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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 Bridgett and this is

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Speaker 1: there Are No Girls on the Internet.

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Speaker 2: Today.

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Speaker 1: I am not joined by my producer Mike. I am

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Speaker 1: joined by my producer, Joey pet. You don't always hear

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Speaker 1: Joey's voice on the show, but you do hear their

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Speaker 1: phenomenal editing and their phenomenal.

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Speaker 2: I don't know, internet cultural takes.

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Speaker 1: We talked about Joey's appearance on the podcast Stuff Mom

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Speaker 1: ever told you really doing a phenomenal and thoughtful job

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Speaker 1: breaking down that kids Online Safety Act that we're all

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Speaker 1: discussing on the show. Joey, thank you so much for

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Speaker 1: being here.

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Speaker 3: Hey, thank you bridget for having me on. Super excited.

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Speaker 1: I'm excited to so First I would love to just

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Speaker 1: sort of let folks get to know you. What is

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Speaker 1: your experience like on the internet, What do you care

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Speaker 1: about online? What brings you to convert stations on the internet?

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Speaker 3: For sure? Yeah, so I again, My name is Jerry.

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Speaker 3: I am a producer here at iHeart I work on

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Speaker 3: a couple of different shows. I do work. When I

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Speaker 3: asked stuff Mom never told you like Britchett said, should

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Speaker 3: check out that episode if you can. But yeah, I

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Speaker 3: am very much an Internet kind of person, a very

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Speaker 3: chronically outlined person. I will call myself sometimes not necessarily

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Speaker 3: in a positive way, but I you know, I am

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Speaker 3: gen Z. I'm an older gen Z person. So I

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Speaker 3: grew up with social media more or less like in

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Speaker 3: I don't know. I got on social media on like

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Speaker 3: high school. I guess that's more or less growing up

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Speaker 3: with social media. Anyways. I was a big like you know,

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Speaker 3: I was really into Tumblr back in the day when

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Speaker 3: that was a big thing. I personally, you know, if

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Speaker 3: you've listened to Smithy, you know this. I'm a big

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Speaker 3: like fandom person, so that always was my introduction to

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Speaker 3: like social media sites and all of that. I am

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Speaker 3: still on Twitter, unfortunately, hanging on. We'll see, although I

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Speaker 3: don't know that that might be ending sued. But yeah,

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Speaker 3: I do use TikTok a lot. I'm big on TikTok.

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Speaker 3: I won't be telling you all my TikTok, but if

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Speaker 3: I find it on your own, congratulations. I don't use

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Speaker 3: the same ad as I do for my other social media,

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Speaker 3: but do love TikTok. Do love sort of you know,

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Speaker 3: my view is always I think social media is it's

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Speaker 3: a medium, it can be a great tool for activism

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Speaker 3: and for you know, one thing I talk about a

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Speaker 3: lot is like I grew up queer in the early

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Speaker 3: two thousands, early two tens or two thousands early twenty

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Speaker 3: tens kind of era. Like I think I was probably

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Speaker 3: like twenty fourteen when I first came.

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Speaker 2: Out, like.

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Speaker 3: But which was still like a time where there wasn't

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Speaker 3: a lot of like visibility with queerness. And I really

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Speaker 3: credit like the internet and social media for being my

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Speaker 3: sort of space, safe space, so that always going to

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Speaker 3: have a place in my heart, you know. At the

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Speaker 3: same time, I think really appreciate the show for being

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Speaker 3: a place to talk about like some of the negatives too.

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Speaker 1: But yeah, we have such similar trajectories when it comes

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Speaker 1: to how we think about the Internet. Like similarly, the Internet,

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Speaker 1: I guess it's a double edged sword, you know. I

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Speaker 1: think for marginalized youth, it can be this new this

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Speaker 1: place to find community and explore yourself sometimes depending on

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Speaker 1: where you grew up and what your upbringing was like

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Speaker 1: sometimes for the very first time.

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Speaker 2: And it also can.

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Speaker 1: Be this place where that very identity, that very thing

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Speaker 1: that the Internet is this way to explore, can be

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Speaker 1: weaponized against you, can be used to further marginalize you.

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Speaker 1: So yeah, it's it's all of the promise and all

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Speaker 1: of the peril. Like that is what it means to

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Speaker 1: be a marginalized person online today, I feel and you

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Speaker 1: really bring your fandom lens. I think is really helpful

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Speaker 1: to understanding a lot of what happens on the internet.

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Speaker 1: We were just talking about, you know, how you could

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Speaker 1: say something about activism or radical politics because people are like, okay,

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Speaker 1: you talk about a fandom, then you can really upset

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Speaker 1: some folks.

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Speaker 3: Yeah, I was like Richard before we started recording. I

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Speaker 3: recently got in some i'm gonna say accidental beef with

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Speaker 3: a very prominent TikToker. Uh was not my intention, That's

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Speaker 3: why I'm saying accidental. I pointed out that he made

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Speaker 3: a comment that was a little bit misogynistic, and you know,

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Speaker 3: accusing a male ally of not being a feminist is

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Speaker 3: apparently the worst defense to feminism that's been a week. Anyway,

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Speaker 3: It's not gonna go super into that, but I yeah,

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Speaker 3: it's I existing as a marginalized person on the internet.

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Speaker 3: It's always always a fun time. Always get some fun comments,

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Speaker 3: you know, always Yeah.

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Speaker 1: Well, speaking of TikTok, that's a great segue into talking

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Speaker 1: about news y'all might have missed on the Internet this week.

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Speaker 1: So we talked on the show about TikTok, the Chinese

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Speaker 1: owned social media platform owned by the parent company fit Dance,

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Speaker 1: was potentially going to be banned in the United States,

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Speaker 1: and how the platform was working with the US government

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Speaker 1: to figure out what it needed to do to avoid

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Speaker 1: being banned. Well, now you've gotten our very first look

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Speaker 1: about what all that might entail, because Forbes obtained a

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Speaker 1: draft of a deal from around this time last year

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Speaker 1: between TikTok and the Biden Administration's Committee on Foreign Investment

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Speaker 1: in the United States or CFIUS. So one quick sort

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Speaker 1: of side note here is that Forbes has a little

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Speaker 1: bit of a history with TikTok. Fite Dance did confirm

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Speaker 1: to Forbes that they had essentially been spying on Forbes

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Speaker 1: journalists through monitoring their physical locations via their IP addresses

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Speaker 1: in an attempt to sniff out leaks from inside the company.

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Speaker 1: A TikTok executive actually had to resign over this. So

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Speaker 1: Forbes has really been on this TikTok Beat, which is

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Speaker 1: probably why they were the outlet that got this agreement

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Speaker 1: between the government and TikTok in the first place. So

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Speaker 1: their reporting sos that if it were to be finalized,

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Speaker 1: the agreement would provide the government near unfettered access to

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Speaker 1: internal TikTok information and unpreceded control over essential functions that

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Speaker 1: it does not have over any other major platform. The

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Speaker 1: draft agreement as it's being negotiated or as it was

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Speaker 1: being negotiated at the time around this time last summer,

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Speaker 1: would give government agencies that the DOJ and the DoD

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Speaker 1: the authority to examine TikTok's US facilities, records, equipment, and

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Speaker 1: servers with minimal or no notice block changes to the apps,

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Speaker 1: US terms of service, moderation policies and privacy policy, veto

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Speaker 1: the hiring of any executive involved in leading TikTok's US

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Speaker 1: data Security. Org order TikTok and byte Dance to pay

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Speaker 1: for and subject themselves to various audits, assessments, and other

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Speaker 1: reports on the security of TikTok's US functions, and in

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Speaker 1: some circumstances require bytdance to temporarily stop TikTok from functioning

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Speaker 1: within the United States. So what's really interesting to me

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Speaker 1: here that Forbes points out is that this agreement would

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Speaker 1: essentially give the United States government a very similar kind

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Speaker 1: of act success and power that lawmakers have worried about

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Speaker 1: China having forbus rights. In one revealing comment, Exchange attorneys

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Speaker 1: for Byteedance explained to c si US that they have

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Speaker 1: addressed language that prevents the government from demanding changes of

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Speaker 1: TikTok's recommendation algorithm simply because it recommended content that the

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Speaker 1: government does not like. And so that is exactly what

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Speaker 1: foes have been like, Oh, China will do that, and

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Speaker 1: now this agreement is saying maybe the United States government

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Speaker 1: should be able to do that.

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Speaker 3: Yeah, I that is so weird to be about this

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Speaker 3: whole debate, which is like, I mean and like obviously

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Speaker 3: that and I guess not obviously, but like they've made

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Speaker 3: it clear the attention behind it is just that they're bad,

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Speaker 3: that they're not the ones that are like Facebook has

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Speaker 3: been doing worship for like the law. I don't know,

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Speaker 3: it's very strange. I also I don't know. Whenever this

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Speaker 3: comes up, I'm always like, what did politicians think people

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Speaker 3: are like doing on TikTok like what I don't know

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Speaker 3: most for you page is like I get a lot

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Speaker 3: of those, like the things with people playing video games

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Speaker 3: and there's like climb over it, like I don't.

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Speaker 2: Yeah, I don't know.

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Speaker 3: Anyways, that's it's this whole thing is so silly and

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Speaker 3: somehow also terrifying. Yeah.

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Speaker 1: Yeah, And you make a good point that so much

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Speaker 1: of what lawmakers in the US have talked about when

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Speaker 1: it comes to this threat that they say that TikTok

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Speaker 1: poses as from being a Chinese owned company is very hypothetical.

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Speaker 1: It's not like they're saying the videos where somebody is

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Speaker 1: playing Mario Kart and then there's a sitcom on top

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Speaker 1: of it, those are national securities. It's not like they

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Speaker 1: have some smoking gun. It's all very hypothetical. And if

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Speaker 1: the government and TikTok were to go ahead with this agreement,

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Speaker 1: that would mean that TikTok would be subjected to more

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Speaker 1: government oversight and scrutiny than any other social media platform

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Speaker 1: that's run in the United States, like Facebook or Twitter,

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Speaker 1: which I'm sure is making folks like Zuckerberg and Elon

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Speaker 1: Musk very very happy. That's sort of one of my

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Speaker 1: overall concerns about legislation around TikTok is that I feel

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Speaker 1: like the United States their real quibble is that they

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Speaker 1: want the US to be the only game in town

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Speaker 1: when it comes to big tech, and so they want

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Speaker 1: to have this dynamic where American companies basically go unscrutinized

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Speaker 1: by the American government, you know, unless they really break

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Speaker 1: the law, and even then it's not really that much scrutiny,

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Speaker 1: and where TikTok has this completely different operating standard where

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Speaker 1: they have complete scrutiny and oversight by the American government.

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Speaker 1: And like, it just a weird dynamic that I just

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Speaker 1: feel like is not actually rooted in keeping our data

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Speaker 1: safe and secure.

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Speaker 2: It's rooted in something else.

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Speaker 3: Yeah, yeah, I mean it's it's again, it's very clearly

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Speaker 3: sort of rooted in this weird sort of like anti

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Speaker 3: China cetophobia that happens. The thing to me too, is

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Speaker 3: it's always like, look, I'm not I don't think any

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Speaker 3: country should be getting my data collecting my data. But

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Speaker 3: at the same time, it's like, it doesn't the US

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Speaker 3: do that to like every other country that uses are like, like,

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Speaker 3: it isn't that every country thing? It is the Facebook

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Speaker 3: like we also, I don't know, it's the whole Yeah,

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Speaker 3: like America wants to control all the big tag I

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Speaker 3: just think it's kind of silly. Yeah. I remember also

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Speaker 3: like when Facebook China banned like Facebook, and that was

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Speaker 3: like a big thing, and they were like, see how

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Speaker 3: repressive this government. It's like, this is not the same

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Speaker 3: vision of tech. I don't not saying that was a

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Speaker 3: good move either, but yes, but.

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Speaker 1: Yeah, it's the it's like the same things. It's like

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Speaker 1: if that was horrible and you know, repressive, what is

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Speaker 1: this like? You know, I'm I'm I just have a

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Speaker 1: lot of questions. And I also think it puts a

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Speaker 1: lot of power in the hands of government. Patrick Toomey,

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Speaker 1: deputy director of the ACLU's National Security Project, told Forbes

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Speaker 1: if this agreement would give the US government the power

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Speaker 1: to dictate what content TikTok can or cannot carry or

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Speaker 1: how it makes those decisions, that would raise serious concerns

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Speaker 1: about the government's ability to censor or distort what people

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Speaker 1: are saying or what it on TikTok. And I completely agree,

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Speaker 1: and I think that doing that in service of quote,

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Speaker 1: you know, having a more secure data privacy practice doesn't really.

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Speaker 1: I mean it's just I just think that we're it's

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Speaker 1: such a big grand standing around one app when what

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Speaker 1: we actually need is meaningful, comprehensive data privacy laws. Like

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Speaker 1: if if this agreement were to be in place, I

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Speaker 1: don't think that our data would be made that much

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Speaker 1: more safe.

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Speaker 3: Yeah, no, I totally agree. I also the point about

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Speaker 3: like it's the government is going to be allowed to

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Speaker 3: kind of dictate what can and cannot be seen. I

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Speaker 3: mean the whole that episode that I did for Spin

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Speaker 3: to about the whole problem with like Kosa and all

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Speaker 3: these other like internet bills that are going around, is that, yeah,

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Speaker 3: they're not really addressing the data privacy issue. They're just

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Speaker 3: sort of trying to monitor what is or is not

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Speaker 3: going to be on the Internet. And honestly, like I

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Speaker 3: said in that episode, or you listen back to it,

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Speaker 3: I learned about that issue from TikTok, from a lot

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Speaker 3: of like TikTokers that we're doing activism and trying to

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Speaker 3: weareness raise awareness about this bell Yeah, I it's just

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Speaker 3: so weird. This also makes me thing I remember, like

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Speaker 3: because this started with Trump, right, Trump tried to ban TikTok,

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Speaker 3: and it was also right after there was that whole

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Speaker 3: thing where like he had some huge rally that was

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Speaker 3: supposed to happen and then like it ended up being

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Speaker 3: like a fraction of like the people that it bought tickets,

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Speaker 3: because it was like there was a whole thing on

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Speaker 3: TikTok that like kids were like reserving seats and then

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Speaker 3: not so yeah yeah, And I was like because I

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Speaker 3: remember when that happened, and it was like, oh, this

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Speaker 3: is yeah, Trump's clearly trying to ban TikTok because he's

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Speaker 3: just mad about that. Like this just seems like it's

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Speaker 3: carried over from that, which I don't know. It's all

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Speaker 3: very strange to me.

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Speaker 1: That is Actually when I got TikTok, I was like

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Speaker 1: I was totally one of those people who was like,

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Speaker 1: it's just kids dancing, Like I was like a TikTok skeptic.

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Speaker 1: And then when Trump was gonna ban it, I remember

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Speaker 1: reading a news headline. It was one of those headlines

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Speaker 1: where like I didn't click into it, and like, actually,

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Speaker 1: one of the reasons why I do the newscast. It's

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Speaker 1: like headlines that you might see you didn't click into,

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Speaker 1: and like here's the here's what you need to know.

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Speaker 1: But so like the headline I saw was like, oh,

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Speaker 1: you have until X day to download TikTok if you

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Speaker 1: want to download it, and I was like, well, I

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Speaker 1: don't want the chance. I don't want to like want

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Speaker 1: to have it and not have it, so let me

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Speaker 1: download it. So like that was the first time that

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Speaker 1: I ever got it. And then I was like, oh, there,

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Speaker 1: it's actually an app that I enjoy.

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Speaker 2: So yeah, I forgot about that, but I remember it now.

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Speaker 3: Yeah, that was that was a really funny time to

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Speaker 3: be on tik tik. I also, I think it was

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Speaker 3: before I was also like using it a lot, like

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Speaker 3: I was working on I just like had that app

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Speaker 3: because everybody was downloading it. I was like, but yeah,

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Speaker 3: that was like again, yeah, it's it's a it's a platform.

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Speaker 3: There are problems with the platform. I would be happy

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Speaker 3: to go into all the many, many problems that I

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Speaker 3: have a TikTok, but also like, it's a platform that

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Speaker 3: exists in the world. There's good and bad and yeah,

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Speaker 3: I ultimately it's you know, this is just going to

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Speaker 3: be used to curb organizing and free speech. None of

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Speaker 3: the free speech absolutists are shown up around this issue.

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Speaker 3: I wonder why, like, yeah, where are they?

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Speaker 4: Let's take a quick break at our back.

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Speaker 1: Okay, So question for you, Joey. This has never happened

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Speaker 1: to me, but I know that it's happened to people.

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Speaker 1: Have you ever taken an uber or gotten an Instacart

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Speaker 1: order or something like that and then had a worker

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Speaker 1: contact you after the fact because they had your information

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Speaker 1: because they wanted to date or wanted to ask you out,

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Speaker 1: or like some other inappropriate vibes given?

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Speaker 2: Has it ever happened to you?

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Speaker 3: So that has not happened to me. That's definitely something

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Speaker 3: I've been worried about. Like, it's something I feel like,

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Speaker 3: especially if you're somebody who like designs female or feminine,

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Speaker 3: you're gonna have that kind of anxiety in the back

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Speaker 3: of your head. I mean maybe everybody does. I'm making assumptions,

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Speaker 3: but yeah, it's definitely I'm definitely like worried about that.

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Speaker 3: Haven't had it happen though.

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Speaker 1: Same, I've not had it happen, but I it's definitely

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Speaker 1: something in the back of my head. And we are

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Speaker 1: not alone because, according to a new survey, the rise

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Speaker 1: of things like gig work also means a rise in

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Speaker 1: this kind of inappropriate behavior. Two thousand British adults were

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Speaker 1: surveyed by the Information Commissioner's Office or the ICO, and

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Speaker 1: the survey found that seventeen percent of people have had

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Speaker 1: their personal information used for a romantic or sexual proposition

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Speaker 1: after handing it over to a business. That figure rises

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Speaker 1: to thirty three percent in London, where apparently this kind

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Speaker 1: of thing is more common. Almost one in three Brits

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Speaker 1: between the ages eighteen and thirty four have received unwanted

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Speaker 1: contact from delivery drivers or other workers, asking them out

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Speaker 1: on dates or propositioning them for sex. It is the

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Speaker 1: way that you put it, Joey, is so true, Like

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Speaker 1: this has not happened to me before. I've heard of

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Speaker 1: it happening. I've seen reports of it happening. It's like

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Speaker 1: a thing in the back of your mind. Whenever you

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Speaker 1: use a service like this, you know, and I think

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Speaker 1: it's one of the reasons why it feels that way

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Speaker 1: is by nature, when you are giving your information and

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Speaker 1: your contact information over to a company, by default, you're

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Speaker 1: like a little bit vulnerable, especially if somebody is coming

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Speaker 1: to your house to deliver something or dropping you off

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Speaker 1: at home if you're alone. Like I understand the feeling

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Speaker 1: of like, oh, no, is this person going to proposition me?

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Speaker 1: You know, And I don't have any data on it,

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Speaker 1: but I have to assume that this is an issue

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Speaker 1: that impacts women or people who present as women disproportionately,

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Speaker 1: Like I just have to assume, And I think, like, yeah,

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Speaker 1: it can feel unsafe. Despite never having had to deal

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Speaker 1: with this myself, I can imagine feeling really unsafe. And

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Speaker 1: Emily Keiney, a deputy missioner at the ICO, said people

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Speaker 1: have the right to order a pizza or give their

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Speaker 1: email for a seat, or have shop being delivered without

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Speaker 1: being asked for sex, or on a date a little

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Speaker 1: while later, And I completely agree. I also think that

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Speaker 1: some of this might be like partly cultural. I think

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Speaker 1: that we have so many signals in society that suggest

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Speaker 1: that this is the kind of quirky or romantic or

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Speaker 1: like big grand gesture way to sort of show someone

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Speaker 1: that you're taking the initiative to show romantic interest. Like

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Speaker 1: how many movies do we have that suggest that, like, oh,

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Speaker 1: just just go up and like do some big grand gesture,

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Speaker 1: They're gonna love it. And I think that like we

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Speaker 1: have been training people that this is a romantic thing

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Speaker 1: and that the person on the receiving end of it

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Speaker 1: is going to be is going to like it. But

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Speaker 1: you know, we're just trying to get take like get

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Speaker 1: our delivery food and go back into our apartment, right Like,

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Speaker 1: we're not trying to get a date. Everybody should have

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Speaker 1: the right to use services and not feel like the

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Speaker 1: person is going to be texting them after that because

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Speaker 1: they have their number, you know.

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Speaker 3: No, that's so true. Though. I feel like I'm not

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Speaker 3: a big rom com person. I feel like this is

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Speaker 3: what I always say that that I'm of course, whenever

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Speaker 3: I watch a rom com that I really like, I'm like,

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Speaker 3: this is so cut. But I think that's the issue

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Speaker 3: is there's so many where it's like this is a

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Speaker 3: little weird, like there's there's something a little off here. Yeah. No,

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Speaker 3: I also like I could totally see that being something

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Speaker 3: that would be like go viral on like Twitter, where

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Speaker 3: it's like, oh my god, this person messaged me after

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Speaker 3: dropping off my pizza and now we're marry, Like it's

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Speaker 3: it's it's weird and it's weird like the kind of

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Speaker 3: stories that we see as normal.

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Speaker 1: Yeah, I it's something it's something that I really I

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Speaker 1: like rom coms, but it's something that I hate in

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Speaker 1: rom coms, where it's like it's like in the romantic

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Speaker 1: comedy universe boundaries, it's not a thing, like, you know,

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Speaker 1: I don't like that. I did once get into a

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Speaker 1: long conversation that lasted after the fact with my insta

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Speaker 1: cart shopper because I had ordered Tabasco sauce and rather

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Speaker 1: than Tabasco sauce, the instacart shopper gave me liquid smoke

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Speaker 1: and I was like, it wasn't a big deal, like whatever,

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Speaker 1: but I was like, this person should know that, like

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Speaker 1: Tabasco sauce and liquid smoke are not synonymous. And so

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Speaker 1: I messaged back and I was like, hey, just so

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Speaker 1: you know, like for your own cooking, this is not

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Speaker 1: a like synonymous thing. And it was a perfectly pleasant interaction,

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Speaker 1: but it ended with, boy, you really have a lot

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Speaker 1: of opinions about cooking like I should do.

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Speaker 2: That's accurate.

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Speaker 3: I love that. So that's like a nice interaction. Yeah,

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Speaker 3: it was nice interactions. Yes, it was a nice plenty

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Speaker 3: of really great conversations with the uber drivers. Yeah, that's

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Speaker 3: always some thing too. I feel like, you know, the

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Speaker 3: particular typographics that always responds to these sort of things

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Speaker 3: with like, oh, so we can't be nice to do

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Speaker 3: It's like, no, I've hat like lovely conversations with like

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Speaker 3: white uber drivers about like I'm pretty young, I look

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Speaker 3: like I'm still in college. So usually it's like, you know,

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Speaker 3: parents telling me about their kids are in college that

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Speaker 3: kind of stuff. But it's always like it's super nice

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Speaker 3: and cute, and I don't know, there's definitely a very

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Speaker 3: clear line about what's what is too far and what

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Speaker 3: is at least I think there is.

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Speaker 2: I think I agree.

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Speaker 1: I think if your proposition is for somebody propositioning somebody

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Speaker 1: for sex or a date, that's probably on the other

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Speaker 1: side of that line. And England is actually cracking down

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Speaker 1: on this. The ICO is urging people who have been

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Speaker 1: victims of this kind of thing to come forward and

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Speaker 1: reminding companies of their data protection responsibilities that they are

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Speaker 1: bound to uphold by law. Companies who are violating data

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Speaker 1: protection laws can be fined up to seventeen point five

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Speaker 1: million pounds that's about twenty two point one million dollars,

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Speaker 1: and so yeah, they're just reminding folks that like it

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Speaker 1: is doing this getting someone's number because they did a

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Speaker 1: service with you is against the law, and contacting them

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Speaker 1: after the fact is against the law. And I think

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Speaker 1: there needs to be a cultural understanding that somebody who

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Speaker 1: is doing service with you, just because you have their

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Speaker 1: information that is not give you the green light to

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Speaker 1: like text them after the fact for a date.

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Speaker 2: So I'm happy to.

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Speaker 1: See England cracking down on this. So let's move to

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Speaker 1: the AI world, something we talk quite a bit about

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Speaker 1: on the show. So this week we spoke to actor

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Speaker 1: and producer Francesca Ramsey on the podcast about the Hollywood

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Speaker 1: strikes and how writers and actors are demanding guardrails about

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Speaker 1: how AI will be used in writers' rooms and on sets.

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Speaker 2: Hollywood studios have already put up.

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Speaker 1: Job listings for like very lucrative jobs for AI specialists

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Speaker 1: amidst the strike, which kind of gives you a lens

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Speaker 1: and to how they're thinking about the strike and how

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Speaker 1: they're thinking about the culture of how content gets made already,

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Speaker 1: and what writers really want to know is like how

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Speaker 1: AI is going to impact their presence on set, Like

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Speaker 1: are studios going to try to be funny with the

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Speaker 1: math and sneak in AI and then use that to

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Speaker 1: not pay human and writers?

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Speaker 2: You know, they want to know if AI is. The

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Speaker 2: example that.

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Speaker 1: Francesca Ramsay used in this week's episode was if AI

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Speaker 1: generates the first janki version of a script and then

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Speaker 1: a human writer punches that script up, who gets credit

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Speaker 1: for that script? Who gets paid for that script? Does

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Speaker 1: the human writer get paid for it? I bet the

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Speaker 1: studios would love it if they were to say, like, oh, no,

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Speaker 1: you just get paid on making tweaks, even though you

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Speaker 1: know the original script is probably super janky and did

434
00:22:27,800 --> 00:22:29,480
Speaker 1: need a heavy lift from a human hand. And so

435
00:22:29,960 --> 00:22:34,040
Speaker 1: in Hollywood, writers and actors really want some guardrails around

436
00:22:34,160 --> 00:22:37,600
Speaker 1: how studios plan to use AI. But this new ruling

437
00:22:37,840 --> 00:22:40,879
Speaker 1: might throw revenge and studios plans around AI because a

438
00:22:40,880 --> 00:22:43,720
Speaker 1: federal judge just ruled that a piece of art generated

439
00:22:43,760 --> 00:22:47,199
Speaker 1: by AI cannot be copyrighted. The lawsuit was brought by

440
00:22:47,200 --> 00:22:49,920
Speaker 1: plaintiffs Stephen th Taylor, who wanted to list his own

441
00:22:50,000 --> 00:22:52,920
Speaker 1: AI system as the sole creator of an artwork called

442
00:22:53,040 --> 00:22:56,840
Speaker 1: a Recent Entrance to Paradise, and the courts basically said, nah,

443
00:22:57,000 --> 00:23:01,080
Speaker 1: humans only. Copyright is for human work only. The judge

444
00:23:01,119 --> 00:23:04,480
Speaker 1: said humans are an essential part of a valid copyright

445
00:23:04,480 --> 00:23:08,719
Speaker 1: claim and that human authorship is a bedrock requirement of copyright.

446
00:23:09,080 --> 00:23:11,280
Speaker 1: Plaintiff can point to no case in which a court

447
00:23:11,320 --> 00:23:13,960
Speaker 1: has recognized copyright and a work originating with a non

448
00:23:14,040 --> 00:23:17,040
Speaker 1: human This is not a cut and drive ruling, I

449
00:23:17,080 --> 00:23:19,679
Speaker 1: should say. The judge did suggest that cases in the

450
00:23:19,720 --> 00:23:24,160
Speaker 1: future could be more complicated and quote will prompt challenging

451
00:23:24,240 --> 00:23:27,840
Speaker 1: questions regarding how much human input is necessary to qualify

452
00:23:27,920 --> 00:23:30,679
Speaker 1: the user of an AI system as an author of

453
00:23:30,720 --> 00:23:35,119
Speaker 1: a generated work. So if studios are not able to

454
00:23:35,200 --> 00:23:39,080
Speaker 1: retain copyright of creative work created solely by AI, the

455
00:23:39,119 --> 00:23:41,560
Speaker 1: whole you know, we'll just let AI write scripts instead

456
00:23:41,560 --> 00:23:44,760
Speaker 1: of paying writers. Thing might not shake out in their favor.

457
00:23:45,359 --> 00:23:47,840
Speaker 3: This whole story is so funny. I mean obviously, okay,

458
00:23:47,880 --> 00:23:50,280
Speaker 3: the like everything that has happened to do this let

459
00:23:50,359 --> 00:23:52,719
Speaker 3: up to this with like the fact that you know,

460
00:23:53,320 --> 00:23:55,879
Speaker 3: the writers and the actors have had to go on strike,

461
00:23:55,920 --> 00:23:58,600
Speaker 3: which full solidarity with them of course lost by the

462
00:23:58,600 --> 00:24:01,320
Speaker 3: picket line earlier today, etc. Some errands in Mintown and

463
00:24:02,280 --> 00:24:05,000
Speaker 3: love it love seeing the Yeah it was it was great.

464
00:24:05,000 --> 00:24:08,160
Speaker 3: But anyways, this whole I just.

465
00:24:09,640 --> 00:24:14,080
Speaker 5: This whole situation because especially watching like what the way

466
00:24:14,119 --> 00:24:17,080
Speaker 5: that these big companies and these big production companies have

467
00:24:17,240 --> 00:24:20,720
Speaker 5: used copyright to like screw over.

468
00:24:21,440 --> 00:24:24,600
Speaker 3: People in the past. I like, you know, I'm a

469
00:24:24,640 --> 00:24:27,240
Speaker 3: big Marble fan, but like the way that the company

470
00:24:27,280 --> 00:24:31,399
Speaker 3: runs is totally messed up. Like again, they use copyright

471
00:24:31,440 --> 00:24:33,439
Speaker 3: to screw over a lot of their writers and to

472
00:24:33,600 --> 00:24:35,760
Speaker 3: like kind of not give people credit for shit that

473
00:24:35,840 --> 00:24:37,359
Speaker 3: we were doing in the past. And it's I don't know,

474
00:24:37,359 --> 00:24:40,240
Speaker 3: it's just funny to see this now them being like, oh, nope,

475
00:24:40,280 --> 00:24:42,200
Speaker 3: we can't use copyright for AI.

476
00:24:44,119 --> 00:24:45,560
Speaker 2: Yeah, I don't know.

477
00:24:45,600 --> 00:24:47,480
Speaker 3: I also have like the whole idea of like AI

478
00:24:47,600 --> 00:24:50,199
Speaker 3: writing a script like that's not gonna work. It's not

479
00:24:50,240 --> 00:24:51,199
Speaker 3: gonna be good.

480
00:24:51,520 --> 00:24:52,240
Speaker 2: No exactly.

481
00:24:52,280 --> 00:24:54,320
Speaker 1: So that was the question that I had for Francesca

482
00:24:54,480 --> 00:24:57,879
Speaker 1: Ramsey in the episode. It was like, I believe the

483
00:24:57,920 --> 00:25:00,560
Speaker 1: idea that AI would write a good script like a

484
00:25:00,720 --> 00:25:03,800
Speaker 1: like like you know, good enough to be a show.

485
00:25:04,080 --> 00:25:06,240
Speaker 2: Is like a fantasy. And Francisca agrees.

486
00:25:06,280 --> 00:25:08,880
Speaker 1: She's like, right now, there has to be a human

487
00:25:08,880 --> 00:25:10,720
Speaker 1: in the mix somewhere for a script to be good,

488
00:25:11,000 --> 00:25:13,720
Speaker 1: and like we probably all I don't know if you

489
00:25:13,760 --> 00:25:15,679
Speaker 1: ever did those parlor tricks where it's like, oh, we

490
00:25:17,119 --> 00:25:20,639
Speaker 1: trained AI on a bunch of episodes of Seinfeld and

491
00:25:20,680 --> 00:25:22,680
Speaker 1: then here's what they came up with, and the joke

492
00:25:22,920 --> 00:25:25,440
Speaker 1: is that they're always janky and funny and don't make sense,

493
00:25:25,520 --> 00:25:28,280
Speaker 1: Like the reason why we do those is because they're

494
00:25:28,320 --> 00:25:32,000
Speaker 1: bad at it. But Francesca points out that while that

495
00:25:32,080 --> 00:25:34,560
Speaker 1: might be the case now, that might not be the

496
00:25:34,560 --> 00:25:37,160
Speaker 1: case forever. And so like three years down the road,

497
00:25:37,760 --> 00:25:41,359
Speaker 1: you know, is what if AI is can write a

498
00:25:41,400 --> 00:25:44,400
Speaker 1: passable first draft, and so really looking to the future

499
00:25:45,040 --> 00:25:49,199
Speaker 1: and sort of anticipating what's on the horizon, because you know,

500
00:25:49,600 --> 00:25:51,720
Speaker 1: again she it's it's it's an inn. I keep referencing,

501
00:25:51,840 --> 00:25:54,040
Speaker 1: referencing the interview because it was a fascinating conversation. But

502
00:25:54,080 --> 00:25:58,640
Speaker 1: she references how when in twenty twelve there the new

503
00:25:59,119 --> 00:26:04,639
Speaker 1: contracts with the Writers' union talked about like content made

504
00:26:04,720 --> 00:26:07,400
Speaker 1: solely for the Internet, but that was like in twenty twelve,

505
00:26:07,440 --> 00:26:09,639
Speaker 1: there weren't really like shows on the internet like that,

506
00:26:09,680 --> 00:26:11,679
Speaker 1: like maybe you had a web series, but it was

507
00:26:11,760 --> 00:26:15,359
Speaker 1: before streaming, and so people didn't know. People couldn't predict

508
00:26:15,400 --> 00:26:18,920
Speaker 1: like how content made for the Internet would take off

509
00:26:18,960 --> 00:26:21,000
Speaker 1: and like streaming would become a thing, and it kind

510
00:26:21,040 --> 00:26:24,520
Speaker 1: of bit them in the ass, not being future forward,

511
00:26:24,560 --> 00:26:27,600
Speaker 1: and so I think with the AI stuff, the writers

512
00:26:27,640 --> 00:26:30,399
Speaker 1: and actors are trying to think, what's on the horizon,

513
00:26:30,480 --> 00:26:32,919
Speaker 1: How will this be used to exploit my labor? How

514
00:26:32,960 --> 00:26:35,560
Speaker 1: will this be used to like undercut me and underpay me?

515
00:26:35,600 --> 00:26:39,800
Speaker 1: Because that's unfortunately, that's how a lot of these executives think, like,

516
00:26:39,880 --> 00:26:43,119
Speaker 1: how can we use this technology to like scam the

517
00:26:43,240 --> 00:26:45,400
Speaker 1: people who make our jobs possible?

518
00:26:45,800 --> 00:26:50,000
Speaker 3: Exactly. Yeah, it's so there's so many scary things that

519
00:26:50,000 --> 00:26:54,480
Speaker 3: could happen because this I feel like already wasn't there

520
00:26:54,520 --> 00:26:57,800
Speaker 3: some movies they made that they like they like used

521
00:26:58,080 --> 00:27:01,520
Speaker 3: CGI to like make like James Dean to be in it.

522
00:27:01,520 --> 00:27:03,600
Speaker 3: It was some whole thing or like yeah, the most

523
00:27:03,640 --> 00:27:06,120
Speaker 3: recent like Star Wars movie when they had like after

524
00:27:06,160 --> 00:27:08,439
Speaker 3: Carrie Fisher died, they had like a CGI carry Fisher

525
00:27:08,520 --> 00:27:09,760
Speaker 3: Like that was the whole thing where it's like it's

526
00:27:09,800 --> 00:27:12,960
Speaker 3: stealing people's likeness. How does that? That was weird? Like

527
00:27:13,000 --> 00:27:16,720
Speaker 3: we already have that, and that's personally as somebody watching

528
00:27:16,840 --> 00:27:19,000
Speaker 3: like watching those movies, it's kind of creepy, Like I

529
00:27:19,040 --> 00:27:20,399
Speaker 3: don't know, I didn't like that when they did that

530
00:27:20,440 --> 00:27:22,680
Speaker 3: in like Star Wars. I haven't seen any other movies

531
00:27:22,680 --> 00:27:24,800
Speaker 3: where they've done it yet, but it's like it's a

532
00:27:24,800 --> 00:27:27,320
Speaker 3: little weird, but yeah, it's it's it's scary to see

533
00:27:27,320 --> 00:27:32,240
Speaker 3: that that's that's a possibility, and they definitely are like yeah, yeah.

534
00:27:31,560 --> 00:27:35,679
Speaker 1: So I've even I've heard of actors and musicians adding

535
00:27:35,720 --> 00:27:39,399
Speaker 1: provisions into their contracts saying that like, don't use my

536
00:27:39,600 --> 00:27:43,280
Speaker 1: likeness in AI, do not use technology to make AI,

537
00:27:44,119 --> 00:27:46,680
Speaker 1: you know, fabricated versions of me. And I think we're

538
00:27:46,680 --> 00:27:50,280
Speaker 1: gonna see more and more of that of creatives really

539
00:27:50,359 --> 00:27:54,240
Speaker 1: getting particular about how their their usage and their likeness

540
00:27:54,240 --> 00:27:57,680
Speaker 1: shows up even in death. Like there's just something very

541
00:27:59,800 --> 00:28:05,560
Speaker 1: all to me personally about like Robin Williams is no

542
00:28:05,600 --> 00:28:10,400
Speaker 1: longer with us. Robin Williams died before AI took off.

543
00:28:11,440 --> 00:28:16,000
Speaker 1: Using that fact that someone died before this technology was

544
00:28:16,080 --> 00:28:18,480
Speaker 1: ubiquitous to be like, oh, we're gonna put them in,

545
00:28:19,119 --> 00:28:22,320
Speaker 1: you know, after their death in a movie that they

546
00:28:22,359 --> 00:28:25,200
Speaker 1: can't they can't agree to do, they can't say whether

547
00:28:25,280 --> 00:28:25,960
Speaker 1: or not they do it.

548
00:28:26,760 --> 00:28:28,359
Speaker 2: That just feels really off to me.

549
00:28:29,680 --> 00:28:32,760
Speaker 3: Yeah, it is. It's it's really strange. It feels very like,

550
00:28:34,200 --> 00:28:36,280
Speaker 3: I don't know, especially if you're an actor, like you're

551
00:28:36,320 --> 00:28:39,920
Speaker 3: putting yourself in that role you are, you are putting

552
00:28:39,960 --> 00:28:42,479
Speaker 3: your image, you're attaching your image to that movie or whatever.

553
00:28:42,600 --> 00:28:45,640
Speaker 3: And it's these people aren't giving they're not giving their

554
00:28:45,640 --> 00:28:48,960
Speaker 3: consent to be, you know, a part of whatever they're

555
00:28:49,000 --> 00:28:52,920
Speaker 3: being put in now after their death. Yeah, it's it's

556
00:28:52,920 --> 00:28:55,560
Speaker 3: so weird. The music thing, too, I think is interesting

557
00:28:55,600 --> 00:28:57,560
Speaker 3: because kind of going back to the tik tok and

558
00:28:57,600 --> 00:28:59,160
Speaker 3: all this thing. One thing that I've been seeing lately

559
00:28:59,200 --> 00:29:00,800
Speaker 3: that has been annoying me. So there's all these like

560
00:29:00,920 --> 00:29:04,200
Speaker 3: videos going around that are like have you ever wondered

561
00:29:04,280 --> 00:29:06,280
Speaker 3: like this pop song but like we did a Frank

562
00:29:06,320 --> 00:29:10,160
Speaker 3: Sinatra ay, and it's really like it's always presented as

563
00:29:10,160 --> 00:29:12,600
Speaker 3: this like oh this is so cool, like mashing job,

564
00:29:12,680 --> 00:29:16,200
Speaker 3: and it's like or you could have like found somebody

565
00:29:16,240 --> 00:29:17,960
Speaker 3: to like do a cover of it in like a

566
00:29:18,080 --> 00:29:22,040
Speaker 3: jazz style or whatever like that. It's it's weird. I think,

567
00:29:22,120 --> 00:29:24,840
Speaker 3: like I get the appeal of it, and I get

568
00:29:24,880 --> 00:29:26,960
Speaker 3: the like especially like I've gotten a couple times where

569
00:29:26,960 --> 00:29:28,200
Speaker 3: I've like seen on a re be like this is

570
00:29:28,200 --> 00:29:31,000
Speaker 3: really cool and then been like huh, like maybe it

571
00:29:31,080 --> 00:29:32,840
Speaker 3: is a little messed up that they're like having an ai

572
00:29:32,920 --> 00:29:37,280
Speaker 3: do this when but yeah, it's it's strange and weird,

573
00:29:37,360 --> 00:29:40,880
Speaker 3: and I think we should just let people be creative

574
00:29:41,040 --> 00:29:44,680
Speaker 3: because that's part of being human and it's I don't know.

575
00:29:45,600 --> 00:29:49,080
Speaker 1: Yeah, I gotta take my cues from Prince on that one.

576
00:29:49,560 --> 00:29:54,760
Speaker 1: Prince was notoriously against, like I'm a huge Prince Man

577
00:29:54,760 --> 00:29:57,160
Speaker 1: by the way, I'm like obsessed with. Prince was notoriously

578
00:29:57,200 --> 00:30:03,040
Speaker 1: against things like holograms or a digital likenesses of his image.

579
00:30:03,280 --> 00:30:05,200
Speaker 1: And he has this line that says, that is the

580
00:30:05,240 --> 00:30:08,880
Speaker 1: most demonic thing imaginable. Everything is as it is and

581
00:30:08,920 --> 00:30:10,800
Speaker 1: as it should be. If I was meant to jam

582
00:30:10,840 --> 00:30:13,320
Speaker 1: with Duke Ellington, we would have lived in the same age.

583
00:30:13,640 --> 00:30:16,680
Speaker 1: And that really speaks to me of like some things

584
00:30:16,840 --> 00:30:19,160
Speaker 1: aren't and they weren't meant to be, and like some

585
00:30:19,360 --> 00:30:21,480
Speaker 1: it can be like a cool parlor trick to be like, oh,

586
00:30:21,480 --> 00:30:24,240
Speaker 1: this is what it would sound like if like Biggie

587
00:30:24,280 --> 00:30:27,920
Speaker 1: Small's sang a Olivia Rodrigo song or whatever. Like I

588
00:30:28,520 --> 00:30:30,640
Speaker 1: get it, I get it, I get it, But like

589
00:30:30,880 --> 00:30:32,960
Speaker 1: some things are not because they were not meant to be.

590
00:30:33,440 --> 00:30:35,200
Speaker 2: I think some of those are good examples.

591
00:30:36,520 --> 00:30:39,640
Speaker 3: But yeah, now the question of the hour, bridget I'm

592
00:30:39,680 --> 00:30:41,560
Speaker 3: on your show for the first time. I gotta know

593
00:30:43,160 --> 00:30:44,440
Speaker 3: what's Elan done.

594
00:30:44,200 --> 00:30:47,600
Speaker 1: Now, glad you asked, Well, he's talking about wanting to

595
00:30:47,640 --> 00:30:50,360
Speaker 1: remove all of the headlines, all of the text from

596
00:30:50,480 --> 00:30:52,960
Speaker 1: news articles on Twitter, so that when you see a

597
00:30:53,000 --> 00:30:56,800
Speaker 1: news article, all you would see is an image, no headline,

598
00:30:56,800 --> 00:31:00,440
Speaker 1: no help or text. It's because he wants like journalists,

599
00:31:00,560 --> 00:31:03,280
Speaker 1: this is what he says. He wants journalists to publish

600
00:31:03,360 --> 00:31:06,200
Speaker 1: directly on Twitter, and like see that as a valuable

601
00:31:06,200 --> 00:31:08,560
Speaker 1: thing to do. I think the outcome will be really obvious.

602
00:31:08,880 --> 00:31:12,160
Speaker 1: People already like barely click into the article to read

603
00:31:12,160 --> 00:31:14,240
Speaker 1: what they're about, and I can only imagine that's going

604
00:31:14,280 --> 00:31:17,320
Speaker 1: to happen even less with this move, further eroding the

605
00:31:17,320 --> 00:31:21,120
Speaker 1: platform as a place to get accurate news and undermined

606
00:31:21,320 --> 00:31:24,120
Speaker 1: news publishers as well. They don't have their name on them,

607
00:31:24,160 --> 00:31:26,320
Speaker 1: that will undermine the ability for them to really get

608
00:31:26,360 --> 00:31:28,480
Speaker 1: their name out there. But I also think it's meant

609
00:31:28,520 --> 00:31:31,240
Speaker 1: to sort of further blow up the division between journalists

610
00:31:31,240 --> 00:31:35,440
Speaker 1: from reputable platforms and people who just like make content

611
00:31:36,040 --> 00:31:39,720
Speaker 1: because Elon does not respect journalists or a free press.

612
00:31:39,720 --> 00:31:42,400
Speaker 1: That has been a notable thing about him. It's been

613
00:31:42,440 --> 00:31:45,800
Speaker 1: a clear he has shown us that with his actions

614
00:31:45,800 --> 00:31:48,000
Speaker 1: and behavior time and time again, and so I think

615
00:31:48,000 --> 00:31:53,160
Speaker 1: that he wants internet personalities like you're Brian Cransnstein's and

616
00:31:53,200 --> 00:31:58,400
Speaker 1: you're like, I don't know, right wing grievance s grifters

617
00:31:58,480 --> 00:32:02,040
Speaker 1: who use social media platforms. I think that by removing

618
00:32:02,800 --> 00:32:05,480
Speaker 1: anything but the image, those people are going to be

619
00:32:05,520 --> 00:32:08,920
Speaker 1: seen in the same way as like people from legit

620
00:32:09,240 --> 00:32:11,720
Speaker 1: news platforms like Reuter's or something right, And so I

621
00:32:11,720 --> 00:32:13,320
Speaker 1: think that, like, that's why he's doing it.

622
00:32:13,440 --> 00:32:14,840
Speaker 2: We hate it, hate to see it.

623
00:32:15,800 --> 00:32:20,000
Speaker 3: Yeah, that is weird. It is like it's a text

624
00:32:20,080 --> 00:32:23,920
Speaker 3: based platform. People are mostly going for the you know.

625
00:32:24,000 --> 00:32:26,640
Speaker 3: And also I guess again it is sort of frustrating

626
00:32:26,640 --> 00:32:29,720
Speaker 3: that people just read the headlines. We'll repost things based

627
00:32:29,760 --> 00:32:33,520
Speaker 3: on the headlines. But I don't know, this doesn't seem

628
00:32:33,720 --> 00:32:36,640
Speaker 3: like the way to assault that this is a really

629
00:32:36,760 --> 00:32:38,480
Speaker 3: weird move, weird to saying it.

630
00:32:38,600 --> 00:32:44,360
Speaker 1: Yeah, yeah, Oh, and he's maybe making his terrible jokes

631
00:32:44,400 --> 00:32:47,960
Speaker 1: and like tweeting the word concerning with a thinking face emoji,

632
00:32:47,960 --> 00:32:51,040
Speaker 1: which he loves to tweet to nobody because his followers

633
00:32:51,240 --> 00:32:53,480
Speaker 1: might be fake. If that was true, there will be

634
00:32:53,560 --> 00:32:56,280
Speaker 1: it will be very housewives of him to have fake followers,

635
00:32:56,320 --> 00:32:59,800
Speaker 1: just saying going back to my point about how all

636
00:32:59,800 --> 00:33:02,280
Speaker 1: of that our behavior can be understood to glens of housewives.

637
00:33:02,400 --> 00:33:05,200
Speaker 1: Mashable reports that a significant chunk of Elon Musk's more

638
00:33:05,200 --> 00:33:08,080
Speaker 1: than one hundred and fifty three million followers on Twitter

639
00:33:08,400 --> 00:33:11,040
Speaker 1: appear to be fake or at the very least inactive.

640
00:33:11,560 --> 00:33:15,080
Speaker 1: Here's some stats. Forty two percent of Musk's followers have

641
00:33:15,360 --> 00:33:18,720
Speaker 1: zero followers on their own account. Over seventy two percent

642
00:33:18,760 --> 00:33:21,240
Speaker 1: of the users who follow Musk have less than ten

643
00:33:21,360 --> 00:33:24,200
Speaker 1: followers on their accounts. More than sixty two point five

644
00:33:24,280 --> 00:33:27,040
Speaker 1: million of musk followers have zero tweets. More than one

645
00:33:27,080 --> 00:33:30,000
Speaker 1: hundred million of Musk's followers have less than ten tweets.

646
00:33:30,320 --> 00:33:32,760
Speaker 1: And another very interesting detail is the timing of his

647
00:33:32,880 --> 00:33:35,840
Speaker 1: follow when he got followers, so he's currently the most

648
00:33:35,920 --> 00:33:40,400
Speaker 1: followed person on Twitter, surprise surprise. When his followers created

649
00:33:40,400 --> 00:33:44,120
Speaker 1: their accounts is pretty interesting. Musk completed his acquisition of

650
00:33:44,160 --> 00:33:46,880
Speaker 1: Twitter on October twenty seven, twenty twenty two. Out of

651
00:33:46,960 --> 00:33:50,080
Speaker 1: all of Musk's current followers, more than twenty five percent,

652
00:33:50,280 --> 00:33:53,880
Speaker 1: or thirty eight point nine million, were created on or

653
00:33:53,920 --> 00:33:58,440
Speaker 1: after that date. So that's suspicious, like, and I think

654
00:33:58,520 --> 00:34:03,880
Speaker 1: it really, if we're to understand Mosqu's followers as a

655
00:34:03,920 --> 00:34:08,040
Speaker 1: micropasm of Twitter itself and who's showing up there, it

656
00:34:08,080 --> 00:34:11,760
Speaker 1: doesn't paint a very like this is a very damning report,

657
00:34:11,920 --> 00:34:14,200
Speaker 1: and I think it's not just about his followers, but

658
00:34:14,239 --> 00:34:17,160
Speaker 1: also about like who's using the platform, who's showing up

659
00:34:17,160 --> 00:34:18,000
Speaker 1: on the platform in.

660
00:34:17,920 --> 00:34:21,319
Speaker 2: General, Like just very not good.

661
00:34:21,640 --> 00:34:24,400
Speaker 3: That's fun that's funny because I feel like recently, I

662
00:34:24,520 --> 00:34:28,600
Speaker 3: just recently was like getting on Twitter again, mostly because

663
00:34:28,600 --> 00:34:30,760
Speaker 3: I was affording TikTok because I was trying to avoid

664
00:34:30,800 --> 00:34:34,759
Speaker 3: all this drama that was happening recently. But I like

665
00:34:34,920 --> 00:34:37,360
Speaker 3: was on my Twitter again and like posted something for

666
00:34:37,480 --> 00:34:39,239
Speaker 3: the first time in like a couple of months, and

667
00:34:40,160 --> 00:34:42,000
Speaker 3: like the first account that liked it was some random

668
00:34:42,080 --> 00:34:44,279
Speaker 3: like blue check mark account that I was like like

669
00:34:44,400 --> 00:34:46,200
Speaker 3: with no Like, I was like, where did this person

670
00:34:46,280 --> 00:34:48,120
Speaker 3: come from? And then it was like going through it,

671
00:34:48,280 --> 00:34:50,400
Speaker 3: there was just so many like blue check mark accounts

672
00:34:50,400 --> 00:34:51,680
Speaker 3: that I was seeing and I was like, there's no

673
00:34:51,880 --> 00:34:55,120
Speaker 3: way that this many people as many people sent to

674
00:34:55,200 --> 00:35:01,400
Speaker 3: the like eight dollars or whatever. It's yeah, yeah, it

675
00:35:01,480 --> 00:35:04,359
Speaker 3: adds up. It makes sense if that is the case.

676
00:35:04,920 --> 00:35:08,440
Speaker 1: Well, speaking of blue check marks. Apparently, in order to

677
00:35:08,560 --> 00:35:12,719
Speaker 1: get blue check marks, Twitter is testing out a verification

678
00:35:12,840 --> 00:35:16,360
Speaker 1: process for Twitter Blue to have your blue check that

679
00:35:16,360 --> 00:35:19,360
Speaker 1: would involve submitting the front and back of your government

680
00:35:19,440 --> 00:35:22,560
Speaker 1: ID along with the SELTE to verify your identity, to

681
00:35:22,719 --> 00:35:25,759
Speaker 1: verify your account to get a blue check mark. So

682
00:35:26,400 --> 00:35:30,040
Speaker 1: hopefully I don't need to say this, but please don't

683
00:35:30,040 --> 00:35:32,560
Speaker 1: give Elon Musk a copy of your government ID. That

684
00:35:32,719 --> 00:35:34,800
Speaker 1: is a terrible idea. Please don't do it.

685
00:35:35,160 --> 00:35:41,400
Speaker 2: Don't do it. Mistake more.

686
00:35:41,440 --> 00:35:53,840
Speaker 6: After a quick break, let's get right back into it.

687
00:35:57,400 --> 00:36:01,319
Speaker 1: Okay, So, speaking of Elon Musk, let's talk a little

688
00:36:01,360 --> 00:36:04,920
Speaker 1: bit more about how unsafe autonomous vehicles can be. We

689
00:36:05,040 --> 00:36:08,720
Speaker 1: talked about the dismal record on autonomous vehicles and crashes

690
00:36:08,719 --> 00:36:11,760
Speaker 1: a few weeks ago. Well, the technology on those vehicles

691
00:36:12,080 --> 00:36:15,120
Speaker 1: does a worse job of identifying people with darker skin

692
00:36:15,640 --> 00:36:20,040
Speaker 1: and children as pedestrians, so pretty concerning. This is according

693
00:36:20,080 --> 00:36:23,920
Speaker 1: to new research from doctor Jizong from the Department of

694
00:36:23,960 --> 00:36:27,840
Speaker 1: Infomatics at King's College, who assessed eight artificial intelligence powered

695
00:36:27,880 --> 00:36:31,960
Speaker 1: pedestrian detection systems used in autonomous vehicle research. Their research

696
00:36:32,080 --> 00:36:36,040
Speaker 1: found that detection accuracy for adults was almost twenty percent

697
00:36:36,200 --> 00:36:39,040
Speaker 1: higher than it was for children, and just over seven

698
00:36:39,080 --> 00:36:42,960
Speaker 1: point five percent more accurate for light skin pedestrians compared

699
00:36:43,000 --> 00:36:46,160
Speaker 1: to their darker skin counterparts. The researchers also found that

700
00:36:46,239 --> 00:36:52,000
Speaker 1: this bias toward darker skin pedestrians increases significantly under scenarios

701
00:36:52,080 --> 00:36:55,440
Speaker 1: of low contrast and low brightness, posing increased issues for

702
00:36:55,560 --> 00:36:59,160
Speaker 1: night time driving. So basically, children and people with darker

703
00:36:59,239 --> 00:37:02,879
Speaker 1: skin are less likely to be recognized as pedestrians by

704
00:37:03,080 --> 00:37:08,000
Speaker 1: autonomous driving vehicles. Pretty scary to me, I think, especially

705
00:37:08,080 --> 00:37:10,560
Speaker 1: the thing about kids is that if you spent any

706
00:37:10,600 --> 00:37:14,080
Speaker 1: time at all around kids, they like, you really can't

707
00:37:14,120 --> 00:37:17,080
Speaker 1: trust a little one to not dart out in an

708
00:37:17,160 --> 00:37:18,040
Speaker 1: unexpected way.

709
00:37:18,160 --> 00:37:21,640
Speaker 2: And so if these vehicles are already less likely.

710
00:37:21,600 --> 00:37:24,160
Speaker 1: To recognize them as pedestrians, and then add on the

711
00:37:24,239 --> 00:37:26,919
Speaker 1: fact that they're squirmy little six year olds or whatever,

712
00:37:27,560 --> 00:37:31,520
Speaker 1: it paints a pretty terrifying picture. And you know, it

713
00:37:31,560 --> 00:37:35,200
Speaker 1: should be said that black women technologists and researchers I'm

714
00:37:35,200 --> 00:37:38,280
Speaker 1: thinking of doctor Sophia Noble and doctor Joe bol Lawini

715
00:37:38,800 --> 00:37:41,520
Speaker 1: have been saying this for a really long time, and

716
00:37:41,600 --> 00:37:44,320
Speaker 1: it actually points back to the reason why I was

717
00:37:44,400 --> 00:37:45,920
Speaker 1: inspired to start this podcast.

718
00:37:46,000 --> 00:37:46,640
Speaker 2: In the first place.

719
00:37:47,280 --> 00:37:50,160
Speaker 1: Side note, there was a much must read Rolling Stone

720
00:37:50,200 --> 00:37:52,560
Speaker 1: profile of how black women like them have been trying

721
00:37:52,600 --> 00:37:55,360
Speaker 1: to sound the alarm about II for years, and it

722
00:37:55,480 --> 00:37:58,279
Speaker 1: is just a really good reminder that inclusion in technology

723
00:37:58,880 --> 00:38:01,400
Speaker 1: is not important because it's like the right thing to

724
00:38:01,480 --> 00:38:03,640
Speaker 1: do or the nice thing to do. It is, but

725
00:38:03,760 --> 00:38:06,520
Speaker 1: that's not the only reason it matters. It is because

726
00:38:06,560 --> 00:38:11,279
Speaker 1: there are real consequences when the spaces that where technology

727
00:38:11,440 --> 00:38:14,839
Speaker 1: is made are not inclusive. Right, When the people who

728
00:38:14,960 --> 00:38:19,399
Speaker 1: make technology like autonomous vehicles are too homogenous or they're

729
00:38:19,440 --> 00:38:22,920
Speaker 1: not thinking enough about being inclusive and different kinds of people,

730
00:38:23,400 --> 00:38:26,040
Speaker 1: they're not then testing them and training them on a

731
00:38:26,160 --> 00:38:30,319
Speaker 1: diverse enough data set, right, And so it's this weird

732
00:38:30,440 --> 00:38:34,840
Speaker 1: thing where the erasure or lack of inclusion means that

733
00:38:34,960 --> 00:38:37,080
Speaker 1: we aren't just not represented in the rooms of the

734
00:38:37,120 --> 00:38:41,280
Speaker 1: technology is being made. In turn, that erasure can actually

735
00:38:41,440 --> 00:38:45,239
Speaker 1: get us killed and have real world light or death consequences.

736
00:38:45,600 --> 00:38:48,439
Speaker 1: As doctor Zong explains, fairness when it comes to AI

737
00:38:48,960 --> 00:38:52,239
Speaker 1: is when an AI system treats privileged and underprivileged groups

738
00:38:52,280 --> 00:38:54,360
Speaker 1: the same, which is not what is happening when it

739
00:38:54,400 --> 00:38:58,200
Speaker 1: comes to autonomous vehicles. Car manufacturers don't release the details

740
00:38:58,239 --> 00:39:00,520
Speaker 1: of the software they use for pedestrian to tech, but

741
00:39:00,600 --> 00:39:03,520
Speaker 1: they are usually built upon the same open source systems

742
00:39:03,800 --> 00:39:06,279
Speaker 1: we used in our research. We can be quite sure

743
00:39:06,360 --> 00:39:08,520
Speaker 1: that they are running into the same issues of bias.

744
00:39:08,920 --> 00:39:12,600
Speaker 1: While the impact of unfair AI systems is already well documented,

745
00:39:12,920 --> 00:39:16,640
Speaker 1: from AI recruitment software favoring male applicants to facial recognition

746
00:39:16,719 --> 00:39:19,359
Speaker 1: software being less accurate for black women than for white men,

747
00:39:19,800 --> 00:39:22,920
Speaker 1: the danger that self driving cars can pose is acute.

748
00:39:23,200 --> 00:39:27,319
Speaker 1: Before minority individuals may have been denied vital services. Now

749
00:39:27,840 --> 00:39:31,759
Speaker 1: they might face severe injury. And I think that is

750
00:39:31,840 --> 00:39:34,239
Speaker 1: exactly right. It is like one of the reasons why

751
00:39:34,320 --> 00:39:38,520
Speaker 1: I will scream from the rooftops that this technology. It's

752
00:39:38,560 --> 00:39:41,680
Speaker 1: so easy to think of technology like AI, like it's

753
00:39:41,719 --> 00:39:45,400
Speaker 1: some kind of robot computer brain that is like acting

754
00:39:45,480 --> 00:39:48,040
Speaker 1: on a different plane than we humans are. But in reality,

755
00:39:48,480 --> 00:39:52,280
Speaker 1: that technology is trained and built and designed by humans,

756
00:39:52,280 --> 00:39:55,120
Speaker 1: and so it's going to be replicating the very same

757
00:39:55,239 --> 00:39:58,359
Speaker 1: biases that we know that humans have. And so if

758
00:39:58,440 --> 00:40:01,520
Speaker 1: that is the case, and these biases come into play

759
00:40:01,560 --> 00:40:05,200
Speaker 1: where the technology is trained to not fully recognized children

760
00:40:05,320 --> 00:40:09,120
Speaker 1: or people of color as human as pedestrians, that autonomous

761
00:40:09,200 --> 00:40:11,680
Speaker 1: vehicles should not hit that is a problem.

762
00:40:12,360 --> 00:40:16,279
Speaker 3: Yeah, yeah, I think it's honestly, like all of this,

763
00:40:16,440 --> 00:40:18,120
Speaker 3: it's not a coincidence that this is happening at the

764
00:40:18,120 --> 00:40:20,520
Speaker 3: same time, Like we're having this conversation about like facial

765
00:40:20,600 --> 00:40:23,480
Speaker 3: recognition and facial because you did that story last week

766
00:40:23,520 --> 00:40:26,640
Speaker 3: about the woman who is like, you know, arrested for

767
00:40:26,719 --> 00:40:30,000
Speaker 3: a crime she didn't commit. Because these facial recognition systems

768
00:40:30,880 --> 00:40:33,359
Speaker 3: they are best of to begin with, I don't think

769
00:40:33,400 --> 00:40:35,080
Speaker 3: we should be having them to begin with. But that's

770
00:40:35,480 --> 00:40:38,640
Speaker 3: another point would be it's like they're they're being designed

771
00:40:38,680 --> 00:40:41,200
Speaker 3: a specific way, They're being designed with a specific type

772
00:40:41,239 --> 00:40:46,239
Speaker 3: of person in mind, and oftentimes it's very exclusionary. And yeah,

773
00:40:46,280 --> 00:40:48,640
Speaker 3: again this is this is another example that was that

774
00:40:48,840 --> 00:40:51,120
Speaker 3: is going to lead to people being killed and that

775
00:40:51,400 --> 00:40:52,120
Speaker 3: that's terrifying.

776
00:40:52,239 --> 00:40:54,120
Speaker 2: Yeah, it's absolutely terrifying.

777
00:40:54,239 --> 00:40:55,920
Speaker 3: I don't know, I don't know if you've ever been

778
00:40:56,040 --> 00:40:58,640
Speaker 3: in a self driving car, but in a self driving

779
00:40:58,680 --> 00:41:01,160
Speaker 3: tessela specifically, but I have this experience and it is

780
00:41:01,239 --> 00:41:05,680
Speaker 3: not fun. It is very scary. That was my personal experience.

781
00:41:05,680 --> 00:41:10,239
Speaker 3: I'm sure other people, but it is I don't think

782
00:41:10,360 --> 00:41:13,479
Speaker 3: that that technology is anywhere near safe enough for people

783
00:41:13,520 --> 00:41:14,719
Speaker 3: to be using a public.

784
00:41:14,520 --> 00:41:17,879
Speaker 1: But yeah, Joey, I never have, nor would I ever

785
00:41:18,239 --> 00:41:21,239
Speaker 1: like this is it was. I don't need choice, Oh

786
00:41:21,320 --> 00:41:23,279
Speaker 1: my god, I want to know everything, Like I don't.

787
00:41:23,320 --> 00:41:25,680
Speaker 1: I'm sure people listening are like, actually it's safe for them.

788
00:41:25,840 --> 00:41:27,759
Speaker 2: I don't care. It's it is not for me.

789
00:41:28,080 --> 00:41:28,239
Speaker 4: I have.

790
00:41:28,400 --> 00:41:29,960
Speaker 2: It's a personal choice. What was it like?

791
00:41:30,400 --> 00:41:32,080
Speaker 3: This was from my experience. Okay, this is a couple

792
00:41:32,080 --> 00:41:33,320
Speaker 3: of years ago, so it's a little but I just

793
00:41:33,400 --> 00:41:37,440
Speaker 3: remember like it was super rocky. It was like very

794
00:41:37,520 --> 00:41:38,799
Speaker 3: there were a lot of like clothes cop we were

795
00:41:38,880 --> 00:41:41,280
Speaker 3: we weren't like we were in like a pretty empty

796
00:41:41,640 --> 00:41:44,760
Speaker 3: kind of shriek, but it was just like very sharp turns.

797
00:41:45,400 --> 00:41:47,480
Speaker 3: I it's just and again, this was a couple of

798
00:41:47,520 --> 00:41:49,320
Speaker 3: years ago. This is my experience. I'm not saying this

799
00:41:49,440 --> 00:41:51,759
Speaker 3: is I'm sure somebody is gonna be like, actually, it's

800
00:41:51,800 --> 00:41:54,839
Speaker 3: really great. I'm sure whatever. This is not me trying

801
00:41:54,880 --> 00:41:58,600
Speaker 3: to slander Tasla. But I my personal experience from the

802
00:41:58,600 --> 00:42:00,279
Speaker 3: one time that I was in a self driving car,

803
00:42:01,040 --> 00:42:02,600
Speaker 3: from the time that I was in a Tesla and

804
00:42:02,680 --> 00:42:05,640
Speaker 3: the person driving it went, hey, actually do turn on

805
00:42:05,760 --> 00:42:08,960
Speaker 3: this self driving feature, and then did it was really scary,

806
00:42:09,000 --> 00:42:10,279
Speaker 3: and I would not do that again, and I would

807
00:42:10,320 --> 00:42:11,520
Speaker 3: not recommend that you could do that.

808
00:42:11,920 --> 00:42:16,880
Speaker 2: So yeah, yeah that I mean, I it's just not

809
00:42:17,040 --> 00:42:17,239
Speaker 2: for me.

810
00:42:17,320 --> 00:42:19,360
Speaker 1: There are certain things I don't need to see the

811
00:42:19,480 --> 00:42:24,040
Speaker 1: stats on the safety, like self driving cars not for me. Uh,

812
00:42:24,360 --> 00:42:26,600
Speaker 1: small planes not for me. There are just certain things.

813
00:42:26,640 --> 00:42:29,279
Speaker 1: It's like we've we've assessed it, we've made a call.

814
00:42:29,800 --> 00:42:31,719
Speaker 3: Not for us, just being in a car that is

815
00:42:31,719 --> 00:42:35,520
Speaker 3: already so dangerous, like oh my god, Yes, we don't

816
00:42:35,520 --> 00:42:37,800
Speaker 3: really think about that because we use cars for like everything.

817
00:42:37,880 --> 00:42:40,160
Speaker 3: We live in a very car based kind of society.

818
00:42:40,280 --> 00:42:42,880
Speaker 3: But it's it's it is like, it's very dangerous to

819
00:42:42,920 --> 00:42:45,399
Speaker 3: be in a car, and I don't know, I don't

820
00:42:45,440 --> 00:42:48,000
Speaker 3: really want to add another layer to that of another

821
00:42:48,080 --> 00:42:50,439
Speaker 3: reason that I could get killed or somebody else could

822
00:42:50,440 --> 00:42:52,200
Speaker 3: get killed because of a vehicle that I'm in.

823
00:42:52,719 --> 00:42:56,040
Speaker 2: Yeah, let's there are plenty of ways to die.

824
00:42:56,239 --> 00:42:57,640
Speaker 1: I don't need to add one to the I don't

825
00:42:57,640 --> 00:42:59,920
Speaker 1: need to add pylon to already a high list.

826
00:43:04,760 --> 00:43:04,880
Speaker 4: More.

827
00:43:04,920 --> 00:43:17,680
Speaker 6: After a quick break, let's get right back into it.

828
00:43:21,320 --> 00:43:24,880
Speaker 1: Okay, So this might sound like kind of a niche story,

829
00:43:25,080 --> 00:43:27,719
Speaker 1: but I promise you have a bigger point in why

830
00:43:27,719 --> 00:43:30,040
Speaker 1: I'm mentioning it. Okay, So did you ever do you

831
00:43:30,040 --> 00:43:32,160
Speaker 1: ever read auto Straddle the outlet?

832
00:43:32,640 --> 00:43:34,840
Speaker 3: I do, Yes, that was I think one of the

833
00:43:34,920 --> 00:43:38,760
Speaker 3: first like queer publications I remember learning like hearing about Yeah.

834
00:43:38,960 --> 00:43:43,640
Speaker 1: Yes, okay, same. So it is an independent LGBTQ media outlet.

835
00:43:43,920 --> 00:43:46,120
Speaker 1: It is very influential publish as some of the most

836
00:43:46,160 --> 00:43:48,440
Speaker 1: thoughtful writing from queer voices and has for the last

837
00:43:48,480 --> 00:43:51,239
Speaker 1: fourteen years. I would put it up there in terms

838
00:43:51,320 --> 00:43:54,799
Speaker 1: of like foundational media outlets. I'd put it up there

839
00:43:54,840 --> 00:43:57,680
Speaker 1: with like Bitch Magazine, The hair Pen, and Jazz Bell

840
00:43:58,120 --> 00:44:02,080
Speaker 1: in terms of being foundational parts of the Internet of

841
00:44:02,200 --> 00:44:05,560
Speaker 1: Internet discourse from marginalized perspectives and voices. The Hairpin and

842
00:44:05,680 --> 00:44:09,239
Speaker 1: Bitch Magazine have sadly closed their doors. Rip jess Bell

843
00:44:09,320 --> 00:44:12,200
Speaker 1: still exists, but like as a shell of its former self. So,

844
00:44:12,400 --> 00:44:15,880
Speaker 1: like many independent media outlets, it had a lot of

845
00:44:16,440 --> 00:44:20,879
Speaker 1: economic ups and downs and struggles. It seemed like Autostraddle

846
00:44:21,040 --> 00:44:24,160
Speaker 1: was either going to fold or be sold or something like.

847
00:44:24,280 --> 00:44:27,279
Speaker 1: They've had a lot of issues and it turns out

848
00:44:27,360 --> 00:44:27,959
Speaker 1: it's the latter.

849
00:44:28,239 --> 00:44:28,680
Speaker 2: Defector.

850
00:44:28,800 --> 00:44:31,560
Speaker 1: Media reports that auto Straddle is being acquired by the

851
00:44:31,680 --> 00:44:35,040
Speaker 1: two year old queer wellness company for them. In an

852
00:44:35,080 --> 00:44:39,320
Speaker 1: all equity deal, auto Straddle would maintain their editorial independence.

853
00:44:39,520 --> 00:44:42,359
Speaker 1: According to a press release from for Them, for Them

854
00:44:42,440 --> 00:44:45,359
Speaker 1: plans to continue to build on Auto Strale's editorial work

855
00:44:45,480 --> 00:44:48,520
Speaker 1: like podcasts and for what it's worth, it sounds like

856
00:44:48,640 --> 00:44:52,200
Speaker 1: the staff at auto Straddle may be kind of cautiously

857
00:44:52,280 --> 00:44:56,200
Speaker 1: optimistic because like, at least this move avoids a shutdown

858
00:44:56,239 --> 00:44:58,040
Speaker 1: on the site, at least they get to still be

859
00:44:58,239 --> 00:45:01,719
Speaker 1: paid to make their content. Totally sounds like it's something

860
00:45:01,760 --> 00:45:05,440
Speaker 1: that they're like cautiously optimistic about. And this is all

861
00:45:05,480 --> 00:45:08,960
Speaker 1: happening at a time when independent media, let alone independent

862
00:45:09,080 --> 00:45:11,640
Speaker 1: queer focus media, is really suffering, and so I think

863
00:45:11,680 --> 00:45:14,600
Speaker 1: it's definitely good to be funding these vital voices and

864
00:45:14,680 --> 00:45:17,719
Speaker 1: perspectives for them. I was not a company that I

865
00:45:17,800 --> 00:45:20,640
Speaker 1: was really familiar with before this story. You know, they

866
00:45:20,680 --> 00:45:23,320
Speaker 1: seem like a cool company. They have all the right branding,

867
00:45:23,480 --> 00:45:26,320
Speaker 1: They use all the right like words and phrases in

868
00:45:26,400 --> 00:45:29,480
Speaker 1: their in their on their website. Their flagship product is

869
00:45:29,520 --> 00:45:32,840
Speaker 1: a chessbinder, but they also offer a subscription and membership.

870
00:45:33,239 --> 00:45:36,800
Speaker 1: So it sounds like for Them's plan was to bundle

871
00:45:37,000 --> 00:45:43,160
Speaker 1: Auto Straddle's existing bonus subscription plan into their membership program,

872
00:45:43,560 --> 00:45:47,600
Speaker 1: which includes access to a gender tracking app, which they

873
00:45:47,680 --> 00:45:51,520
Speaker 1: describe as quote the first gender tracking app for real

874
00:45:51,640 --> 00:45:56,680
Speaker 1: time gender evolution using biometric data. So it sounds like

875
00:45:56,880 --> 00:45:58,960
Speaker 1: this app was meant to be something where it was

876
00:45:58,960 --> 00:46:01,640
Speaker 1: like you would put in in information about how you're

877
00:46:01,680 --> 00:46:04,279
Speaker 1: feeling about your gender so that you can track your

878
00:46:04,360 --> 00:46:07,200
Speaker 1: evolution of like where you're at with your own understanding

879
00:46:07,520 --> 00:46:10,120
Speaker 1: of and relationship to your gender. But the use of

880
00:46:10,200 --> 00:46:14,080
Speaker 1: the word biometric data to describe this process is like

881
00:46:14,800 --> 00:46:17,400
Speaker 1: red flag city to me, and taking a look at

882
00:46:17,440 --> 00:46:21,120
Speaker 1: the app, their privacy policy is like pretty minimal. It's

883
00:46:21,160 --> 00:46:24,080
Speaker 1: like pretty scant. As we discussed in our episode around Betterhelp,

884
00:46:24,320 --> 00:46:27,239
Speaker 1: a lot of these apps that seem great on their face,

885
00:46:27,320 --> 00:46:29,960
Speaker 1: and maybe are great on their face when you actually

886
00:46:30,080 --> 00:46:33,760
Speaker 1: look down into them, what they're actually doing is taking

887
00:46:34,000 --> 00:46:37,800
Speaker 1: pretty intimate information and data about us and selling it.

888
00:46:38,120 --> 00:46:40,400
Speaker 1: To be clear, this is not the kind of information

889
00:46:40,480 --> 00:46:42,479
Speaker 1: that I would suggest anyone to share with an app

890
00:46:42,840 --> 00:46:45,480
Speaker 1: that does not have a clear and robust privacy policy,

891
00:46:45,800 --> 00:46:47,959
Speaker 1: And even then it's not I wouldn't suggest that people

892
00:46:48,040 --> 00:46:49,680
Speaker 1: share this kind of information with an app.

893
00:46:50,120 --> 00:46:53,719
Speaker 3: Yeah, I so I have heard it for them before

894
00:46:53,760 --> 00:46:58,000
Speaker 3: this because I because of the way data works, I'm somebody,

895
00:46:58,080 --> 00:47:02,200
Speaker 3: I'm non binary, I'm transmit esclin. I do buind on occasion.

896
00:47:04,120 --> 00:47:06,040
Speaker 3: I actually was considering buying a binder from them. I

897
00:47:06,120 --> 00:47:09,680
Speaker 3: still might, we'll see, but I've heard good things. Again,

898
00:47:09,680 --> 00:47:11,600
Speaker 3: I've heard good things about their binders. I'm sure that's great.

899
00:47:11,640 --> 00:47:14,120
Speaker 3: But I get targeted ads from them a lot, and

900
00:47:14,160 --> 00:47:16,120
Speaker 3: I get a lot of like these companies that are

901
00:47:16,200 --> 00:47:20,319
Speaker 3: like targeted for queer people, for trans people, for non

902
00:47:20,360 --> 00:47:25,200
Speaker 3: binary people, and yeah, that that really the whole Like

903
00:47:25,560 --> 00:47:27,920
Speaker 3: having an app to track your gender journey that seems

904
00:47:27,960 --> 00:47:30,560
Speaker 3: really nice. But also, like I said this in this

905
00:47:30,600 --> 00:47:32,520
Speaker 3: Spincy episode that I was on, I said this before,

906
00:47:32,600 --> 00:47:33,960
Speaker 3: like we're living at a time when there's like this

907
00:47:34,160 --> 00:47:37,399
Speaker 3: active attack on trans people. Like I think any sort

908
00:47:37,440 --> 00:47:41,359
Speaker 3: of situation where you are, and again, like I'm again,

909
00:47:41,360 --> 00:47:43,160
Speaker 3: I'm somebody who's I'm trans. I talk a lot about

910
00:47:43,160 --> 00:47:44,839
Speaker 3: being transce I talk a lot about being queer. I'm

911
00:47:44,920 --> 00:47:49,200
Speaker 3: very open about that. But like, you don't necessarily want

912
00:47:49,280 --> 00:47:51,720
Speaker 3: people to like be tracking that, Like you don't necessarily

913
00:47:51,760 --> 00:47:54,120
Speaker 3: want these like third party companies to be tracking that

914
00:47:54,400 --> 00:47:58,960
Speaker 3: there's so many ways that could go wrong that to

915
00:47:59,080 --> 00:48:01,279
Speaker 3: sound like I don't know. I feel like when I

916
00:48:01,320 --> 00:48:03,560
Speaker 3: say that, people are like, it's a little conspiracy theorist, but.

917
00:48:03,560 --> 00:48:05,120
Speaker 2: I'm like, no, it's just being paucious.

918
00:48:05,400 --> 00:48:08,759
Speaker 3: If you haven't martialized identity, there's a very good chant Like, yeah, no,

919
00:48:08,880 --> 00:48:10,680
Speaker 3: I kind of want to keep that under apps much

920
00:48:10,719 --> 00:48:14,000
Speaker 3: I can, because like I could be targeted for that. Yeah,

921
00:48:14,080 --> 00:48:16,600
Speaker 3: that is so weird, especially but it's like you can journal.

922
00:48:17,840 --> 00:48:21,799
Speaker 3: I journal, I'm a journaling I'm I draw a lot.

923
00:48:22,200 --> 00:48:25,320
Speaker 3: And again, like I I deal with gender dysphoria and

924
00:48:25,480 --> 00:48:27,040
Speaker 3: and all that, all the things that come up being

925
00:48:28,400 --> 00:48:30,800
Speaker 3: you know, a trans person. There's a lot of outlets

926
00:48:30,920 --> 00:48:35,200
Speaker 3: that are that are not you putting more information into

927
00:48:35,320 --> 00:48:37,920
Speaker 3: some random company. But yeah, I get And this is

928
00:48:37,960 --> 00:48:39,560
Speaker 3: the thing that I think is so frustrating. And there's

929
00:48:39,560 --> 00:48:41,239
Speaker 3: all there's always these kind of things that pop up

930
00:48:41,239 --> 00:48:44,040
Speaker 3: where it's like a you know, company that is for

931
00:48:44,280 --> 00:48:47,879
Speaker 3: queer people by queer people, and it seems good and theory,

932
00:48:48,080 --> 00:48:51,160
Speaker 3: but it's like, what are they gonna do with that data?

933
00:48:51,280 --> 00:48:51,560
Speaker 2: Again?

934
00:48:51,800 --> 00:48:53,400
Speaker 3: Yeah, Like going back to Better Help, it's like a

935
00:48:53,440 --> 00:48:55,120
Speaker 3: similar thing where it seems like a great idea, but

936
00:48:55,760 --> 00:48:59,800
Speaker 3: I yeah, and again I think like having a company

937
00:48:59,880 --> 00:49:02,160
Speaker 3: that those binders that is run by queer people and

938
00:49:02,320 --> 00:49:04,560
Speaker 3: is run by transmask people in particular, Like that's really

939
00:49:04,600 --> 00:49:07,760
Speaker 3: important because there are people who are going to understand

940
00:49:08,560 --> 00:49:11,799
Speaker 3: what's needed and how like how different ways it's gonna

941
00:49:11,800 --> 00:49:16,440
Speaker 3: affect you. But it's like, yeah, that that seems concerning

942
00:49:16,600 --> 00:49:19,400
Speaker 3: any sort of like it's also having like a publication

943
00:49:19,520 --> 00:49:23,760
Speaker 3: attached to some sort of like corporation or capitalist structure

944
00:49:23,840 --> 00:49:27,239
Speaker 3: seems you know, concerning. But that's a whole other conversation.

945
00:49:27,480 --> 00:49:29,800
Speaker 2: Yeah, well that's definitely part of it.

946
00:49:29,920 --> 00:49:34,200
Speaker 1: And I think I think that's probably why the one

947
00:49:34,239 --> 00:49:36,640
Speaker 1: of the reasons why the gender tracking app hit me

948
00:49:36,840 --> 00:49:41,279
Speaker 1: wrong is that what you said does not sound.

949
00:49:41,280 --> 00:49:42,239
Speaker 2: Conspiratorial to me.

950
00:49:42,360 --> 00:49:47,319
Speaker 1: It's just unfortunately the reality that like marginalized people are

951
00:49:47,480 --> 00:49:50,719
Speaker 1: under attack in this country and abroad globally as well,

952
00:49:51,239 --> 00:49:55,120
Speaker 1: and so having a company that again it's like like

953
00:49:55,200 --> 00:49:56,840
Speaker 1: this is no shade to them, like they seem like

954
00:49:56,880 --> 00:49:59,960
Speaker 1: a cool company by all accounts. I'm not trying to

955
00:50:00,080 --> 00:50:03,879
Speaker 1: to apply nefarious intent here, but I'm just keeping it real,

956
00:50:03,920 --> 00:50:07,400
Speaker 1: which is that like if you are asking trans people

957
00:50:07,560 --> 00:50:11,480
Speaker 1: and non binary people and clear people to put intimate

958
00:50:11,560 --> 00:50:14,840
Speaker 1: information about their gender and their identity into an app,

959
00:50:15,360 --> 00:50:19,239
Speaker 1: you better you need a really robust privacy policy, And

960
00:50:19,280 --> 00:50:21,960
Speaker 1: if you don't have one, to me, that sounds like

961
00:50:22,600 --> 00:50:28,200
Speaker 1: you're not really attuned to the realities of what people

962
00:50:28,360 --> 00:50:30,719
Speaker 1: what the community is up against right now.

963
00:50:31,200 --> 00:50:34,640
Speaker 2: Because yeah, I just so like that's one two.

964
00:50:35,000 --> 00:50:36,920
Speaker 1: I think that, you know, I don't want to make

965
00:50:36,920 --> 00:50:39,800
Speaker 1: this about for them or auto staddle specifically, because I

966
00:50:39,880 --> 00:50:42,719
Speaker 1: do think it seems like a genuine partnership with possibility.

967
00:50:43,040 --> 00:50:45,319
Speaker 2: But our communities really do deserve.

968
00:50:45,160 --> 00:50:48,800
Speaker 1: Better than to only be able to get thoughtful stories

969
00:50:49,080 --> 00:50:54,440
Speaker 1: that really center our perspectives if also our complex understandings

970
00:50:54,480 --> 00:50:57,640
Speaker 1: and relationships to gender and sexuality and identity are turned

971
00:50:57,640 --> 00:51:01,920
Speaker 1: into a product commodified mind and taken from us to

972
00:51:02,000 --> 00:51:04,680
Speaker 1: make someone else money. Like and that is what that

973
00:51:04,840 --> 00:51:07,799
Speaker 1: gender tracking app sounded like to me when I first

974
00:51:07,840 --> 00:51:13,040
Speaker 1: heard it, Like our experiences are so much more thoughtful

975
00:51:13,440 --> 00:51:18,279
Speaker 1: and complex and complicated and hard and fucked up and

976
00:51:18,400 --> 00:51:20,960
Speaker 1: beautiful and joys. It's like there's so much there, and

977
00:51:21,600 --> 00:51:24,960
Speaker 1: there ares there. They don't belong to some third party

978
00:51:25,120 --> 00:51:28,839
Speaker 1: app or they don't exist, like we're not grappling with them.

979
00:51:30,120 --> 00:51:32,520
Speaker 1: Just to have them taken from us so that someone

980
00:51:32,560 --> 00:51:34,920
Speaker 1: else can profit. And I feel like that relationship, I

981
00:51:34,960 --> 00:51:39,239
Speaker 1: feel like I really take issue with like that being

982
00:51:39,320 --> 00:51:42,480
Speaker 1: presented to us as liberation, as something that is good.

983
00:51:43,040 --> 00:51:45,879
Speaker 3: Yeah, no, I totally agree. I mean it's I feel

984
00:51:45,880 --> 00:51:48,840
Speaker 3: like we have this conversation every pride about like corporations

985
00:51:48,920 --> 00:51:50,759
Speaker 3: being involved in pride, and it's the whole idea of

986
00:51:50,880 --> 00:51:54,160
Speaker 3: like rainbow capitalism and all of that. It's it's definitely

987
00:51:54,719 --> 00:51:57,920
Speaker 3: that the end kind of end game of all of

988
00:51:58,000 --> 00:52:01,239
Speaker 3: this is making somebody a lot of money. Again, It's

989
00:52:01,440 --> 00:52:05,319
Speaker 3: it's nice to it's nice to have resource. It's nice

990
00:52:05,360 --> 00:52:09,160
Speaker 3: to have you know, resources for talking about gender identity

991
00:52:09,200 --> 00:52:12,080
Speaker 3: and sexuality and all that that are you know, run

992
00:52:12,160 --> 00:52:16,000
Speaker 3: by queer people aimed towards queer people. But yeah, I

993
00:52:16,080 --> 00:52:19,239
Speaker 3: don't want somebody to be like capitalizing off of my

994
00:52:19,360 --> 00:52:24,640
Speaker 3: experience just existing as somebody like who is not cis gender,

995
00:52:24,800 --> 00:52:29,399
Speaker 3: who is not straight like that that it's very weird. Yeah,

996
00:52:29,440 --> 00:52:31,239
Speaker 3: because then yeah, because how much of a how much

997
00:52:31,280 --> 00:52:33,600
Speaker 3: of a obviously not in this situation because they are

998
00:52:33,760 --> 00:52:35,799
Speaker 3: like a queer company, but like other companies that are

999
00:52:35,840 --> 00:52:38,160
Speaker 3: like positioning themselves as like allies. But then if you're

1000
00:52:38,160 --> 00:52:40,560
Speaker 3: putting people in danger because you're not protecting their data,

1001
00:52:41,360 --> 00:52:42,920
Speaker 3: how much of an ally are you or how much

1002
00:52:42,960 --> 00:52:45,160
Speaker 3: of like an activist for your community are you If

1003
00:52:45,160 --> 00:52:48,400
Speaker 3: you're actively putting people in danger. This whole situation just

1004
00:52:48,440 --> 00:52:49,800
Speaker 3: seems very shady to me.

1005
00:52:50,719 --> 00:52:54,600
Speaker 1: Yeah, allies, active allyship, this is gonna sound nerdy as hell,

1006
00:52:54,640 --> 00:52:56,840
Speaker 1: but it's what I believe. Like, you can't be an

1007
00:52:56,880 --> 00:53:00,800
Speaker 1: active allyship is protecting my I have see and protecting

1008
00:53:00,880 --> 00:53:02,200
Speaker 1: my data as a marginalized person.

1009
00:53:02,719 --> 00:53:05,520
Speaker 2: Right, Active allyship is do not track.

1010
00:53:05,640 --> 00:53:07,840
Speaker 1: Active allyship is like when we're talking about tech, like

1011
00:53:08,480 --> 00:53:12,200
Speaker 1: it's not just like, yeah, give us your information. We'll

1012
00:53:12,239 --> 00:53:15,040
Speaker 1: probably keep it safe. We don't really have a policy

1013
00:53:15,080 --> 00:53:16,840
Speaker 1: about it, but we'll do our best. Like that is

1014
00:53:16,920 --> 00:53:19,880
Speaker 1: not allyship And that's not That is not like a

1015
00:53:20,000 --> 00:53:22,040
Speaker 1: community of safety that I want to be part of.

1016
00:53:22,320 --> 00:53:25,200
Speaker 1: And I think that we are both in good company.

1017
00:53:25,280 --> 00:53:28,800
Speaker 1: Because after some outcry for them did change the wording

1018
00:53:28,840 --> 00:53:32,239
Speaker 1: on their site, Defector Reports for them has removed the

1019
00:53:32,280 --> 00:53:36,040
Speaker 1: phrase biometric data from its gender tracking apps ad copy.

1020
00:53:36,360 --> 00:53:39,120
Speaker 1: In an Instagram comment addressing the concerns, they characterize the

1021
00:53:39,160 --> 00:53:43,080
Speaker 1: app as more of a reflective journal and said biometric

1022
00:53:43,200 --> 00:53:45,399
Speaker 1: data is likely the wrong word to be used here,

1023
00:53:45,719 --> 00:53:48,400
Speaker 1: and we will course correct to not raise false alarms.

1024
00:53:48,719 --> 00:53:51,960
Speaker 1: So I get that, Like, I'm glad that they're backing

1025
00:53:51,960 --> 00:53:55,000
Speaker 1: away from that. I feel like the word biometric data

1026
00:53:55,560 --> 00:53:58,360
Speaker 1: has such a specific like.

1027
00:54:00,200 --> 00:54:02,919
Speaker 2: Journal every morning I do my morning pages, I can't.

1028
00:54:03,000 --> 00:54:06,319
Speaker 1: I'm not like writing biometric data like that has such

1029
00:54:06,360 --> 00:54:10,000
Speaker 1: a specific definition and connotation that I have to imagine

1030
00:54:10,040 --> 00:54:12,960
Speaker 1: that someone somewhere thought that was going to be like

1031
00:54:13,000 --> 00:54:17,200
Speaker 1: a buzzy phrase to get to make these like venture

1032
00:54:17,320 --> 00:54:19,080
Speaker 1: capital funders happy or.

1033
00:54:19,080 --> 00:54:20,640
Speaker 2: Excited, and that's how it got in there.

1034
00:54:20,719 --> 00:54:22,439
Speaker 1: But it's why it's wild to me that they could

1035
00:54:22,440 --> 00:54:24,239
Speaker 1: be like, oh, we didn't really mean biometric data. We

1036
00:54:24,360 --> 00:54:27,080
Speaker 1: really met more like feelings like a journal, because like

1037
00:54:27,120 --> 00:54:29,160
Speaker 1: biometric data is like a very specific thing.

1038
00:54:29,920 --> 00:54:34,879
Speaker 3: Yeah, that's so weird, especially like gender and sexuality. They're

1039
00:54:34,920 --> 00:54:40,360
Speaker 3: such complicated experiences and there's such personal experiences for people

1040
00:54:40,880 --> 00:54:44,359
Speaker 3: like I don't know, that's not necessarily something I want

1041
00:54:44,520 --> 00:54:46,560
Speaker 3: on like an Allienka, that's the thing I keep in

1042
00:54:46,640 --> 00:54:49,240
Speaker 3: my journal. That's for me, don't. I don't want anybody

1043
00:54:49,440 --> 00:54:52,160
Speaker 3: like it is a Yeah, that is a weird way

1044
00:54:52,200 --> 00:54:53,680
Speaker 3: of putting it. It also is zero I got it,

1045
00:54:54,239 --> 00:54:57,600
Speaker 3: especially in the middle of this whole like anti trans backlash,

1046
00:54:57,719 --> 00:55:00,359
Speaker 3: so much of which revolves around sort of of these

1047
00:55:00,520 --> 00:55:02,520
Speaker 3: weird you know, the thing that keep coming up to

1048
00:55:02,520 --> 00:55:05,040
Speaker 3: people being like, oh, it's just biology, is just science,

1049
00:55:05,120 --> 00:55:08,240
Speaker 3: and none of these people have actually, you know, studied

1050
00:55:08,280 --> 00:55:11,879
Speaker 3: biology and whatever. But that being said, like, it's it's

1051
00:55:11,880 --> 00:55:13,960
Speaker 3: such a like it also is almost kind of like

1052
00:55:13,960 --> 00:55:16,120
Speaker 3: a buzzword for like the other side too, where it's like,

1053
00:55:16,160 --> 00:55:18,320
Speaker 3: why are you focusing so much on like the biologic

1054
00:55:18,400 --> 00:55:20,839
Speaker 3: I'm never I'm not waking up and being like, hm,

1055
00:55:21,000 --> 00:55:23,719
Speaker 3: thinking about my gender identity right now, Like I'm not

1056
00:55:23,880 --> 00:55:28,080
Speaker 3: like how does this work biochemically? Or that's such a

1057
00:55:28,120 --> 00:55:30,000
Speaker 3: weird way of like putting it.

1058
00:55:31,480 --> 00:55:32,360
Speaker 2: It's very weird.

1059
00:55:32,560 --> 00:55:32,759
Speaker 3: Yeah.

1060
00:55:33,520 --> 00:55:35,760
Speaker 1: So here's one other reason why I think this story

1061
00:55:35,960 --> 00:55:38,759
Speaker 1: is important because I think it says something about the

1062
00:55:38,800 --> 00:55:42,160
Speaker 1: state of media more generally right now, say, the media

1063
00:55:42,320 --> 00:55:45,400
Speaker 1: is like very sad and very scary to me, Like

1064
00:55:45,480 --> 00:55:47,960
Speaker 1: I don't know anybody who works in media or journalism

1065
00:55:48,440 --> 00:55:51,800
Speaker 1: who is not kind of having a tough time of

1066
00:55:51,880 --> 00:55:54,239
Speaker 1: it right now. You know, for a while, I feel

1067
00:55:54,280 --> 00:55:56,800
Speaker 1: like we were told that the path forward was venture

1068
00:55:57,000 --> 00:56:00,279
Speaker 1: capital funding, you know VC, a rich funder come by

1069
00:56:00,360 --> 00:56:03,359
Speaker 1: and pump's a media brand full of cash and then

1070
00:56:03,480 --> 00:56:06,840
Speaker 1: profit right. Well, not if you're BuzzFeed News or Vice

1071
00:56:07,000 --> 00:56:09,719
Speaker 1: or etcetera, etcetera, etcetera, like any any number of the

1072
00:56:09,760 --> 00:56:13,040
Speaker 1: different media outlets that got fat VC funding and then

1073
00:56:13,160 --> 00:56:16,799
Speaker 1: shut down. The journalists at Vice who were all laid

1074
00:56:16,840 --> 00:56:19,720
Speaker 1: off recently, like Samantha Cole, who's one of my favorite

1075
00:56:19,800 --> 00:56:22,080
Speaker 1: journalists who writes about the intersection of sex on the Internet,

1076
00:56:22,400 --> 00:56:25,960
Speaker 1: started their own independent media brand called for for Media recently,

1077
00:56:26,120 --> 00:56:28,400
Speaker 1: and I know that a lot of folks are pivoting

1078
00:56:28,480 --> 00:56:31,920
Speaker 1: to things like that, like like worker and writer owned

1079
00:56:32,120 --> 00:56:36,160
Speaker 1: media collaboratives or independent you know, media entities, and they're

1080
00:56:36,520 --> 00:56:38,960
Speaker 1: privoting toward things like substack, which I have my issues

1081
00:56:39,000 --> 00:56:42,360
Speaker 1: with but I get it, and Patreon myself included. And

1082
00:56:42,480 --> 00:56:45,040
Speaker 1: so I think that we're coming to see that if

1083
00:56:45,080 --> 00:56:48,719
Speaker 1: you want to be making media and like telling stories

1084
00:56:49,400 --> 00:56:54,080
Speaker 1: not just but also about marginalized voices, that center marginalized voices,

1085
00:56:54,080 --> 00:56:56,600
Speaker 1: and you want to do it independently, I think that

1086
00:56:57,239 --> 00:57:01,040
Speaker 1: the way forward seems to be not attaching yourself to

1087
00:57:01,120 --> 00:57:04,160
Speaker 1: an outlet that is going to financially mismanage things or

1088
00:57:04,280 --> 00:57:08,000
Speaker 1: like be beholden to VC expectations because they took that

1089
00:57:08,160 --> 00:57:09,360
Speaker 1: big VC funding.

1090
00:57:09,800 --> 00:57:10,920
Speaker 2: I think that this is like a.

1091
00:57:11,960 --> 00:57:15,600
Speaker 1: Larger story about the precarity of independent media right now.

1092
00:57:16,080 --> 00:57:19,040
Speaker 1: And I think more and more folks like writers and

1093
00:57:19,120 --> 00:57:22,320
Speaker 1: creators and podcasters and journalists that you love are going

1094
00:57:22,400 --> 00:57:24,720
Speaker 1: to start doing their own things. And what's funny to

1095
00:57:24,760 --> 00:57:28,160
Speaker 1: me about that is like, as I'm kind of taking

1096
00:57:28,280 --> 00:57:34,200
Speaker 1: that journey myself, it feels very like precarious and unstable,

1097
00:57:34,680 --> 00:57:39,640
Speaker 1: trying to pin your livelihood on Patreon or substack, subscriptions

1098
00:57:39,720 --> 00:57:40,040
Speaker 1: or whatever.

1099
00:57:40,440 --> 00:57:41,760
Speaker 2: But here's the thing, the.

1100
00:57:41,880 --> 00:57:45,280
Speaker 1: Alternative of being at like a big VC backed media

1101
00:57:45,320 --> 00:57:49,640
Speaker 1: company right now also feels unstable and uncertain and precarious.

1102
00:57:49,680 --> 00:57:52,000
Speaker 1: So like, you may as well if it's going to

1103
00:57:52,040 --> 00:57:54,160
Speaker 1: be precarious regardless, you may as well be like fuck it.

1104
00:57:54,280 --> 00:57:56,280
Speaker 1: I'll at least be the person who's in charge of

1105
00:57:56,400 --> 00:58:00,520
Speaker 1: my precarious feeling destiny and like the sort of my

1106
00:58:00,600 --> 00:58:03,280
Speaker 1: own ship. And so I don't know all the auto

1107
00:58:03,320 --> 00:58:07,320
Speaker 1: straddle thing, it signals to me that we are really

1108
00:58:08,040 --> 00:58:12,360
Speaker 1: experimenting with paths forward of how we have a media

1109
00:58:12,720 --> 00:58:16,400
Speaker 1: climate that is sustainable, And I don't know maybe like

1110
00:58:16,440 --> 00:58:18,240
Speaker 1: I don't want to be all doom and gloom. Maybe

1111
00:58:18,320 --> 00:58:19,880
Speaker 1: this reshapes the space for the better.

1112
00:58:21,120 --> 00:58:24,840
Speaker 3: Yeah, I mean, as somebody who's in like the early

1113
00:58:24,920 --> 00:58:27,240
Speaker 3: parts of their career is like a media person, it is.

1114
00:58:27,520 --> 00:58:30,200
Speaker 3: It is. I mean when I was getting Get Too

1115
00:58:30,240 --> 00:58:32,600
Speaker 3: and it was gonna be best, but it's it's scary.

1116
00:58:32,680 --> 00:58:35,320
Speaker 3: And I mean I always say, like the things that

1117
00:58:35,480 --> 00:58:39,040
Speaker 3: I really was watching when I was in high school

1118
00:58:39,080 --> 00:58:41,920
Speaker 3: in college that really like inspired me were things that

1119
00:58:42,000 --> 00:58:45,800
Speaker 3: were coming from like Vice and BuzzFeed and teen Vogue

1120
00:58:45,880 --> 00:58:47,440
Speaker 3: was a big one. I remember, like with the big

1121
00:58:47,520 --> 00:58:49,520
Speaker 3: teen Vogue sort of shift happened. That was like a

1122
00:58:49,600 --> 00:58:53,680
Speaker 3: big thing. And it's it's it's so weird to see

1123
00:58:53,800 --> 00:58:56,320
Speaker 3: now like a lot of these sites coming down are

1124
00:58:56,440 --> 00:58:58,600
Speaker 3: like these kind of yeah, like BuzzFeed and Vice sort

1125
00:58:58,600 --> 00:59:01,000
Speaker 3: of falling apart. And also yeah, like a lot of

1126
00:59:01,040 --> 00:59:03,360
Speaker 3: my mentors, a lot of my journalism like mentors have

1127
00:59:03,400 --> 00:59:05,760
Speaker 3: come from these companies too, and I'm like, I don't know,

1128
00:59:05,840 --> 00:59:09,000
Speaker 3: I'm super grateful for like them and everything they sort

1129
00:59:09,040 --> 00:59:11,280
Speaker 3: of taught me and everything I've learned from them. But

1130
00:59:11,360 --> 00:59:13,840
Speaker 3: it's it's it is sort of like interesting to be

1131
00:59:14,000 --> 00:59:16,320
Speaker 3: like seeing people that have gone through that and now

1132
00:59:16,440 --> 00:59:19,480
Speaker 3: being somebody who's getting into the media industry. And yeah,

1133
00:59:19,600 --> 00:59:22,160
Speaker 3: I sort of straddled the line between like production and

1134
00:59:22,320 --> 00:59:26,280
Speaker 3: journalism too, where like again I work at iHeart. I

1135
00:59:26,360 --> 00:59:29,200
Speaker 3: am an iHeart employee, so like I do work for

1136
00:59:29,280 --> 00:59:34,800
Speaker 3: a big media company. But yeah, it is is definitely

1137
00:59:35,440 --> 00:59:37,720
Speaker 3: a strange time for media, when is it not? But

1138
00:59:38,120 --> 00:59:40,959
Speaker 3: it's yeah, and yeah, I guess we'll see.

1139
00:59:41,360 --> 00:59:44,320
Speaker 1: Yeah, it's funny that you talk about where your mentors

1140
00:59:44,480 --> 00:59:47,840
Speaker 1: were when before you got into the media game, because

1141
00:59:48,040 --> 00:59:49,720
Speaker 1: I might be a little bit older than you. My

1142
00:59:49,960 --> 00:59:53,120
Speaker 1: mentors all came from an era that, like I was

1143
00:59:53,200 --> 00:59:55,080
Speaker 1: like too young to have been in any kind of

1144
00:59:56,240 --> 01:00:01,480
Speaker 1: meaningful profession. But the era of like the late nineties

1145
01:00:01,560 --> 01:00:04,760
Speaker 1: early aughts were like it seemed like being in media

1146
01:00:05,080 --> 01:00:07,680
Speaker 1: was like the fucking best where it was like, yeah,

1147
01:00:07,720 --> 01:00:10,760
Speaker 1: you get like a dollar per word, and you like

1148
01:00:11,120 --> 01:00:13,040
Speaker 1: get to go to all these swinky parties, like the

1149
01:00:13,280 --> 01:00:14,000
Speaker 1: like the era.

1150
01:00:14,040 --> 01:00:18,400
Speaker 3: Of like City, Yeah, yeah, yeah, who was that?

1151
01:00:18,560 --> 01:00:18,680
Speaker 4: Was it?

1152
01:00:18,960 --> 01:00:19,800
Speaker 2: G Magazine?

1153
01:00:19,840 --> 01:00:23,800
Speaker 1: Who was the Kennedy that had his own like vanity magazine? Okay,

1154
01:00:23,920 --> 01:00:27,439
Speaker 1: it was so John F. Kennedy Junior before he died

1155
01:00:27,560 --> 01:00:31,040
Speaker 1: had a vanity magazine. Called George, and like it was

1156
01:00:31,120 --> 01:00:34,960
Speaker 1: just the time of like glossy magazines and like big

1157
01:00:35,240 --> 01:00:38,040
Speaker 1: parties and like all the magazines seemed really cool.

1158
01:00:38,080 --> 01:00:40,080
Speaker 2: It was like if you ever watched a movie.

1159
01:00:40,000 --> 01:00:42,720
Speaker 1: Like I feel like, Also, it's no coincidence that that

1160
01:00:42,880 --> 01:00:46,800
Speaker 1: coincided with the aughts, where the cool career in like

1161
01:00:47,040 --> 01:00:50,040
Speaker 1: movies was I'm a journalist, like The Devil Wears Prada

1162
01:00:50,200 --> 01:00:51,920
Speaker 1: and How to Lose a Guy in Ten Days. That

1163
01:00:52,120 --> 01:00:53,960
Speaker 1: was that was an era that we will probably never

1164
01:00:54,120 --> 01:00:57,560
Speaker 1: see again in media where that you know, it just

1165
01:00:57,720 --> 01:00:59,960
Speaker 1: was flush with cash and it was like the coolest time,

1166
01:01:00,200 --> 01:01:03,520
Speaker 1: and it's a time that like most people, it's like

1167
01:01:03,520 --> 01:01:06,680
Speaker 1: a it's like a bygone era, Like what you would

1168
01:01:06,720 --> 01:01:10,320
Speaker 1: make for a story then and what you would make

1169
01:01:10,360 --> 01:01:13,160
Speaker 1: for a story now is like night and day. Like

1170
01:01:13,280 --> 01:01:15,960
Speaker 1: I was watching Almost Famous the other day, and that

1171
01:01:16,120 --> 01:01:19,480
Speaker 1: kid is getting a cover like a feature in Rolling

1172
01:01:19,520 --> 01:01:21,880
Speaker 1: Stone and they're gonna pay him like seven thousand dollars

1173
01:01:22,040 --> 01:01:24,880
Speaker 1: or something, which like today he paid three hundred dollars.

1174
01:01:25,160 --> 01:01:26,880
Speaker 1: Like that was supposed to be the seventies, you know.

1175
01:01:27,040 --> 01:01:30,600
Speaker 1: So it's it is a very weird field and always

1176
01:01:30,680 --> 01:01:31,080
Speaker 1: has been.

1177
01:01:31,320 --> 01:01:32,640
Speaker 2: And yeah, it's just.

1178
01:01:32,760 --> 01:01:35,840
Speaker 1: I'm I don't know the answer. Maybe it is Patreon

1179
01:01:35,920 --> 01:01:38,960
Speaker 1: and substack. Maybe it is starting your own media brands.

1180
01:01:39,000 --> 01:01:45,080
Speaker 1: Maybe it is you know, queer binder companies acquiring your brand.

1181
01:01:45,320 --> 01:01:47,560
Speaker 1: Like we'll see, it's just everyone's doing what they.

1182
01:01:47,520 --> 01:01:51,960
Speaker 3: Can real Oh man, it's so funny because like, yeah,

1183
01:01:51,960 --> 01:01:54,120
Speaker 3: I also grew up like watching yea like Devil's Words,

1184
01:01:54,240 --> 01:01:57,080
Speaker 3: Prada and a bit Young for Sex in the City.

1185
01:01:57,160 --> 01:01:58,880
Speaker 3: I sort of missed that, but like that whole era

1186
01:01:58,960 --> 01:02:01,560
Speaker 3: of like yeah, I was always the you know, the

1187
01:02:01,640 --> 01:02:04,520
Speaker 3: New York journalist kind of thing, and I just it's

1188
01:02:04,840 --> 01:02:07,640
Speaker 3: like it's just such a different world. Like I that

1189
01:02:07,800 --> 01:02:10,120
Speaker 3: never like crossed my mind that that would be me,

1190
01:02:10,280 --> 01:02:12,040
Speaker 3: like that that would be my life, because yeah, I

1191
01:02:12,120 --> 01:02:15,520
Speaker 3: got like what I was starting to get interested in journalism.

1192
01:02:15,600 --> 01:02:18,760
Speaker 3: In the media, it was always like yep, you're gonna

1193
01:02:18,760 --> 01:02:20,800
Speaker 3: have to freelance, You're gonna have to like you're gonna

1194
01:02:20,800 --> 01:02:24,520
Speaker 3: be like starving artists kind of thing from all and

1195
01:02:25,120 --> 01:02:28,160
Speaker 3: I don't know, it was it was very bleak. Yeah,

1196
01:02:28,440 --> 01:02:31,680
Speaker 3: I guess, I guess we'll see. Oh yeah, almost famous

1197
01:02:31,680 --> 01:02:33,400
Speaker 3: is always the one that pisses me off because I'm

1198
01:02:33,440 --> 01:02:37,000
Speaker 3: like I that would never happen it's.

1199
01:02:37,160 --> 01:02:43,040
Speaker 2: Never none of it like not yeah. Okay.

1200
01:02:43,120 --> 01:02:46,640
Speaker 1: So one last thing, which is that I am celebrating

1201
01:02:46,680 --> 01:02:51,680
Speaker 1: today a little bit. I think slash hope question mark

1202
01:02:51,760 --> 01:02:54,600
Speaker 1: maybe that people probably think of me as like a

1203
01:02:55,120 --> 01:02:59,800
Speaker 1: pretty thoughtful, measured person and not someone who is really

1204
01:03:00,080 --> 01:03:03,880
Speaker 1: headyuh not someone who would revel in the downfall of

1205
01:03:03,960 --> 01:03:07,080
Speaker 1: her enemies. Luckily, that means if you think that way,

1206
01:03:07,200 --> 01:03:09,120
Speaker 1: I have fooled you into thinking that's the kind of

1207
01:03:09,160 --> 01:03:11,760
Speaker 1: person that I am. Because I am celebrating a little

1208
01:03:11,800 --> 01:03:15,560
Speaker 1: bit today because enemy of the pod, Walter Weeks and

1209
01:03:15,680 --> 01:03:19,920
Speaker 1: Myron Gains of the podcast Fresh in Fit were demonetized

1210
01:03:19,960 --> 01:03:22,000
Speaker 1: on YouTube this week. That does not mean that their

1211
01:03:22,040 --> 01:03:24,960
Speaker 1: content is going away, don't I wish. It just means

1212
01:03:24,960 --> 01:03:26,400
Speaker 1: that they will not be able to earn money from

1213
01:03:26,440 --> 01:03:28,800
Speaker 1: it on YouTube. Fresh and Fit is one plank of

1214
01:03:28,880 --> 01:03:33,160
Speaker 1: what is sometimes called the manosphere. Basically, just like the

1215
01:03:33,240 --> 01:03:35,880
Speaker 1: misogynistic man who should not have podcast brigade, think of

1216
01:03:35,920 --> 01:03:38,080
Speaker 1: them that way anytime you've ever thought to yourself, like,

1217
01:03:38,520 --> 01:03:40,640
Speaker 1: why does this man have a podcast that is them?

1218
01:03:40,720 --> 01:03:41,600
Speaker 2: Like they are in that group.

1219
01:03:41,960 --> 01:03:44,760
Speaker 1: When they got the news that their YouTube was going

1220
01:03:44,800 --> 01:03:49,160
Speaker 1: to be demonetized, they made a very tearful video about

1221
01:03:49,200 --> 01:03:51,920
Speaker 1: how hard it was to be demonetized and how it

1222
01:03:52,040 --> 01:03:54,760
Speaker 1: wasn't about the money for them, it was about wanting

1223
01:03:54,800 --> 01:03:57,760
Speaker 1: to have a platform to help people. Yeah, if by

1224
01:03:57,840 --> 01:04:01,720
Speaker 1: help people you mean stoke their worst instincts for personal gain,

1225
01:04:01,840 --> 01:04:04,000
Speaker 1: because that is what the guys at Fresh and Fit

1226
01:04:04,120 --> 01:04:06,640
Speaker 1: podcast do. They have said that they believe they were

1227
01:04:06,680 --> 01:04:10,840
Speaker 1: demonetized because of their quote controversial takes, but my years

1228
01:04:10,880 --> 01:04:14,520
Speaker 1: of platform accountability work urging platforms like YouTube the takedown

1229
01:04:14,640 --> 01:04:17,640
Speaker 1: content that breaks community guidelines tells me that that is

1230
01:04:17,720 --> 01:04:21,160
Speaker 1: probably not likely. I just do not think that that

1231
01:04:21,480 --> 01:04:24,320
Speaker 1: is the reason why they were removed. Joey, it is

1232
01:04:24,360 --> 01:04:27,160
Speaker 1: your first time recording with me. I had written out

1233
01:04:27,160 --> 01:04:29,320
Speaker 1: a whole thing and that I was showing it to

1234
01:04:29,400 --> 01:04:31,520
Speaker 1: producer Mike, and Michael was like, this might be a

1235
01:04:31,600 --> 01:04:34,640
Speaker 1: little much for their first recording with you, Like, you

1236
01:04:34,680 --> 01:04:36,840
Speaker 1: don't want to make Joey fully think that, like they

1237
01:04:36,880 --> 01:04:41,080
Speaker 1: are recording with a crazy person, a petty betty. So

1238
01:04:41,240 --> 01:04:44,360
Speaker 1: I have a lot more to say about Fresh and

1239
01:04:44,440 --> 01:04:48,280
Speaker 1: Fit if you want to hear my full, full, uncensored

1240
01:04:48,360 --> 01:04:51,240
Speaker 1: take on why Fresh and Fit is a boil on

1241
01:04:51,320 --> 01:04:54,120
Speaker 1: the ass of the Internet and why I believe they

1242
01:04:54,120 --> 01:04:57,080
Speaker 1: were actually demonetized from YouTube and why this is something

1243
01:04:57,120 --> 01:05:00,000
Speaker 1: that we should all be celebrating. Please check out the Patriot.

1244
01:05:00,240 --> 01:05:02,160
Speaker 1: I have a lot to say. I've written about it

1245
01:05:02,200 --> 01:05:03,560
Speaker 1: for the Nation. I will throw the link to my

1246
01:05:03,680 --> 01:05:06,080
Speaker 1: Nation piece about fresh and Fit in the in the

1247
01:05:06,160 --> 01:05:08,320
Speaker 1: show notes. But yeah, this is personal for me. If

1248
01:05:08,320 --> 01:05:11,520
Speaker 1: you can't tell, I haven't there. I try really hard

1249
01:05:11,720 --> 01:05:15,960
Speaker 1: to when we cover unsavory characters, I try very hard

1250
01:05:16,000 --> 01:05:18,120
Speaker 1: to just make it about like what they say, what

1251
01:05:18,240 --> 01:05:21,960
Speaker 1: they do, and not reveal my personal thoughts on them.

1252
01:05:22,040 --> 01:05:24,840
Speaker 1: But this is different. I have a real personal distaste

1253
01:05:24,880 --> 01:05:27,080
Speaker 1: for these two. So if you want to know more

1254
01:05:27,160 --> 01:05:29,400
Speaker 1: about how I feel, check out the Patreon.

1255
01:05:30,240 --> 01:05:33,880
Speaker 3: I yeah, no, I am. I'm I will admit that

1256
01:05:33,920 --> 01:05:37,680
Speaker 3: I am a very petty yeah there, And I would

1257
01:05:37,680 --> 01:05:40,160
Speaker 3: be like, I open our conversation being like, do you

1258
01:05:40,160 --> 01:05:41,320
Speaker 3: want to hear my tiktokter on?

1259
01:05:44,200 --> 01:05:45,120
Speaker 2: I had to gauge it.

1260
01:05:45,160 --> 01:05:46,960
Speaker 1: I was like, I don't know how petty they are, Like,

1261
01:05:47,160 --> 01:05:49,160
Speaker 1: let's well have to Like now that I know I can,

1262
01:05:49,320 --> 01:05:50,800
Speaker 1: I can like, Okay, this is good to know, but.

1263
01:05:50,800 --> 01:05:53,560
Speaker 2: It's good to know. Joey, thank you so much for

1264
01:05:53,680 --> 01:05:55,720
Speaker 2: being here. Where can folks, keep up with what you're

1265
01:05:55,760 --> 01:05:56,520
Speaker 2: doing for sure.

1266
01:05:57,280 --> 01:06:01,240
Speaker 3: You can find me on Twitter for who knows how

1267
01:06:01,320 --> 01:06:04,960
Speaker 3: long and Instagram at Pat not Pratt. That is p

1268
01:06:05,200 --> 01:06:08,760
Speaker 3: A T T not n O T p r a

1269
01:06:08,960 --> 01:06:11,760
Speaker 3: T T Pratt. My last name is Pat and people

1270
01:06:11,840 --> 01:06:13,280
Speaker 3: often misspell it as Pratt.

1271
01:06:13,320 --> 01:06:15,520
Speaker 2: So that was that was how I did the same mistake.

1272
01:06:17,560 --> 01:06:22,000
Speaker 3: Okay, I don't think I've ever had like an employer

1273
01:06:22,480 --> 01:06:24,160
Speaker 3: get it right on the PIRG.

1274
01:06:24,600 --> 01:06:25,280
Speaker 4: What is it? Like?

1275
01:06:25,560 --> 01:06:28,160
Speaker 2: Why did why is it? Why? Why does this happen?

1276
01:06:28,360 --> 01:06:30,800
Speaker 3: I don't know. I think it's just because it's such

1277
01:06:30,840 --> 01:06:32,800
Speaker 3: a like people are so used to saying Pratt. They're

1278
01:06:32,880 --> 01:06:34,920
Speaker 3: like they just look over it. It's okay. There was

1279
01:06:35,040 --> 01:06:36,360
Speaker 3: there was a brief period of time where I kept

1280
01:06:36,400 --> 01:06:38,200
Speaker 3: getting asked if I was related to Chris Pratt, and

1281
01:06:38,240 --> 01:06:40,200
Speaker 3: then once he once it kind of fell out of favor.

1282
01:06:40,320 --> 01:06:46,320
Speaker 3: People stopped doing that, which was nice. But who's weird anyways, Yeah,

1283
01:06:46,320 --> 01:06:49,000
Speaker 3: you can find me on Twitter and Instagram. I'm also

1284
01:06:49,080 --> 01:06:53,480
Speaker 3: occasionally on stuff. Mom never told you of a new

1285
01:06:53,560 --> 01:06:55,600
Speaker 3: show that's coming out in a couple of months that

1286
01:06:55,760 --> 01:06:57,480
Speaker 3: I don't think I could talk about yet, but I

1287
01:06:57,760 --> 01:06:58,720
Speaker 3: probably will at some point.

1288
01:06:58,840 --> 01:07:02,880
Speaker 2: Anyways, yeah, and as always, thanks to you for listening.

1289
01:07:03,280 --> 01:07:04,040
Speaker 2: Please keep in touch.

1290
01:07:04,040 --> 01:07:06,120
Speaker 1: You can check us out on Pagereon, find me on social,

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Speaker 1: and please be well. If you're looking for ways to

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Speaker 1: support the show, check out our merch store at tangodi

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Speaker 1: dot com slash store. Got a story about an interesting

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Speaker 1: thing in tech, or just want to say hi, You

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Speaker 1: can reach us at Hello at tegody dot com. You

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Speaker 1: can also find transcripts for today's episode at tenggody dot com.

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Speaker 1: There Are No Girls on the Internet was created by.

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Speaker 6: Me Bridget Tod.

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Speaker 1: It's a production of iHeartRadio and Unboss Creative, edited by

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Speaker 1: Joey pat Jonathan Strickland is our executive producer. Tari Harrison

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Speaker 1: is our producer and sound engineer. Michael Almada is our

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Speaker 1: contributing producer. I'm your host, Bridget Todd. If you want

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Speaker 1: to help us grow, rate and review us on Apple Podcasts.

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Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

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Speaker 1: Apple Podcasts, or wherever you get your podcasts.