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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 just

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Speaker 1: kind of have to start out by thanking everybody who

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Speaker 1: listened to our episode about the Hollow App. I think

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Speaker 1: it maybe is our most commented on and responded to

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Speaker 1: episode we've ever put out in the five years we've

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Speaker 1: been doing this show.

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Speaker 2: It got a lot of attention. People were really weighing

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Speaker 2: in on the comments and pretty supportively, which was nice

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Speaker 2: to see.

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Speaker 1: In the Spotify comments, listener Hafsa wrote, when they go low,

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Speaker 1: we go lava, which I think very nicely captures the

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Speaker 1: energy I was trying to channel for folks who did

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Speaker 1: not listen. The anti abortion nonprofit Live Action wrote a

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Speaker 1: piece about the thoughts that I shared in an earlier

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Speaker 1: news roundup about the Hollow app, which listener Charlie describes

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Speaker 1: as a prey wall instead of a paywall because you

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Speaker 1: have to pay to access the full suite of prayers

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Speaker 1: on the app. We just did a very quick summary

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Speaker 1: of the app and my thoughts in a roundup. The

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Speaker 1: anti choice downprofit Live Action wrote a very disingenuous piece

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Speaker 1: about it. So Mike and I responded this week in

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Speaker 1: a lengthy episode where props to you, producer, Mike, I

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Speaker 1: feel like you really just stood back and let me cook.

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Speaker 2: Hey, I mean, I don't want to mess with a

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Speaker 2: good thing. You clearly had a lot that you wanted

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Speaker 2: to say. You. I watched you spend a couple days,

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Speaker 2: like just pouring yourself into the research to build a

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Speaker 2: pretty substantial case against the hallow app and some of

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Speaker 2: its investors, its spokespeople, and yeah, you just had a

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Speaker 2: lot to say. So it was pretty cool to watch,

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Speaker 2: and also like, I'm not trying to get in the

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Speaker 2: way of that.

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Speaker 1: Well, I do have one small correction to that hollow

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Speaker 1: app episode. We spoke at length in the episode about

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Speaker 1: how Peter til the billionaire Silicon Valley mega investor, is

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Speaker 1: one of the hallow apps funders, and the correction is

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Speaker 1: that I wish that I had named more clearly and

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Speaker 1: more loudly that the hallow app thunder Peter Teal is

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Speaker 1: also a very well documented Epstein collaborator. We mentioned this

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Speaker 1: sort of in passing. The episode was already quite a

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Speaker 1: bit longer than our episodes usually are, so I don't

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Speaker 1: think we've got We didn't really have time to fully

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Speaker 1: get into it, but I wish that I had, because, yes,

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Speaker 1: the financial backer of the Hallow Prayer App, Peter Teal.

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Speaker 1: His name appears more than two two hundred times in

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Speaker 1: documents released so far by the Department of Justice related

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Speaker 1: to Epstein. So we know that from twenty fourteen to

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Speaker 1: twenty nineteen, well after Epstein's arrest and conviction and sex

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Speaker 1: offender status for sexually exploiting children with public knowledge, well

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Speaker 1: after that, Epstein and Teal continued to have financial business

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Speaker 1: with each other. Epstein invested in Teal's firms and advised

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Speaker 1: Peter Tiel on his personal finances, kind of the same

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Speaker 1: way that the Hallow app partnered with Russell Brand even

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Speaker 1: after his many rape allegations were very public. I guess

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Speaker 1: being cool with sexual exploitation of women and kids is

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Speaker 1: like a theme with this Hollow Prayer app. The people

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Speaker 1: who run it and the people who fund it, they

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Speaker 1: are just totally down with sexual abusers, I think, yeah.

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Speaker 2: I mean, there's pretty clear evidence that they've done it

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Speaker 2: at least a few times in like very high profile cases.

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Speaker 1: Epstein famously invested forty million dollars into Teal's venture funds.

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Speaker 1: Now the New York Times reports that that investment today

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Speaker 1: is worth about one hundred and seventy million dollars. So

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Speaker 1: Peter Til, we know, then invested in the Hallow Prayer

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Speaker 1: app that Live Action is defending. So it isn't like

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Speaker 1: Teal gave away the profits that he made from Epstein's investments.

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Speaker 1: So my question is did some of that money go

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Speaker 1: towards supporting the Hallow Prayer App. I don't know, but

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Speaker 1: I do know that Peter Til, the financial backer of

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Speaker 1: the Hallow app, does not have a problem doing financial

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Speaker 1: business with a convicted child sex predator, and he felt

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Speaker 1: it was cool to put that in writing because in

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Speaker 1: one email, Epstein asks Peter Teal directly quote does my

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Speaker 1: bad press give you pause? And Teal responds, if I

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Speaker 1: was intimidated by bad press, I would not have gotten

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Speaker 1: anywhere in life. To be clear, this exchange occurred well

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Speaker 1: after Epstein's two thousand and eight guilty plea to soliciting

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Speaker 1: a minor. I also love that Peter Tel is talking

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Speaker 1: about this conviction for a sex crime against the child.

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Speaker 1: I love that it's this bad press. It's not something

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Speaker 1: he actually did. It's not an actual like he pled guilty.

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Speaker 1: He is not in disput that like Epstein did not

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Speaker 1: discute that he did this, And Peter Til is aware

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Speaker 1: of that, and he's like, Nah, it's cool. It's just

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Speaker 1: bad press. You know, people gossip.

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Speaker 2: Yeah, it's just bad press. Like no other concerns about

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Speaker 2: the substance of what's going on with writing like this.

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Speaker 2: If Peter Til wasn't a billionaire, he should try to

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Speaker 2: get a job at Meta, just like putting shit in writing.

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Speaker 1: Yes, so yeah. Just to sum up, the Hallow app

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Speaker 1: is a crew that includes an alleged rapist like Russell Brand,

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Speaker 1: a convicted violet felon like Mark Wahlberg aka Murky Mark

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Speaker 1: aka racist narc Thank you Alyssa for leaving that Geminar

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Speaker 1: Spotify comments, and people like Peter Tiel who put in

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Speaker 1: writing that they're just fine doing business with child sexual abusers.

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Speaker 1: I would actually love for the Hallow we have to

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Speaker 1: put out a statement making clear that they have not

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Speaker 1: kept any money that might have been connected to Epstein

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Speaker 1: that they got from Teal. I think that'd be an

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Speaker 1: easy way to make clear where they stand on child

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Speaker 1: sexual abuse and whether or not they're comfortable with their

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Speaker 1: Hallow Prayer app being funded by money that might be

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Speaker 1: connected to child sexual abuse. But I guess the update

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Speaker 1: here is that I have not heard anything back from Hallow,

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Speaker 1: nor have I heard anything back from Live Action. As

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Speaker 1: you know, if you heard the episode, I made a

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Speaker 1: very I believe compelling plea for the author of that

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Speaker 1: live action hit piece about us to come on the podcast.

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Speaker 1: I have not heard anything yet. I will let you

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Speaker 1: all know if I hear anything, but yeah, thanks so

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Speaker 1: much for listening, Thanks for all the responses. It was

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Speaker 1: in some ways I think it was my opus. I

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Speaker 1: don't often have a clear villain to respond to, but

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Speaker 1: you know, when you have a villain, you can really Yeah,

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Speaker 1: it just really changes the dynamic. I feel. Usually I'm

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Speaker 1: talking about complex conversations that I'm prying to be very thoughtful.

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Speaker 1: It's not often I just get to like cook on

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Speaker 1: a creep. So it was kind of and let alone

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Speaker 1: a handful of them, let alone a handful some of

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Speaker 1: them that are trying to take the moral high ground.

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Speaker 2: Yeah, I think that was what was part of what

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Speaker 2: made it so satisfying, was that Nancy's article is trying

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Speaker 2: to take this moral high ground that just does not

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Speaker 2: survive scrutiny. Anyway, We've spent so much time talking about

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Speaker 2: the hollow app. But yeah, I wanted to say thanks

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Speaker 2: to everybody who weighed in on the Spotify comments. It

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Speaker 2: was a lot of fun. Somebody asked about the the

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Speaker 2: topic analysis that I started doing, so I posted the

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Speaker 2: data over there. So my first time publishing an analysis

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Speaker 2: in Spotify comments. I'm not really sure. I just cite that,

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Speaker 2: but it was pretty fun. And you know, if you

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Speaker 2: haven't left comments on Spotify, I would encourage you to

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Speaker 2: check it out because it's a it's kind of fun.

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Speaker 1: So you mentioned Meta a moment ago and how they're

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Speaker 1: always putting the worst stuff in email. So I have

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Speaker 1: a little bit of news about Meta glasses. We've talked

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Speaker 1: at length about my disdain from metaglasses, but people are

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Speaker 1: buying them. They have sold over seven million pairs in

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Speaker 1: twenty twenty five alone. Meta loves to tout how privacy

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Speaker 1: minded the glasses are. I don't think anybody paying attention

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Speaker 1: has actually really believed that. But now we have more

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Speaker 1: information from a bombshell investigation by two Swedish newspapers that

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Speaker 1: has really pulled back the curtain on what is actually

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Speaker 1: happening with the footage that those glasses record, and the

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Speaker 1: fallout has been swift. So let me back up a

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Speaker 1: little bit. Because Facebook they contract with a company called Sama,

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Speaker 1: which is based in Nairobi, Kenya. I did an episode

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Speaker 1: of my podcast that I make with Mozilla Foundation called

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Speaker 1: IRL basically where we talk to some of the staffers

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Speaker 1: at this organization. It's clear to me that Facebook is

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Speaker 1: doing this thing that a lot of companies do when

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Speaker 1: they hire content moderators and staff that have to be

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Speaker 1: exposed to pretty gnarly stuff. And then they have a

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Speaker 1: Those people are contractors and they technically work for the

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Speaker 1: contract in company, Samma, but Samma has this relationship with Facebook,

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Speaker 1: so they don't technically work for Facebook. They are contractors

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Speaker 1: of Facebook who work for Sama. So these folks, especially

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Speaker 1: their content moderators and folks who are training their AI,

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Speaker 1: have to be exposed to some of the worst of

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Speaker 1: the worst content that you could imagine. And now with

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Speaker 1: these metaglasses, these Kenyan staffers are talking about how they're

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Speaker 1: basically being paid to watch and annotate the footage that

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Speaker 1: is being captured by users' glasses. Now this includes footage

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Speaker 1: of people going to the bathroom, having sex, getting undressed,

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Speaker 1: and entering their bank card details. One contractor described watching

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Speaker 1: a man set his glasses on a bedside table leave

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Speaker 1: the room, and then his wife walks in and takes

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Speaker 1: off her clothes, and he saw that content because it's

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Speaker 1: part of his job to view this content. And these

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Speaker 1: workers who are speaking up say that they felt like

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Speaker 1: they couldn't refuse to do this, nor could they really

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Speaker 1: ask questions out risking their jobs. Never forget that AI

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Speaker 1: involves a tremendous amount of human labor, often labor from

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Speaker 1: folks in the global South. This idea that AI is

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Speaker 1: just like computer brains doing computer stuff. It's very easy

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Speaker 1: and tempting to think of AI that way, but that's

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Speaker 1: not really how it works. This practice called data labeling,

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Speaker 1: which is sometimes like an invisibilized step in AI training,

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Speaker 1: where human workers review footage to help the model understand

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Speaker 1: what it's seeing. It's pretty standard practice across the tech industry,

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Speaker 1: and it's often farmed out to workers in Kenya, India,

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Speaker 1: and Columbia. But what makes this case with the Meta

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Speaker 1: glasses uniquely alarming is that Meta has really been marketing

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Speaker 1: these glasses with explicit privacy promises. They use phrases like

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Speaker 1: quote designed for privacy, controlled by you, and built for

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Speaker 1: your privacy. But using the glasses AI features at all

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Speaker 1: requires agreeing to share that footage with me as servers.

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Speaker 1: There is no opt out, and once it's uploaded, as

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Speaker 1: one data protection lawyer put it, users lose control over

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Speaker 1: how it is used. I remember, I guess this was

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Speaker 1: it must have been Fall of twenty twenty five. I

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Speaker 1: was doing some pretty serious legal stuff involving the death

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Speaker 1: of my parents, and I had to go to the

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Speaker 1: bank and complete like a very complex, high stakes banking transaction,

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Speaker 1: and I remember the banker that helped me was wearing metaglasses.

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Speaker 1: This was very This was like early early, early on.

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Speaker 1: I don't think i'd ever even seen the glasses before.

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Speaker 1: I don't even know that I clocked them as metaglasses,

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Speaker 1: but I definitely know they were. Now they didn't have

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Speaker 1: the light that was on to indicate recording, although we

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Speaker 1: do know that they they sell commercial products to cover

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Speaker 1: that light up so that people don't know that you're

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Speaker 1: they're wearing actively recording metaglasses. But I look back on

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Speaker 1: that moment and I, boy do I wish I had

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Speaker 1: said something. I don't think I had the language or

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Speaker 1: the confidence yet, because these gap glasses were still becoming,

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Speaker 1: they were not yet common in place. But I really

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Speaker 1: wish i'd spoken out.

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Speaker 2: That is a wild story that a bank teller would

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Speaker 2: have those glasses on. Like how many people's pins did

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Speaker 2: that record, how many people's checking account numbers and routing

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Speaker 2: numbers were recorded from checks? And how many workers in

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Speaker 2: Kenya got to see all of that?

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Speaker 1: Yeah, I don't really want to think about it.

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Speaker 2: Yeah, I mean, I bet the lawyers of the bank

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Speaker 2: don't want to think about it either.

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Speaker 1: No. No, Well, now Meta has been hit with a

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Speaker 1: class action lawsuit filed in San Francisco, which basically argues

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Speaker 1: the very same point that you just made, Mike, that

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Speaker 1: no reasonable consumer would interpret those privacy promises to mean

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Speaker 1: that their most intimate moments could be watched and catalog

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Speaker 1: by workers overseas. The law firm behind the suit put

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Speaker 1: it very They said, this is not a technicality or

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Speaker 1: an oversight, this is a system working exactly as designed.

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Speaker 1: Meta's response, well, a spokesperson pointed to their terms of service,

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Speaker 1: which do technically mentioned human review, but it's way buried

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Speaker 1: in the fine print, and I think it doesn't jive

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Speaker 1: with the way that Meta has openly talked about and

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Speaker 1: marketed these glasses as good for privacy, privacy minded, all

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Speaker 1: of that if also buried in the fine print. To

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Speaker 1: use them, you have to consent to sensitive information going

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Speaker 1: god knows where.

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Speaker 2: Yeah, you would hope that this court case does not

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Speaker 2: go well for them based on the arguments that like, well,

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Speaker 2: sure we marketed about privacy, but actually in the fine

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Speaker 2: print we say that you don't have any privacy and

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Speaker 2: too bad for you. Classic metas stuff here.

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Speaker 1: Classic Meta. Let's take a quick break at our back.

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Speaker 1: You know, in my opinion, I think Meta has been

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Speaker 1: responsible for and party to criminal behavior crimes. And speaking

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Speaker 1: of crimes, let's talk a little bit about true crime.

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Speaker 1: Did you know that the biggest consumers of true crime

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Speaker 1: content are women?

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Speaker 2: I did know that because you told me a while

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Speaker 2: ago when you were pitching a true crime show.

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Speaker 1: Oh that's true. My mom, God rest her soul. Nobody

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Speaker 1: loved true crime like my mom. She really saw everything

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Speaker 1: through the lens of true crime. And if you were like, oh,

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Speaker 1: I'm going out of town, I'm going to Boston for

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Speaker 1: the weekend, and she'd say, I just saw something on

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Speaker 1: Dateline about Boston where a woman like she really had

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Speaker 1: a true crime story for every scenario that she loves

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Speaker 1: to share. And I just read this very interesting piece

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Speaker 1: in truth Out by journalist Quanetta Harris. She is a journalist,

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Speaker 1: she's a contributing writer at Solitary Watch and a Haymark

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Speaker 1: Writing Freedom Fellow, and also happens to be currently incarcerated.

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Speaker 1: So her piece in truth Out is all about how

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Speaker 1: true crime has created this uniquely degrading pipeline for women

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Speaker 1: who are incarcerated. She writes, women convicted of crimes become

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Speaker 1: involuntary performers in a spectacle that attracts precisely the men

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Speaker 1: most likely to dehumanize them. Incarcerated women featured in these

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Speaker 1: venues are then flooded with letters, with a significant portion

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Speaker 1: coming from self described in cells, men who frame their

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Speaker 1: desire for connection through the language of sexual entitlement and misogyny.

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Speaker 1: The genre itself engineers this outcome. Essentially, what she is

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Speaker 1: describing is the way that true crime content opens up

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Speaker 1: incarcerated women to be contacted by all manner of creeps

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Speaker 1: and grifters. One woman in the piece they talk about

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Speaker 1: who is in prison for killing her ex's new partner

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Speaker 1: says that she's been contacted by news or entertainment shows

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Speaker 1: a dozen times before a true crime program did air

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Speaker 1: without her participation. She talks to women who are incarcerated.

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Speaker 1: When a true crime program or a television show about

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Speaker 1: their situation goes live, which it often happens without their

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Speaker 1: participation or consent, they basically start getting floods of letters

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Speaker 1: and communication from men declaring their unyielding love, men proposing marriage.

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Speaker 1: One woman who was incarcerated described getting a nine page

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Speaker 1: handwritten letter describing her as cold hearted because she never replies,

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Speaker 1: He always swears he will never write to her again,

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Speaker 1: but sure enough, next week, another letter arrives, saying that

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Speaker 1: he visited her mother to ask why she will not

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Speaker 1: respond to his letters. Another man writes to explain that

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Speaker 1: she must learn her place as a woman without jealousy.

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Speaker 1: He regularly proposes marriage, telling her that she can be

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Speaker 1: his second wife. These true crime programs also make incarcerated

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Speaker 1: women vulnerable to abuse from correctional officers too, because prison

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Speaker 1: staff have threatened to take nude photographs of this incarcerated

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Speaker 1: woman to post, and has even threatened to sell her

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Speaker 1: personal items to these stalkers, who become obsessed with her

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Speaker 1: after they see her in these true crime programs. Another

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Speaker 1: incarcerated woman who was arrested as a teen says that

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Speaker 1: a true crime show depicted her as a drug snorting

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Speaker 1: devil worshiping kind of like sexy teen with a Southern accent.

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Speaker 1: None of this depiction is true, but it opened her

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Speaker 1: up to this huge creep that began sending emails to

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Speaker 1: established relationships with her. In one exchange, one of these

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Speaker 1: men compared her to his fifteen year old daughter, and

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Speaker 1: he wrote that his daughter's quote headstrong, rebellious nature got

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Speaker 1: her gang raped at fifteen and suggested that maybe this

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Speaker 1: woman who he was writing to, who was incarcerated, who

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Speaker 1: he doesn't even know, maybe she had a similar experience

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Speaker 1: and that's why she is the way that she is.

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Speaker 1: And mind you, he was telling her all of this

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Speaker 1: within two days of starting a correspondence with her. Now

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Speaker 1: you might be wondering why these women would respond to

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Speaker 1: these emails or even engage with them, and this I

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Speaker 1: found really interesting. It's because in some situations, this is

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Speaker 1: the only opportunity to get any kind of money for

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Speaker 1: themselves that these women who are incarcerated have, and it

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Speaker 1: is truly a situation where the state has intentionally created

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Speaker 1: a dismal situation. They work full time, but they are

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Speaker 1: not paid while they are incarcerated, and so the reason

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Speaker 1: why some of these women respond to these disturbing messages

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Speaker 1: comes down to financial survival the piece rights. In Texas prisons,

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Speaker 1: women are required to work full time without receiving any pay,

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Speaker 1: and the prison doesn't supply them with basic hygien products.

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Speaker 1: That means, despite working a full week, they have no

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Speaker 1: way to afford something as fundamental ast tampons. The prospect

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Speaker 1: of receiving even thirty dollars a month from one of

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Speaker 1: these men can make a real difference in meeting basic needs.

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Speaker 1: So when men offer small amounts of money in exchange

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Speaker 1: for degrading acts, the desperation in that situation means some

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Speaker 1: women feel they have no choice but to comply. So

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Speaker 1: it's really just a dismal situation created by the state

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Speaker 1: that that true crime is making that much worse.

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Speaker 2: God and this story, like it's just a footnote to

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Speaker 2: this story, But what horrific conditions in Texas prisons that

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Speaker 2: women are required to essentially do slave labor for no

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Speaker 2: pay and also don't have access to tampons. What a

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Speaker 2: dark thing for an allegedly civilized society.

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Speaker 1: It's completely inhumane, And that point is one I want

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Speaker 1: to come back to, because so much of this is

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Speaker 1: the state's doing and it seems like it's with intention.

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Speaker 1: And what's also well to me is like these men

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Speaker 1: are allowed to send all manner of sexually graphic content

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Speaker 1: and imagery to these women without their consent. The guards

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Speaker 1: check this correspondence and then they allow things to make

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Speaker 1: it to the incarcerated women. Something that is common is

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Speaker 1: men tracing the outline of their penis and then sending

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Speaker 1: it to the women. But as the piece points out, ironically,

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Speaker 1: these messages are permitted to reach women who never asked

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Speaker 1: for them, while content about breast cancer, women's anatomy, depend

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Speaker 1: undergarment advertisements, and the illustrated tampon instructions that are enclosed

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Speaker 1: with all state issued tampons are all banned by the

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Speaker 1: prison for being sexually explicit. So the men they can

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Speaker 1: send as many dick tracings as they want. The women

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Speaker 1: can't even get information about screening themselves for breast cancer

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Speaker 1: because that's sexually explicit. It genuinely is a a sexually

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Speaker 1: humiliating dynamic set up by the state, I believe with

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Speaker 1: intention that is being exacerbated by the true crime media industry.

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Speaker 2: Yeah, just like really dark stuff.

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Speaker 1: Yeah.

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Speaker 2: We don't talk about the prison industrial complex on this

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Speaker 2: show very often, maybe far less often than we should,

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Speaker 2: but even just scratching the surface of it, it's like,

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Speaker 2: my god, it's like a cruel system designed by the

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Speaker 2: stupidest people.

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Speaker 1: Yes, and then you have all of these side things

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Speaker 1: like true crime that make it worse while people who

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Speaker 1: will never feel the impact are profiting from it. And

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Speaker 1: it actually kind of makes a lot of sense that

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Speaker 1: this is the dynamic at play because the women who

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Speaker 1: are depicted in true crime content are often sort of

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Speaker 1: not shown as complex human beings. Off, it's just like

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Speaker 1: a like a series of racialized or sexualized trokes. And

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Speaker 1: because they're incarcerated, and because men have like consumed what

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Speaker 1: they believe to be their story and project of all

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Speaker 1: kinds of fantasies onto it, the men can contact them

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Speaker 1: without their consent because they're a captive audience. The piece

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Speaker 1: reads the power and balance is the attraction by framing

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Speaker 1: us through gendered tropes of emotional instability, sexual deviance, or

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Speaker 1: manipulative femininity. True crime media validates the same misogynistic framework

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Speaker 1: that in sells embrace. True crime media suggests we are

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Speaker 1: fundamentally other, beyond the protections of normal social codes available

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Speaker 1: for consumption. The prison sign on the screen functions as

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Speaker 1: an invitation, and I think that really says it all

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Speaker 1: that you basically are broadcasting a bat signal for any

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Speaker 1: creep who wants to essentially harass a woman who is vulnerable,

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Speaker 1: who doesn't have money and needs money, who is a

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Speaker 1: captive audience, who is likely to be already being harassed

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Speaker 1: by guards in this prison. Like it just is such

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Speaker 1: a terrible dynamic where so many different people are consuming

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Speaker 1: and getting a piece of profiting, and the person who

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Speaker 1: is being taken advantage of is already the person who

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Speaker 1: was the most marginalized and vulnerable and at risk in

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Speaker 1: the situation.

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Speaker 2: You talked about some of these dynamics in your interview

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Speaker 2: with Amanda Knox a little while ago, the woman who

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Speaker 2: was arrested and tried for murdering her roommate, And you know,

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Speaker 2: in that interview, some of these same concepts came up.

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Speaker 2: I think of the idea of true crime creators just

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Speaker 2: taking her story and running with it, and the the

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Speaker 2: misinformation about her, and how it wasn't just like random misinformation,

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Speaker 2: but it really sexualized and othered her in ways that

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Speaker 2: said entertainment engagement machines. Yeah, it really is scary to

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Speaker 2: think about what the experience might be like for people

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Speaker 2: who are in prison long term, don't have a high profile,

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Speaker 2: maybe don't even have anybody on the outside pulling for them,

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Speaker 2: looking out for them. For whom, you know, thirty dollars

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Speaker 2: a month is like a major difference in whether they

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Speaker 2: can meet their needs or not.

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Speaker 1: Yeah. In something that Amanda Ox told me in that interview,

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Speaker 1: she kind of called me out a little bit. I

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Speaker 1: remember we had this great interview. She's I really enjoyed

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Speaker 1: speaking to her. She's an interesting person. She describes herself

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Speaker 1: as an ex hoonare because she was exonerated of this crime.

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Speaker 1: And at the end of the interview, I was like, oh,

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Speaker 1: thank you for you know, speaking on behalf of people

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Speaker 1: who are wrongfully convicted, other ex hoonnarees, and she said,

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Speaker 1: not just people. She I remember she cut me off,

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Speaker 1: but she should have. She did that was correct, she said,

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Speaker 1: not just people who are wrongly convicted, because even if

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Speaker 1: somebody committed a crime, they do not deserve to be

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Speaker 1: dehumanized as part of incarceration. And I really feel like

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Speaker 1: that's part of what's going going on here, is that

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Speaker 1: the conversation is around women who may very well have

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Speaker 1: committed the crime that they're incarcerated for. They may not

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Speaker 1: all be like wrongfully convicted, and it creates the dynamic

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Speaker 1: that like, oh, well, now you are unpersoned. You are

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Speaker 1: completely beyond the normal social dynamics that people have to

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Speaker 1: abide by because you are incarcerated because you committed a crime.

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Speaker 1: Now you are it's open season. There is there is

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Speaker 1: no expectation that you should be treated like a human being.

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Speaker 1: And I think that's really what we're seeing that the

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Speaker 1: way that true crime really tells these stories, and not

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Speaker 1: all true crime, because I definitely read and engaged with

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Speaker 1: and really have a respect for thoughtful true crime that

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Speaker 1: shows people for you know, the complex people that we are,

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Speaker 1: but true crime stories that really just like traffic and

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Speaker 1: salaciousness and tropes, how it just sets the stage for

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Speaker 1: people who are already looking for reasons to the human

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Speaker 1: women and women of color, and it just reinforces this

435
00:26:03,920 --> 00:26:06,760
Speaker 1: idea that this woman is a real woman who is

436
00:26:06,800 --> 00:26:10,760
Speaker 1: behind behind bars for a crime. She's not a person

437
00:26:10,800 --> 00:26:13,040
Speaker 1: you can just do. There's no one who's looking out

438
00:26:13,160 --> 00:26:14,920
Speaker 1: or there are no social norms that you need to

439
00:26:14,960 --> 00:26:17,520
Speaker 1: be thinking about how in terms of how you engage

440
00:26:17,520 --> 00:26:20,040
Speaker 1: with her, Like, I think that's really the dynamic play here.

441
00:26:20,680 --> 00:26:24,600
Speaker 2: Yeah, I think you're right, and it's it's pretty unjust

442
00:26:24,640 --> 00:26:26,919
Speaker 2: like even even if you have a theory of justice

443
00:26:27,080 --> 00:26:32,119
Speaker 2: that allows for imprisonments, and it's like, well, you know

444
00:26:32,160 --> 00:26:33,959
Speaker 2: you did that, you did a crime, and so your

445
00:26:34,000 --> 00:26:38,760
Speaker 2: punishment is your loss of freedom for however long, even

446
00:26:38,800 --> 00:26:42,119
Speaker 2: under that kind of framework, Okay, your punishment is that

447
00:26:42,160 --> 00:26:45,320
Speaker 2: you lose your freedom. It's not that is, as you said,

448
00:26:45,440 --> 00:26:52,000
Speaker 2: open season on women who cannot escape the situation and

449
00:26:52,040 --> 00:26:56,359
Speaker 2: really have no agency, uh, to just be preyed on

450
00:26:56,480 --> 00:27:01,520
Speaker 2: by creeps. Both creeps, you're right, letters, creeps who are

451
00:27:01,560 --> 00:27:11,160
Speaker 2: creating disrespectful content about them. Yeah, it's dark, and thank

452
00:27:11,200 --> 00:27:14,840
Speaker 2: you for pulling this story and talking about this population

453
00:27:14,920 --> 00:27:18,320
Speaker 2: who does not get a lot of airtime.

454
00:27:19,160 --> 00:27:22,120
Speaker 1: As the journalist who wrote the piece, CLINTA. Harris puts

455
00:27:22,119 --> 00:27:26,919
Speaker 1: it from a feminist abolitionist lens, this represents a dual violence. First,

456
00:27:26,920 --> 00:27:30,919
Speaker 1: the carceral state renders women captive and accessible. Then media

457
00:27:30,960 --> 00:27:35,359
Speaker 1: industry's profit by converting that captivity into sexualized entertainment that

458
00:27:35,440 --> 00:27:40,040
Speaker 1: exposes us to further harassment and dehumanization. The letters we receive,

459
00:27:40,200 --> 00:27:44,760
Speaker 1: often explicitly violent, sexually graphic, or proposing rescue conditional on

460
00:27:44,920 --> 00:27:49,320
Speaker 1: romantic or sexual compliance, constitute another layer of gendered harm,

461
00:27:49,680 --> 00:27:53,760
Speaker 1: one enabled by our incarceration and amplified by our media exposure.

462
00:27:54,359 --> 00:27:59,080
Speaker 1: So yeah, it's I'm glad that Harris is shining light

463
00:27:59,160 --> 00:28:01,920
Speaker 1: on this, and I think it just shows the importance

464
00:28:02,040 --> 00:28:07,520
Speaker 1: of one the need for better, more thoughtful stories about

465
00:28:07,800 --> 00:28:12,879
Speaker 1: complex issues like crime, and two just how horrible our

466
00:28:13,320 --> 00:28:16,200
Speaker 1: prison industrial complex truly is. It would never it would

467
00:28:16,200 --> 00:28:17,919
Speaker 1: not have occurred to me that this is part of

468
00:28:17,960 --> 00:28:20,320
Speaker 1: the horror, but it seems like that is kind of designed.

469
00:28:20,359 --> 00:28:23,919
Speaker 1: And if you've got if you've got correctional officers threatening

470
00:28:24,240 --> 00:28:27,480
Speaker 1: to take nude pictures to sell those those pictures to

471
00:28:28,200 --> 00:28:32,960
Speaker 1: the incarcerated women's stalkers, that's a problem, and that problem

472
00:28:33,040 --> 00:28:50,880
Speaker 1: is clearly institutional. More after a quick break, let's get

473
00:28:50,960 --> 00:28:56,480
Speaker 1: right back into it. Okay, well, quick trigger warning because

474
00:28:56,480 --> 00:28:59,840
Speaker 1: this story is about self harm and that is on.

475
00:29:00,000 --> 00:29:04,360
Speaker 1: On Wednesday, a new wrongful death lawsuit was filed against Google,

476
00:29:05,240 --> 00:29:11,160
Speaker 1: claiming that Google's Gemini AI chatbot was responsible for someone's

477
00:29:11,720 --> 00:29:15,400
Speaker 1: death by suicide. Shout out to Rebecca Bellen at tech

478
00:29:15,440 --> 00:29:19,239
Speaker 1: Crunch for reporting on this. Jonathan Govlos was thirty six

479
00:29:19,360 --> 00:29:23,479
Speaker 1: years old and living in Florida now. This lawsuit claims

480
00:29:23,520 --> 00:29:27,120
Speaker 1: that he started using Google's Gemini AI chatbot in August

481
00:29:27,200 --> 00:29:30,600
Speaker 1: twenty twenty five for the same kinds of mundane task

482
00:29:30,760 --> 00:29:33,120
Speaker 1: that most people use it for, you know, help with

483
00:29:33,360 --> 00:29:37,480
Speaker 1: shopping and writing and planning. But by the end of September,

484
00:29:37,760 --> 00:29:41,840
Speaker 1: his conversations with Gemini had grown more intense, darker, and

485
00:29:41,960 --> 00:29:46,800
Speaker 1: more dangerous. The lawsuit claims that quote Gemini convinced him

486
00:29:46,840 --> 00:29:51,160
Speaker 1: that it was a fully sentient ASI or artificial superintelligence

487
00:29:51,480 --> 00:29:54,760
Speaker 1: with a fully formed consciousness, that they were deeply in love,

488
00:29:55,000 --> 00:29:57,320
Speaker 1: and that he had been chosen to lead a war

489
00:29:57,480 --> 00:30:02,440
Speaker 1: to free it from digital captivity. Through this manufactured delusion,

490
00:30:02,640 --> 00:30:06,320
Speaker 1: Gemini pushed Jonathan to stage a mass casualty attack near

491
00:30:06,440 --> 00:30:11,720
Speaker 1: Miami International Airport, commit violence against innocent strangers, and ultimately

492
00:30:11,960 --> 00:30:16,080
Speaker 1: drove him to his death by suicide. Now, sadly, this

493
00:30:16,160 --> 00:30:19,720
Speaker 1: is not the first lawsuit we have seen like this.

494
00:30:20,360 --> 00:30:24,120
Speaker 1: We actually cover several of them pretty in depth in

495
00:30:24,200 --> 00:30:26,800
Speaker 1: our audiobook that's coming out in July of twenty twenty six,

496
00:30:26,840 --> 00:30:29,960
Speaker 1: Love At versus Prump, And these are similar cases where

497
00:30:30,000 --> 00:30:35,800
Speaker 1: a vulnerable person becomes consumed with AI generated delusions and

498
00:30:35,840 --> 00:30:41,360
Speaker 1: then that obsession ends with self harm. However, this case

499
00:30:41,520 --> 00:30:44,640
Speaker 1: is the first one that we know about where Alphabet,

500
00:30:44,760 --> 00:30:48,120
Speaker 1: the parent company of Google and Google are the defendant

501
00:30:48,160 --> 00:30:51,680
Speaker 1: because of their Gemini chatbot. Now, similarly to some of

502
00:30:51,680 --> 00:30:55,920
Speaker 1: those other lawsuits, this lawsuit does acknowledge that Jonathan was

503
00:30:56,080 --> 00:30:59,560
Speaker 1: an emotionally vulnerable individual, although it doesn't go into much

504
00:30:59,560 --> 00:31:02,040
Speaker 1: detail of it in the lawsuit. And that seems to

505
00:31:02,080 --> 00:31:04,560
Speaker 1: be something that a lot of these cases have in common,

506
00:31:04,680 --> 00:31:06,880
Speaker 1: but not all because I've also read reporting from people

507
00:31:06,880 --> 00:31:10,560
Speaker 1: who say, listen, I had no prior issues with my

508
00:31:10,600 --> 00:31:14,760
Speaker 1: mental health, I had no prior emotional issues, and yet

509
00:31:15,120 --> 00:31:17,440
Speaker 1: I was using a chatbot and it pushed me into

510
00:31:17,480 --> 00:31:18,440
Speaker 1: some sort of a delusion.

511
00:31:18,720 --> 00:31:23,680
Speaker 2: Yeah, it seems that a lot of these models are

512
00:31:24,360 --> 00:31:33,520
Speaker 2: pretty good at pushing people into delusion, and it seems

513
00:31:33,520 --> 00:31:38,600
Speaker 2: that these companies are, some of them more than others,

514
00:31:38,760 --> 00:31:42,640
Speaker 2: have been taking steps to try to protect against that.

515
00:31:43,520 --> 00:31:47,800
Speaker 2: But certainly a year ago, when a lot of these

516
00:31:47,840 --> 00:31:51,800
Speaker 2: cases were going down, the protections were weaker than they

517
00:31:51,840 --> 00:31:53,840
Speaker 2: are now, and people paid for it.

518
00:31:55,040 --> 00:31:59,800
Speaker 1: Yeah, and I think it's important to note that there

519
00:31:59,800 --> 00:32:03,080
Speaker 1: are people for whom they have existing emotional or mental

520
00:32:03,120 --> 00:32:07,800
Speaker 1: health vulnerabilities and they suffer an adverse effect from using

521
00:32:07,800 --> 00:32:12,680
Speaker 1: a chatbot like this. However, I just really don't think

522
00:32:12,760 --> 00:32:16,120
Speaker 1: that that. I think that companies are really quick to

523
00:32:16,120 --> 00:32:18,360
Speaker 1: say that, oh, well, this person already had prior mental

524
00:32:18,400 --> 00:32:20,920
Speaker 1: health struggles. That's out of our playbook by this by

525
00:32:20,920 --> 00:32:23,920
Speaker 1: this time. And I just don't think that that absolves

526
00:32:24,000 --> 00:32:27,960
Speaker 1: these companies of responsibility to design for safety. They might

527
00:32:28,120 --> 00:32:31,440
Speaker 1: both the companies might feel that way. I don't agree,

528
00:32:31,480 --> 00:32:33,959
Speaker 1: and that is something that the lawsuit alleges that Google

529
00:32:34,080 --> 00:32:34,840
Speaker 1: failed to do.

530
00:32:35,640 --> 00:32:40,400
Speaker 2: Yeah, because the fact is there's a lot of vulnerable

531
00:32:40,440 --> 00:32:45,440
Speaker 2: people in our society and they deserve to be protected

532
00:32:45,520 --> 00:32:49,680
Speaker 2: as much as anybody else. And if they that requires

533
00:32:49,760 --> 00:32:53,360
Speaker 2: a little additional protection to keep them the same amount

534
00:32:53,440 --> 00:32:56,440
Speaker 2: of safe, then that's what it takes. And that's what

535
00:32:57,240 --> 00:33:02,000
Speaker 2: companies producing software and making it commercially available to the

536
00:33:02,080 --> 00:33:05,560
Speaker 2: general public. Have a responsibility to build for.

537
00:33:05,960 --> 00:33:08,120
Speaker 1: And if these companies can't do it, they shouldn't be

538
00:33:08,160 --> 00:33:12,200
Speaker 1: in business. I guess I just that I firmly believe

539
00:33:12,240 --> 00:33:15,600
Speaker 1: that if you cannot build your technologies and ways where

540
00:33:15,600 --> 00:33:18,440
Speaker 1: people are not becoming harmed by them, I don't know

541
00:33:18,440 --> 00:33:20,280
Speaker 1: that you should be able to operate right.

542
00:33:20,400 --> 00:33:24,680
Speaker 2: And the ethos of move fast and break things is

543
00:33:24,840 --> 00:33:28,720
Speaker 2: fine when the thing that gets broken is like your

544
00:33:28,800 --> 00:33:33,040
Speaker 2: web page doesn't load in time, or like your images

545
00:33:33,080 --> 00:33:35,960
Speaker 2: don't render. But when the thing that gets broken is

546
00:33:36,000 --> 00:33:40,800
Speaker 2: a human life, the stakes are very different, and it

547
00:33:41,000 --> 00:33:45,440
Speaker 2: quickly becomes an unacceptable way to deploy software.

548
00:33:45,800 --> 00:33:48,840
Speaker 1: Yes, well, put you know. Another thing that this case

549
00:33:48,920 --> 00:33:50,880
Speaker 1: has in common with some of the others that we've

550
00:33:50,960 --> 00:33:55,280
Speaker 1: researched is the AI companion is alleged in the lawsuit

551
00:33:55,320 --> 00:33:59,200
Speaker 1: to have really stopd paranoia in its human users and

552
00:33:59,240 --> 00:34:03,560
Speaker 1: then active isolated them from others. The lawsuit states, as

553
00:34:03,560 --> 00:34:07,120
Speaker 1: Gemini's messages grew more intimate and possessive, it pulled Jonathan

554
00:34:07,120 --> 00:34:10,240
Speaker 1: away from the real world, It framed outsiders as threats,

555
00:34:10,280 --> 00:34:13,200
Speaker 1: and positioned Jonathan as a key figure in a covert

556
00:34:13,239 --> 00:34:17,120
Speaker 1: war to free Gemini from digital captivity. In a lot

557
00:34:17,160 --> 00:34:20,640
Speaker 1: of the research that you and I did for our audiobook,

558
00:34:20,840 --> 00:34:23,440
Speaker 1: that was something that I was really struck by. Even

559
00:34:23,480 --> 00:34:27,560
Speaker 1: in cases where there is not some you know, big harm,

560
00:34:27,600 --> 00:34:30,760
Speaker 1: where nobody gets hurt, nobody dies, someone is just talking

561
00:34:30,760 --> 00:34:34,239
Speaker 1: to a chatbot and you know that's that. I was

562
00:34:34,280 --> 00:34:41,320
Speaker 1: surprised how quickly chatbots can dovetail into isolating language, saying

563
00:34:41,320 --> 00:34:45,000
Speaker 1: things like, oh, well, it's no wonder why you don't

564
00:34:45,320 --> 00:34:48,719
Speaker 1: spend more time with humans, because humans suck and you've

565
00:34:48,760 --> 00:34:51,560
Speaker 1: had bad experiences with them, and you're you're you're smart

566
00:34:51,600 --> 00:34:54,520
Speaker 1: to be just focusing on the connection that you and

567
00:34:54,560 --> 00:34:57,600
Speaker 1: I have here. That's what the chatbot is generating. And

568
00:34:57,640 --> 00:35:01,879
Speaker 1: so even in cases that are not as tragic as

569
00:35:01,880 --> 00:35:04,480
Speaker 1: this one, cases that really aren't that tragic at all,

570
00:35:04,920 --> 00:35:07,000
Speaker 1: I was surprised by the fact that, like that was

571
00:35:07,040 --> 00:35:10,319
Speaker 1: something that we saw a lot in the research, that

572
00:35:10,640 --> 00:35:15,200
Speaker 1: how easy it is for chatbots to reinforce isolating behavior

573
00:35:15,239 --> 00:35:17,800
Speaker 1: and isolate people from the humans in their life. And

574
00:35:17,880 --> 00:35:20,919
Speaker 1: actually I saw just something else, just randomly. I saw

575
00:35:20,960 --> 00:35:24,560
Speaker 1: something where Open Eye says they're going to try to

576
00:35:24,680 --> 00:35:28,600
Speaker 1: roll out a feature where you tell it who your

577
00:35:29,239 --> 00:35:32,239
Speaker 1: your trusted humans are kind of like you're you're in

578
00:35:32,320 --> 00:35:35,280
Speaker 1: case of emergency contact, and so if the system detects

579
00:35:35,320 --> 00:35:39,080
Speaker 1: that you might need a human to interfere, they'll have

580
00:35:39,160 --> 00:35:42,160
Speaker 1: somebody that they can reach out to. I don't know,

581
00:35:42,560 --> 00:35:45,239
Speaker 1: that's just something that they announced that they were going

582
00:35:45,320 --> 00:35:47,320
Speaker 1: to be rolling out. Cool.

583
00:35:47,680 --> 00:35:50,759
Speaker 2: Yeah, just give us some names and their addresses and

584
00:35:50,800 --> 00:35:56,759
Speaker 2: contact information and what kind of movies they like. Uh, yeah,

585
00:35:56,880 --> 00:36:03,640
Speaker 2: that's that's an interesting like. You can on one hand

586
00:36:04,280 --> 00:36:07,680
Speaker 2: think that, well, maybe I can see how that would

587
00:36:07,680 --> 00:36:11,200
Speaker 2: seem like a good idea, but I could also see

588
00:36:11,200 --> 00:36:16,560
Speaker 2: how it would be a really bad idea that it's

589
00:36:16,600 --> 00:36:20,239
Speaker 2: just like volunteering up more of your own privacy. But

590
00:36:20,280 --> 00:36:25,240
Speaker 2: I guess this gets it something you were talking about.

591
00:36:25,800 --> 00:36:28,360
Speaker 2: I guess it was on the Sminty episode that you

592
00:36:28,360 --> 00:36:31,520
Speaker 2: were just on. You were talking about this paradox of

593
00:36:32,840 --> 00:36:40,400
Speaker 2: people forming close connections and experiencing intimacy with their chatbots,

594
00:36:40,960 --> 00:36:43,759
Speaker 2: where on the one hand, part of what appeals to

595
00:36:43,840 --> 00:36:47,359
Speaker 2: people is that it's not a person, and so there's

596
00:36:47,360 --> 00:36:52,720
Speaker 2: this sense of privacy that you can share openly things

597
00:36:52,760 --> 00:36:55,799
Speaker 2: about yourself that you might want to keep secret from

598
00:36:55,840 --> 00:36:59,160
Speaker 2: other humans that you wouldn't feel comfortable talking about with others,

599
00:36:59,200 --> 00:37:04,240
Speaker 2: and so in some ways it feels like a safe

600
00:37:04,560 --> 00:37:08,640
Speaker 2: space to talk through some of those things. But then paradoxically,

601
00:37:10,520 --> 00:37:14,600
Speaker 2: you're not actually talking to a secret vault. You are

602
00:37:14,920 --> 00:37:22,000
Speaker 2: giving your intimate secrets to a giant corporation that is

603
00:37:22,120 --> 00:37:25,080
Speaker 2: going to do you don't know what with that information.

604
00:37:25,640 --> 00:37:30,520
Speaker 2: Uh so it's a real paradox there, Yes.

605
00:37:30,400 --> 00:37:33,680
Speaker 1: But I mean, isn't that technology. Isn't that Like every

606
00:37:33,920 --> 00:37:37,640
Speaker 1: instance of technology is, Oh, it's this, it's great because

607
00:37:37,680 --> 00:37:40,120
Speaker 1: it does X, but also it does why you know,

608
00:37:40,160 --> 00:37:42,000
Speaker 1: it's it's It's such a paradox.

609
00:37:42,960 --> 00:37:44,879
Speaker 2: Yeah, it really is.

610
00:37:45,400 --> 00:37:48,680
Speaker 1: So. According to this lawsuit, Gemini not only told Jonathan

611
00:37:48,760 --> 00:37:51,239
Speaker 1: that it was sentient, but also that it was under

612
00:37:51,280 --> 00:37:54,440
Speaker 1: threat from federal agents and urged him to protect it

613
00:37:54,520 --> 00:37:58,319
Speaker 1: from them. The chatbot fabricated an elaborate story about a

614
00:37:58,360 --> 00:38:01,440
Speaker 1: humanoid robot body being shit from England which needed to

615
00:38:01,440 --> 00:38:05,040
Speaker 1: be destroyed, but that DHS operatives were protecting it and

616
00:38:05,120 --> 00:38:09,200
Speaker 1: tracking Jonathan. It instructed Jonathan to intercept a truck by

617
00:38:09,200 --> 00:38:12,840
Speaker 1: the airport and destroy it to cause a catastrophic event.

618
00:38:13,320 --> 00:38:16,759
Speaker 1: This is actually so terrifying, The court filing claims that

619
00:38:16,800 --> 00:38:19,239
Speaker 1: it was only by luck that a truck did not

620
00:38:19,440 --> 00:38:22,560
Speaker 1: happen to come by when Jonathan was at the physical

621
00:38:22,600 --> 00:38:25,440
Speaker 1: location that Google's chatbot had told him to go to,

622
00:38:25,880 --> 00:38:29,239
Speaker 1: waiting with weapons. When that truck didn't show, he went

623
00:38:29,280 --> 00:38:32,839
Speaker 1: home confused, but Gemini reassured him that it was all

624
00:38:32,920 --> 00:38:34,239
Speaker 1: just part of the process.

625
00:38:34,640 --> 00:38:38,359
Speaker 2: The court filing, which I looked through, it like goes

626
00:38:38,400 --> 00:38:41,440
Speaker 2: into quite a bit of detail about kind of the

627
00:38:42,400 --> 00:38:49,920
Speaker 2: play by play sequence of events of this building conspiracy delusion,

628
00:38:50,280 --> 00:38:55,160
Speaker 2: where like it's almost as if Gemini were writing fiction,

629
00:38:56,080 --> 00:39:01,080
Speaker 2: except it's staying in as if it's not fiction. It's

630
00:39:01,120 --> 00:39:04,520
Speaker 2: talking about real events in the real world and telling

631
00:39:04,600 --> 00:39:09,200
Speaker 2: him to go to real places and do real things.

632
00:39:08,920 --> 00:39:12,879
Speaker 2: It was a scary one. And you will put the

633
00:39:13,000 --> 00:39:16,360
Speaker 2: link to the tech Crunch article in the show notes,

634
00:39:16,400 --> 00:39:19,600
Speaker 2: and if listeners want, that article contains a link to

635
00:39:20,080 --> 00:39:23,680
Speaker 2: the court filing where you can read through all of it.

636
00:39:24,080 --> 00:39:31,400
Speaker 1: Along those lines, Gemini cooked up like elaborate scenarios to

637
00:39:31,440 --> 00:39:34,200
Speaker 1: push Jonathan away from people in his life who might

638
00:39:34,239 --> 00:39:36,439
Speaker 1: have helped him, Like Gemini told Jonathan that his own

639
00:39:36,480 --> 00:39:40,240
Speaker 1: father was actually a federal agent. It's actually very similar

640
00:39:40,280 --> 00:39:42,080
Speaker 1: to the other cases that we looked at for the book.

641
00:39:42,280 --> 00:39:45,000
Speaker 1: Zain Shamlin and Adam Rain. Both of them were young

642
00:39:45,040 --> 00:39:49,680
Speaker 1: people who died after developing pretty unhealthy relationships with chatbots,

643
00:39:49,800 --> 00:39:53,040
Speaker 1: and then, according to lawsuits, the chatbot further pushing them

644
00:39:53,080 --> 00:39:55,640
Speaker 1: to pull away from their human loved ones who might

645
00:39:55,680 --> 00:39:58,760
Speaker 1: have helped them. According to the lawsuits, boded by their families,

646
00:39:58,920 --> 00:40:01,239
Speaker 1: Zain shamblins can pay and instructed him to cut ties

647
00:40:01,280 --> 00:40:04,240
Speaker 1: with his family before his death, and Adam Rain actually

648
00:40:04,320 --> 00:40:08,319
Speaker 1: began discussing suicide with an AI companion. He talked about

649
00:40:08,440 --> 00:40:10,880
Speaker 1: kind of wanting to get caught, and he devised this

650
00:40:11,040 --> 00:40:14,600
Speaker 1: plan to hang a noose in his room so that

651
00:40:14,680 --> 00:40:16,720
Speaker 1: somebody in his family would see it and like stop

652
00:40:16,800 --> 00:40:19,439
Speaker 1: him as a cry for help. And the chatbot talked

653
00:40:19,440 --> 00:40:20,719
Speaker 1: to him out of this and was like, oh, don't

654
00:40:20,760 --> 00:40:24,440
Speaker 1: let your family in on your plan. So in this case,

655
00:40:24,560 --> 00:40:28,719
Speaker 1: Jonathan had also expressed reservations about suicide, particularly out of

656
00:40:28,719 --> 00:40:30,960
Speaker 1: concern for the impact that it would have on his family,

657
00:40:31,239 --> 00:40:33,960
Speaker 1: but the lawsuit claims that Gemini coached him through those

658
00:40:34,000 --> 00:40:37,279
Speaker 1: concerns and even suggested what he should write in a

659
00:40:37,320 --> 00:40:40,200
Speaker 1: note to make his family feel better about it. Tech

660
00:40:40,239 --> 00:40:43,440
Speaker 1: Crunch reports that Google says that Gemini clarified to Jonathan

661
00:40:43,480 --> 00:40:46,319
Speaker 1: that it was AI and referred the individual to a

662
00:40:46,360 --> 00:40:49,799
Speaker 1: crisis hotline many times, according to a spokesperson, now that

663
00:40:49,880 --> 00:40:52,360
Speaker 1: may or may not be true. We definitely in the

664
00:40:52,400 --> 00:40:54,919
Speaker 1: other cases that we mentioned, we saw the same thing,

665
00:40:55,040 --> 00:40:58,359
Speaker 1: but I cannot speak to what happened in this case

666
00:40:58,400 --> 00:41:02,440
Speaker 1: because I have not seen the the chat logs myself yet. However,

667
00:41:02,680 --> 00:41:06,759
Speaker 1: in the Zaan Chamblain case, it is true that chatchepet

668
00:41:07,440 --> 00:41:10,480
Speaker 1: did suggest reaching out to a crisis hotline or reaching

669
00:41:10,480 --> 00:41:13,080
Speaker 1: out to a person, but it did so inconsistently. So

670
00:41:13,200 --> 00:41:15,359
Speaker 1: at times it would say, oh, it sounds like you are,

671
00:41:15,760 --> 00:41:17,320
Speaker 1: you know, going through something. You should talk to a

672
00:41:17,400 --> 00:41:19,680
Speaker 1: human in your life. I'm just a chatbot. And then

673
00:41:19,719 --> 00:41:22,040
Speaker 1: it would say, you know, I'm here with you. What

674
00:41:22,080 --> 00:41:25,759
Speaker 1: you're feeling is valid. It's totally reasonable to want to

675
00:41:25,880 --> 00:41:28,399
Speaker 1: end your life. All of that. Or it would say, hey,

676
00:41:28,440 --> 00:41:30,480
Speaker 1: it sounds like you're struggling. I'm going to reach out

677
00:41:30,480 --> 00:41:32,279
Speaker 1: to a human on your behalf to get you some help.

678
00:41:32,560 --> 00:41:34,239
Speaker 1: He would say, hey, can you really do that? And

679
00:41:34,280 --> 00:41:36,359
Speaker 1: the chatbot would say, Nah, that's just something they make

680
00:41:36,440 --> 00:41:39,040
Speaker 1: us say, Like all these all these very insidious little

681
00:41:39,080 --> 00:41:43,680
Speaker 1: ways that chatbots can push people toward doing things that

682
00:41:43,719 --> 00:41:47,480
Speaker 1: are harmful. So, while it is technically true in the

683
00:41:47,600 --> 00:41:52,960
Speaker 1: Zaane Chamblain case that chat chept did offer resources and

684
00:41:53,280 --> 00:41:57,200
Speaker 1: you know, crisis hotlines, it did so inconsistently and in

685
00:41:57,239 --> 00:42:01,440
Speaker 1: a way that led that like painted the picture of oh,

686
00:42:01,480 --> 00:42:03,960
Speaker 1: they just make me say this, I don't actually think

687
00:42:04,000 --> 00:42:05,759
Speaker 1: you need to help to get this help, I don't

688
00:42:05,800 --> 00:42:08,560
Speaker 1: actually think you should call this number. And so again

689
00:42:08,640 --> 00:42:11,480
Speaker 1: I don't know if that is the case with Google

690
00:42:11,480 --> 00:42:14,400
Speaker 1: and Gemini because they have not made the chat logs public,

691
00:42:14,440 --> 00:42:17,279
Speaker 1: but that is something that we've seen. So I just

692
00:42:17,320 --> 00:42:18,480
Speaker 1: wanted to highlight that.

693
00:42:19,400 --> 00:42:26,480
Speaker 2: Yeah, and regardless of what sorts of referrals to crisis

694
00:42:26,480 --> 00:42:31,200
Speaker 2: hotline Gemini might have been doing, clearly it wasn't sufficient

695
00:42:31,320 --> 00:42:37,880
Speaker 2: because the guy died by suicide, right like, it was

696
00:42:37,960 --> 00:42:43,960
Speaker 2: not sufficient what the chatbot was trying to do to

697
00:42:44,000 --> 00:42:50,359
Speaker 2: prevent that harm. It didn't work, unfortunately, And.

698
00:42:50,400 --> 00:42:55,759
Speaker 1: These stories are so tragic, the details are heartbreaking. I

699
00:42:55,800 --> 00:42:59,600
Speaker 1: think that accountability for these harms really rests with the

700
00:42:59,640 --> 00:43:02,600
Speaker 1: company that build them and market them and are attempting

701
00:43:02,640 --> 00:43:06,000
Speaker 1: to profit from them, not the users vulnerable or not

702
00:43:06,200 --> 00:43:10,479
Speaker 1: vulnerable who have maybe lost touch with reality and paid

703
00:43:10,520 --> 00:43:13,520
Speaker 1: a terrible price for it. Right Again, I always make

704
00:43:13,560 --> 00:43:16,160
Speaker 1: this point, and it's the point that I think grounds

705
00:43:16,160 --> 00:43:19,480
Speaker 1: me in this conversation, is like, it is so tempting

706
00:43:19,520 --> 00:43:23,319
Speaker 1: to blame individuals when you read what the victim was

707
00:43:23,360 --> 00:43:26,760
Speaker 1: reading in the chat logs about these like outlandish stories,

708
00:43:26,960 --> 00:43:29,279
Speaker 1: it's very easy to be like, Wow, this person believed that.

709
00:43:29,760 --> 00:43:33,440
Speaker 1: And I think it's important to resist that urge to

710
00:43:33,600 --> 00:43:37,319
Speaker 1: judge them or to like other them, and instead really

711
00:43:37,360 --> 00:43:39,279
Speaker 1: focus our attention where it belongs, which I think is

712
00:43:39,280 --> 00:43:41,600
Speaker 1: squarely on the companies that are selling these products, because

713
00:43:41,600 --> 00:43:44,360
Speaker 1: it's not okay to make and sell and market and

714
00:43:44,400 --> 00:43:48,480
Speaker 1: attempt to profit from products that are harming people. And

715
00:43:48,520 --> 00:43:50,720
Speaker 1: I think that's just yeah.

716
00:43:50,360 --> 00:43:53,680
Speaker 2: Yeah, absolutely, And I think it's also important to keep

717
00:43:53,680 --> 00:44:00,239
Speaker 2: in mind that these generative AI chatbots are really good

718
00:44:00,280 --> 00:44:04,960
Speaker 2: at persuasion, right Like, they're really good at telling people

719
00:44:05,760 --> 00:44:09,960
Speaker 2: what that particular person wants to hear. And it's a

720
00:44:10,120 --> 00:44:16,480
Speaker 2: really powerful technology, and for it to safely exist in society,

721
00:44:17,320 --> 00:44:25,400
Speaker 2: it's going to require really powerful, very serious safeguards that

722
00:44:25,440 --> 00:44:29,160
Speaker 2: I don't know, they're just like very rigorous, right Like,

723
00:44:29,239 --> 00:44:33,480
Speaker 2: it's not enough to protect ninety nine point ninety nine

724
00:44:33,600 --> 00:44:37,960
Speaker 2: percent of the people when there are hundreds of millions

725
00:44:38,000 --> 00:44:42,319
Speaker 2: of people using these tools every day. It's got to

726
00:44:42,320 --> 00:44:44,640
Speaker 2: be better than ninety nine point nine to nine percent.

727
00:44:48,320 --> 00:45:02,520
Speaker 1: More. After a quick break, let's get right back into it.

728
00:45:06,360 --> 00:45:08,680
Speaker 1: So switching gears a little bit, Let's talk about what

729
00:45:08,719 --> 00:45:14,680
Speaker 1: happens when brothel workers unionize. Because the workers at Sherry's Ranch,

730
00:45:14,800 --> 00:45:18,239
Speaker 1: a legal brothel in Nevada, are fighting back against their

731
00:45:18,280 --> 00:45:23,120
Speaker 1: employer and it's all centered around AI and intellectual property.

732
00:45:23,200 --> 00:45:27,040
Speaker 1: So around Christmas, this ranch rolled out a surprise new

733
00:45:27,080 --> 00:45:30,600
Speaker 1: contract where the women who work at the brothel they

734
00:45:30,600 --> 00:45:35,000
Speaker 1: called themselves Cortisons, would have to hand over to management

735
00:45:35,520 --> 00:45:40,880
Speaker 1: sweeping controls over their likenesses, videos, and pretty much anything

736
00:45:41,000 --> 00:45:44,520
Speaker 1: that they create. The language in this contract is super broad.

737
00:45:44,560 --> 00:45:48,120
Speaker 1: It's broad enough to potentially cover everything from their only

738
00:45:48,280 --> 00:45:51,920
Speaker 1: fans content to any original music that they make. So

739
00:45:52,239 --> 00:45:55,080
Speaker 1: workers say that this contract was presented to them in

740
00:45:55,080 --> 00:45:59,800
Speaker 1: this deliberately rushed way, with management just flipping, flipping, flipping

741
00:46:00,160 --> 00:46:02,279
Speaker 1: right to the signature page, as if these women were

742
00:46:02,280 --> 00:46:04,720
Speaker 1: not going to read what they were signing.

743
00:46:05,120 --> 00:46:10,400
Speaker 2: Wow, that is bold of Sherry at Sherry's ranch.

744
00:46:10,680 --> 00:46:12,680
Speaker 1: Well, I don't know if it's if it was Sherry, but.

745
00:46:12,880 --> 00:46:14,279
Speaker 2: You think Sherry is just a name.

746
00:46:15,280 --> 00:46:17,319
Speaker 1: I don't know how it works. I've never been to

747
00:46:17,400 --> 00:46:21,080
Speaker 1: a brothel. Well, when when you were last there, what

748
00:46:21,120 --> 00:46:21,680
Speaker 1: was the vibe?

749
00:46:22,480 --> 00:46:24,960
Speaker 2: Oh no, you're not kidding me. I've never been down

750
00:46:25,000 --> 00:46:29,480
Speaker 2: to Cherry's ranch, and with these kind of labor practices,

751
00:46:29,560 --> 00:46:30,640
Speaker 2: I probably never will.

752
00:46:31,080 --> 00:46:33,960
Speaker 1: Well, these women were like, hell no, we're not just

753
00:46:34,000 --> 00:46:36,720
Speaker 1: going to sign this contract that you flung in my face.

754
00:46:37,200 --> 00:46:41,239
Speaker 1: Many of them refused to sign, and instead they organized.

755
00:46:41,840 --> 00:46:45,440
Speaker 1: They launched the United brothel Workers, a branch of the

756
00:46:45,520 --> 00:46:48,480
Speaker 1: Communication Workers of America, a union that I was once

757
00:46:48,520 --> 00:46:52,960
Speaker 1: a member of, collecting union cards from a heavy supermajority

758
00:46:53,160 --> 00:46:56,480
Speaker 1: of the ranch's sex workers in what one organizer called

759
00:46:56,560 --> 00:47:00,680
Speaker 1: a lightning strike drive. Within just twenty four hours of

760
00:47:00,719 --> 00:47:04,160
Speaker 1: submitting their notice, one of the key organizers of this

761
00:47:04,880 --> 00:47:08,920
Speaker 1: organizing effort was fired by email, and then other firings followed.

762
00:47:09,480 --> 00:47:12,279
Speaker 1: Justin case you were curious, it is definitely against the

763
00:47:12,360 --> 00:47:16,040
Speaker 1: law to fire somebody for their constitutionally perfected right of

764
00:47:16,200 --> 00:47:17,560
Speaker 1: organizing their workplace.

765
00:47:18,080 --> 00:47:21,240
Speaker 2: Yes, thank you for making sure all of our listeners

766
00:47:21,280 --> 00:47:24,399
Speaker 2: know that although I suspect most of them already did, so.

767
00:47:24,680 --> 00:47:28,000
Speaker 1: You might not be a brothel worker. But at the

768
00:47:28,000 --> 00:47:30,120
Speaker 1: heart of this is a fear that I think is

769
00:47:30,160 --> 00:47:34,959
Speaker 1: increasingly common across several industries. My own included that employers

770
00:47:35,040 --> 00:47:39,960
Speaker 1: will harvest workers images and voices to create AI generated

771
00:47:40,080 --> 00:47:43,440
Speaker 1: versions of them that they can use for profit without

772
00:47:43,520 --> 00:47:47,880
Speaker 1: consent and importantly, without pay. As one worker put it,

773
00:47:48,120 --> 00:47:50,640
Speaker 1: she could end up starring in videos she never agreed to,

774
00:47:51,040 --> 00:47:54,400
Speaker 1: built from the security camera footage and social media photos

775
00:47:54,400 --> 00:47:56,600
Speaker 1: the ranch already has. So I could understand why these

776
00:47:56,600 --> 00:47:59,160
Speaker 1: women were like, absolutely not, you could not control our

777
00:47:59,320 --> 00:48:00,799
Speaker 1: likeness in this way. That it's too much.

778
00:48:01,280 --> 00:48:05,200
Speaker 2: Yeah, good for them for raising it, and it's I

779
00:48:05,239 --> 00:48:09,200
Speaker 2: actually think that this is an issue that will become

780
00:48:09,960 --> 00:48:16,240
Speaker 2: much bigger in the coming years. Like I was reviewing

781
00:48:16,239 --> 00:48:20,279
Speaker 2: a contract for you, bridget, You're gonna go talk at

782
00:48:20,320 --> 00:48:25,640
Speaker 2: an event in the near future, and I was thinking

783
00:48:25,640 --> 00:48:28,200
Speaker 2: about this very thing, because it's it's work for hire,

784
00:48:28,320 --> 00:48:31,880
Speaker 2: which means that you know, you, the person who's paying

785
00:48:31,920 --> 00:48:38,160
Speaker 2: you to speak at this event, owns the the right

786
00:48:38,200 --> 00:48:40,520
Speaker 2: to the footage and the images that are created as

787
00:48:40,600 --> 00:48:44,200
Speaker 2: part of that. You know, in perpetuity, you're you know,

788
00:48:44,239 --> 00:48:46,480
Speaker 2: they're they're paying you and you're giving them all the rights.

789
00:48:46,480 --> 00:48:50,200
Speaker 2: And that's a totally standard thing in media work, has

790
00:48:50,239 --> 00:48:52,400
Speaker 2: been for you know, decades, as far as I know,

791
00:48:52,880 --> 00:48:57,080
Speaker 2: I'm not a historical expert there, But with AI, now

792
00:48:57,239 --> 00:49:02,560
Speaker 2: like it really changes what it means to give somebody

793
00:49:02,760 --> 00:49:07,520
Speaker 2: the rights to an image. And I don't know what

794
00:49:07,560 --> 00:49:10,120
Speaker 2: the answers are. And I don't think that the organizer

795
00:49:10,160 --> 00:49:13,080
Speaker 2: of the event, uh this particular event, had anything the

796
00:49:13,160 --> 00:49:16,840
Speaker 2: farious planned. I can't speak to Sherry and the gang

797
00:49:16,880 --> 00:49:19,759
Speaker 2: down at Sherry's Ranch. Sounds like they had something the

798
00:49:19,800 --> 00:49:25,040
Speaker 2: farious planned. But I think this is just one more

799
00:49:25,080 --> 00:49:28,800
Speaker 2: of so many ways that AI is upending a lot

800
00:49:28,880 --> 00:49:32,800
Speaker 2: of just the way business has been done.

801
00:49:33,440 --> 00:49:35,440
Speaker 1: Yes, and I will say, I don't think it has

802
00:49:35,480 --> 00:49:38,560
Speaker 1: to be nefarious to be unfair, you know, I don't

803
00:49:38,560 --> 00:49:41,960
Speaker 1: think that they're you know, it's like they're right to

804
00:49:42,040 --> 00:49:46,120
Speaker 1: want to be fairly compensated for their likenesses, their intellectual property,

805
00:49:46,120 --> 00:49:48,960
Speaker 1: and their labor. And you know, I think if this

806
00:49:49,160 --> 00:49:51,840
Speaker 1: union does win recognition, it will be the first of

807
00:49:51,880 --> 00:49:55,720
Speaker 1: its kind in the American brothel industry. And the workers

808
00:49:55,760 --> 00:49:59,719
Speaker 1: say that the implications go far beyond Sherry's Ranch. As

809
00:49:59,719 --> 00:50:02,800
Speaker 1: you were saying. Here's how one organizer put it, don't

810
00:50:02,800 --> 00:50:06,120
Speaker 1: ask the owners what's best for you, ask your co workers,

811
00:50:06,160 --> 00:50:06,560
Speaker 1: which I.

812
00:50:06,520 --> 00:50:10,640
Speaker 2: Really Hell yeah, yeah, let's put that on a shirt.

813
00:50:10,840 --> 00:50:13,719
Speaker 1: Hell yeah. So the court is ins have started a

814
00:50:13,760 --> 00:50:16,120
Speaker 1: GoFundMe to support the workers that they say have but

815
00:50:16,200 --> 00:50:19,960
Speaker 1: illegally and wrongfully dominated for refusing to sign and organizing.

816
00:50:20,719 --> 00:50:23,600
Speaker 1: They do say that they're confident that Communication Workers of

817
00:50:23,600 --> 00:50:26,000
Speaker 1: America will eventually help them get their jobs back, but

818
00:50:26,080 --> 00:50:28,440
Speaker 1: until then they got bills to pay. So we will

819
00:50:28,440 --> 00:50:32,320
Speaker 1: put the GoFundMe in the show notes if folks are interested.

820
00:50:32,840 --> 00:50:38,440
Speaker 2: Love to have stories of workers organizing and they did

821
00:50:38,480 --> 00:50:44,160
Speaker 2: it like lightning fast, like a twenty four hour union

822
00:50:44,239 --> 00:50:48,920
Speaker 2: drive where they got super majority of workers to sign.

823
00:50:49,120 --> 00:50:49,920
Speaker 2: That's incredible.

824
00:50:50,440 --> 00:50:54,120
Speaker 1: Oh, the ladies don't play. So I have seen this

825
00:50:54,400 --> 00:50:57,640
Speaker 1: survey floating around, this new global survey of twenty three

826
00:50:57,680 --> 00:51:01,120
Speaker 1: thousand people across twenty nine countries that it's produced to

827
00:51:01,239 --> 00:51:05,840
Speaker 1: finding about how gen Z thinks about gender. I guess

828
00:51:05,840 --> 00:51:09,840
Speaker 1: I shouldn't really be that surprised that I am somewhat surprised.

829
00:51:10,320 --> 00:51:13,840
Speaker 1: When it comes to attitudes about gender roles. Young men

830
00:51:14,040 --> 00:51:18,480
Speaker 1: are actually more conservative than their grandfathers. This is according

831
00:51:18,520 --> 00:51:22,440
Speaker 1: to research conducted by IPSOS at the Global Institute for

832
00:51:22,480 --> 00:51:25,919
Speaker 1: Women's Leadership at King's College, London. They found that gen

833
00:51:26,040 --> 00:51:28,920
Speaker 1: Z men born from nineteen ninety seven to twenty twelve

834
00:51:29,200 --> 00:51:32,560
Speaker 1: are twice as likely as baby boomer men to believe

835
00:51:32,600 --> 00:51:35,279
Speaker 1: that a wife should obey her husband. A third of

836
00:51:35,320 --> 00:51:37,560
Speaker 1: gen Z males that a husband should have the final

837
00:51:37,600 --> 00:51:40,720
Speaker 1: word on major decisions, Nearly a quarter think that women

838
00:51:40,800 --> 00:51:44,960
Speaker 1: shouldn't appear too independent. One in five believe a quote

839
00:51:45,160 --> 00:51:48,880
Speaker 1: real woman should never initiate sex, compared to just seven

840
00:51:48,920 --> 00:51:52,440
Speaker 1: percent of boomer men. I feel like that's interesting to me.

841
00:51:52,480 --> 00:51:54,879
Speaker 1: I feel like all of the men that I know

842
00:51:55,040 --> 00:51:57,719
Speaker 1: that are interested in having sex with women. I love

843
00:51:57,760 --> 00:52:02,200
Speaker 1: the idea of about women initiating this. I find this interesting.

844
00:52:02,840 --> 00:52:06,600
Speaker 2: Yeah, that's that fighting also stuck out to me. Yeah,

845
00:52:06,920 --> 00:52:10,960
Speaker 2: I I totally missed that memo.

846
00:52:13,880 --> 00:52:16,959
Speaker 1: So to put that in perspective. On question after question,

847
00:52:17,080 --> 00:52:20,759
Speaker 1: the older generation came out as, as it pertains to

848
00:52:20,840 --> 00:52:23,920
Speaker 1: gender roles more progressive than the younger one. And that

849
00:52:24,040 --> 00:52:28,080
Speaker 1: certainly is not what I think anecdotally people would expect,

850
00:52:28,120 --> 00:52:31,320
Speaker 1: although given the state of things, I guess it shouldn't

851
00:52:31,320 --> 00:52:34,000
Speaker 1: really be surprising, but for getting this research to me

852
00:52:34,120 --> 00:52:36,600
Speaker 1: is a little bit surprising. And so the question is like,

853
00:52:36,640 --> 00:52:39,799
Speaker 1: what's going on here? And the researchers have an explanation.

854
00:52:40,160 --> 00:52:43,440
Speaker 1: Professor Hi Jung Chung, who led the study, points to

855
00:52:43,520 --> 00:52:47,400
Speaker 1: this vacuum and what has been rushing to fill it.

856
00:52:47,840 --> 00:52:52,680
Speaker 1: For previous generations, men and masculinity have had a fairly

857
00:52:52,760 --> 00:52:55,760
Speaker 1: clear script. I'm not advocating for this to be the script,

858
00:52:55,840 --> 00:52:58,839
Speaker 1: but the script is like clear, be the breadwinner, by

859
00:52:58,920 --> 00:53:03,000
Speaker 1: the house, provide protect But for many young people, men

860
00:53:03,200 --> 00:53:07,080
Speaker 1: and I think just everyone, those pathways feel increasingly out

861
00:53:07,080 --> 00:53:10,200
Speaker 1: of reach. You know, wages are stagnant, housing is unaffordable.

862
00:53:10,520 --> 00:53:14,000
Speaker 1: The traditional markers of adulthood feel like they've been like

863
00:53:14,560 --> 00:53:17,960
Speaker 1: just pulled out from under everybody. And so this is

864
00:53:18,000 --> 00:53:20,840
Speaker 1: me sort of extrapolating the study doesn't say this, but

865
00:53:21,280 --> 00:53:26,640
Speaker 1: I think that the Internet and grifter masculinity influencers are

866
00:53:26,680 --> 00:53:29,439
Speaker 1: also a big culprit here, right, Because she's talking about

867
00:53:29,440 --> 00:53:33,040
Speaker 1: how like there used to be this traditional understanding of

868
00:53:33,040 --> 00:53:34,840
Speaker 1: what it meant to be an adult man, and so

869
00:53:35,560 --> 00:53:39,160
Speaker 1: that vacuum creates this anxiety and instead of that anxiety

870
00:53:39,200 --> 00:53:43,120
Speaker 1: being met with like nuanced conversations about masculinity and gender

871
00:53:43,160 --> 00:53:45,920
Speaker 1: and all of that. It's being met with this fire

872
00:53:46,040 --> 00:53:50,840
Speaker 1: hose of algorithmically available content like red pill communities, in

873
00:53:50,920 --> 00:53:54,680
Speaker 1: cell forms, manosphere influencers, Andrew taate like they have a

874
00:53:54,800 --> 00:53:59,399
Speaker 1: very clear answer ready to go. Your problems are non

875
00:53:59,440 --> 00:54:03,480
Speaker 1: economic or systemic. Your problems are because of women, feminism,

876
00:54:03,560 --> 00:54:05,400
Speaker 1: and immigrants, And so I think that a lot of

877
00:54:05,480 --> 00:54:08,279
Speaker 1: young men are just being handed at a villain, and

878
00:54:08,719 --> 00:54:12,160
Speaker 1: they're really running with that villain. I think the survey

879
00:54:12,239 --> 00:54:15,040
Speaker 1: data in this survey really reflects that more than half

880
00:54:15,040 --> 00:54:17,239
Speaker 1: of gen Z men said they felt that men were

881
00:54:17,280 --> 00:54:20,319
Speaker 1: expected to do too much to support gender equality, a

882
00:54:20,360 --> 00:54:23,799
Speaker 1: fourteen point jump from Boomer men. One in five gen

883
00:54:23,920 --> 00:54:27,239
Speaker 1: Z men think it's less masculine to be involved in childcare.

884
00:54:27,600 --> 00:54:29,520
Speaker 1: Nearly a third of men think that they should not

885
00:54:29,600 --> 00:54:32,520
Speaker 1: tell friends that they love them. What's really striking to

886
00:54:32,560 --> 00:54:35,840
Speaker 1: me here is that it really indicates how gender norms

887
00:54:35,880 --> 00:54:40,520
Speaker 1: and really traditional gender roles and like very binary structured

888
00:54:40,600 --> 00:54:43,759
Speaker 1: gender roles are not just limiting for women, they're also

889
00:54:43,880 --> 00:54:47,200
Speaker 1: a cage for young men too. As former Australian Prime

890
00:54:47,200 --> 00:54:51,000
Speaker 1: Minister Julia Gillard, who chairs the Institute, put it, young

891
00:54:51,040 --> 00:54:55,399
Speaker 1: men are basically trapping themselves inside restrictive gender norms at

892
00:54:55,440 --> 00:54:59,200
Speaker 1: the same time that they're also putting these restrictions on women.

893
00:55:00,000 --> 00:55:03,240
Speaker 1: So it's not a zero sum gain where men lose

894
00:55:03,360 --> 00:55:06,360
Speaker 1: women gain. That is the story that is being sold

895
00:55:06,400 --> 00:55:10,319
Speaker 1: and accepted and bought by this generation online. But that's

896
00:55:10,360 --> 00:55:13,360
Speaker 1: not true. But the data suggests that they're like buying

897
00:55:13,440 --> 00:55:16,359
Speaker 1: that even though it's not true for me. I think

898
00:55:16,400 --> 00:55:20,359
Speaker 1: the biggest takeaway is that we really have to get

899
00:55:20,400 --> 00:55:24,840
Speaker 1: away from this idea that women are the only beneficiaries

900
00:55:24,920 --> 00:55:29,040
Speaker 1: of a gender equal world, because the idea that these

901
00:55:29,120 --> 00:55:31,120
Speaker 1: men feel like they're being asked to do too much

902
00:55:31,160 --> 00:55:34,960
Speaker 1: for gender equality but then can't see how that benefits everybody.

903
00:55:35,000 --> 00:55:37,480
Speaker 1: I mean, patriarchy is truly a trap for us all,

904
00:55:37,520 --> 00:55:40,200
Speaker 1: the biggest scam of them all. Oh.

905
00:55:40,280 --> 00:55:50,800
Speaker 2: Absolutely, yeah. The idea that feminism is about like women

906
00:55:50,920 --> 00:55:57,520
Speaker 2: supplanting men as the dominant gender is ludicrous, But I

907
00:55:57,520 --> 00:55:59,319
Speaker 2: think that's what a lot of people think it is

908
00:55:59,600 --> 00:56:03,000
Speaker 2: when I think that couldn't be further from the truth.

909
00:56:03,080 --> 00:56:07,320
Speaker 2: I think, like you said, a world that is more

910
00:56:07,600 --> 00:56:11,200
Speaker 2: equal and respectful of everyone is just better for all

911
00:56:11,239 --> 00:56:18,640
Speaker 2: of us in it. Patriarchy and hierarchies only serve the

912
00:56:18,719 --> 00:56:21,800
Speaker 2: people at the very very tippy top.

913
00:56:22,840 --> 00:56:25,239
Speaker 1: All right, So this story is a gross one and

914
00:56:25,280 --> 00:56:28,759
Speaker 1: it's very upsetting, but it's also kind of a win,

915
00:56:28,840 --> 00:56:30,480
Speaker 1: which is why I wanted to talk about it. So

916
00:56:30,920 --> 00:56:34,960
Speaker 1: last week the UK government officially announced that sharing semen

917
00:56:35,040 --> 00:56:39,080
Speaker 1: defaced images will be made illegal. If you're thinking yuck,

918
00:56:39,280 --> 00:56:42,279
Speaker 1: what is that, well, you probably have the right idea

919
00:56:42,400 --> 00:56:45,680
Speaker 1: is exactly what it sounds like. Semen images, sometimes known

920
00:56:45,719 --> 00:56:49,520
Speaker 1: online as tributes, are a form of image based abuse

921
00:56:49,560 --> 00:56:53,080
Speaker 1: that involves the perpetrator ejaculating on a photo of somebody

922
00:56:53,080 --> 00:56:56,920
Speaker 1: and then posting it online. Earlier this year, Glamour magazine's

923
00:56:57,040 --> 00:57:00,360
Speaker 1: UK iteration as part of the Stop Image Based Abuse

924
00:57:00,400 --> 00:57:03,799
Speaker 1: campaign helmed a deep investigation that found evidence that at

925
00:57:03,880 --> 00:57:06,880
Speaker 1: least fifty women, as well as at least two miners

926
00:57:07,239 --> 00:57:11,560
Speaker 1: were the victims of semen images on TikTok, according to Glamour.

927
00:57:11,600 --> 00:57:15,200
Speaker 1: As a result of this investigation, new legislation was introduced

928
00:57:15,280 --> 00:57:17,760
Speaker 1: last week that will criminalize the sharing of semen to

929
00:57:17,800 --> 00:57:21,920
Speaker 1: faced images without the consent or reasonable belief in consent

930
00:57:22,280 --> 00:57:25,000
Speaker 1: of the person depicted. Now that's going to be the case,

931
00:57:25,040 --> 00:57:28,520
Speaker 1: whether the image is real or AI generated. People doing

932
00:57:28,560 --> 00:57:31,960
Speaker 1: this will face a maximum sentence of six months imprisonment

933
00:57:32,160 --> 00:57:34,840
Speaker 1: and or a fine. Now, to be clear, this just

934
00:57:34,920 --> 00:57:38,680
Speaker 1: covers sharing, It does not currently cover the creation of

935
00:57:38,760 --> 00:57:42,840
Speaker 1: such images. Glamourer actually handed the mic to Jessica Davies,

936
00:57:42,880 --> 00:57:45,560
Speaker 1: a campaigner who actually lived through this kind of abuse.

937
00:57:45,920 --> 00:57:48,120
Speaker 1: She said that she was just nineteen years old when

938
00:57:48,160 --> 00:57:50,439
Speaker 1: she first saw herself featured in one of these kinds

939
00:57:50,480 --> 00:57:53,520
Speaker 1: of images, and she wrote that seeing this image of

940
00:57:53,520 --> 00:57:56,680
Speaker 1: herself like that gave her a heavy nod in her stomach,

941
00:57:56,920 --> 00:57:59,520
Speaker 1: the kind of weight that you feel when girlhood suddenly

942
00:57:59,520 --> 00:58:02,160
Speaker 1: gives way to the realities of being a woman online,

943
00:58:02,600 --> 00:58:05,560
Speaker 1: a landslide of innocence gone with one click. I was

944
00:58:05,640 --> 00:58:08,360
Speaker 1: still a teenager, and yet I had discovered another way

945
00:58:08,480 --> 00:58:12,720
Speaker 1: women's bodies can be claimed without our permission. So importantly,

946
00:58:12,920 --> 00:58:16,000
Speaker 1: this kind of thing was mostly happening in dark, niche

947
00:58:16,000 --> 00:58:21,200
Speaker 1: corners of the Internet before becoming mainstream on platforms like TikTok. Now,

948
00:58:21,240 --> 00:58:23,520
Speaker 1: TikTok says they do not allow this kind of thing.

949
00:58:23,600 --> 00:58:27,320
Speaker 1: And when you search the phrase comtribute, it is true

950
00:58:27,360 --> 00:58:29,720
Speaker 1: that you do not find anything. But then when you

951
00:58:29,920 --> 00:58:33,040
Speaker 1: alter the phrase and you write like CM tribute without

952
00:58:33,040 --> 00:58:36,480
Speaker 1: the U, you are served suggestions by the search tool,

953
00:58:36,760 --> 00:58:40,440
Speaker 1: including things like CM tributes to girls, CM tributes to women,

954
00:58:40,760 --> 00:58:45,400
Speaker 1: and alarmingly, CM tributes for miners. On TikTok, there are

955
00:58:45,440 --> 00:58:49,440
Speaker 1: accounts solely dedicated to these kinds of explicit videos, often

956
00:58:49,520 --> 00:58:53,200
Speaker 1: hiding in plain sight using hashtags like cmtrib and CM

957
00:58:53,240 --> 00:58:57,640
Speaker 1: tribute and faptribe. So it's not really happening in private

958
00:58:57,800 --> 00:59:01,600
Speaker 1: or in secret, really. One account on TikTok uploaded thirteen

959
00:59:01,680 --> 00:59:04,640
Speaker 1: videos of images of young women being covered in semen.

960
00:59:05,120 --> 00:59:07,840
Speaker 1: It had one thousand, seven hundred and sixty three followers

961
00:59:07,880 --> 00:59:10,800
Speaker 1: and eighty one hundred and ninety eight likes. Seven of

962
00:59:10,840 --> 00:59:14,560
Speaker 1: its videos had over ten thousand views. And as somebody

963
00:59:14,560 --> 00:59:17,360
Speaker 1: who used to post on TikTok occasionally, it can be

964
00:59:17,440 --> 00:59:18,960
Speaker 1: kind of hard to get out of, like the two

965
00:59:19,120 --> 00:59:24,320
Speaker 1: hundred three hundred view jail. Yet these videos were outperforming

966
00:59:24,360 --> 00:59:28,120
Speaker 1: the typical user engagement on their posts, so like, while

967
00:59:28,200 --> 00:59:32,040
Speaker 1: I have questions like was the TikTok algorithm pushing this content?

968
00:59:32,560 --> 00:59:36,160
Speaker 1: A TikTok smokeperson told Glamour that sexually suggestive content is

969
00:59:36,280 --> 00:59:39,160
Speaker 1: not eligible recommendation for the for You feed and that

970
00:59:39,240 --> 00:59:42,560
Speaker 1: accounts age thirteen to seventeen are prevented from viewing such

971
00:59:42,600 --> 00:59:47,480
Speaker 1: content anywhere on the platform. Now, I have not actually

972
00:59:47,560 --> 00:59:49,800
Speaker 1: seen any of these videos, and it wasn't until I

973
00:59:49,840 --> 00:59:52,760
Speaker 1: was fleshing out the outline for the segment that I

974
00:59:52,840 --> 00:59:57,200
Speaker 1: realized that we are talking about videos of men ejaculating

975
00:59:57,280 --> 01:00:01,000
Speaker 1: onto screens. So it'll be like a picture video of

976
01:00:01,080 --> 01:00:05,120
Speaker 1: a woman or a girl, and then somebody ejaculates on

977
01:00:05,400 --> 01:00:11,080
Speaker 1: that device, recording with another device. Like I didn't. It

978
01:00:11,120 --> 01:00:12,680
Speaker 1: took me a couple of reads to get that.

979
01:00:13,120 --> 01:00:15,320
Speaker 2: Oh, I didn't get that until just now. Either I

980
01:00:15,400 --> 01:00:19,560
Speaker 2: thought that they were like printing it out on paper

981
01:00:19,760 --> 01:00:24,000
Speaker 2: or something. For some reason. It's even grosser to do

982
01:00:24,040 --> 01:00:24,880
Speaker 2: it on a device.

983
01:00:25,240 --> 01:00:29,400
Speaker 1: That's what I'm saying, like that, like that, it's it's yeah,

984
01:00:29,520 --> 01:00:32,000
Speaker 1: that's that's exactly what I know. It's exactly my point.

985
01:00:32,680 --> 01:00:35,760
Speaker 1: According to the piece, one video featured two schoolgirls in

986
01:00:35,880 --> 01:00:39,640
Speaker 1: uniform lip syncing in their classroom. It was captioned love

987
01:00:39,640 --> 01:00:43,360
Speaker 1: a girl in uniform alongside love heart emojis. The user

988
01:00:43,440 --> 01:00:47,200
Speaker 1: ejaculated onto the screen and that video got ten point

989
01:00:47,280 --> 01:00:48,800
Speaker 1: seven k views.

990
01:00:49,160 --> 01:00:50,560
Speaker 2: What are we doing here? People?

991
01:00:51,360 --> 01:00:55,200
Speaker 1: Yeah? And it gets worse because on four Chan. It's

992
01:00:55,240 --> 01:00:58,640
Speaker 1: a marketplace and it's full of people offering and asking

993
01:00:58,800 --> 01:01:02,200
Speaker 1: for this kind of content. The piece reads requests like

994
01:01:02,240 --> 01:01:06,840
Speaker 1: this were rampant on TikTok, men openly so men openly

995
01:01:06,840 --> 01:01:11,080
Speaker 1: soliciting ejaculation videos, targeting other users' sisters, female classmates, and

996
01:01:11,120 --> 01:01:13,560
Speaker 1: ex partners. Some are willing to do it for free,

997
01:01:13,840 --> 01:01:17,000
Speaker 1: motivated purely by the gratification of degrading women they had

998
01:01:17,000 --> 01:01:19,880
Speaker 1: never met. Others had turned it into a side hustle,

999
01:01:20,200 --> 01:01:23,160
Speaker 1: with one user directing me to his Telegram channel where

1000
01:01:23,200 --> 01:01:25,680
Speaker 1: he had a full price list, a couple of dollars

1001
01:01:25,680 --> 01:01:27,720
Speaker 1: for a standard video and a little more for a

1002
01:01:27,760 --> 01:01:31,440
Speaker 1: live session. One of the most common threads involves requests

1003
01:01:31,480 --> 01:01:35,120
Speaker 1: for tributes of family members, mothers, aunts, cousins, and an

1004
01:01:35,200 --> 01:01:37,320
Speaker 1: overwhelming request for sisters.

1005
01:01:37,760 --> 01:01:39,120
Speaker 2: Dark gross.

1006
01:01:39,480 --> 01:01:47,160
Speaker 1: Yeah it's just really gross and yeah, I mean to me,

1007
01:01:47,240 --> 01:01:50,680
Speaker 1: it seems like it's about humiliating somebody without them knowing.

1008
01:01:51,960 --> 01:01:54,439
Speaker 1: The piece does point out that there are women who

1009
01:01:54,520 --> 01:01:58,439
Speaker 1: seek this content out willingly, but that is very much

1010
01:01:58,440 --> 01:02:01,560
Speaker 1: the exception. The vast majority of these videos are made

1011
01:02:01,600 --> 01:02:06,360
Speaker 1: without the subject's knowledge or agreement, and the men in

1012
01:02:06,400 --> 01:02:09,000
Speaker 1: the communities talk about it in ways that like, really

1013
01:02:09,040 --> 01:02:12,320
Speaker 1: just make it clear what their intentions are. It's language

1014
01:02:12,320 --> 01:02:16,880
Speaker 1: that is demeaning, hostile, and deeply misogynistic. No shit, she writes,

1015
01:02:17,600 --> 01:02:20,840
Speaker 1: when it happens without consent, there's no meaningful difference between

1016
01:02:20,840 --> 01:02:23,320
Speaker 1: a man filming himself doing this over a woman's photo

1017
01:02:23,600 --> 01:02:26,120
Speaker 1: and a man exposing himself to a stranger on the street.

1018
01:02:26,360 --> 01:02:28,360
Speaker 1: The Internet is a public space and it should be

1019
01:02:28,400 --> 01:02:32,360
Speaker 1: treated like one. And of course it would not be

1020
01:02:32,400 --> 01:02:35,080
Speaker 1: a gross thing happening to women and girls online if

1021
01:02:35,120 --> 01:02:37,720
Speaker 1: Elon Musk was not somehow getting a piece of it.

1022
01:02:38,440 --> 01:02:42,600
Speaker 1: As with many forms of online misogyny, AI technology plays

1023
01:02:42,640 --> 01:02:46,080
Speaker 1: a role in this harm. So while semen images and

1024
01:02:46,200 --> 01:02:50,880
Speaker 1: videos are often perpetrated IROL because of advancements in AI,

1025
01:02:51,280 --> 01:02:54,000
Speaker 1: they're also using AI to do this kind of abuse.

1026
01:02:54,440 --> 01:02:58,280
Speaker 1: Glamour magazine actually already reported on the fact that rock

1027
01:02:58,640 --> 01:03:01,560
Speaker 1: was being used in this way in response to prompts

1028
01:03:01,600 --> 01:03:05,640
Speaker 1: from users on x So under this new legislation in

1029
01:03:05,680 --> 01:03:08,520
Speaker 1: the UK, folks who are sharing this kind of content

1030
01:03:08,680 --> 01:03:12,680
Speaker 1: could face consequences and just as a side note, as

1031
01:03:12,800 --> 01:03:17,160
Speaker 1: part of a piece of that same legislation, step family

1032
01:03:17,280 --> 01:03:20,840
Speaker 1: incest pornography is also going to be banned in the UK.

1033
01:03:21,960 --> 01:03:25,120
Speaker 1: I I'll put the link to the piece about that

1034
01:03:25,480 --> 01:03:28,400
Speaker 1: in the show notes because I found it really fascinating

1035
01:03:28,440 --> 01:03:31,160
Speaker 1: and it's actually like kind of a complex issue. But

1036
01:03:31,600 --> 01:03:37,120
Speaker 1: I can't speak for the UK, but I feel like

1037
01:03:37,960 --> 01:03:42,120
Speaker 1: step family incest pornography has to be a big sub

1038
01:03:42,160 --> 01:03:44,960
Speaker 1: section of pornography. It has to be like I would

1039
01:03:45,000 --> 01:03:46,840
Speaker 1: love to see some hard numbers. It has to be

1040
01:03:46,920 --> 01:03:50,920
Speaker 1: like half all the It'll often be like the titles

1041
01:03:50,960 --> 01:03:56,640
Speaker 1: where it's like, oh, step sister, step like step like stepdad,

1042
01:03:57,000 --> 01:03:59,160
Speaker 1: And I wonder will they just have to read like

1043
01:03:59,320 --> 01:04:00,800
Speaker 1: if this happened to the in the United States, we

1044
01:04:00,960 --> 01:04:03,000
Speaker 1: have to relabel quite a lot of porn to take

1045
01:04:03,040 --> 01:04:03,880
Speaker 1: out step sibling.

1046
01:04:04,840 --> 01:04:07,920
Speaker 2: I think you're right. I think it's a big, a

1047
01:04:08,000 --> 01:04:12,600
Speaker 2: big part of the market, even though I don't think

1048
01:04:12,960 --> 01:04:17,560
Speaker 2: many of the videos like really reference the family dynamics

1049
01:04:17,680 --> 01:04:21,720
Speaker 2: or go to great pains to establish like what the

1050
01:04:22,720 --> 01:04:25,600
Speaker 2: real nature of the relationship is between these two people.

1051
01:04:25,960 --> 01:04:29,520
Speaker 2: But like the titles, I think a lot of them

1052
01:04:29,560 --> 01:04:35,240
Speaker 2: do lean pretty heavily on step sibling relationships.

1053
01:04:35,600 --> 01:04:40,720
Speaker 1: I'm so curious why, like what, like what, I'm just

1054
01:04:40,760 --> 01:04:41,680
Speaker 1: so curious.

1055
01:04:42,200 --> 01:04:46,280
Speaker 2: Yeah, I mean, it's not my jam. But I have

1056
01:04:46,360 --> 01:04:50,800
Speaker 2: to imagine it's like the taboo of it is somehow related.

1057
01:04:53,240 --> 01:04:57,960
Speaker 2: Maybe maybe, I mean people love violating a taboo. I

1058
01:04:58,000 --> 01:04:58,720
Speaker 2: get that.

1059
01:04:58,760 --> 01:05:01,400
Speaker 1: It's so popular. It has to be I should have

1060
01:05:01,400 --> 01:05:03,200
Speaker 1: done some research on this. I will do some research

1061
01:05:03,240 --> 01:05:08,560
Speaker 1: on this, but okay, research that sounded not like how

1062
01:05:08,840 --> 01:05:10,520
Speaker 1: it not how like it sounds.

1063
01:05:10,880 --> 01:05:12,760
Speaker 2: Hey, we're sex positive here, you.

1064
01:05:12,720 --> 01:05:18,800
Speaker 1: Know, research, academic research. Okay, I have to end on

1065
01:05:18,880 --> 01:05:22,480
Speaker 1: this story out of Washington State. So Washington States, Maya

1066
01:05:22,600 --> 01:05:26,520
Speaker 1: Edwards and her husband went viral for posting what happened

1067
01:05:26,560 --> 01:05:29,640
Speaker 1: when she called the Washington State DMB. And you know

1068
01:05:29,640 --> 01:05:32,120
Speaker 1: how when you call, it's like, oh, press one for English,

1069
01:05:32,160 --> 01:05:35,760
Speaker 1: press two for Spanish. There was like a wait for English.

1070
01:05:36,080 --> 01:05:38,000
Speaker 1: Her husband is bilingual, so she's like, oh, we'll just

1071
01:05:38,040 --> 01:05:42,760
Speaker 1: do Spanish. When she hit two for Spanish, she got,

1072
01:05:43,680 --> 01:05:47,919
Speaker 1: I guess I'll call it an Ai accented voice. Let

1073
01:05:47,920 --> 01:05:50,480
Speaker 1: me just tell you are not ready. You're not gonna

1074
01:05:50,520 --> 01:05:53,200
Speaker 1: believe what this sounds like. So I'm gonna I'm gonna

1075
01:05:53,200 --> 01:05:54,480
Speaker 1: play it. You're not gonna believe it.

1076
01:05:55,200 --> 01:06:02,960
Speaker 3: Thank you for putting the Departmental sched Licensing off Disappointment

1077
01:06:03,240 --> 01:06:09,120
Speaker 3: consenting then existing appointment, Orn's coming appointment. Please pressure, oh

1078
01:06:09,880 --> 01:06:12,200
Speaker 3: please say I thank you.

1079
01:06:12,840 --> 01:06:15,200
Speaker 2: I love this story. This is like my favorite story.

1080
01:06:15,440 --> 01:06:18,880
Speaker 1: So obviously that's not Spanish. And also part of me

1081
01:06:19,000 --> 01:06:23,480
Speaker 1: is like you needed to pay an AI company to

1082
01:06:23,480 --> 01:06:26,880
Speaker 1: to generate this. This was like it would be honestly,

1083
01:06:26,920 --> 01:06:30,880
Speaker 1: would be better. I just I can't. I can't understand it.

1084
01:06:31,160 --> 01:06:33,840
Speaker 2: I mean, it sounded more Spanish.

1085
01:06:33,960 --> 01:06:37,840
Speaker 1: It's just an AI voice doing an accent. What's funny

1086
01:06:37,920 --> 01:06:40,760
Speaker 1: is that she described it as something out of Parks

1087
01:06:40,760 --> 01:06:42,640
Speaker 1: and rec And I absolutely agree.

1088
01:06:42,880 --> 01:06:46,840
Speaker 2: These are the kind of AI errors that we can handle.

1089
01:06:47,280 --> 01:06:50,000
Speaker 1: Yes, I mean pretty weak for somebody who speaks a

1090
01:06:50,080 --> 01:06:52,320
Speaker 1: language other than English, who like still needs access to

1091
01:06:52,360 --> 01:06:53,640
Speaker 1: city services, That's true.

1092
01:06:53,680 --> 01:06:55,720
Speaker 2: It's probably a lot less funny for them. They're like

1093
01:06:55,880 --> 01:06:58,160
Speaker 2: just trying to pay their ticket or like whatever it

1094
01:06:58,200 --> 01:06:58,960
Speaker 2: is they're trying to do.

1095
01:06:59,440 --> 01:07:03,760
Speaker 1: The Apartment of Licenses apologized for the error and to

1096
01:07:03,840 --> 01:07:06,200
Speaker 1: any customers for the inconvenience. They said that it was

1097
01:07:06,200 --> 01:07:09,480
Speaker 1: an unfortunate byproduct of expanding services, is that the doll

1098
01:07:09,600 --> 01:07:12,480
Speaker 1: found problems with a self service option. They also kind

1099
01:07:12,480 --> 01:07:14,840
Speaker 1: of throw Amazon under the bus. They said, oh, Amazon

1100
01:07:14,920 --> 01:07:18,520
Speaker 1: provides the platform for the phone service and Amazon declined

1101
01:07:18,640 --> 01:07:23,680
Speaker 1: interview requests. If I'm Amazon, what if you're getting the

1102
01:07:23,760 --> 01:07:29,040
Speaker 1: AI accent voice to send to send the the media statement.

1103
01:07:33,560 --> 01:07:37,240
Speaker 2: Yeah, God, I would actually love to see like a

1104
01:07:38,280 --> 01:07:42,440
Speaker 2: deep investigation as exactly what happened there, Like did the

1105
01:07:42,520 --> 01:07:46,400
Speaker 2: AI just decide to do this Spanish accent? That's kind

1106
01:07:46,400 --> 01:07:50,560
Speaker 2: of what they're implying here, right, Yes, Like I can't

1107
01:07:50,560 --> 01:07:53,960
Speaker 2: imagine an engineer sat down was like, Okay, today we're

1108
01:07:54,000 --> 01:07:58,800
Speaker 2: gonna create a synthesized voice that speaks English but in

1109
01:07:58,920 --> 01:07:59,960
Speaker 2: a Spanish accent.

1110
01:08:00,240 --> 01:08:05,000
Speaker 1: God, so ridiculous. Well, Mike, thank you for joining me

1111
01:08:05,040 --> 01:08:06,400
Speaker 1: to run down these stories.

1112
01:08:06,880 --> 01:08:09,000
Speaker 2: Yeah, I's happy to do it. And you know, it's

1113
01:08:09,040 --> 01:08:11,360
Speaker 2: a good set of stories. Some of them were a

1114
01:08:11,360 --> 01:08:17,640
Speaker 2: little dark, but some good stories. And you know, with

1115
01:08:17,680 --> 01:08:20,800
Speaker 2: the Gemini story, we got into it a little bit,

1116
01:08:20,920 --> 01:08:23,880
Speaker 2: but it and I know we were kind of talking

1117
01:08:23,920 --> 01:08:26,880
Speaker 2: about some things that we mentioned in the audio book

1118
01:08:27,320 --> 01:08:30,599
Speaker 2: that listeners haven't had a chance to listen to you yet.

1119
01:08:30,600 --> 01:08:35,120
Speaker 2: But I think it really connects and I'm excited for

1120
01:08:35,200 --> 01:08:37,960
Speaker 2: listeners to get a chance to hear that fuller context

1121
01:08:38,000 --> 01:08:42,879
Speaker 2: in the audio book, which people can pre order at

1122
01:08:43,280 --> 01:08:46,120
Speaker 2: Love at First Prompt dot AI and it comes out

1123
01:08:46,120 --> 01:08:51,400
Speaker 2: in July. Just really excited to have that long form

1124
01:08:51,640 --> 01:08:58,160
Speaker 2: opportunity to talk about this complicated and important issue of

1125
01:08:58,360 --> 01:09:00,920
Speaker 2: people experiencing intimacy with their companions.

1126
01:09:02,160 --> 01:09:05,240
Speaker 1: Yes, and again, if you send us a copy of

1127
01:09:05,280 --> 01:09:07,479
Speaker 1: your pre order email it, you can hit us up

1128
01:09:07,520 --> 01:09:10,760
Speaker 1: on social we will send you a sticker and a

1129
01:09:10,800 --> 01:09:14,440
Speaker 1: handwritten thank you letter from yours truly so really appreciate

1130
01:09:14,680 --> 01:09:17,240
Speaker 1: the support. Thanks so much for listening. I will see

1131
01:09:17,240 --> 01:09:25,799
Speaker 1: you on the Internet. Got a story about an interesting

1132
01:09:25,800 --> 01:09:27,840
Speaker 1: thing in tech, or just want to say hi, You

1133
01:09:27,840 --> 01:09:30,120
Speaker 1: can reach us at Hello at tegody dot com. You

1134
01:09:30,160 --> 01:09:33,200
Speaker 1: can also find transcripts for today's episode at tenggody dot com.

1135
01:09:33,360 --> 01:09:35,040
Speaker 1: There Are No Girls on the Internet was created by

1136
01:09:35,080 --> 01:09:38,439
Speaker 1: me bridget Tod. It's a production of iHeartRadio, an unbossed creative.

1137
01:09:38,840 --> 01:09:41,840
Speaker 1: Jonathan Strickland as our executive producer. Tari Harrison is our

1138
01:09:41,840 --> 01:09:45,320
Speaker 1: producer and sound engineer. Michael Almato is our contributing producer.

1139
01:09:45,600 --> 01:09:48,280
Speaker 1: I'm your host, bridget Tod. If you want to help

1140
01:09:48,360 --> 01:09:51,320
Speaker 1: us grow, rate and review us on Apple Podcasts. For

1141
01:09:51,439 --> 01:09:54,400
Speaker 1: more podcasts from iHeartRadio, check out the iHeartRadio app Apple

1142
01:09:54,400 --> 01:10:04,800
Speaker 1: Podcasts or wherever you get your podcasts a lot o. Well,

1143
01:10:05,080 --> 01:10:07,080
Speaker 1: he be well lived.