stretched in some way.

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Speaker 3: It's very round and fleck It's very funny. It's like,

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Speaker 3: he's not just parodying her, but he's parodying the parodies

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Speaker 3: of her.

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Speaker 1: Yes, he's parodying AI generated Erica Kirk means. And what's

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Speaker 1: really funny is that nowhere in that video does Drewski

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Speaker 1: say that it's supposed to be Erica Kirk. Specifically, he

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Speaker 1: just says conservative white women in America. I mean, it's

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Speaker 1: obviously supposed to be her. But I just find it

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Speaker 1: funny that everybody saw this skit where you have somebody

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Speaker 1: who looks grotesque and everybody agrees, oh, that's obviously Erica Kirk,

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Speaker 1: even though it was unsaid.

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Speaker 3: That's a mark of like, well done. I don't know, parody,

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Speaker 3: mockery that he doesn't even need to say her name,

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Speaker 3: but we all know. Well.

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Speaker 1: What's even wilder is that when users on X showed

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Speaker 1: Groc a picture of Drewski in this get up, Groch

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Speaker 1: was like, oh, baby, that's Erica Kirk in no yeah,

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Speaker 1: Groc identified it as the real Erica Kirk, saying, quote,

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Speaker 1: the photo matches her public appearances, including podcasts and event

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Speaker 1: shots with the blonde hair, blue eyes, and makeup style.

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Speaker 1: So even Grock was like, that's Erica Kirk that's her croc.

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Speaker 3: Come on, that's She's on your side, buddy.

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Speaker 1: That's what I'm saying. Also, funny enough, Google Gemini did

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Speaker 1: get it right that it was not the real Erica Kirk,

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Speaker 1: but it said that it was a face swap meme

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Speaker 1: that okay, it was Erica Kirk and Drewsky had had

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Speaker 1: swapped faces.

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Speaker 3: And so it's like, well, everything else about it checked out,

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Speaker 3: like the dancing, the press conference. Wow, uh, maybe this

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Speaker 3: could be a new benchmark for AI systems. You know

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Speaker 3: that they have like different things that they test them

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Speaker 3: how well they can perform them, the like recognizing Erica Kirk.

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Speaker 1: Task, Yes, that's gonna be the new Will Smith eating spaghetti.

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Speaker 1: What's funny is like, so I said that his stuff

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Speaker 1: is hard, it's like barely even pair. The parody that

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Speaker 1: he has of black megachurch preachers is like true, like

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Speaker 1: if you've not been you're not if you were not

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Speaker 1: someone who has spent time in church, you might it

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Speaker 1: might you might think it's parody, but it is hardly

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Speaker 1: even parody. He's like coming down from the ceiling with

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Speaker 1: like jet packs and like steam and shit, Like.

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Speaker 3: Are you sure you are just watching righteous Gemstones?

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Speaker 1: No, but that's the thing. Righteous Gemstones is also barely parody,

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Speaker 1: because that really is how it is sometimes so in

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Speaker 1: that same vein, here is what Drewski says, as Erica

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Speaker 1: Kirk at the end of a skit.

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Speaker 4: We have to protect all men in America, especially all

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Speaker 4: white men in America. Those are the boys that we

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Speaker 4: care about in this country. America is built on their backs, yes,

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Speaker 4: because they are the ones who mattered most.

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Speaker 1: And it's funny because that is barely even parody. Because

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Speaker 1: here is what Erica Kirk took a little bit of

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Speaker 1: heat for saying earlier this month at an event in Arkansas.

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Speaker 5: Don't let anyone disenfranchise you because you're a young man,

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Speaker 5: especially a young white male man. Don't ever let anyone

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Speaker 5: talk down to you. We need strong men out there,

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Speaker 5: strong men who are convicted that will be good leaders,

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Speaker 5: good husbands, good father's like mine.

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Speaker 1: What's also funny to me about this is the reaction

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Speaker 1: a lot of people are, like, he's using white face,

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Speaker 1: and white face is not a thing. I mean, there's

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Speaker 1: not an analogous It would be convenient for these critics

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Speaker 1: if it was, but there's not an analogous kind of

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Speaker 1: historical context and precedent for whiteface.

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Speaker 3: In the United States. That's such a funny thing to say,

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Speaker 3: like through doing white face, because yeah, white face is

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Speaker 3: not a thing like a person just putting on weight makeup,

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Speaker 3: not a thing.

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Speaker 1: Like Ted Cruz tweeted that the skit was horrible. All

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Speaker 1: the voices that you would expect to be speaking out

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Speaker 1: about this or speaking out about it. What's funny is,

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Speaker 1: are these not the same people who just spent all

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Speaker 1: this time being like, oh, you can't even make jokes anymore?

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Speaker 1: Oh everything is so whoa can't even make jokes? Can

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Speaker 1: you take a joke? Drewski? Say what you want about Drewski.

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Speaker 1: He is very clever, because now I think the conversation

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Speaker 1: has kind of become like, oh, well, this is really

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Speaker 1: offensive and tonight as that conversation is really cresting about

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Speaker 1: people who are upset by this. Drewsky posted a picture

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Speaker 1: of him as a child with his grandfather. His grandfather

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Speaker 1: is white, so I can I feel like he's like, oh,

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Speaker 1: I hate white people. Explain this.

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Speaker 3: Oh wow.

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Speaker 1: I feel like he really thought the reaction through with

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Speaker 1: this one.

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Speaker 3: He set them up. He anticipated the like stolen valor

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Speaker 3: of being faux outraged by white face.

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Speaker 1: Yes.

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Speaker 2: Yes.

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Speaker 1: When I sat down to do this next segment about Sora,

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Speaker 1: I start, I wanted to start by saying, oh, I

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Speaker 1: hardly ever say I told you so. I'm not the

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Speaker 1: kind of person who goes back and says I can so,

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Speaker 1: I can see the face that you just made on

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Speaker 1: our recording software.

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Speaker 3: Yeah, why would you lie to our listeners like that?

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Speaker 1: So you know me in a different context, irl, so

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Speaker 1: you you might be able to say, like, Okay, well,

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Speaker 1: Bridget is not somebody who holds back on it.

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Speaker 4: And I told you so.

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Speaker 1: Often you live for it.

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Speaker 3: I told you so, Bridget. That's like your favorite thing.

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Speaker 1: I do love that I told you The only thing

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Speaker 1: you liked.

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Speaker 3: Better that it'll I told you so is like acting

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Speaker 3: above it, as if you aren't secretly relishing in it.

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Speaker 1: Sometimes the best I told you so is the is

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Speaker 1: the I told you so that you don't even need

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Speaker 1: to say where It's like they already know, they already know. Yeah,

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Speaker 1: Oh god. I do love being mayor of I told

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Speaker 1: you so Town. But I often will say on the

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Speaker 1: podcast like, oh, I'm not one to say I told

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Speaker 1: you so, But I realized that I'm usually saying that

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Speaker 1: before I put on my mare of I told you so,

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Speaker 1: par Sash and I.

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Speaker 3: Just kind of like sweeping the podium a little bit

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Speaker 3: before you step up and claim you're rightful I told

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Speaker 3: you so.

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Speaker 1: I kind of got that because if you listen to

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Speaker 1: Kara Swisher's podcast, she loves and I told you So.

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Speaker 1: And I was like, Oh, it's so. It's kind of

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Speaker 1: obnoxious to catalog all the times that you were right

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Speaker 1: about something. But I guess I do it too, So

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Speaker 1: I need to stop saying, oh, I never say and

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Speaker 1: I told you so, But this is and I told

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Speaker 1: you so. Because do you remember the conversation that you

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Speaker 1: and I had six months ago when Open Ai released Sora,

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Speaker 1: and all these people from people that I respected even

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Speaker 1: and trusted, were talking about how this was going to

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Speaker 1: be the thing that turned the tides on AI, that

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Speaker 1: that Sora was a mark for the idea that AI

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Speaker 1: was going to truly revolutionize film and television and creativity.

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Speaker 1: Do you remember those conversations?

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Speaker 3: I do. I do remember. It came out and it

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Speaker 3: was it made such a big splash. People were using it,

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Speaker 3: posting silly little videos. There was a lot of buzz.

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Speaker 3: All the media was talking about it, but not everyone

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Speaker 3: was so impressed.

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Speaker 1: Yes, so Soa, if you don't remember, the short lived fanfare,

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Speaker 1: was kind of like an AI TikTok. It was a

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Speaker 1: vertical scroll video platform that you could use AI to

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Speaker 1: make content. People genuinely acted like Soa was going to

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Speaker 1: be the thing in AI contact creation. I was not

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Speaker 1: so sure. Here was our prediction when it first came out.

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Speaker 1: And maybe I will eat my words. But I have

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Speaker 1: seen people say this platform is so fun and it's

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Speaker 1: such a joyful experience that it's going to rock social media,

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Speaker 1: and I just don't see it. I really just don't

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Speaker 1: see it again. Maybe I'll eat my words. I'm happy

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Speaker 1: to take this back if it turns out that I'm incorrect,

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Speaker 1: But it really reminds me of a few years ago

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Speaker 1: when a lot of folks are posting those AI generated

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Speaker 1: futuristic looking selfies on Facebook a few years ago. I

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Speaker 1: think the reason why people who are using it are

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Speaker 1: saying it's so fun, they're just excited to see themselves

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Speaker 1: and their friends in AI generated scenarios.

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Speaker 3: Do you know what I mean?

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Speaker 1: I don't think that that necessarily means that the kind

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Speaker 1: of content that is coming out of these platforms are interesting, unique,

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Speaker 1: the kind of thing that's actually going to hold someone's attention.

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Speaker 1: I mean, it just sounds like exactly what I thought

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Speaker 1: was going to happen. The novelty wore off rather quickly,

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Speaker 1: Sora's daily users dropped, and this week open Ai announced

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Speaker 1: that it was shutting down SOA after just six months.

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Speaker 3: Hard to believe it was so fast. It seems like

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Speaker 3: it was longer ago. But yeah, you really that. There

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Speaker 3: was so much buzz, and then after that initial splash,

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Speaker 3: I don't know that I ever even heard about it again.

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Speaker 1: No, there were a couple of interesting pieces I want

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Speaker 1: to say, in Business Insider or perhaps four or four

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Speaker 1: about some of the ways that people were using SOA

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Speaker 1: after that initial spike in interest, and it was all

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Speaker 1: just creepy slop, like making AI generated video after AI

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Speaker 1: generated video of women being strangled, women, videos where women

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Speaker 1: look grotesquely pregnant. All of these just like very weird, gross,

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Speaker 1: cheap AI generated slop, vaguely misogynistic content. That was really

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Speaker 1: what it was used for. And you'll remember that during

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Speaker 1: our episode about SOA's launch, we talked about how it

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Speaker 1: was using the likeness of real dead people and real

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Speaker 1: living public figures like Robin Williams and Martin Luther King Junior,

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Speaker 1: both of whom their families objected to them being used

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Speaker 1: in this way, and because they did not explicitly opt in,

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Speaker 1: and it was just like very easy to get around

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Speaker 1: whatever guardrails that they had put in there. People were

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Speaker 1: making Sora content, including copyrighted characters like Pikachu smoking a blunt,

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Speaker 1: or like Hitler SpongeBob, just like very weird stuff. And

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Speaker 1: then we had our whole little segue with Disney, because Disney,

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Speaker 1: which we know was a company that is notorious for

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Speaker 1: protecting their IP at all costs, took a very different

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Speaker 1: approach this time. Instead of going to court, they ended

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Speaker 1: up striking a deal, a billion dollar investment in Open Ai,

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Speaker 1: paired with a licensing agreement that would have let Sora

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Speaker 1: users generate videos using characters from Marvel, Pixar, Star Wars,

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Speaker 1: and Disney. And that was really the moment that I

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Speaker 1: felt like people were saying, this is gonna be a

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Speaker 1: defining moment in for AI in Hollywood. The fact that Disney,

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Speaker 1: rather than you know, what they usually do, which is

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Speaker 1: to protect their IP, was like, actually, we'll give you

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Speaker 1: a billion dollar and y'all gonna use whatever IP you want.

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Speaker 1: That To me, it's it seemed so strange to me.

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Speaker 3: What did Open AI get in return for this, you know,

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Speaker 3: giving a billion dollars of stock to Disney.

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Speaker 1: Tech Crunch reported that even after that deal, that deal

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Speaker 1: died and that no money actually changed hands before that

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Speaker 1: deal fell apart. When asked, Disney kept things very diplomatic.

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Speaker 1: They told Hollywood Reporter like, oh, we planned to continue

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Speaker 1: exploring partnerships with AI platforms going forward, but this particular partnership,

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Speaker 1: we're not doing that anymore. To your point, there was

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Speaker 1: a really good piece in FRO four that Will put

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Speaker 1: in the show notes that basically tried to answer my

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Speaker 1: big question, which is like why a company like Disney

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Speaker 1: that has been so protective of their copyright on IP

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Speaker 1: in Hollywood would suddenly open the doors to what run

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Speaker 1: writer called Sam Altman's plagiarism machine. And in the piece

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Speaker 1: they talk about how the only real explanation that makes

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Speaker 1: sense is that the studio executives just really really hate

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Speaker 1: paying for human labor. They hate it so much that

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Speaker 1: they maybe convince themselves that audiences would accept AI content

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Speaker 1: if it was like packaged the right way. But I

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Speaker 1: think to your point, when the use cases on Sora

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Speaker 1: are mostly low effort porny, kind of weird, gross stuff.

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Speaker 1: When you strip that away, there just really isn't a

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Speaker 1: lot there. As they noted in the piece for four

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Speaker 1: or four, that's just not something anybody really wants to

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Speaker 1: pay for.

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Speaker 3: Yeah, just the lowest common denominator slop that is the easiest,

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Speaker 3: cheapest to mass produce. Why would anybody pay for that?

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Speaker 3: Why would anybody even really want to look at it?

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Speaker 1: Yeah, there's been a lot of people on social media

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Speaker 1: complaining about this and saying I built up my sore

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Speaker 1: presence from zero. I have made hours and hours and

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Speaker 1: hours of AI generated content there, and then Sora just

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Speaker 1: makes this decision and it wipes out my entire portfolio

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Speaker 1: for have a better word overnight. And then they would

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Speaker 1: show some of the stuff that they would make, and

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Speaker 1: it's stuff, Yeah, it's stuff, Like it's a lot of

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Speaker 1: trademarked characters from different universes interacting in some way, right,

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Speaker 1: So it's Spider Man hanging out with SpongeBob square Pants

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Speaker 1: or something like that. And I saw this interesting conversation

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Speaker 1: on threads where somebody explained that it was essentially playing

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Speaker 1: with action figures. I remember playing with action figures, and

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Speaker 1: that the quote storylines that they come up with, it's

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Speaker 1: always like what if Ross from Friends hung out with

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Speaker 1: the Fresh Prince of bel Air, Like they're all the

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Speaker 1: most trite ideas imaginable that really are not cinematic and

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Speaker 1: not really good stories. And truly that my bedroom production

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Speaker 1: of two Barbies and a g I Joe Kiss was

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Speaker 1: on the same level as what they're coming up with.

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Speaker 1: It just happens to be a generated Yeah.

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Speaker 3: I think that's a great analogy for that slop because

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Speaker 3: that's it really gets it why people like characters in

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Speaker 3: the first place.

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Speaker 4: Right, Like.

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Speaker 3: Ross from Friends and the Fresh Prince of Beller, they

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Speaker 3: had good storytelling around them and like relationships with other characters.

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Speaker 3: What if you take that away, it's just trash exactly.

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Speaker 2: Let's take a quick break at our back.

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Speaker 1: Speaking of trash, let's check in on my favorite trash platform, Facebook. Mike,

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Speaker 1: I know you have been following some pretty big news

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Speaker 1: from Facebook this week.

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Speaker 3: Yeah, there were two, uh not just one, but two

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Speaker 3: big lawsuits against social media platforms this week. One was

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Speaker 3: against Meta and YouTube, which is owned by Google. The

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Speaker 3: other one was just against the Meta. The New York

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Speaker 3: Times and The Guardian and a bunch of other outlets

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Speaker 3: covered in both, and we'll put some links in the

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Speaker 3: show notes. Both of these I think are pretty landmark

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Speaker 3: cases that will be cited a lot as similar cases

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Speaker 3: continue to pop up and be heard by juris. So

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Speaker 3: just real quick what they actually were. Two different cases.

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Speaker 3: One was in California, brought by a young woman's named Kaylee,

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Speaker 3: who won her suit claiming the design choices by Meta

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Speaker 3: and YouTube contributed to her poor mental health. Tiktop and

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Speaker 3: Snap had also been originally named as play TIFFs when

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Speaker 3: she filed her lawsuit, but they settled out of court,

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Speaker 3: so they weren't part of this jury's decision, and now

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Speaker 3: that we know which way the jury went, that was

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Speaker 3: probably a wise move on their part. The other case

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Speaker 3: was in New Mexico and like quite different. So instead

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Speaker 3: of being brought by a single individual who was harmed,

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Speaker 3: in the New Mexico case, it was brought by the

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Speaker 3: state attorney general on behalf of young people across the state,

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Speaker 3: specifically claiming that design choices that Meta had made facilitated

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Speaker 3: the sexual exploitation of children. So we can break down

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Speaker 3: both of them. But first, I think it's important to

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Speaker 3: set up the context that these aren't just two random

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Speaker 3: court cases. There are literally thousands of similar lawsuits filed

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Speaker 3: across America, and these are the first two. And of

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Speaker 3: course I am not a lawyer, but this is something

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Speaker 3: that I'm pretty interested in, just because you know that

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Speaker 3: I have a history working in tobacco control, and so

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Speaker 3: there's a lot of parallels between what these types of

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Speaker 3: lawsuits being brought against social media companies and lawsuits that

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Speaker 3: people were bringing against the big tobacco companies in the

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Speaker 3: nineteen nineties.

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Speaker 1: I've seen so many people kind of compare this to

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Speaker 1: the moment that led us to the Master's settlement agreement

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Speaker 1: with big tobacco, that are are we hitting the watershed

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Speaker 1: moment of people speaking out against tech harms?

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Speaker 3: Yeah, and I think that's a very reasonable question, right

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Speaker 3: because these are the first two cases. There are thousands

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Speaker 3: more across the country. These are the first two, and

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Speaker 3: in both of them, the social media companies lost. Right,

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Speaker 3: So certainly not any sort of guarantee about how those

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Speaker 3: other cases are going to go, but it certainly does

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Speaker 3: demonstrate that they're not just a bunch of nonsense that's

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Speaker 3: going to be thrown out of court immediately. In both cases,

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Speaker 3: juris agreed that there is enough evidence to reasonably conclude

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Speaker 3: that these companies were aware their products were harming young people,

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Speaker 3: but allowed the harm to continue because it was profitable. Right,

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Speaker 3: So let's talk about these two cases. In the interest

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Speaker 3: of time, I'm just going to briefly go through Kiley's

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Speaker 3: case against Meta and Facebook, and then spent a little

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Speaker 3: bit more time on the other case. But so Kaylee,

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Speaker 3: she originally filed her lawsuit in California in twenty twenty three.

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Speaker 3: Today she's twenty years old, but she says she started

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Speaker 3: using social media back when she was six. During the trial,

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Speaker 3: she testified about using social media back then and how

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Speaker 3: as a child she treated it as a creative outlet

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Speaker 3: and escape from bullying at school. It was a really

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Speaker 3: big part of her life. She posted very often. For example,

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Speaker 3: she posted hundreds of photos on Instagram, and she used

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Speaker 3: a lot of beauty filters that the platform provided, which

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Speaker 3: she said magnified her insecurities and led to body dysmorphia,

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Speaker 3: which is something that we've talked about before on this show. Right,

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Speaker 3: it's not the first time anybody is hearing these ideas,

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Speaker 3: and so the jury believed her. The prosecution presented a

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Speaker 3: lot of evidence. The court ordered Meta to pay her

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Speaker 3: four point two million dollars in damages and ordered YouTube

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Speaker 3: to pay one point eight million.

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Speaker 1: That is not a lot of money for these companies.

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Speaker 1: That is pocket change for these companies.

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Speaker 3: Yes, that is complete pocket change for these companies, just

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Speaker 3: like a drop in the bucket for them. In the

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Speaker 3: during the trial, Kayley's lawyer held up a jar full

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Speaker 3: of em and ms to talk to the jury about

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Speaker 3: what the damages should be awarded. He compared the jar

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Speaker 3: full of eminems. Who's like, each of these eminem's is

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Speaker 3: a million dollars. You could take a handful out and

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Speaker 3: it's not even going to make any difference. Trying to

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Speaker 3: make that same point. So, let's right, the artists a

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Speaker 3: drop in the bucket for these companies. But it does

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Speaker 3: set precedent for the thousands of other cases that are pending,

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Speaker 3: and so we'll just have to wait and see what

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Speaker 3: happens with all of them.

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Speaker 1: Okay, So what about the case in New Mexico.

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Speaker 3: So the New Mexico case was filed against Meta, specifically

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Speaker 3: just Meta by the States to turn General in May

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Speaker 3: of twenty twenty three, who accused it of misleading its

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Speaker 3: users about safety. The evidence that they submitted included both

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Speaker 3: private and public statements by staff and executives at Meta.

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Speaker 3: It also included evidence that state investigators had collected by

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Speaker 3: posing his underage kids online, documenting cases where they were

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Speaker 3: sexually solicited on the platform. The lawsuit also cited a

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Speaker 3: two year investigation of Meta that The Guardian had published

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Speaker 3: in April of twenty twenty three, which found that Facebook

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Speaker 3: and Instagram had both become marketplaces for child sex trafficking.

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Speaker 3: So it was just a lot of evidence that the

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Speaker 3: prosecutors had been accumulating and working on for a very

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Speaker 3: long time. Former Facebook engineering director Arturo Behar, who designed

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Speaker 3: social media safety features for the platform before leaving in

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Speaker 3: twenty twenty one, he testified as part of the trial.

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Speaker 1: When we were first covering this trial early on. We've

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Speaker 1: actually played his testimony to Congress on the podcast before.

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Speaker 1: I would recommend people listen to what he has to say.

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Speaker 1: He's just a very he just makes thought. I thought

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Speaker 1: his testimony was very compelling.

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Speaker 3: Yes, he's a very compelling speaker, and he like clearly

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Speaker 3: cares a lot about this issue after working at Meta

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Speaker 3: for many years and then becoming a whistleblower. We'll definitely

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Speaker 3: link to that episode so people can listen to his

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Speaker 3: testimony that he gave to Congress back in twenty twenty three.

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Speaker 3: But his story is that he said, you know, he

435
00:25:28,200 --> 00:25:32,240
Speaker 3: was in charge of building safety features and then he

436
00:25:32,320 --> 00:25:35,160
Speaker 3: felt that safety was just not being taken seriously at Meta,

437
00:25:35,600 --> 00:25:39,920
Speaker 3: that the company often prioritized safety theater over actually making

438
00:25:39,960 --> 00:25:44,040
Speaker 3: the product safe. He says that he initially realized there's

439
00:25:44,080 --> 00:25:47,119
Speaker 3: a problem when his young daughter showed him the deluge

440
00:25:47,119 --> 00:25:51,320
Speaker 3: of sexual solicitations and content on her Facebook account, and

441
00:25:51,400 --> 00:25:53,120
Speaker 3: she told him that it was the same for all

442
00:25:53,160 --> 00:25:55,919
Speaker 3: of her friends, that they didn't even bother reporting it

443
00:25:55,960 --> 00:26:00,199
Speaker 3: because they knew that nothing would be done. So from

444
00:26:00,240 --> 00:26:04,720
Speaker 3: the stand during this trial, part of his testimony was

445
00:26:04,760 --> 00:26:08,240
Speaker 3: that he gave a statement that quote, we don't need

446
00:26:08,280 --> 00:26:10,879
Speaker 3: to live in a world where an unsolicited penis picture

447
00:26:10,960 --> 00:26:13,680
Speaker 3: is something that you just dismissed because it happens end.

448
00:26:13,760 --> 00:26:21,040
Speaker 3: Quote Yeah, like pretty powerful stuff, right. He's a compelling speaker,

449
00:26:21,600 --> 00:26:24,040
Speaker 3: and after he was done, Metas lawyers didn't even bother

450
00:26:24,080 --> 00:26:28,040
Speaker 3: a cross examining him. The jury found Facebook liable and

451
00:26:28,600 --> 00:26:31,159
Speaker 3: find the company three hundred and seventy five million dollars

452
00:26:31,200 --> 00:26:36,360
Speaker 3: in damages for violating New Mexico consumer protection laws. So that's,

453
00:26:36,720 --> 00:26:40,040
Speaker 3: you know, two orders of magnitude more than Kaylee got

454
00:26:40,119 --> 00:26:41,800
Speaker 3: already right there in New Mexico.

455
00:26:42,240 --> 00:26:44,320
Speaker 1: But it's still a drop in the bucket for a

456
00:26:44,359 --> 00:26:45,440
Speaker 1: company like Facebook.

457
00:26:45,920 --> 00:26:50,080
Speaker 3: It's still a drop in the bucket drop, but it's

458
00:26:50,080 --> 00:26:54,479
Speaker 3: a bigger Yeah, that's right. They probably make that amount

459
00:26:54,480 --> 00:26:58,920
Speaker 3: of revenue in like a couple hours. It's going to

460
00:26:58,960 --> 00:27:01,440
Speaker 3: be a long process. We are going to be hearing

461
00:27:01,480 --> 00:27:04,600
Speaker 3: about great cases like this for a long time, with

462
00:27:04,720 --> 00:27:05,720
Speaker 3: increasing frequency.

463
00:27:05,720 --> 00:27:06,320
Speaker 1: I think.

464
00:27:07,680 --> 00:27:11,040
Speaker 3: One notable thing about this case is that the company

465
00:27:11,200 --> 00:27:15,040
Speaker 3: wasn't able to hide behind Section two thirty. So, as

466
00:27:15,119 --> 00:27:18,040
Speaker 3: you know, Section two thirty is a controversial law that

467
00:27:18,119 --> 00:27:21,719
Speaker 3: shields social media companies from liability for content posted by

468
00:27:21,720 --> 00:27:24,200
Speaker 3: their users. We've had people in the show who have

469
00:27:24,440 --> 00:27:27,800
Speaker 3: argued that it needs to be reformed. We've had other

470
00:27:27,800 --> 00:27:30,320
Speaker 3: people on the show who have argued that it needs

471
00:27:30,359 --> 00:27:32,600
Speaker 3: to be left alone because it is an essential pillar

472
00:27:32,680 --> 00:27:36,000
Speaker 3: of a free and open internet. In my opinion, very

473
00:27:36,040 --> 00:27:38,800
Speaker 3: reasonable people disagree sharply on it.

474
00:27:39,280 --> 00:27:42,600
Speaker 1: Yeah, I am in a lot of coalitions and working

475
00:27:42,600 --> 00:27:46,760
Speaker 1: groups for Internet safety, and for the most part, we

476
00:27:46,840 --> 00:27:49,480
Speaker 1: all sort of agree on you know, we all have

477
00:27:49,720 --> 00:27:52,600
Speaker 1: a similar orientation for whatever reason, when it comes to

478
00:27:52,640 --> 00:27:57,480
Speaker 1: Section two thirty. I have been surprised how much disagreement

479
00:27:57,600 --> 00:28:01,159
Speaker 1: there is among people who like broughtly advocate for the

480
00:28:01,160 --> 00:28:01,720
Speaker 1: same things.

481
00:28:02,200 --> 00:28:04,520
Speaker 3: And that's a whole other show, right, Like we've had

482
00:28:04,560 --> 00:28:06,679
Speaker 3: shows on it, we probably will have shows in the

483
00:28:06,720 --> 00:28:10,560
Speaker 3: future about Section two thirty. But notably in this lawsuit,

484
00:28:11,200 --> 00:28:18,200
Speaker 3: the prosecutors stepped around it completely, right, because in this case,

485
00:28:18,520 --> 00:28:21,480
Speaker 3: it wasn't the content that was on trial. It was

486
00:28:21,520 --> 00:28:26,000
Speaker 3: the way Meta had designed their apps to be addictive,

487
00:28:26,600 --> 00:28:29,040
Speaker 3: and the fact that they knew that they were harmful

488
00:28:29,680 --> 00:28:35,879
Speaker 3: and deliberately tried to mislead the public about that harm.

489
00:28:36,320 --> 00:28:38,160
Speaker 3: That is what was on trial here.

490
00:28:38,800 --> 00:28:41,040
Speaker 1: Yes, and Mike, you and I were out for a

491
00:28:41,120 --> 00:28:43,640
Speaker 1: drink the other night, and I won't say we got

492
00:28:43,640 --> 00:28:48,600
Speaker 1: into an argument. We had a discussion about this. I

493
00:28:48,640 --> 00:28:56,000
Speaker 1: take issue sometimes with the framing of social media as addictive.

494
00:28:56,120 --> 00:28:59,520
Speaker 1: I guess per this case, it has set a legal

495
00:29:00,320 --> 00:29:05,000
Speaker 1: precedent that, legally speaking, a jury has found that these

496
00:29:05,040 --> 00:29:09,080
Speaker 1: design choices were addictive, and I know that this is

497
00:29:09,120 --> 00:29:12,440
Speaker 1: something that you have a background in as like working

498
00:29:12,480 --> 00:29:14,160
Speaker 1: with addiction, studying addiction.

499
00:29:14,720 --> 00:29:17,800
Speaker 3: That's right. I've spent more than a decade working in

500
00:29:18,160 --> 00:29:22,880
Speaker 3: tobacco and nicotine addiction, you know, doing building apps to

501
00:29:22,880 --> 00:29:26,880
Speaker 3: help people with treatment, doing research on policies, research on addiction.

502
00:29:27,640 --> 00:29:31,360
Speaker 3: So it is something that I know a bit about.

503
00:29:31,600 --> 00:29:37,280
Speaker 3: And one of the things that really struck me as

504
00:29:37,320 --> 00:29:39,720
Speaker 3: I got into this field is that there is not

505
00:29:39,960 --> 00:29:43,840
Speaker 3: a single accepted definition of addiction or what it means

506
00:29:43,840 --> 00:29:49,880
Speaker 3: to be addicted. There just isn't. And I think personally,

507
00:29:49,880 --> 00:29:54,200
Speaker 3: one of the best definitions of addiction that I've found

508
00:29:54,840 --> 00:29:58,480
Speaker 3: is framing it as a learning problem where a person

509
00:29:58,640 --> 00:30:03,480
Speaker 3: keeps doing something despite being harmed by it, and for

510
00:30:03,520 --> 00:30:07,160
Speaker 3: whatever reason, just keeps doing it. Because that's different from

511
00:30:07,200 --> 00:30:10,640
Speaker 3: most behaviors, right Like, humans are really good at learning.

512
00:30:11,200 --> 00:30:14,760
Speaker 3: If a little baby touches a hot stove, it quickly

513
00:30:14,840 --> 00:30:17,920
Speaker 3: learns that stoves are hot, and it hurts, and hopefully

514
00:30:17,920 --> 00:30:21,120
Speaker 3: the baby's going to be okay, but it's almost certainly

515
00:30:21,200 --> 00:30:22,840
Speaker 3: not going to touch a stove again. Right Like, that's

516
00:30:22,840 --> 00:30:27,560
Speaker 3: a very sharp learning signal that doing this behavior leads

517
00:30:27,600 --> 00:30:31,600
Speaker 3: to bad consequences, and so an addiction, the same sort

518
00:30:31,640 --> 00:30:35,080
Speaker 3: of thing is going on. There's this behavior that leaves

519
00:30:35,080 --> 00:30:38,160
Speaker 3: to bad consequences, and yet the person just keeps doing

520
00:30:38,200 --> 00:30:42,000
Speaker 3: it and fails to learn that actually it would be

521
00:30:42,000 --> 00:30:44,760
Speaker 3: better to not do it. And you know, of course,

522
00:30:44,800 --> 00:30:49,840
Speaker 3: it's very complicated, and addiction is used to describe a

523
00:30:49,880 --> 00:30:54,120
Speaker 3: whole range of things, and some addictions are obviously so

524
00:30:54,240 --> 00:30:57,880
Speaker 3: much stronger and also so much more devastating than others.

525
00:30:58,640 --> 00:31:02,680
Speaker 3: And thinking about behaviors that are addictive, I think is

526
00:31:02,760 --> 00:31:07,240
Speaker 3: often more useful than questions of like, well is someone

527
00:31:07,240 --> 00:31:11,160
Speaker 3: addicted or not? And trying to put things in categories

528
00:31:11,200 --> 00:31:14,160
Speaker 3: of like this is something to which one can be

529
00:31:14,200 --> 00:31:17,400
Speaker 3: addicted and this is something to which it's not possible

530
00:31:17,440 --> 00:31:20,240
Speaker 3: to be addicted. I don't think that ladder framing is

531
00:31:20,320 --> 00:31:22,480
Speaker 3: very helpful. That's a really good distinction.

532
00:31:22,640 --> 00:31:26,480
Speaker 1: So it's not even really necessarily a question of can

533
00:31:26,600 --> 00:31:30,959
Speaker 1: someone be addicted to social media? The question really is

534
00:31:31,400 --> 00:31:36,880
Speaker 1: did this company create platforms knowing that they might present

535
00:31:37,040 --> 00:31:40,840
Speaker 1: in addiction like behaviors in their users? So it's not

536
00:31:40,880 --> 00:31:44,120
Speaker 1: really it's a small distinction, but I see what you mean.

537
00:31:45,040 --> 00:31:52,040
Speaker 3: Yeah, did they design their product? Facebook and Instagram to

538
00:31:52,320 --> 00:31:59,000
Speaker 3: make users, in particular kids just want to keep using

539
00:31:59,040 --> 00:32:01,760
Speaker 3: them more and more, or even though it was harmful

540
00:32:01,800 --> 00:32:04,560
Speaker 3: to those kids, and did they go out of their

541
00:32:04,560 --> 00:32:07,720
Speaker 3: way to hide that harm. A lot of the internal

542
00:32:07,760 --> 00:32:12,360
Speaker 3: documents that were submitted as evidence, from memos and emails

543
00:32:12,360 --> 00:32:15,240
Speaker 3: that Facebook folks had sent to each other within the company,

544
00:32:15,280 --> 00:32:18,960
Speaker 3: as well as some public statements, a lot of them

545
00:32:19,120 --> 00:32:21,960
Speaker 3: made the case that Facebook was indeed designing its products

546
00:32:21,960 --> 00:32:27,120
Speaker 3: to maximize engagement, even when people on the team pushed back.

547
00:32:27,160 --> 00:32:31,320
Speaker 3: In some cases, the directives from up top were to

548
00:32:31,440 --> 00:32:34,040
Speaker 3: keep pushing to maximize engagement, get children to be using

549
00:32:34,080 --> 00:32:37,480
Speaker 3: it more and more, in some cases using tactics that

550
00:32:37,520 --> 00:32:41,280
Speaker 3: were similar to tactics that casinos used to keep people gambling,

551
00:32:41,760 --> 00:32:43,760
Speaker 3: except in this case they were using them on kids,

552
00:32:43,800 --> 00:32:47,360
Speaker 3: designing products to keep the kids more and more engaged,

553
00:32:48,120 --> 00:32:50,840
Speaker 3: holding their attention in the apps so that it could

554
00:32:50,880 --> 00:32:54,880
Speaker 3: sell more ads. That's one of the big connections between

555
00:32:54,920 --> 00:32:57,240
Speaker 3: this case and the case against the big tobacco companies,

556
00:32:57,280 --> 00:33:00,000
Speaker 3: the idea that the executives at the company were intentionally

557
00:33:00,120 --> 00:33:06,120
Speaker 3: designing their product to be addictive to keep the user

558
00:33:06,280 --> 00:33:09,640
Speaker 3: using it over and over again. Another similarity is that

559
00:33:09,720 --> 00:33:12,920
Speaker 3: prosecutors didn't just claim that Meta had harmed people, but

560
00:33:13,120 --> 00:33:15,200
Speaker 3: also claimed that the company had taken steps to hide

561
00:33:15,240 --> 00:33:18,479
Speaker 3: that harm from the public. Evidence for that included some

562
00:33:18,600 --> 00:33:21,480
Speaker 3: internal meta documents about the results of an internal study

563
00:33:21,480 --> 00:33:24,120
Speaker 3: of the company had conducted to measure the mental health

564
00:33:24,120 --> 00:33:28,560
Speaker 3: effects of disconnecting from Facebook. The study found that quote,

565
00:33:28,960 --> 00:33:32,080
Speaker 3: people who stopped using Facebook for a week reported lower

566
00:33:32,120 --> 00:33:37,240
Speaker 3: feelings of depression, anxiety, loathliness, and social comparison end quote,

567
00:33:37,400 --> 00:33:41,200
Speaker 3: according to internal meta documents. So they knew that this

568
00:33:41,320 --> 00:33:43,959
Speaker 3: was their own study that found that harm, but they

569
00:33:44,000 --> 00:33:50,800
Speaker 3: never published that study. They buried it and period the end.

570
00:33:51,080 --> 00:33:53,880
Speaker 1: You know, Yeah, I guess I'm not surprised by the

571
00:33:53,880 --> 00:33:56,920
Speaker 1: fact that Facebook did not put that study out about

572
00:33:56,920 --> 00:33:57,960
Speaker 1: their own products.

573
00:33:58,480 --> 00:33:58,680
Speaker 4: Yeah.

574
00:33:58,720 --> 00:34:01,719
Speaker 3: I mean, either it's not so, and the fact that

575
00:34:01,760 --> 00:34:05,240
Speaker 3: it's not surprising kind of reminds me of Behar's daughter

576
00:34:05,320 --> 00:34:08,160
Speaker 3: and her friends, who were like, oh, yeah, of course

577
00:34:08,200 --> 00:34:12,960
Speaker 3: people are showing us penises, like we've normalized the idea

578
00:34:13,000 --> 00:34:17,440
Speaker 3: that a company like Facebook could have this direct line

579
00:34:17,760 --> 00:34:23,960
Speaker 3: to children, to teens, to adults to elderly people and

580
00:34:24,400 --> 00:34:29,239
Speaker 3: have so much potential to influence the information that they

581
00:34:29,280 --> 00:34:31,160
Speaker 3: see and what they're exposed to and the people that

582
00:34:31,200 --> 00:34:35,160
Speaker 3: they're exposed to, and they could just do this study

583
00:34:35,400 --> 00:34:37,879
Speaker 3: and it could show harm and they could just bury it.

584
00:34:37,960 --> 00:34:44,920
Speaker 3: Like that's bananas, right, But that's unfortunately the world we

585
00:34:44,960 --> 00:34:48,600
Speaker 3: increasingly live in as companies like Meta have shut down

586
00:34:48,680 --> 00:34:50,759
Speaker 3: research or access and made it harder and harder for

587
00:34:51,120 --> 00:34:55,840
Speaker 3: outside sources to measure what's going on with them just

588
00:34:55,880 --> 00:35:00,000
Speaker 3: making more challenging and protecting themselves and their ability to

589
00:35:00,000 --> 00:35:04,000
Speaker 3: design products. However, they want more and more. It's a

590
00:35:04,000 --> 00:35:06,319
Speaker 3: little bit of a digression, but I just wanted to

591
00:35:06,320 --> 00:35:07,439
Speaker 3: get that in there too.

592
00:35:07,719 --> 00:35:14,279
Speaker 1: Yes, So I don't think anybody listening thinks that I

593
00:35:14,320 --> 00:35:19,520
Speaker 1: am pro big tech, pro Facebook. I think that if

594
00:35:19,560 --> 00:35:25,000
Speaker 1: Facebook wanted to make safer products, they would shut down,

595
00:35:25,080 --> 00:35:32,719
Speaker 1: they would stop existing. I am no friend to Mark Zuckerberg. However,

596
00:35:33,719 --> 00:35:37,239
Speaker 1: I am very cautious about some of the precedent that

597
00:35:37,320 --> 00:35:41,760
Speaker 1: I think this is setting. Facebook hurts people, They hurt kids.

598
00:35:41,800 --> 00:35:44,880
Speaker 1: They profit from harming people. That is not in dispute,

599
00:35:44,920 --> 00:35:47,520
Speaker 1: that as a fact, And I think that anybody who

600
00:35:47,960 --> 00:35:51,160
Speaker 1: has been harmed by them should get paid for it

601
00:35:51,200 --> 00:35:53,560
Speaker 1: because that harm should is real and like should have

602
00:35:53,600 --> 00:35:58,480
Speaker 1: a monetary cost, because Facebook certainly made a monetary profit

603
00:35:58,600 --> 00:36:03,560
Speaker 1: from that harm and trafficking and that harm. However, I

604
00:36:03,760 --> 00:36:08,920
Speaker 1: believe that a lot of these cases are so easily

605
00:36:09,080 --> 00:36:14,399
Speaker 1: turned into more fodder for the way that elected officials

606
00:36:14,520 --> 00:36:17,759
Speaker 1: are really chopping at the bit to age gate and

607
00:36:17,920 --> 00:36:24,560
Speaker 1: restrict an open, free Internet. I do not think that

608
00:36:24,640 --> 00:36:27,480
Speaker 1: Facebook and big tech company should be allowed to get

609
00:36:27,480 --> 00:36:30,239
Speaker 1: away with harm without any kind of accountability. Absolutely not.

610
00:36:31,160 --> 00:36:35,520
Speaker 1: But I'm just incredibly touchy about the This just to

611
00:36:35,560 --> 00:36:41,200
Speaker 1: me seems like it fits so neatly into the hands

612
00:36:41,320 --> 00:36:45,880
Speaker 1: of a right wing elected official who wants to restrict

613
00:36:45,920 --> 00:36:50,600
Speaker 1: the Internet from young people and will use that to say,

614
00:36:50,719 --> 00:36:52,560
Speaker 1: and we need to make sure that they don't see

615
00:36:52,560 --> 00:36:56,840
Speaker 1: any content about queerness or transness or anything that we

616
00:36:56,920 --> 00:37:00,640
Speaker 1: deem a harm to kids. And so, while I'm certainly

617
00:37:00,680 --> 00:37:03,640
Speaker 1: not crying Henny tears for Facebook and Mark Zuckerberg and

618
00:37:03,680 --> 00:37:06,440
Speaker 1: Meta and YouTube and Google here, I just wanted to

619
00:37:06,520 --> 00:37:11,840
Speaker 1: say that because I think a lot of the plaintiffs

620
00:37:11,880 --> 00:37:15,719
Speaker 1: in these cases are very sympathetic, like rightly so understandably so.

621
00:37:16,560 --> 00:37:20,440
Speaker 1: And I just don't want people to see these cases

622
00:37:20,520 --> 00:37:23,600
Speaker 1: and say and get on board with the growing chorus

623
00:37:23,640 --> 00:37:26,440
Speaker 1: of voices that are trying to restrict an open internet,

624
00:37:26,600 --> 00:37:29,000
Speaker 1: especially for young people, because the Internet is a lifeline

625
00:37:29,000 --> 00:37:31,560
Speaker 1: for young people, particularly marginalized young people, And so I

626
00:37:31,600 --> 00:37:32,960
Speaker 1: don't want to make I don't want anyone to think

627
00:37:32,960 --> 00:37:36,080
Speaker 1: that I'm saying Facebook is good and kids need to

628
00:37:36,120 --> 00:37:42,279
Speaker 1: have unrestricted access to these platforms that we know are harmful.

629
00:37:42,320 --> 00:37:44,480
Speaker 1: Like it's not in dispute, but I just wanted to

630
00:37:44,520 --> 00:37:49,239
Speaker 1: say that context because I think it. I just can

631
00:37:49,280 --> 00:37:51,120
Speaker 1: sort of see the writing on the wall of how

632
00:37:51,200 --> 00:37:53,160
Speaker 1: easily this would play into the way that a lot

633
00:37:53,200 --> 00:37:55,040
Speaker 1: of people who are trying to restrict the Internet right

634
00:37:55,040 --> 00:37:57,520
Speaker 1: now and successfully so in a lot of cases, are

635
00:37:57,560 --> 00:37:59,480
Speaker 1: thinking about it and thinking about how they can publicly

636
00:37:59,480 --> 00:38:01,040
Speaker 1: message aroun that. Does that make sense?

637
00:38:01,600 --> 00:38:05,879
Speaker 3: It does make sense, and I think that's absolutely a concern, right.

638
00:38:06,000 --> 00:38:12,040
Speaker 3: I think what these One of the effects of these

639
00:38:12,120 --> 00:38:19,080
Speaker 3: cases is that it increases pressure on the tech companies

640
00:38:19,640 --> 00:38:26,000
Speaker 3: to do something about this problem. And it also I

641
00:38:26,040 --> 00:38:31,520
Speaker 3: think increases pressure on regulators and legislators to do something

642
00:38:31,520 --> 00:38:36,879
Speaker 3: about the problem, and also, to your point, gives them

643
00:38:37,000 --> 00:38:44,799
Speaker 3: more fodder and momentum behind policies that might be misguided

644
00:38:45,040 --> 00:38:49,879
Speaker 3: and maybe are more interested in, you know, staging the

645
00:38:50,080 --> 00:38:54,000
Speaker 3: safety theater for themselves, or using this as like a

646
00:38:54,040 --> 00:38:58,360
Speaker 3: trojan horse to crack down on content for morality reasons

647
00:38:58,600 --> 00:39:02,759
Speaker 3: or because it's too queer or two trends, or just

648
00:39:02,800 --> 00:39:04,520
Speaker 3: like content that they don't like. I think that's an

649
00:39:04,600 --> 00:39:07,200
Speaker 3: absolutely valid concern.

650
00:39:08,560 --> 00:39:13,160
Speaker 1: And right now you have Meta really angling to be

651
00:39:13,400 --> 00:39:17,000
Speaker 1: the one to set what these policies look like. Metta

652
00:39:17,440 --> 00:39:20,719
Speaker 1: is really playing a very effective pr trick on a

653
00:39:20,719 --> 00:39:22,360
Speaker 1: lot of us right now by being like, oh, we

654
00:39:22,440 --> 00:39:26,560
Speaker 1: are so interested in like please regulate us. You want

655
00:39:26,600 --> 00:39:28,960
Speaker 1: to come to the table with some guidelines and regulations.

656
00:39:29,120 --> 00:39:31,480
Speaker 1: And I actually don't think that Meta should be able

657
00:39:31,560 --> 00:39:34,480
Speaker 1: to design and set the rule book for how they

658
00:39:34,480 --> 00:39:37,040
Speaker 1: will behave they obviously can't be trusted. And right now

659
00:39:37,080 --> 00:39:41,800
Speaker 1: I think that like we're all sort of watching as

660
00:39:42,239 --> 00:39:47,200
Speaker 1: these very tidy ways of big tech continuing to harm

661
00:39:47,280 --> 00:39:51,160
Speaker 1: our kids for profit while advocating for the companies to

662
00:39:51,200 --> 00:39:53,320
Speaker 1: be the fox is watching the henhouse.

663
00:39:53,560 --> 00:39:58,440
Speaker 3: Absolutely, and I'm glad you brought that up, because that

664
00:39:58,600 --> 00:40:02,120
Speaker 3: absolutely is another pair of to big tobacco. Right Like,

665
00:40:03,200 --> 00:40:05,319
Speaker 3: once it was clear to the big tobacco companies that

666
00:40:05,360 --> 00:40:08,560
Speaker 3: they were going to lose and that regulation was going

667
00:40:08,640 --> 00:40:11,920
Speaker 3: to be the new order of the day. They pivoted

668
00:40:12,000 --> 00:40:17,600
Speaker 3: and they weaponized the regulation to effectively cement their monopolies

669
00:40:18,960 --> 00:40:22,920
Speaker 3: and prevent other competitors from breaking in. So that's absolutely

670
00:40:22,960 --> 00:40:25,480
Speaker 3: a risk, and they're definitely going to try to do that.

671
00:40:25,560 --> 00:40:27,879
Speaker 3: Like you say, Facebook is already trying to do that

672
00:40:28,600 --> 00:40:33,600
Speaker 3: with its big push and lobbying effort for its age gating.

673
00:40:33,920 --> 00:40:38,319
Speaker 3: That is doomed to fail, right, And so there are

674
00:40:38,400 --> 00:40:43,840
Speaker 3: people in Congress who have introduced alternative approaches that, in

675
00:40:43,920 --> 00:40:47,640
Speaker 3: my opinion, are much more reasonable, much more workable, and

676
00:40:48,120 --> 00:40:53,960
Speaker 3: do not and importantly do not put the power to

677
00:40:54,080 --> 00:40:56,680
Speaker 3: be making these decisions in the hands of the companies

678
00:40:56,680 --> 00:40:58,880
Speaker 3: who are supposed to be policing themselves, which is like

679
00:40:59,000 --> 00:41:03,880
Speaker 3: a joke. So yeah, I hear you that these cases

680
00:41:05,040 --> 00:41:09,520
Speaker 3: I think will be fodder for people who want to

681
00:41:09,560 --> 00:41:16,000
Speaker 3: pass bad laws and create bad policies. But I don't

682
00:41:16,040 --> 00:41:19,480
Speaker 3: think that's the fault of the cases. I think that's

683
00:41:19,520 --> 00:41:22,240
Speaker 3: just like a natural thing that is going to happen

684
00:41:22,440 --> 00:41:27,160
Speaker 3: as these companies continue to become more and more powerful

685
00:41:27,360 --> 00:41:35,960
Speaker 3: and and members of the public demand something be done right,

686
00:41:36,000 --> 00:41:39,839
Speaker 3: and so hopefully we can collectively do something smart and

687
00:41:39,880 --> 00:41:41,480
Speaker 3: not something stupid or harmful.

688
00:41:41,640 --> 00:41:44,160
Speaker 1: Yes, so we know these companies are going to appeal,

689
00:41:44,320 --> 00:41:47,319
Speaker 1: so we will keep you posted on the latest when

690
00:41:47,360 --> 00:41:47,839
Speaker 1: we get it.

691
00:41:50,800 --> 00:42:05,040
Speaker 2: More. After a quick break, let's get right back into it.

692
00:42:09,360 --> 00:42:12,400
Speaker 1: Okay. So there is this romantic comedy movie coming to

693
00:42:12,480 --> 00:42:16,840
Speaker 1: theaters called You, Me and Tuscany. It stars Hallie Bailey

694
00:42:16,960 --> 00:42:20,160
Speaker 1: and it's about a black woman who impulsively jets off

695
00:42:20,160 --> 00:42:23,600
Speaker 1: to Tuscany to find love. It actually has Na Vardos

696
00:42:23,640 --> 00:42:26,160
Speaker 1: from that movie My Big Bat Greek Wedding in it too,

697
00:42:26,200 --> 00:42:28,680
Speaker 1: who I love. It does not have a black director,

698
00:42:28,760 --> 00:42:32,680
Speaker 1: but it has two black leads. I am not a

699
00:42:32,719 --> 00:42:35,600
Speaker 1: big rom com person, like I will watch them if

700
00:42:35,600 --> 00:42:39,000
Speaker 1: I am hungover on the couch and How to Lose

701
00:42:39,040 --> 00:42:41,719
Speaker 1: a Guy and ten Days is on TBS, I'll watch it.

702
00:42:41,719 --> 00:42:44,560
Speaker 1: But I'm not a big rom com person. But even

703
00:42:44,680 --> 00:42:47,239
Speaker 1: I thought, oh, this could be a cute movie. So

704
00:42:47,800 --> 00:42:51,560
Speaker 1: Nina Lee is a black woman filmmaker who has no

705
00:42:51,719 --> 00:42:55,080
Speaker 1: connection whatsoever to that movie that I just described, You

706
00:42:55,160 --> 00:42:58,440
Speaker 1: Me and Tuscany, But she is posting on social media

707
00:42:59,239 --> 00:43:02,200
Speaker 1: begging people to go see this movie that she has

708
00:43:02,200 --> 00:43:06,759
Speaker 1: nothing to do with. Why Because Nina says that two

709
00:43:06,840 --> 00:43:10,960
Speaker 1: of her film projects are currently stuck in limbo because

710
00:43:11,000 --> 00:43:15,000
Speaker 1: she has been told by film studio executives that they're

711
00:43:15,040 --> 00:43:19,560
Speaker 1: holding to see whether that movie You Me and Tuscany

712
00:43:20,200 --> 00:43:23,959
Speaker 1: is going to be successful. If that rom com does

713
00:43:24,080 --> 00:43:26,759
Speaker 1: well at the box office, they said that they can

714
00:43:26,760 --> 00:43:30,600
Speaker 1: move forward with her movie. If it doesn't, it might

715
00:43:30,640 --> 00:43:34,040
Speaker 1: not go forward. So that filmmaker Nina Lee tweeted, please

716
00:43:34,080 --> 00:43:37,600
Speaker 1: go see this film about You Me and Tuscany. I

717
00:43:37,680 --> 00:43:40,359
Speaker 1: met with a studio about my already shot rom com

718
00:43:40,400 --> 00:43:42,279
Speaker 1: and they won't buy it until they see how You

719
00:43:42,400 --> 00:43:44,960
Speaker 1: Me and Tuscany does. Met with an executive about a

720
00:43:45,040 --> 00:43:47,480
Speaker 1: romance script I have. They won't buy it until they

721
00:43:47,520 --> 00:43:51,239
Speaker 1: see how You Me and Tuscany does. A film that

722
00:43:51,280 --> 00:43:53,720
Speaker 1: has nothing to do with me could quite literally change

723
00:43:53,800 --> 00:43:55,880
Speaker 1: my life. Plus, I've heard it's really great, so I'm

724
00:43:55,920 --> 00:43:59,640
Speaker 1: looking forward to supporting. She also cited the RAPS coverage

725
00:43:59,719 --> 00:44:03,520
Speaker 1: of the twenty twenty five Reframe Report, which is basically

726
00:44:03,560 --> 00:44:08,319
Speaker 1: a report showing how inclusive the film industry is and

727
00:44:09,239 --> 00:44:12,319
Speaker 1: the news is not good. The report showed the backsliding

728
00:44:12,360 --> 00:44:15,839
Speaker 1: and the hiring of women for key roles among Hollywood's

729
00:44:15,880 --> 00:44:20,799
Speaker 1: top films last year. Notably, women directed films dropped to

730
00:44:20,920 --> 00:44:23,760
Speaker 1: just eleven out of the top one hundred, the lowest

731
00:44:23,840 --> 00:44:27,240
Speaker 1: number since twenty nineteen, and a sharp fall from twenty

732
00:44:27,280 --> 00:44:30,840
Speaker 1: to twenty twenty three. Female's central roles fell from fifty

733
00:44:30,880 --> 00:44:32,920
Speaker 1: one and twenty twenty four to thirty nine and twenty

734
00:44:32,960 --> 00:44:36,120
Speaker 1: twenty five, only seven of those roles went to women

735
00:44:36,120 --> 00:44:40,280
Speaker 1: of color, the lowest count in that category since twenty eighteen.

736
00:44:40,760 --> 00:44:46,080
Speaker 1: So yes, it just seems like whatever gaines marginalized folks

737
00:44:46,080 --> 00:44:48,920
Speaker 1: were making in Hollywood, and we have made serious gains,

738
00:44:49,360 --> 00:44:53,080
Speaker 1: we are really backsliding. And thus Nina Lee is like,

739
00:44:53,280 --> 00:44:58,279
Speaker 1: please see this movie, because studio executives are saying that

740
00:44:58,320 --> 00:45:01,239
Speaker 1: they're waiting to see how this won rom com does

741
00:45:01,320 --> 00:45:05,759
Speaker 1: before they greenlight other movies with diverse casts. And I've

742
00:45:05,800 --> 00:45:08,480
Speaker 1: talked about this in the podcast before. I hate this

743
00:45:08,719 --> 00:45:13,080
Speaker 1: for so many reasons. One, I just feel that when

744
00:45:13,120 --> 00:45:16,360
Speaker 1: an audience is sort of guilted into seeing a movie,

745
00:45:16,400 --> 00:45:18,720
Speaker 1: that they're made to feel like they have to support

746
00:45:18,719 --> 00:45:21,200
Speaker 1: a movie because you want it to be successful. To

747
00:45:21,239 --> 00:45:24,480
Speaker 1: commit studios to give women or black folks or Asian

748
00:45:24,520 --> 00:45:27,800
Speaker 1: folks or other marginalized people more shots to make movies

749
00:45:28,480 --> 00:45:30,640
Speaker 1: that is not compelling to me. That is not. That's

750
00:45:30,680 --> 00:45:33,480
Speaker 1: not a reasonable reason to go see a movie. You

751
00:45:33,480 --> 00:45:35,279
Speaker 1: should go see a movie because you like it, because

752
00:45:35,280 --> 00:45:37,640
Speaker 1: you're curious, because you want, you want, you want to

753
00:45:37,680 --> 00:45:40,840
Speaker 1: support it. I you know, I don't know if you

754
00:45:40,880 --> 00:45:44,799
Speaker 1: remember the movie Bros. Which was kind of billed as oh,

755
00:45:44,840 --> 00:45:48,840
Speaker 1: the First Day, the first rom com with the first

756
00:45:48,920 --> 00:45:52,040
Speaker 1: major rom com with a gay man at the heart

757
00:45:52,080 --> 00:45:55,200
Speaker 1: of the love story. I found the marketing for that

758
00:45:55,280 --> 00:45:57,480
Speaker 1: movie to be very heavy on the like, you have

759
00:45:57,520 --> 00:46:00,000
Speaker 1: to support this movie. It's very historic, it's very important,

760
00:46:00,520 --> 00:46:03,160
Speaker 1: and I just don't think that's a compelling reason to

761
00:46:03,200 --> 00:46:05,319
Speaker 1: see a movie. Going to see a movie should not

762
00:46:05,360 --> 00:46:09,880
Speaker 1: be activism. And so also, I hate this because movies

763
00:46:09,960 --> 00:46:15,120
Speaker 1: with mostly white casts or white directors flop all the time,

764
00:46:15,520 --> 00:46:17,400
Speaker 1: and then it's not turned around and used as a

765
00:46:17,400 --> 00:46:19,520
Speaker 1: way for executives to say, oh, we shouldn't make more

766
00:46:19,560 --> 00:46:22,520
Speaker 1: movies like this. I don't think it's a fair precedent

767
00:46:22,560 --> 00:46:25,360
Speaker 1: that marginalized people and people of color and women we

768
00:46:25,480 --> 00:46:27,600
Speaker 1: have to wait and see how this movie that has

769
00:46:27,680 --> 00:46:31,600
Speaker 1: nothing to do with us pans out until we can

770
00:46:31,640 --> 00:46:34,759
Speaker 1: actually make the project that we want to make. And furthermore,

771
00:46:35,000 --> 00:46:39,920
Speaker 1: even when movies with black casts or black directors are successful,

772
00:46:40,280 --> 00:46:42,360
Speaker 1: it's talked about like it's some kind of a fluke.

773
00:46:42,640 --> 00:46:45,080
Speaker 1: Black Panther is a great example of this, and Sinners

774
00:46:45,160 --> 00:46:47,719
Speaker 1: is a great example of this. So like, diverse and

775
00:46:47,800 --> 00:46:53,280
Speaker 1: inclusive casts and diverse directors are putting butts in seats,

776
00:46:53,400 --> 00:46:55,520
Speaker 1: but it's like it doesn't matter. It's like any time

777
00:46:55,520 --> 00:46:58,239
Speaker 1: when that happens, it's going to be another goalpost move.

778
00:46:58,280 --> 00:47:01,720
Speaker 1: It's going to be like, oh, well, you know, Hallie

779
00:47:01,719 --> 00:47:03,520
Speaker 1: Bailey was in this movie and maybe it did well

780
00:47:03,560 --> 00:47:05,600
Speaker 1: because she's a big star. So if she's not in

781
00:47:05,640 --> 00:47:07,160
Speaker 1: the next movie, we're not going to green light it.

782
00:47:07,160 --> 00:47:09,759
Speaker 1: It's like it's always going to be another goalpost or

783
00:47:09,800 --> 00:47:12,759
Speaker 1: another benchmark for folks to just not want to tell

784
00:47:12,800 --> 00:47:15,480
Speaker 1: our stories. And I'm sick of it. The film critic

785
00:47:15,520 --> 00:47:17,840
Speaker 1: Carolyn Linz put it really well on Twitter. She wrote,

786
00:47:18,000 --> 00:47:20,240
Speaker 1: at the end of the day, the same racist studios

787
00:47:20,239 --> 00:47:23,239
Speaker 1: and executives win, either they save money on a film

788
00:47:23,280 --> 00:47:26,000
Speaker 1: they never wanted to make or make money from a

789
00:47:26,000 --> 00:47:28,560
Speaker 1: film they never wanted to make. All that happens is

790
00:47:28,560 --> 00:47:32,280
Speaker 1: the cycle of manipulation and exploitation is spun and culture

791
00:47:32,360 --> 00:47:35,920
Speaker 1: goes nowhere. It is beyond time. We stop demanding loyalty

792
00:47:35,960 --> 00:47:39,320
Speaker 1: from racialized audiences just because we're black, Indigenous, or Asian,

793
00:47:39,719 --> 00:47:41,840
Speaker 1: because it's proven time and time again that when it

794
00:47:41,840 --> 00:47:45,279
Speaker 1: comes to Hollywood, that representation is a tool they get

795
00:47:45,280 --> 00:47:48,480
Speaker 1: to use way more against us than we against them.

796
00:47:48,800 --> 00:47:51,840
Speaker 1: I think this critic is absolutely right, and I'm just

797
00:47:51,920 --> 00:47:56,520
Speaker 1: sick of this. I really respect that I see a

798
00:47:56,560 --> 00:48:02,440
Speaker 1: lot of black movie people and critics and and appreciators saying, well,

799
00:48:02,440 --> 00:48:05,000
Speaker 1: obviously we're gonna support this. Obviously we're gonna support this.

800
00:48:05,160 --> 00:48:09,640
Speaker 1: Like I saw somebody tweet, you know, we understand the assignment. Hell,

801
00:48:09,680 --> 00:48:12,319
Speaker 1: I don't even like rom coms. I'll probably go see

802
00:48:12,320 --> 00:48:15,200
Speaker 1: this because I do want to set a precedent that

803
00:48:15,280 --> 00:48:19,560
Speaker 1: black films have merit, but we that our stories have

804
00:48:19,719 --> 00:48:22,480
Speaker 1: merit and telling. But I just don't like this. I

805
00:48:22,520 --> 00:48:25,439
Speaker 1: don't like it. Just feels very manipulative, and I don't

806
00:48:25,560 --> 00:48:28,480
Speaker 1: like being manipulated this way as an audience member. We

807
00:48:28,520 --> 00:48:31,960
Speaker 1: deserve so much better. Our stories and our storytellers deserve

808
00:48:32,080 --> 00:48:35,400
Speaker 1: so much better. I have another little update for you

809
00:48:35,440 --> 00:48:37,800
Speaker 1: all about something happening on x SO. We told you

810
00:48:37,840 --> 00:48:40,600
Speaker 1: all about this when it first happened. But Elon Musk,

811
00:48:40,800 --> 00:48:45,120
Speaker 1: remember his big strategy to woo advertisers back to X.

812
00:48:45,880 --> 00:48:48,160
Speaker 1: It was extortion.

813
00:48:49,280 --> 00:48:51,239
Speaker 3: Extortion. Oh that's pretty good.

814
00:48:51,520 --> 00:48:54,680
Speaker 1: So back in twenty twenty four, X sued the World

815
00:48:54,719 --> 00:48:58,440
Speaker 1: Beeneration of Advertisers and a string of big name companies

816
00:48:58,440 --> 00:49:03,200
Speaker 1: and brands, including CBS Health, Colgate Mars, claiming that these

817
00:49:03,239 --> 00:49:07,560
Speaker 1: companies illegally conspired to withhold billions of dollars in ad

818
00:49:07,600 --> 00:49:11,360
Speaker 1: revenue that he was entitled to from the platform by

819
00:49:11,560 --> 00:49:14,920
Speaker 1: choosing not to advertise with him. So there are so

820
00:49:15,000 --> 00:49:19,239
Speaker 1: many companies that don't advertise on my podcast. Are they

821
00:49:19,280 --> 00:49:22,560
Speaker 1: Are they illegally colluding against me? Could I sue them?

822
00:49:22,640 --> 00:49:23,279
Speaker 4: I think they are.

823
00:49:23,360 --> 00:49:26,040
Speaker 3: You could sue them. I mean they they should be

824
00:49:26,120 --> 00:49:29,880
Speaker 3: spending millions of dollars on this podcast every week and

825
00:49:29,920 --> 00:49:30,279
Speaker 3: they're not.

826
00:49:31,040 --> 00:49:32,400
Speaker 1: Otherwise it's illegal.

827
00:49:32,880 --> 00:49:35,520
Speaker 3: Yeah, it's illegal not to give us millions of dollars.

828
00:49:35,719 --> 00:49:38,840
Speaker 1: That's essentially what X what he is saying. So X

829
00:49:38,960 --> 00:49:41,799
Speaker 1: argued that the advertisers coordinated through an initiative called the

830
00:49:41,840 --> 00:49:46,560
Speaker 1: Global Alliance for Responsible Media to effectively boycott X back

831
00:49:46,600 --> 00:49:49,960
Speaker 1: in twenty twenty four. This actually did work on some

832
00:49:50,080 --> 00:49:54,319
Speaker 1: pretty big brands, low key extortion Verizon, which had not

833
00:49:54,520 --> 00:49:57,759
Speaker 1: advertised on X since twenty twenty two. Pledged to spend

834
00:49:57,800 --> 00:50:00,160
Speaker 1: at least ten million dollars in twenty twenty five for

835
00:50:00,719 --> 00:50:02,880
Speaker 1: on X after X threaten to sue them if they

836
00:50:02,880 --> 00:50:07,160
Speaker 1: didn't the same thing for the luxury fashion brand Ralph Lauren,

837
00:50:07,840 --> 00:50:10,520
Speaker 1: but the company also agreed to resume buying ads on

838
00:50:10,800 --> 00:50:13,960
Speaker 1: X after receiving a threat of a lawsuit. We talked

839
00:50:14,000 --> 00:50:16,239
Speaker 1: about this in an episode of the podcast. But I

840
00:50:16,280 --> 00:50:20,120
Speaker 1: think that these companies were just like Musk is very litigious.

841
00:50:20,120 --> 00:50:23,719
Speaker 1: He has a billion dollars, it's cheaper just to throw

842
00:50:23,800 --> 00:50:26,239
Speaker 1: them a little bit of advertising money. Which if I'm

843
00:50:26,280 --> 00:50:28,960
Speaker 1: Elon Musk, I don't want people advertising on my platform

844
00:50:28,960 --> 00:50:30,879
Speaker 1: because I have extorted them into doing it.

845
00:50:30,920 --> 00:50:33,200
Speaker 3: But whatever I mean, I think if you're Elon Musk,

846
00:50:33,239 --> 00:50:36,000
Speaker 3: you want exactly that, you just want them giving you money.

847
00:50:37,760 --> 00:50:39,800
Speaker 3: I don't know about Ralph Lauren, but I know Verizon

848
00:50:39,920 --> 00:50:45,239
Speaker 3: spends a ton of money on political contributions to candidates

849
00:50:45,480 --> 00:50:48,239
Speaker 3: from both parties, and I have to assume that they

850
00:50:48,320 --> 00:50:52,960
Speaker 3: just like wrote this off as another drop out of

851
00:50:52,960 --> 00:50:59,960
Speaker 3: that bucket to just keep Musk, who is politically connected

852
00:51:00,080 --> 00:51:03,880
Speaker 3: to the Trump administration happy, just easier to write her

853
00:51:03,880 --> 00:51:06,600
Speaker 3: a ten million dollar checks than stand up for themselves.

854
00:51:06,680 --> 00:51:08,279
Speaker 1: Well, it turns out they maybe didn't even have to

855
00:51:08,320 --> 00:51:11,640
Speaker 1: do that, because this week a judge in Dallas dismissed

856
00:51:12,000 --> 00:51:15,000
Speaker 1: X's lawsuit, and this judge did not mince words. She

857
00:51:15,080 --> 00:51:18,880
Speaker 1: wrote that the very nature of this alleged conspiracy does

858
00:51:18,920 --> 00:51:21,640
Speaker 1: not hold up as an antitrust claim and dismissed it

859
00:51:21,920 --> 00:51:25,200
Speaker 1: with prejudice. So that means that X cannot refile this lawsuit.

860
00:51:25,200 --> 00:51:31,920
Speaker 1: It's over. The advertiser's defense was like very straightforward. They

861
00:51:31,960 --> 00:51:34,800
Speaker 1: basically said that they each made an independent business decision

862
00:51:34,800 --> 00:51:36,879
Speaker 1: about where to put their ad money and they were

863
00:51:37,000 --> 00:51:41,040
Speaker 1: largely driven by brand safety concerns following Mosque's takeover of

864
00:51:41,080 --> 00:51:43,640
Speaker 1: Twitter back in twenty twenty two. They talked about how

865
00:51:43,640 --> 00:51:46,480
Speaker 1: that was a time of layoffs and chaos and it just,

866
00:51:46,560 --> 00:51:50,040
Speaker 1: in their words, a less brand friendly environment, which is

867
00:51:50,080 --> 00:51:52,360
Speaker 1: one way to put it. When there's Nazi stuff on

868
00:51:52,480 --> 00:51:56,960
Speaker 1: your platform, AI generated child sexual abuse material on your platforms,

869
00:51:57,760 --> 00:51:59,680
Speaker 1: less brand friendly is one way to put that.

870
00:52:01,239 --> 00:52:04,239
Speaker 3: Yeah, and once again, that shit is just like normal. Now,

871
00:52:04,480 --> 00:52:07,279
Speaker 3: that's just what you get with X. Don't even just

872
00:52:07,880 --> 00:52:09,600
Speaker 3: try to pretend that it's something else.

873
00:52:10,200 --> 00:52:13,080
Speaker 1: Yes, so the court agreed, and this is a pretty

874
00:52:13,120 --> 00:52:17,120
Speaker 1: big loss for Elon Musk. He's been super vocal about

875
00:52:17,120 --> 00:52:21,640
Speaker 1: what he sees as this politically motivated advertiser pullback since

876
00:52:21,680 --> 00:52:24,239
Speaker 1: taking over the platform, and at least for right now,

877
00:52:24,280 --> 00:52:27,440
Speaker 1: the courts are like, yeah, that's not what's going on.

878
00:52:31,440 --> 00:52:31,600
Speaker 5: More.

879
00:52:31,600 --> 00:52:45,680
Speaker 2: After a quick break, let's get right back into it.

880
00:52:48,840 --> 00:52:53,040
Speaker 1: Okay, So you flagged this story for me. During the

881
00:52:53,080 --> 00:52:56,440
Speaker 1: first few days of the military strike on Iran, Trump

882
00:52:56,520 --> 00:53:01,799
Speaker 1: was photographed walking alongside Jessica Foster, who is this beautiful, blonde,

883
00:53:01,840 --> 00:53:06,080
Speaker 1: fit soldier. They were strutting down the tarmac together. After

884
00:53:06,120 --> 00:53:09,719
Speaker 1: this photograph with Trump, her Instagram page exploded with a

885
00:53:09,760 --> 00:53:15,040
Speaker 1: million followers. Only one problem, she is fake. The Washington

886
00:53:15,080 --> 00:53:18,439
Speaker 1: Post reports Foster is an illusion, a fake woman who

887
00:53:18,480 --> 00:53:22,200
Speaker 1: experts say was probably created by an artificial image by

888
00:53:22,239 --> 00:53:25,840
Speaker 1: an artificial intelligence image generator. There's no public record of

889
00:53:25,880 --> 00:53:29,120
Speaker 1: Foster's military service, and the account, despite not being labeled

890
00:53:29,120 --> 00:53:32,120
Speaker 1: as AI, is packed with indicators that she is fake.

891
00:53:32,600 --> 00:53:36,000
Speaker 1: Between many of her pro Trump post, Foster also prominently

892
00:53:36,000 --> 00:53:41,040
Speaker 1: displays her feet. Foster's viral takeoff highlights an increasingly prevalent

893
00:53:41,080 --> 00:53:43,960
Speaker 1: strategy for winning attention online a slew of right wing

894
00:53:43,960 --> 00:53:48,520
Speaker 1: accounts peddling patriotism mixed with soft core pornography, use fake

895
00:53:48,560 --> 00:53:52,680
Speaker 1: women and condensing imagery to grab viewers across a distracted Internet,

896
00:53:53,080 --> 00:53:56,480
Speaker 1: monetize their interest, and score political points.

897
00:53:56,719 --> 00:54:00,560
Speaker 3: Boy, we've like had some pretty tough words for the

898
00:54:00,680 --> 00:54:03,319
Speaker 3: Washington Post over the past couple of years, and like

899
00:54:03,440 --> 00:54:06,680
Speaker 3: all of them deserved. But that sentence is really a

900
00:54:06,760 --> 00:54:07,480
Speaker 3: thing of glory.

901
00:54:07,560 --> 00:54:11,120
Speaker 1: I think I am with you on that one. So

902
00:54:11,320 --> 00:54:14,240
Speaker 1: this whole thing is basically just a way to capitalize

903
00:54:14,239 --> 00:54:18,640
Speaker 1: on political content as a marketing strategy for more AI

904
00:54:18,719 --> 00:54:23,960
Speaker 1: generated content. Like by wrapping these fake personas in politics

905
00:54:24,040 --> 00:54:27,839
Speaker 1: and current events, they're basically just gaming the algorithm by

906
00:54:27,840 --> 00:54:31,040
Speaker 1: making the content feel timely and shareable, right, So, like,

907
00:54:31,560 --> 00:54:33,720
Speaker 1: then they build in this audience and then the grift

908
00:54:33,840 --> 00:54:37,719
Speaker 1: kicks in followers get nudged toward a paid platform where

909
00:54:37,800 --> 00:54:40,719
Speaker 1: whoever is running this AI generated account can make some

910
00:54:40,800 --> 00:54:45,200
Speaker 1: real cash. Some of her content is just so funny

911
00:54:45,239 --> 00:54:48,760
Speaker 1: to me because it's just hard for me to imagine

912
00:54:48,840 --> 00:54:53,040
Speaker 1: the person who doesn't understand that it's fake. Her first

913
00:54:53,239 --> 00:54:55,480
Speaker 1: picture was an image of her in a really tight

914
00:54:55,560 --> 00:54:59,759
Speaker 1: kind of skimpy shirt asking for comments from quote every

915
00:55:00,120 --> 00:55:03,279
Speaker 1: Drake guy that likes an American Army girl. You know,

916
00:55:03,320 --> 00:55:05,440
Speaker 1: it's like, who would post that on Instagram?

917
00:55:05,640 --> 00:55:07,320
Speaker 3: The dream girl for some guys.

918
00:55:07,360 --> 00:55:12,120
Speaker 1: I guess that's what I'm saying. It's so it seems

919
00:55:12,160 --> 00:55:14,120
Speaker 1: so obviously fake to me that I have a hard

920
00:55:14,120 --> 00:55:17,040
Speaker 1: time picturing like, who is like, oh, yeah, this is

921
00:55:17,080 --> 00:55:19,360
Speaker 1: a real woman. And it's a good thing that Foster

922
00:55:19,520 --> 00:55:21,919
Speaker 1: is not real, because if she was real, she would

923
00:55:21,960 --> 00:55:24,960
Speaker 1: be super fucking busy, because more than fifty photos and

924
00:55:25,080 --> 00:55:28,480
Speaker 1: videos have followed in the last few months revealing that

925
00:55:28,600 --> 00:55:33,040
Speaker 1: this person met with First Lady Malania Trump, with Ukrainian

926
00:55:33,120 --> 00:55:38,760
Speaker 1: President Zealinski, with Russian President Putin, soccer star Lionel Messi.

927
00:55:39,880 --> 00:55:42,880
Speaker 1: When she's not doing that, she also finds time to

928
00:55:42,880 --> 00:55:47,319
Speaker 1: give speeches, have sexy pillow fights with her fellow comrades.

929
00:55:47,640 --> 00:55:51,280
Speaker 1: And also she had a picture of herself holding Maduro captive.

930
00:55:52,440 --> 00:55:56,719
Speaker 3: She's been busy. One of the things that comes up

931
00:55:56,719 --> 00:55:59,680
Speaker 3: on this show all the time is how fan fiction

932
00:56:00,120 --> 00:56:03,640
Speaker 3: explains like sixty percent of the Internet at this point, Yeah,

933
00:56:03,800 --> 00:56:07,160
Speaker 3: and I feel this fits that too. It's like fan fiction.

934
00:56:08,239 --> 00:56:12,840
Speaker 3: What did the Washington Post say? Like patriotism mixed with

935
00:56:12,920 --> 00:56:14,320
Speaker 3: soft core pornography.

936
00:56:14,600 --> 00:56:17,799
Speaker 1: This was some really interesting reporting from the post. They

937
00:56:17,840 --> 00:56:20,440
Speaker 1: did include one little bit. I was like, dang, y'all

938
00:56:20,520 --> 00:56:24,360
Speaker 1: really blowing up this guy spot so so many of

939
00:56:24,360 --> 00:56:27,919
Speaker 1: the comments on this Instagram are from people who think

940
00:56:27,960 --> 00:56:30,000
Speaker 1: this person is real, right, They're like, oh, you're so beautiful,

941
00:56:30,040 --> 00:56:33,800
Speaker 1: You're so beautiful. The post writes the verified Instagram account

942
00:56:33,880 --> 00:56:37,560
Speaker 1: of a Brazilian transportation official liked most of her photos

943
00:56:37,800 --> 00:56:41,600
Speaker 1: and told Foster that she was Linda or beautiful. Another

944
00:56:41,760 --> 00:56:45,719
Speaker 1: user asked, why do you never reply? Part of me

945
00:56:45,800 --> 00:56:48,080
Speaker 1: is like, damn, you gotta call y'all, gotta like put

946
00:56:48,080 --> 00:56:51,799
Speaker 1: this Brazilian transportation official on blast. Guess who liked all

947
00:56:51,800 --> 00:56:54,959
Speaker 1: her posts? I left pathetic emoji comments on all of them,

948
00:56:55,200 --> 00:56:57,120
Speaker 1: This Brazilian transportation official.

949
00:56:57,480 --> 00:57:00,879
Speaker 3: You think he did that while he was at work, and.

950
00:57:00,880 --> 00:57:03,680
Speaker 1: He did it on his verified account. I almost feel

951
00:57:03,680 --> 00:57:05,319
Speaker 1: a little bad for this guy that the post is

952
00:57:05,320 --> 00:57:09,040
Speaker 1: calling out his like questionable likes on Instagram.

953
00:57:09,760 --> 00:57:12,399
Speaker 3: I do wonder how many of the people who are

954
00:57:12,480 --> 00:57:18,000
Speaker 3: liking her following her are from countries outside the United States,

955
00:57:18,040 --> 00:57:21,920
Speaker 3: and so maybe that's why the like what seems to

956
00:57:21,960 --> 00:57:27,040
Speaker 3: you is straightforward, like so obviously fake content, They're like, Oh,

957
00:57:27,080 --> 00:57:29,120
Speaker 3: that's just how Americans talk, you know, how they are.

958
00:57:30,560 --> 00:57:34,240
Speaker 1: I mean, reminds me of the guy who made that

959
00:57:34,320 --> 00:57:38,600
Speaker 1: AI generated girlfriend who was on Esther Perel's podcast, was like,

960
00:57:38,640 --> 00:57:41,600
Speaker 1: that's just she sounds like a nine year old chipmunk.

961
00:57:41,680 --> 00:57:43,960
Speaker 1: That's just how women in America sound to me. They

962
00:57:44,000 --> 00:57:45,080
Speaker 1: all sound like that to me.

963
00:57:46,000 --> 00:57:50,280
Speaker 3: Yeah. I think that the way the rest of the

964
00:57:50,280 --> 00:57:52,720
Speaker 3: world sees us is often different than now we see ourselves.

965
00:57:52,800 --> 00:57:53,360
Speaker 2: I think.

966
00:57:53,600 --> 00:57:58,080
Speaker 1: So this AI generated account of this sexy soldier, the

967
00:57:58,120 --> 00:58:01,240
Speaker 1: account was being used to link to only fans, but

968
00:58:01,320 --> 00:58:04,240
Speaker 1: because that violates OnlyFans rules that you have to be

969
00:58:04,320 --> 00:58:07,160
Speaker 1: like a verified person on only fans, it was deleted.

970
00:58:07,960 --> 00:58:10,880
Speaker 1: So now it links to Fanview, which is like a

971
00:58:10,920 --> 00:58:16,040
Speaker 1: smaller OnlyFans competitor with looser rules. The caption of her

972
00:58:16,080 --> 00:58:21,240
Speaker 1: account there reads public servant by day, Troublemaker by night

973
00:58:22,040 --> 00:58:25,760
Speaker 1: bt dubs. I respond to every message, but be patient

974
00:58:25,840 --> 00:58:29,800
Speaker 1: since I'm not a robot. You sure about that? Are

975
00:58:29,840 --> 00:58:33,160
Speaker 1: you sure about that? So the post actually spoke to

976
00:58:33,280 --> 00:58:35,840
Speaker 1: friend of the show, doctor Joan Donovan, an assistant professor

977
00:58:35,840 --> 00:58:38,720
Speaker 1: at Boston University who studies the media manipulation, who we've

978
00:58:38,760 --> 00:58:42,880
Speaker 1: had on the show before. Doctor Donovan said that AI

979
00:58:42,960 --> 00:58:46,440
Speaker 1: has really helped accounts multiply because they're easy to just

980
00:58:46,640 --> 00:58:51,600
Speaker 1: endlessly create and customize and offer creators a clear path

981
00:58:51,680 --> 00:58:55,800
Speaker 1: to making money. The account's kind of political sheen also

982
00:58:55,920 --> 00:58:59,560
Speaker 1: helps ensure that these images appear in people's news feeds,

983
00:58:59,560 --> 00:59:03,640
Speaker 1: because if you were just making sexy AI generated content,

984
00:59:03,880 --> 00:59:06,880
Speaker 1: you might not be prioritized at an algorithm. But if

985
00:59:06,920 --> 00:59:11,360
Speaker 1: you're making content that has this current events vibe, you

986
00:59:11,440 --> 00:59:14,600
Speaker 1: might be getting shown to more people who otherwise you

987
00:59:14,640 --> 00:59:17,680
Speaker 1: probably would not be in their feeds. Doctor Donovan said,

988
00:59:18,040 --> 00:59:19,920
Speaker 1: the danger of this is that we're moving toward a

989
00:59:20,000 --> 00:59:23,760
Speaker 1: society of the unreal. It's one way to get it's

990
00:59:23,800 --> 00:59:26,600
Speaker 1: one way to get political messaging across and it's effective.

991
00:59:27,080 --> 00:59:29,880
Speaker 1: We don't even and this is my favorite part of

992
00:59:29,880 --> 00:59:32,800
Speaker 1: the quote, we don't even know of selling feet picks

993
00:59:32,880 --> 00:59:34,720
Speaker 1: is Jessica Foster's final form.

994
00:59:36,560 --> 00:59:38,200
Speaker 3: No like, as far as we know, we don't know

995
00:59:38,240 --> 00:59:43,600
Speaker 3: anything about Jessica Foster's political beliefs. But I would venture

996
00:59:43,720 --> 00:59:46,320
Speaker 3: that I probably don't like a lot of them, and

997
00:59:46,480 --> 00:59:48,320
Speaker 3: that we might start hearing about them as we get

998
00:59:48,320 --> 00:59:49,600
Speaker 3: closer to November.

999
00:59:50,280 --> 00:59:53,600
Speaker 1: Yeah, I will say what I was thinking about this.

1000
00:59:54,480 --> 00:59:57,200
Speaker 1: I feel like men really ought to be insulted by

1001
00:59:57,240 --> 01:00:01,040
Speaker 1: this kind of content, because whoever is making they think

1002
01:00:01,080 --> 01:00:05,919
Speaker 1: that men are so stupid and horny that they will

1003
01:00:07,280 --> 01:00:11,920
Speaker 1: raw raw the a war if the fake AI generated

1004
01:00:12,000 --> 01:00:16,720
Speaker 1: woman at behind it is hot, you know what I mean. Like,

1005
01:00:17,000 --> 01:00:18,880
Speaker 1: when I was like looking at the content, I was like,

1006
01:00:19,560 --> 01:00:24,120
Speaker 1: how stupid and horny does whoever made this AI generated

1007
01:00:24,120 --> 01:00:26,840
Speaker 1: profile think men are? But then again, this profile is

1008
01:00:26,840 --> 01:00:29,240
Speaker 1: a million followers, so maybe this person is not wrong

1009
01:00:29,280 --> 01:00:31,600
Speaker 1: about just how stupid and horny these folks are.

1010
01:00:32,560 --> 01:00:35,760
Speaker 3: Yeah, I wish I could say that, uh, that you

1011
01:00:35,800 --> 01:00:38,160
Speaker 3: were wrong, but I guess she's got a million followers.

1012
01:00:38,160 --> 01:00:38,959
Speaker 3: Somebody wants it.

1013
01:00:40,040 --> 01:00:43,560
Speaker 1: Okay, So really quickly, I feel like I've really bookended

1014
01:00:43,600 --> 01:00:46,400
Speaker 1: this episode with things that I know that the Internet

1015
01:00:46,480 --> 01:00:48,440
Speaker 1: is all talking about. And you, I'm sure you have

1016
01:00:48,480 --> 01:00:54,040
Speaker 1: no idea. So this super famous Brazilian soccer player, Georgino,

1017
01:00:54,920 --> 01:00:59,120
Speaker 1: said that his eleven year old daughter was very aggressively

1018
01:00:59,200 --> 01:01:04,040
Speaker 1: confronted by a security guard at a hotel where chapel

1019
01:01:04,080 --> 01:01:08,120
Speaker 1: Rone was staying. Basically, him and his wife both said

1020
01:01:08,160 --> 01:01:11,680
Speaker 1: on social media that their daughter was eating breakfast at

1021
01:01:11,680 --> 01:01:14,800
Speaker 1: this hotel the daughter is a huge Chapel Roone fan.

1022
01:01:14,920 --> 01:01:18,280
Speaker 1: She'd even made like a cute little sign that was like, uh,

1023
01:01:19,080 --> 01:01:19,760
Speaker 1: what dos that song?

1024
01:01:19,800 --> 01:01:19,960
Speaker 2: Though?

1025
01:01:20,040 --> 01:01:26,840
Speaker 1: Like h ottogo, I am seeing chapel Rone. Very cute

1026
01:01:26,880 --> 01:01:30,200
Speaker 1: for a kid, I thought. And so she sees chapel

1027
01:01:30,280 --> 01:01:33,040
Speaker 1: Rone and she says, oh, this chapel Roone. According to

1028
01:01:33,080 --> 01:01:36,480
Speaker 1: her parents, she did not ask chapel Rone for anything.

1029
01:01:36,520 --> 01:01:40,360
Speaker 1: She just like got up, walked near chapel Rone to

1030
01:01:40,480 --> 01:01:43,160
Speaker 1: confirm that it was her, saw that it was her,

1031
01:01:43,480 --> 01:01:45,360
Speaker 1: smiled and went back to her table and was like,

1032
01:01:45,360 --> 01:01:47,400
Speaker 1: that was chapel Rone and was very happy about it.

1033
01:01:48,200 --> 01:01:52,200
Speaker 1: Then this security guard, according to her parents, came over

1034
01:01:52,320 --> 01:01:55,240
Speaker 1: and screamed at her and threatened to have them like

1035
01:01:55,320 --> 01:01:57,960
Speaker 1: removed from the hotel. Was a whole thing. I'm not

1036
01:01:58,240 --> 01:02:01,040
Speaker 1: big into the sports world, but like, this soccer player

1037
01:02:01,280 --> 01:02:05,920
Speaker 1: is hugely famous. He's like very very very very famous

1038
01:02:06,240 --> 01:02:11,400
Speaker 1: in Brazil. So Georgino is this kid's stepfather, and the

1039
01:02:11,480 --> 01:02:14,960
Speaker 1: kid's biological father is actually actor Jude Law. So this

1040
01:02:15,040 --> 01:02:18,360
Speaker 1: kid is has very famous parent, is what I am saying.

1041
01:02:18,720 --> 01:02:22,360
Speaker 1: And so it turned into this big controversy. So the

1042
01:02:22,400 --> 01:02:25,720
Speaker 1: mayor of Rio de Jannio banned a chapel erone from

1043
01:02:25,760 --> 01:02:29,280
Speaker 1: performing at this major city sponsored event in Rio de

1044
01:02:29,360 --> 01:02:32,320
Speaker 1: Gannio over this event, which depending on who you ask,

1045
01:02:32,400 --> 01:02:35,600
Speaker 1: might be attle bit of an overreaction. So she put

1046
01:02:35,640 --> 01:02:37,840
Speaker 1: out a statement that she was like, Oh, I don't

1047
01:02:37,960 --> 01:02:40,480
Speaker 1: really know what happened. I don't hate kids. I wouldn't

1048
01:02:40,480 --> 01:02:42,280
Speaker 1: have done that. And I thought like, oh, that's a

1049
01:02:42,280 --> 01:02:45,000
Speaker 1: pretty good statement. And then immediately there was a video

1050
01:02:45,120 --> 01:02:48,959
Speaker 1: of her walking through an airport with security where she's

1051
01:02:48,960 --> 01:02:51,800
Speaker 1: like visibly pointing to security being like, tell him to

1052
01:02:51,800 --> 01:02:53,640
Speaker 1: stop photographing me, tell him to get away from me.

1053
01:02:53,800 --> 01:02:55,160
Speaker 1: So I'm sort of like, I don't know what's the

1054
01:02:55,200 --> 01:02:59,800
Speaker 1: thing now, and I'm still not one hundred percent sure

1055
01:03:00,080 --> 01:03:03,640
Speaker 1: what the truth is here. So chapel Ron clarified that

1056
01:03:03,680 --> 01:03:06,760
Speaker 1: it was not a member of her security team, and

1057
01:03:06,800 --> 01:03:10,720
Speaker 1: then the guard himself put out a statement saying that

1058
01:03:10,840 --> 01:03:14,880
Speaker 1: he was not acting on her behalf, that he was

1059
01:03:15,480 --> 01:03:20,080
Speaker 1: there bodyguarding someone else, and that he made a judgment

1060
01:03:20,160 --> 01:03:24,640
Speaker 1: call based on the vibe of the breakfast and decided

1061
01:03:24,680 --> 01:03:27,560
Speaker 1: on his own to go like scream at this child,

1062
01:03:28,360 --> 01:03:31,360
Speaker 1: which to me is even weirder. Side note, do you

1063
01:03:31,360 --> 01:03:34,200
Speaker 1: ever hear the story about how Kim Kardashian was like

1064
01:03:34,320 --> 01:03:38,520
Speaker 1: violently robbed in a hotel in Paris. Yeah, he was her,

1065
01:03:38,680 --> 01:03:43,120
Speaker 1: He was her bodyguard when that happened. Wow, So this

1066
01:03:43,320 --> 01:03:46,360
Speaker 1: this buddy guard, He's like the forest Gump of celebrities.

1067
01:03:46,400 --> 01:03:49,160
Speaker 1: Whenever there was like a celebrity scandal, he just happens

1068
01:03:49,200 --> 01:03:50,280
Speaker 1: to be there somehow.

1069
01:03:50,880 --> 01:03:53,240
Speaker 3: Also, why do I feel like this hotel was not

1070
01:03:53,360 --> 01:03:57,600
Speaker 3: the Liquina of Rito dejan Aio, Like everyone staying there

1071
01:03:57,680 --> 01:03:58,880
Speaker 3: is an aless celebrity.

1072
01:03:59,600 --> 01:03:59,800
Speaker 2: Yes.

1073
01:04:00,160 --> 01:04:02,600
Speaker 1: Yes, So the reason I want to bring this up

1074
01:04:02,720 --> 01:04:05,520
Speaker 1: not because I have any big take about Chapel Roan

1075
01:04:05,680 --> 01:04:10,000
Speaker 1: or anything like that, but because this conversation got very

1076
01:04:10,080 --> 01:04:14,040
Speaker 1: heated online and it turns out that a substantial amount

1077
01:04:14,040 --> 01:04:18,720
Speaker 1: of the conversation was amplified by bots. We know that

1078
01:04:18,880 --> 01:04:22,000
Speaker 1: from the research firm Goudea, who we heard from the

1079
01:04:22,040 --> 01:04:24,880
Speaker 1: head of Gudea in our episode about Taylor swift Well.

1080
01:04:24,880 --> 01:04:28,760
Speaker 1: Goudea analyzed over one hundred thousand posts across seven platforms

1081
01:04:28,800 --> 01:04:31,400
Speaker 1: in the days following the incident and found that while

1082
01:04:31,520 --> 01:04:34,880
Speaker 1: only about four percent of users were likely bots, those

1083
01:04:34,920 --> 01:04:38,439
Speaker 1: accounts were responsible for more than twenty three percent of

1084
01:04:38,480 --> 01:04:42,400
Speaker 1: the post, meaning that a small number of bot accounts

1085
01:04:42,600 --> 01:04:47,680
Speaker 1: were driving a hugely outsized chunk of the backlash, including

1086
01:04:47,880 --> 01:04:53,440
Speaker 1: misinformation and calls for boycotts. And I say that to

1087
01:04:53,520 --> 01:04:57,200
Speaker 1: say that I spent a non trivial amount of time

1088
01:04:57,560 --> 01:05:03,160
Speaker 1: reading Chapel Roone takes, and I'm I can admit that

1089
01:05:03,400 --> 01:05:06,120
Speaker 1: the ones that were like Chapel Rone, she needs to

1090
01:05:06,200 --> 01:05:09,080
Speaker 1: learn how to be respectful to her fans. I was like, yeah,

1091
01:05:09,080 --> 01:05:12,560
Speaker 1: you tell them, And then it was like chapel Rone,

1092
01:05:12,600 --> 01:05:14,160
Speaker 1: she didn't do anything wrong, and I was like, yeah,

1093
01:05:14,200 --> 01:05:16,760
Speaker 1: that's probably right too, like the way that I was

1094
01:05:16,840 --> 01:05:19,760
Speaker 1: so easily swayed in this convert in a conversation that

1095
01:05:19,800 --> 01:05:22,960
Speaker 1: I have no real stake in. I like Chapel Roone.

1096
01:05:22,960 --> 01:05:24,600
Speaker 1: I'm not a huge fan, so it's not even like

1097
01:05:25,200 --> 01:05:28,760
Speaker 1: I did not feel strongly about it. Yet I threw

1098
01:05:28,920 --> 01:05:35,400
Speaker 1: away half an afternoon reading Chapel Rone Brazil takes, and

1099
01:05:35,440 --> 01:05:37,200
Speaker 1: you know, I have but one life to live, and

1100
01:05:37,280 --> 01:05:40,560
Speaker 1: I spent half of a day of it following takes

1101
01:05:40,600 --> 01:05:42,080
Speaker 1: of a story I don't even really care about, who

1102
01:05:42,080 --> 01:05:43,440
Speaker 1: were probably amplified by bots.

1103
01:05:43,960 --> 01:05:45,480
Speaker 3: And you still don't know what happened.

1104
01:05:45,680 --> 01:05:46,600
Speaker 4: I still don't know.

1105
01:05:47,200 --> 01:05:51,320
Speaker 1: I still don't know anyway, I just wanted to end there,

1106
01:05:51,920 --> 01:05:54,920
Speaker 1: just as a reminder not to let online conversation sway

1107
01:05:55,000 --> 01:05:58,360
Speaker 1: you too much, right like don't get to invested. Nine

1108
01:05:58,360 --> 01:06:01,600
Speaker 1: times out of ten, bots are are very very effective

1109
01:06:01,600 --> 01:06:05,560
Speaker 1: at amplify and slaying the conversations with just tread carefully

1110
01:06:05,600 --> 01:06:07,160
Speaker 1: in these internet streets.

1111
01:06:07,840 --> 01:06:09,800
Speaker 3: If folks want to let us know what they thought

1112
01:06:09,800 --> 01:06:11,960
Speaker 3: about any of these stories, we would love to hear

1113
01:06:12,000 --> 01:06:14,640
Speaker 3: about it at send us an email at Hello at

1114
01:06:14,640 --> 01:06:18,360
Speaker 3: tangoty dot com. You can leave comments in Spotify. We've

1115
01:06:19,080 --> 01:06:20,920
Speaker 3: had a lot over there. We love to see them.

1116
01:06:20,960 --> 01:06:23,680
Speaker 3: Please keep them coming. And you can follow Bridget on

1117
01:06:23,720 --> 01:06:28,560
Speaker 3: social media at Bridget Marie in DC on Instagram and

1118
01:06:29,760 --> 01:06:32,360
Speaker 3: you can follow the show there are no girls on

1119
01:06:32,400 --> 01:06:36,040
Speaker 3: the Internet. On TikTok and YouTube. And don't forget to

1120
01:06:36,040 --> 01:06:40,120
Speaker 3: pre order our book. You can reserve your copy at

1121
01:06:40,200 --> 01:06:44,520
Speaker 3: lovettfirst prompt dot ai. I recently learned that liro dot

1122
01:06:44,560 --> 01:06:49,560
Speaker 3: fm is a really cool way to listen to audiobooks.

1123
01:06:49,640 --> 01:06:51,400
Speaker 3: I don't know if other people have been more into

1124
01:06:51,440 --> 01:06:54,640
Speaker 3: audiobooks than I have. Already knew this, but the thing

1125
01:06:54,680 --> 01:06:58,320
Speaker 3: that makes liro dot fm different is that whenever you

1126
01:06:58,400 --> 01:07:02,040
Speaker 3: reserve an audiobook or buy an audiobook, they make a

1127
01:07:02,120 --> 01:07:07,520
Speaker 3: donation to your local brick and mortar bookstore, helping support

1128
01:07:07,640 --> 01:07:11,880
Speaker 3: small bookstores stay in business. So I was really excited

1129
01:07:11,920 --> 01:07:14,800
Speaker 3: to learn that and have started telling everyone that that's

1130
01:07:14,800 --> 01:07:17,680
Speaker 3: where they should get their audiobook. Love at first prompt,

1131
01:07:17,960 --> 01:07:22,880
Speaker 3: but you should get it wherever you prefer, and when

1132
01:07:22,920 --> 01:07:24,440
Speaker 3: it comes out in July fourteenth, I hope you let

1133
01:07:24,520 --> 01:07:25,440
Speaker 3: us know what you think about it.

1134
01:07:25,720 --> 01:07:28,400
Speaker 1: And as you know, if you send us a screenshot

1135
01:07:28,560 --> 01:07:32,400
Speaker 1: or cag us in your pre order, we have a

1136
01:07:32,440 --> 01:07:34,680
Speaker 1: sticker and a handwritten card coming your way. We've got

1137
01:07:34,680 --> 01:07:37,680
Speaker 1: the first batch a back to go out. So Melanie

1138
01:07:37,760 --> 01:07:40,920
Speaker 1: is getting a card, Elizabeth g Is getting a card,

1139
01:07:41,080 --> 01:07:44,480
Speaker 1: Robin's getting a card, Yan is getting a card, Hannah's

1140
01:07:44,480 --> 01:07:47,200
Speaker 1: getting a card. Some of these places are far away,

1141
01:07:47,240 --> 01:07:50,800
Speaker 1: and I love to see how many different listeners we

1142
01:07:50,880 --> 01:07:54,120
Speaker 1: have all over the world. So cards are the first

1143
01:07:54,200 --> 01:07:56,480
Speaker 1: batch is going out and if you want a handwritten

1144
01:07:56,480 --> 01:07:59,680
Speaker 1: card as a thank you and a sticker, go ahead

1145
01:07:59,720 --> 01:08:00,240
Speaker 1: and pretty.

1146
01:08:00,880 --> 01:08:04,160
Speaker 3: Yeah, we've been getting requests from like all over the world.

1147
01:08:04,200 --> 01:08:09,560
Speaker 3: We had one from New Zealand, one from Australia, bunch

1148
01:08:09,560 --> 01:08:15,440
Speaker 3: from Europe and when people from other countries ask for stickers.

1149
01:08:16,360 --> 01:08:18,040
Speaker 3: They're always like, you don't have to send it as

1150
01:08:18,040 --> 01:08:20,080
Speaker 3: the shipping is too much. We don't mind. We will

1151
01:08:20,080 --> 01:08:22,080
Speaker 3: ship it, and we're also going to send you extra

1152
01:08:22,120 --> 01:08:24,160
Speaker 3: stickers and we hope that you put them up in

1153
01:08:24,280 --> 01:08:27,200
Speaker 3: your little corner of the world to help spread the show.

1154
01:08:27,439 --> 01:08:31,720
Speaker 1: Thank you so much for all the support. Thank you

1155
01:08:31,800 --> 01:08:34,080
Speaker 1: Mike for being here. I will see you on the Internet.

1156
01:08:39,960 --> 01:08:41,920
Speaker 1: Got a story about an interesting thing in tech? I

1157
01:08:42,080 --> 01:08:43,920
Speaker 1: just want to say hi. You can be just at

1158
01:08:43,920 --> 01:08:46,639
Speaker 1: hello at tengody dot com. You can also find transcripts

1159
01:08:46,640 --> 01:08:49,040
Speaker 1: for today's episode at teng Goody dot com. There Are

1160
01:08:49,040 --> 01:08:51,240
Speaker 1: No Girls on the Internet was created by me Bridget Toad.

1161
01:08:51,640 --> 01:08:55,040
Speaker 1: It's a production of iHeartRadio, an unbossed creative Jonathan Strickland

1162
01:08:55,040 --> 01:08:57,800
Speaker 1: as our executive producer. Tari Harrison is our producer and

1163
01:08:57,880 --> 01:09:01,680
Speaker 1: sound engineer. Michael Almada is our ttributing producer. Edited by

1164
01:09:01,760 --> 01:09:05,280
Speaker 1: Joey pat I'm your host, Bridget Todd. If you want

1165
01:09:05,280 --> 01:09:07,880
Speaker 1: to help us grow, rate and review us on Apple Podcasts.

1166
01:09:08,479 --> 01:09:11,280
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1167
01:09:11,320 --> 01:09:22,600
Speaker 1: Apple podcast, or wherever you get your podcasts.