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Speaker 1: There Are No Girls on the Internet, as a production

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Speaker 1: of iHeartRadio and Unbossed Creative. I'm Bridget Todd and there

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Speaker 1: Are No Girls on the Internet. Welcome to There Are

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Speaker 1: No Girls on the Internet, where we explore the intersection

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Speaker 1: of technology, identity, and social media. And this is another

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Speaker 1: installment of our weekly news bound Up, where we dig

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Speaker 1: into the stories online that you might have missed so

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Speaker 1: you don't have to And just to quick heads up,

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Speaker 1: I'm totally fine, but I've just had a somewhat invasive

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Speaker 1: medical procedure. I have been given pain killers. I'm feeling great.

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Speaker 1: But if I found a little loopy, if the show

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Speaker 1: is a little off the rails, off the tracks, whatever

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Speaker 1: that expression is, please forgive us. That's what's going on.

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Speaker 1: So you know this, Mike, listeners know this. My favorite

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Speaker 1: thing in the world to talk about is scams. So

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Speaker 1: I want to start with some scams. No, not the

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Speaker 1: scam of your smart toilet or smart light switch or

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Speaker 1: smart cooling mattress crapping out on you because AWS was down.

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Speaker 1: Did you hear about that?

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Speaker 2: I did. Yeah, during that day earlier this week when

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Speaker 2: AWS was down, and like all of the Internet, I

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Speaker 2: just ground to a Halt. There were people who had

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Speaker 2: like Internet connected mattresses that when Haywire had just like

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Speaker 2: folded up on them.

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Speaker 1: I don't know if they folded up on anybody, but.

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Speaker 2: That's what I heard. They folded up on them.

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Speaker 1: They were crushed. I will say. It really made me

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Speaker 1: realize how much I have taken for granted that Amazon

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Speaker 1: Web Services runs everything, and maybe that's bad, maybe it's

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Speaker 1: not cool that this one company runs everything. And then

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Speaker 1: it also made me realize too how much stuff out

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Speaker 1: there is tech enabled these days. The mattress was probably

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Speaker 1: the weirdest thing. Imagine being like, oh, the Wi Fi

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Speaker 1: is out, so I can't use my smart pillow or whatever.

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Speaker 1: I just was really I had never heard of anything

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Speaker 1: like that. It really blew my mind.

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Speaker 2: Yeah, it's a good reminder that, like, maybe your mattress

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Speaker 2: doesn't need to be connected of the Internet. I have

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Speaker 2: an air conditioner, just like a window unit air conditioner

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Speaker 2: that is Wi Fi enabled. I didn't want that. I've

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Speaker 2: never connected it to Wi Fi. I don't know why

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Speaker 2: I would want that, but it was like a big

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Speaker 2: marketing point on the side of the box when I

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Speaker 2: bought this thing.

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Speaker 1: Oh, I mean you've heard me complain about this for

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Speaker 1: hours and hours, the saga of me trying to pick

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Speaker 1: a sunrise alarm clock. And I almost bought that alarm clock,

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Speaker 1: the Hatch clock, which I did want deeply and badly,

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Speaker 1: but it was way too expensive. I thought, Uh, it

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Speaker 1: might just be worth it. I'm so picky about my

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Speaker 1: alarm clocks. I want such particular features in an alarm clock.

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Speaker 1: And then I realized, oh no, you don't just plunk

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Speaker 1: down two hundred dollars or whatever on the clock. You

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Speaker 1: also have to pay a monthly subscription service fee to

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Speaker 1: use all the features. And you were like, you're not

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Speaker 1: getting an alarm clock subscription. No, A gritch too far.

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Speaker 2: No one should have an alarm clock subscription. I'm sorry

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Speaker 2: to the like subscription model alarm clock industry, but that's

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Speaker 2: not a thing we need. We have the technology to

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Speaker 2: deliver it for free. I cannot imagine what premium features

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Speaker 2: I would gain access to for nine ninety nine a

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Speaker 2: month or whatever it is.

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Speaker 1: They had, like rub Paul doing wake up affirmations. I mean,

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Speaker 1: I will say I'm not gonna lie and say I

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Speaker 1: was totally disinterested in some of these features. I was like,

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Speaker 1: down the rabbit hole on this alarm clock purchase, which

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Speaker 1: in the end I ended up buying one for like

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Speaker 1: thirty dollars A networked just fine. No, so obviously that

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Speaker 1: level of subscription complete scam. But I have a couple

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Speaker 1: of scam updates for you all.

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Speaker 2: That sounds great. What scam you want to start with?

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Speaker 1: First tea app and the Tea on her app Saga.

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Speaker 1: So we've been talking about this since the summertime, y'all

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Speaker 1: will recall that the t app was designed ostensibly for

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Speaker 1: women to be able to spill tea on the men

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Speaker 1: they were interested in potentially dating. It was really marketed

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Speaker 1: as the safety thing, right, a way for women to

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Speaker 1: vet men to make sure that abusers were not warning

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Speaker 1: their way into all of our hearts. But when you

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Speaker 1: look a little deeper, it really seemed like it was

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Speaker 1: more of an exploitation thing because this company did not

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Speaker 1: do the bear Bear Bear minimum to keep their users safe,

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Speaker 1: and then their user's information, including driver's licenses that were

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Speaker 1: used to verify people's genders to sign up, were leaked

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Speaker 1: by four Chan. We also covered the male version of

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Speaker 1: the t app for men to post tea about the

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Speaker 1: women that they were interested in dating, and they also

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Speaker 1: had their information leak. So everybody's getting their informational leaked

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Speaker 1: and exposed. Equality. We've done it, gender equality. What is

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Speaker 1: wild to me is that even as those leaks happened,

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Speaker 1: like when the Tea app was in the news and

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Speaker 1: the Tea on Her app was in the news because

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Speaker 1: of this massive, massive leak, both versions of the app

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Speaker 1: were incredibly popular, more popular than ever and so I

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Speaker 1: don't know what it says about us, but people were

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Speaker 1: continuing to use the apps.

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Speaker 2: Yeah, it's disappointing, but not that surprising. I think there's

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Speaker 2: a lot of digital nihilism out there where people just

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Speaker 2: feel like, well, my data is already out there, so

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Speaker 2: what's the harm of just completely exposing my photo of

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Speaker 2: my driver's license and my name and birth date to

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Speaker 2: my address and my address to all of the internet.

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Speaker 2: To be clear, there's people who are deeply misguided and

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Speaker 2: we need to help them realize that they're incorrect. I'd

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Speaker 2: suspect that's a lot of it.

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Speaker 1: So we have another update, which is that both the

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Speaker 1: Tea app and the Tea on Her app have been

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Speaker 1: removed from the Apple App Store. Four for media who

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Speaker 1: initially broke this Massive story said that Apple told Zam

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Speaker 1: in an email that it had removed the app as

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Speaker 1: well as the tea on her app, for failing to

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Speaker 1: meet the company's terms of use around content moderation and

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Speaker 1: user privacy. Apple also said that it received an excessive

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Speaker 1: number of complaints, including ones about the personal data of

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Speaker 1: miners being posted on the app. So pretty bad. Apple

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Speaker 1: said that it has parts of its guidelines that say

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Speaker 1: that apps are not allowed to share someone's personal data

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Speaker 1: without their permission, and that any app on their app

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Speaker 1: store needs to have a mechanism for reporting objectionable content. Mike,

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Speaker 1: do you have any idea how the t app is

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Speaker 1: responding to all of this being taken down off the

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Speaker 1: app store?

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Speaker 2: Uh? Are they posting on the teon app stores?

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Speaker 1: App? No, but that would be pretty good if they were.

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Speaker 2: Watch out for Apple.

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Speaker 1: This company will kick you off of their app store

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Speaker 1: without any warning. So Weirdly, the te app is still

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Speaker 1: just posting on social media like none of this ever happened.

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Speaker 1: Their most recent post and I checked this was their

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Speaker 1: most recent post as of US recording. This was on Instagram,

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Speaker 1: where they say the first ever girls only space that

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Speaker 1: truly amplifies women's voices and gives them an anonymous space

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Speaker 1: to share their experiences, find comfort, and get the info

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Speaker 1: they need on the men they're talking to. In the

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Speaker 1: name of all caps dating safety heart emoji. So the

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Speaker 1: te app is just posting through it, and I honestly

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Speaker 1: think that says a lot about the ethos of this

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Speaker 1: particular app. You know, the te app branded itself as

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Speaker 1: this tool that was all about women's empowerment and women's safety.

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Speaker 1: The founder shared this story, which turned out to be

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Speaker 1: I would, in my opinion, obvious bs about how he

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Speaker 1: decided to make the app because his mom had been

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Speaker 1: on a bunch of bad days with people who had

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Speaker 1: criminal records, and he just wanted to make a platform

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Speaker 1: for women to be able to stay safe. For for media,

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Speaker 1: talk to a woman who the founder had been trying

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Speaker 1: to come on board as the founder, and she's like, oh,

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Speaker 1: I never heard that story. That story is made up,

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Speaker 1: And I think, you know, it's this platform that was

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Speaker 1: trying to brand itself this way, but at the end

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Speaker 1: of the day, it was just another data hungry startup

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Speaker 1: trying to use feminism and women's safety as a marketing hook.

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Speaker 1: Like a lot of these stories, I feel they end

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Speaker 1: the same way a privacy scandal and then a takedown,

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Speaker 1: and I guess for the tea app, just like fronting

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Speaker 1: on social media pretending that everything is fine.

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Speaker 2: I have to wonder if there's even anybody behind that

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Speaker 2: social media account at this point, Like, given how just

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Speaker 2: piss poor they were about like security and building the

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Speaker 2: app itself, I have to imagine that an equal amount

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Speaker 2: of non care went into their social media work. And

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Speaker 2: like I would not be surprised if they just had

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Speaker 2: chat GPT spit out like a thousand little tweets worth

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Speaker 2: of content and then scheduled them for the rest of time.

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Speaker 2: And so it's just like a zombie account posting the

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Speaker 2: hope that a little bore data trickles it.

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Speaker 1: That's sweet, sweet data. Okay, So, speaking of obvious scams,

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Speaker 1: we told you in our episode about Charlie Kirk how

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Speaker 1: the literal day after Charlie Kirk was murdered, a site

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Speaker 1: sprang up promising to unmask anybody not being sufficiently mournful

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Speaker 1: in the wake of his death. And that site kind

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Speaker 1: of seemed like it was scamming people, Like from the jump,

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Speaker 1: I remember going to that site and being like, who

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Speaker 1: put their information? Into the site. Well, that site, initially

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Speaker 1: called Exposed Charlie's Murderers, promised to make a searchable trove

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Speaker 1: of names and workplaces for quote, the largest firing operation

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Speaker 1: in history. Charlie Kirk would have been so proud. According

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Speaker 1: to drop site News, the website listed six cryptocurrency blockchains

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Speaker 1: and asked supporters to fund a quote highly sophisticated enterprise

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Speaker 1: system that will be impervious to leftist attacks. Basically, it

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Speaker 1: sounds like whoever set this up just collected a bunch

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Speaker 1: of money and then dipped. Over a two day period,

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Speaker 1: the websites anonymous developers collected more than thirty thousand dollars

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Speaker 1: per coin tracker software used for crypto based taxes. Folks

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Speaker 1: went recall, they were initially called Exposed Charlie's Murderers, and

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Speaker 1: then they rebranded as the Charlie Kirk Data Foundation on

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Speaker 1: September fourteenth. Then the site it was deplatformed, and now

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Speaker 1: they've basically disappeared altogether. With that thirty thousand dollars of donation,

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Speaker 1: it really just seems like, according to this piece and

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Speaker 1: drop site News, THETIS took a bunch of people's money

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Speaker 1: and then bounced the group has not responded to several

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Speaker 1: inquiries from drop site about where their donations went and

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Speaker 1: whether or not they're still planning to create that big

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Speaker 1: database for all the firings. And a lot of the

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Speaker 1: folks who spent money on this enterprise are not happy.

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Speaker 2: I bez probably really upsetting to be a dope. I

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Speaker 2: love how they went from let's make a list of

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Speaker 2: leftists we don't like to let's build a highly sophisticated

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Speaker 2: enterprise system with your money, please.

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Speaker 1: And then wound up with just give us the money

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Speaker 1: and we'll be on our way.

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Speaker 2: Yeah, like, you know what this is. This is a scam.

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Speaker 2: Just just hand it over.

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Speaker 1: You know what it is. So yeah, people might have

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Speaker 1: gotten scammed thirty thousand dollars one hundred and ninety crypto

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Speaker 1: payments and what do they have to show for it

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Speaker 1: a ghost website and like a bunch angry donors. And again,

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Speaker 1: just like the t app, I think it is a

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Speaker 1: reminder that in this age of online outrage and online morality,

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Speaker 1: there is always someone ready to monetize your outrage, and

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Speaker 1: sometimes they will just vanish poof done gone. And just

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Speaker 1: like the t app, I mean, my big thing with

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Speaker 1: the tapp was that I could sense that it was

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Speaker 1: a company that I thought was trying to materially capitalize

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Speaker 1: on gender war's outrage, and so that was sort of

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Speaker 1: like how they were getting their their marketing. And so

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Speaker 1: I just think that any company that pops up, in

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Speaker 1: this case literally overnight, promising to turn your anger or

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Speaker 1: your fear into something more is probably a company worth

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Speaker 1: thinking twice about getting involved in.

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Speaker 2: That is such good advice, and there's not a lot

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Speaker 2: of like universal good advice in the day and age.

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Speaker 2: But I feel like anybody who is trying to encourage

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Speaker 2: you to push the gas on your outrage, that's like

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Speaker 2: a huge red flag.

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Speaker 1: Oh yes, Like, oh, you're outraged, you're angry, you're fearful.

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Speaker 1: We'll give us your driver's license and forty dollars. We

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Speaker 1: can take this to the next level. That should be.

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Speaker 1: That should be a big red flag for anybody across

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Speaker 1: the board.

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Speaker 2: Even before they get to the part where they make

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Speaker 2: that ask for the money. Just like you should lean

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Speaker 2: into that anger, you should lean into that fear. You're

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Speaker 2: right to feel those things. We need to punish the

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Speaker 2: people who have done this to you. That's just not

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Speaker 2: not something that somebody who's trying to help is gonna.

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Speaker 1: Say absolutely not. Speaking of not trying to help, big

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Speaker 1: text capitulation to the Trump administration continues. This time it

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Speaker 1: is Spotify and Ice. So we talked a while back

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Speaker 1: about how a number of musicians like Massive Attack, one

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Speaker 1: of my favorite groups Sylvia and Esso, one of your

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Speaker 1: favorite groups Mike Cagen, Gizzard and the Lizard Wizard, which

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Speaker 1: the first time you told me about that group, I

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Speaker 1: for the entirety of the conversation I did not know

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Speaker 1: if you were making it up. We're not. I said, Oh,

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Speaker 1: this is a real band. There's a band that you

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Speaker 1: are you trying to get one over on you with

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Speaker 1: this band name.

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Speaker 2: You have no idea how many albums they have.

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Speaker 1: I still don't know if they're a real band or not.

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Speaker 2: Oh they're real bad. They rock. They are awesome, talented

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Speaker 2: musicians who just like crank out music. Go listen to Rattlesnake.

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Speaker 1: Okay, well you're gonna like them even more now because

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Speaker 1: they have all taken their music off of Spotify. A

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Speaker 1: lot of these artists site Spotify founder Daniel x investment

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Speaker 1: in the AI military company at Helsing for why they

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Speaker 1: have removed their music from the platform, and now it's

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Speaker 1: gone from worse to worser, as they say online, because

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Speaker 1: Spotify is running these horrible ICE recruitment ads on their

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Speaker 1: non paid ad supported tier. The ads say in sanctuary cities,

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Speaker 1: you're ordered to stand down while dangerous illegals walk free,

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Speaker 1: and urge listeners to quote fulfill their mission and join

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Speaker 1: the mission to protect America by joining ICE. I should say,

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Speaker 1: it is not just Spotify either, YouTube, Pandora, HBO, Max

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Speaker 1: and others also have these ads on their platforms.

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Speaker 2: Pretty distasteful, right, it is distasteful.

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Speaker 1: Well, don't worry because a Spotify spokesperson says the ads

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Speaker 1: do not violate Spotify advertising policy and that Spotify has

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Speaker 1: no intention of discontinuing those ads. And the spokesperson says,

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Speaker 1: chill out, y'all, because if you don't want to hear

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Speaker 1: these ads, listeners are free to thumbs up or thumbs

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Speaker 1: down these ads, which would supposedly mean that I guess

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Speaker 1: these ads would play a little less often on your

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Speaker 1: personalized feed. To me, that simply does not address the problem.

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Speaker 1: And the fact that the spokesperson was like, oh, people

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Speaker 1: can just like say they don't like these ads. Via

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Speaker 1: thumbs down. It just shows to me how not seriously

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Speaker 1: Spotify is taking this.

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Speaker 2: I also love how the ads just become like another

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Speaker 2: form of content, like, oh, I love this ad, Give

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Speaker 2: me more of these ads. No one says that no

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Speaker 2: one wants that.

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Speaker 1: Stereo Gum reports at the record label Epitaph, which has

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Speaker 1: bands like Bad Religion and Pennywise, and then their sister

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Speaker 1: label Anti, which has musicians like Nico Kase, Nick Cave,

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Speaker 1: and Tom Waits, have both posted a message calling for

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Speaker 1: Spotify to remove these ads. So Snopes reached out to

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Speaker 1: the Department of Homeland Security, which oversees ICE, about these ads.

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Speaker 1: The assistant DJs secretary responded basically saying, Yeah, We're doing

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Speaker 1: a huge nationwide recruitment campaign, saying, quote, the ICE recruitment

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Speaker 1: campaign is a resounding success, with more than one hundred

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Speaker 1: and fifty applications rolling in from patriotic Americans answering the

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Speaker 1: call to defend the whole land by helping to arrest

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Speaker 1: and remove the worst of the worst from our country.

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Speaker 1: You know people who are being arrested lawfully at court

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Speaker 1: appointments or appointments with their attorney, or people who are

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Speaker 1: trying to pursue gainful employment to put food on their table,

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Speaker 1: you know, the worst of the worst people.

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Speaker 2: It's one of the most cruel and duplicitous things this

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Speaker 2: cruel and DUPLICITUS administration has done, in continues doing, is

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Speaker 2: attempting to smear all immigrants to this country as the

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Speaker 2: worst of the worst, as if everyone who's an immigrant

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Speaker 2: here is like a dangerous criminal, when the data is

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Speaker 2: pretty clear that immigrants in this country are less likely

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Speaker 2: to commit violent crimes than native port Americans.

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Speaker 1: Yes, and I think you know, first of all, don't

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Speaker 1: let anybody ever tell you that technology is neutral ever again,

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Speaker 1: because I think what these Spotify ads really show us

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Speaker 1: is how deeply entwined big tech has become with government

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Speaker 1: power and our current administration, even the most odious parts

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Speaker 1: of it. When you have platforms claiming neutrality while profiting

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Speaker 1: off of these ice recruitment ads, it's not neutral at all.

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Speaker 1: It is a choice for artists who see their music

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Speaker 1: as protests. That choice probably makes Spotify feel a lot

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Speaker 1: less like a platform for creativity and more like an

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Speaker 1: arm of the state. And I think the content of

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Speaker 1: these ads, in the ads, they're not even pretending to

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Speaker 1: be neutral. Right. They don't sound like ads that are

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Speaker 1: recruiting people to nonpartisan civil service or government jobs. They

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Speaker 1: sound like campaign ads for the Trump administration. The ads

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Speaker 1: are partisan as hell.

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Speaker 2: Yeah, they use the exact same language, and like most

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Speaker 2: messaging out of the Trump administration, they're full of lies,

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Speaker 2: lies about people, and lies about our cities being prime

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Speaker 2: ridden hell holes filled with the worst of the worst,

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Speaker 2: as they call it.

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Speaker 1: Yeah, there's nothing neutral about it. And people are leaving

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Speaker 1: Spotify in protests. And so if you are hearing this

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Speaker 1: and you're like, fuck Ice, fuck Spotify, just go to

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Speaker 1: bit dot lea slash cancel Spotify. They have an entire

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Speaker 1: page of resources to help organize folks who are interested

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Speaker 1: in bore hiding Spotify. We will put that in the

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Speaker 1: show notes more after a quick.

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Speaker 3: Break, let's get right back into it.

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Speaker 1: Speaking of ICE lies, I don't think anybody needs to

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Speaker 1: be reminded that Homeland Security Secretary Christy no is a liar.

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Speaker 1: But now she is lying on a specific black youth.

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Speaker 1: So she has been saying that drug cartels were placing

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Speaker 1: bounties on ICE agent's heads. Then the official DHS account

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Speaker 1: posted a video purporting to show a young black man

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Speaker 1: featuring an on screen message that reads Ice We're on

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Speaker 1: the way. Word in the streets cartels put a fifty

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Speaker 1: k bounty on y'all. Only the person in this video said,

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Speaker 1: I never said that. That was not what my video

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Speaker 1: was about. They stole my video and made this up.

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Speaker 1: So he said it was an old TikTok that he

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Speaker 1: made where he wasn't talking about Ice at all. He

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Speaker 1: was actually talking about Iran as a joke, and that

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Speaker 1: DHS took the video edited it to make it seem

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Speaker 1: like he was threatening Ice before disseminating it. Here's a

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Speaker 1: little bit of what he had to say.

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Speaker 4: And I've seen the caps and then I was like, wait,

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Speaker 4: I didn't do that. Still got the video saved on

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Speaker 4: my draft months ago. On TikTok bro, I was like,

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Speaker 4: I'm gonna pull it up right here. Now here's the

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Speaker 4: fucked up part. The federal government is involved with something

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Speaker 4: that I didn't do. Like what, So this is what

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Speaker 4: they reposted offline TikTok. That video got taken down, So

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Speaker 4: here's what they reposted on their page. Now that video

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Speaker 4: was going crazy him on Twitter. Bro, Now I'm a

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Speaker 4: threat to the government or something that I didn't do

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Speaker 4: bro I'm being called I want to be jy in,

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Speaker 4: I want to be gangster over a video that I

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Speaker 4: didn't caption or.

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Speaker 3: What do you post?

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Speaker 1: D just is sticking by what they said. A DHS

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Speaker 1: spokesperson told The Independent, this young man posted violent threats

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Speaker 1: of murder against our law enforcement. He then deleted it

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Speaker 1: when he was called out in attempted to deny all wrongdoing.

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Speaker 1: We have the receipts and the Internet is forever. DHS

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Speaker 1: did not edit, change, use AI or any other manner

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Speaker 1: to alter this video. So I don't know for sure,

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Speaker 1: and this is just my opinion about how they are

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Speaker 1: able to say this about this man's video. This man

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Speaker 1: has been so clear that it was aboudy Run. I

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Speaker 1: never mentioned ice. This video was months old. What they're

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Speaker 1: saying is not true. I think what DHS is saying

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Speaker 1: is that we did not use AI or edit the video,

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Speaker 1: but that putting text on somebody else's video. They're probably

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Speaker 1: banking on people not seeing that as editing because that

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Speaker 1: is so commonplace on TikTok. So they're saying we didn't

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Speaker 1: change what he does in the video via AI or

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Speaker 1: manipulate it. But in my opinion. What they're probably leaving

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Speaker 1: out pretty crucially is that certainly he did not put

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Speaker 1: that text on the screen about a fifty k bounty.

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Speaker 1: They added that, So I think that they're kind of

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Speaker 1: it kind of reminds me of the MS Doen with

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Speaker 1: the MS thirteen thing, where they very clearly went in

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Speaker 1: and typed MS thirteen on a tattoo for President Trump,

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Speaker 1: and then later in a debate, it was clear that

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Speaker 1: President Trump did not understand that somebody had obviously gone

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Speaker 1: in and written MS thirteen, but that was not the

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Speaker 1: literal image of a tattoo. And like, it's the semantics

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Speaker 1: that they get so stupid. But I think that's what's

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Speaker 1: going on here. If I had to say, this is

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Speaker 1: just my opinion, I.

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Speaker 2: Mean, it makes sense what you've described. It's insane that

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Speaker 2: that is something that the Department's Nansecurity could be credibly

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Speaker 2: accused of doing. But absolutely, I mean, they they used

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Speaker 2: that same kind of like gotcha technicalities about semantics and

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Speaker 2: court filings with judges, So why wouldn't they use with

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Speaker 2: the American public the words they had on the screen,

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Speaker 2: ice were on the way word in the street cartels

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Speaker 2: put a fifty K bounty on you all. So, like,

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Speaker 2: what we're supposed to believe is that this youth heard

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Speaker 2: somebody on the streets say that some unspecified cartels have

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Speaker 2: a bounty, and so he's hoping to collect that. He's

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Speaker 2: just gonna like attack an ICE officer and then show

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Speaker 2: up at some unspecified cartel headquarters and be like, where's

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Speaker 2: my fifty thousand dollars. That's what we're meant to believe

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Speaker 2: is a real thing.

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Speaker 1: Yeah, it doesn't quite pass the Smith test with me either,

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Speaker 1: I have to say. And who wouldn't want to join

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Speaker 1: the ranks of an organization like this? Who wouldn't want

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Speaker 1: to work with people like this?

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Speaker 2: God? Yeah, yeah, that's just the I mean, they talk

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Speaker 2: about going after the worst of the worst, they should

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Speaker 2: really look at their own ranks, like these are not

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Speaker 2: the best of the best.

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Speaker 1: They're not sending their best. Okay, So this is a

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Speaker 1: little bit of a tough story, so trigger warning up top,

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Speaker 1: But we have to talk about the climate going on

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Speaker 1: toward women stream right now. Weeks ago, streamers Bealcree and

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Speaker 1: qt Cinderella said that they were really worried about going

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Speaker 1: to TwitchCon this year, which is a big gathering of

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Speaker 1: live streamers and the people that watch them. They were

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Speaker 1: specifically concerned about things like lack security at events where

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Speaker 1: live streamers gather after some pretty high profile events where

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Speaker 1: folks were able to crash those events. They also pointed

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Speaker 1: to an increasing number of violent or harassing incidents, specifically

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Speaker 1: targeting women who make digital content and live stream content.

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Speaker 1: Twitch is CEO Dan Clancy, who runs TwitchCon, which is

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Speaker 1: owned by Amazon, said that the company takes security very seriously,

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Speaker 1: but unfortunately, on the first day of TwitchCon, the popular

435
00:23:43,960 --> 00:23:48,240
Speaker 1: streamer Amiru was assaulted during a meet and greet at

436
00:23:48,320 --> 00:23:50,840
Speaker 1: the event. So I saw the video of what happened.

437
00:23:50,880 --> 00:23:55,480
Speaker 1: It's horrifying. She's standing there, there's people around, and basically

438
00:23:55,520 --> 00:23:58,840
Speaker 1: this big guy rushes up to her and grabs her.

439
00:23:59,040 --> 00:24:01,240
Speaker 1: To me, I mean, I wasn't there, but to me,

440
00:24:01,280 --> 00:24:03,200
Speaker 1: it almost looked like he was trying to choke her,

441
00:24:03,480 --> 00:24:06,240
Speaker 1: but other people said that he was trying to grab

442
00:24:06,280 --> 00:24:09,680
Speaker 1: her to kiss her. Either way, it is terrifying. Her

443
00:24:09,720 --> 00:24:13,160
Speaker 1: security guard, who was standing nearby, shoves the man, and

444
00:24:13,200 --> 00:24:16,320
Speaker 1: then the man is just sort of allowed to visibly

445
00:24:16,440 --> 00:24:19,960
Speaker 1: kind of walk away. Twitch later clarifies that that man

446
00:24:20,160 --> 00:24:22,880
Speaker 1: was found, apprehended and removed from the event and banned

447
00:24:22,880 --> 00:24:26,919
Speaker 1: from future events. According to Tube Filter, they say that

448
00:24:26,960 --> 00:24:29,760
Speaker 1: it's worth noting that the security personnel who pushed that

449
00:24:30,040 --> 00:24:34,360
Speaker 1: man is not this streamer's preferred bodyguard, because her preferred

450
00:24:34,359 --> 00:24:37,480
Speaker 1: bodyguard was perma ban from twitch Con because at a

451
00:24:37,600 --> 00:24:40,959
Speaker 1: previous event he physically stopped a man who had been

452
00:24:41,000 --> 00:24:44,359
Speaker 1: stalking Amru around the convention. Now, she explained that her

453
00:24:44,440 --> 00:24:47,600
Speaker 1: bodyguard did not push, punch, or hurt the stalker in

454
00:24:47,640 --> 00:24:50,240
Speaker 1: any way. He just held him in place. But because

455
00:24:50,280 --> 00:24:53,800
Speaker 1: he physically engaged the stalker, he is no longer allowed

456
00:24:53,840 --> 00:24:57,160
Speaker 1: to work with her or protector at twitch con events.

457
00:24:57,320 --> 00:25:00,000
Speaker 1: So when she was assaulted at twitch Con this time around,

458
00:25:00,280 --> 00:25:03,520
Speaker 1: her preferred security guard was not there because these organizers

459
00:25:03,600 --> 00:25:08,360
Speaker 1: had banned him. So, in a statement, Amiru disputed much

460
00:25:08,400 --> 00:25:11,199
Speaker 1: of Twitch's account of what happened during this assault. She

461
00:25:11,200 --> 00:25:13,919
Speaker 1: said that they what they said was a blatant lie.

462
00:25:14,119 --> 00:25:16,720
Speaker 1: Here's what she had to say about the incident yesterday.

463
00:25:16,720 --> 00:25:19,000
Speaker 1: The man who assaulted me was allowed to cross multiple

464
00:25:19,080 --> 00:25:21,480
Speaker 1: barriers at twitch Con, and even in front of another

465
00:25:21,520 --> 00:25:24,119
Speaker 1: creator's meet and greet, tried to grab me and kiss me.

466
00:25:24,520 --> 00:25:26,800
Speaker 1: Fortunately he wasn't able to, but a lot of people

467
00:25:26,800 --> 00:25:28,679
Speaker 1: have pointed out that could have been a lot worse.

468
00:25:28,800 --> 00:25:30,720
Speaker 1: I'm obviously shaken up by what happened, and it's not

469
00:25:30,800 --> 00:25:32,479
Speaker 1: the first time that I've dealt with something like this,

470
00:25:32,920 --> 00:25:35,720
Speaker 1: But to tell you honestly, I'm a lot more hurt

471
00:25:35,720 --> 00:25:38,480
Speaker 1: and upset by how Twitch handled it during and after

472
00:25:38,560 --> 00:25:40,639
Speaker 1: the fact. I don't understand how he was allowed to

473
00:25:40,640 --> 00:25:43,200
Speaker 1: make it to me in the first place. The security

474
00:25:43,200 --> 00:25:46,000
Speaker 1: in the clip who reacts is my own security. It's true.

475
00:25:46,040 --> 00:25:48,439
Speaker 1: My favorite and usual security guard was banned for holding

476
00:25:48,520 --> 00:25:50,399
Speaker 1: a stalker's arm to bring him to police at a

477
00:25:50,440 --> 00:25:53,720
Speaker 1: past TWITCHCN. However, there were at least three or four

478
00:25:53,800 --> 00:25:57,520
Speaker 1: other TWITCHCN security staff in the area who did not react.

479
00:25:57,520 --> 00:25:59,400
Speaker 1: I left the guy walk away, as you can see

480
00:25:59,480 --> 00:26:01,240
Speaker 1: in the clips since they don't even appear in the

481
00:26:01,240 --> 00:26:03,720
Speaker 1: frame lol. She says that the people in the clip

482
00:26:03,720 --> 00:26:06,920
Speaker 1: who are checking on her are her own friends and staff,

483
00:26:07,240 --> 00:26:09,840
Speaker 1: and that none of the twitch con staff even came

484
00:26:09,880 --> 00:26:12,080
Speaker 1: to ask what was happening or that she was okay.

485
00:26:12,520 --> 00:26:14,840
Speaker 1: It gets worse because she says, my friend who was

486
00:26:14,880 --> 00:26:18,120
Speaker 1: present told me Twitch security were also behind the booth afterward,

487
00:26:18,560 --> 00:26:21,280
Speaker 1: joking about how they didn't see what happened and immediately

488
00:26:21,359 --> 00:26:24,439
Speaker 1: laughing and moving on to talk about something else. So

489
00:26:24,520 --> 00:26:26,439
Speaker 1: if no one was checking if I was okay or

490
00:26:26,520 --> 00:26:28,920
Speaker 1: needed anything, and they let the guy run away initially,

491
00:26:29,160 --> 00:26:31,199
Speaker 1: I have no idea what anyone hired to keep the

492
00:26:31,200 --> 00:26:34,200
Speaker 1: event safe was doing. In Twitch's statement, they said the

493
00:26:34,240 --> 00:26:37,399
Speaker 1: guy was immediately caught and attained. I'm sorry, but that

494
00:26:37,520 --> 00:26:40,120
Speaker 1: is a blatant lie. He was allowed to walk away

495
00:26:40,119 --> 00:26:41,960
Speaker 1: from my meet and greet and I didn't hear that

496
00:26:42,000 --> 00:26:44,639
Speaker 1: he was caught until hours after he attacked me. And

497
00:26:44,680 --> 00:26:46,880
Speaker 1: it felt like this only happened because of my manager

498
00:26:46,920 --> 00:26:50,600
Speaker 1: pressing for it, not because TWITCHCN staff present thought it

499
00:26:50,640 --> 00:26:54,520
Speaker 1: was a big deal. And yeah, I really feel for her.

500
00:26:54,600 --> 00:26:57,760
Speaker 1: It's horrifying that this has happened. You know, there have

501
00:26:57,800 --> 00:27:01,199
Speaker 1: been other incidents where people run up on celebrities or

502
00:27:01,240 --> 00:27:05,160
Speaker 1: public figures that they have parasocial relationships with and things

503
00:27:05,320 --> 00:27:09,000
Speaker 1: end much worse. You know, people like Christina Grimmy who

504
00:27:09,119 --> 00:27:11,520
Speaker 1: in twenty sixteen was doing a meet and greet and

505
00:27:11,720 --> 00:27:16,720
Speaker 1: was killed by a obsessive stalker who went up to

506
00:27:16,760 --> 00:27:19,439
Speaker 1: her meet and greet and murdered her, like, just in

507
00:27:19,480 --> 00:27:21,439
Speaker 1: front of all of her fans. And so what this

508
00:27:21,560 --> 00:27:24,719
Speaker 1: live streamer is saying is absolutely true. As horrifying as

509
00:27:24,760 --> 00:27:27,560
Speaker 1: what happened to her is, it genuinely could have been

510
00:27:27,560 --> 00:27:29,760
Speaker 1: a lot worse. And it sounds like what she's saying

511
00:27:29,840 --> 00:27:33,640
Speaker 1: is that TWITCHCN staff didn't do a ton to make

512
00:27:33,680 --> 00:27:37,000
Speaker 1: sure that she was okay afterward, and that TWITCHCN staff

513
00:27:37,160 --> 00:27:39,919
Speaker 1: really neglected to take this kind of thing seriously, to

514
00:27:39,920 --> 00:27:41,480
Speaker 1: the point where she says, I'm never coming back to

515
00:27:41,480 --> 00:27:43,360
Speaker 1: one of these things again, which honestly, I can't even

516
00:27:43,359 --> 00:27:43,880
Speaker 1: blame her.

517
00:27:44,440 --> 00:27:47,200
Speaker 2: Yeah, I don't blame her either.

518
00:27:47,760 --> 00:27:50,120
Speaker 1: It sounds to me that women in this community had

519
00:27:50,160 --> 00:27:53,200
Speaker 1: been warning about the way that this community treats women

520
00:27:53,400 --> 00:27:56,159
Speaker 1: contact graders for some time. Here's how NBC put it

521
00:27:56,400 --> 00:27:59,879
Speaker 1: to many fans online, her assault validated long running fears

522
00:28:00,040 --> 00:28:03,840
Speaker 1: that streamer safety at in person event, particularly scheduled meet

523
00:28:03,880 --> 00:28:06,399
Speaker 1: and greets where fans know exactly where a particular creator

524
00:28:06,440 --> 00:28:09,359
Speaker 1: will be ahead of time. Some streamers online claim that

525
00:28:09,400 --> 00:28:11,720
Speaker 1: they canceled their meet and greets or skipped this year's

526
00:28:11,720 --> 00:28:15,400
Speaker 1: Switchcon all together because of existing concerns about security, while

527
00:28:15,400 --> 00:28:17,760
Speaker 1: others said that they plan to shun future twitch Con

528
00:28:17,760 --> 00:28:22,200
Speaker 1: events in solidarity with Amiru. So I think that one

529
00:28:22,240 --> 00:28:23,920
Speaker 1: It's the tale as all the time that we talk

530
00:28:23,960 --> 00:28:26,680
Speaker 1: about on the show all the time, of women saying, hey,

531
00:28:27,119 --> 00:28:30,400
Speaker 1: this thing is happening in the community. It is potentially dangerous,

532
00:28:30,400 --> 00:28:33,920
Speaker 1: somebody needs to do something. It not being taken super seriously,

533
00:28:34,560 --> 00:28:37,480
Speaker 1: it being allowed to fester, the women sort of being

534
00:28:37,640 --> 00:28:39,600
Speaker 1: made to just deal with it on their own, and

535
00:28:39,640 --> 00:28:42,320
Speaker 1: this kind of thing happening. But I also do think,

536
00:28:42,480 --> 00:28:45,680
Speaker 1: if I'm just being real, I think that Twitch probably

537
00:28:45,760 --> 00:28:48,160
Speaker 1: is the kind of company where they make their real

538
00:28:48,240 --> 00:28:53,040
Speaker 1: money from their mega users, people who probably are more

539
00:28:53,200 --> 00:28:57,280
Speaker 1: likely to have a deep, unhealthy parasocial relationship with some

540
00:28:57,320 --> 00:28:59,840
Speaker 1: of their favorite contact creators, right, And so I have

541
00:29:00,200 --> 00:29:04,120
Speaker 1: feeling that Twitch perhaps does not want to alienate those

542
00:29:04,160 --> 00:29:06,600
Speaker 1: people because they are their cash cows. They are making

543
00:29:06,640 --> 00:29:08,520
Speaker 1: a ton of money from them, and so the company

544
00:29:08,600 --> 00:29:12,880
Speaker 1: probably can't really alienate the people who there are making

545
00:29:12,920 --> 00:29:15,160
Speaker 1: the company the most money, and that is just got

546
00:29:15,200 --> 00:29:16,760
Speaker 1: to be more likely to be the kind of people

547
00:29:16,800 --> 00:29:22,600
Speaker 1: who have unhealthy relationships or parasocial relationships with streamers, and

548
00:29:22,640 --> 00:29:25,240
Speaker 1: so that has to be part of the problem here.

549
00:29:25,320 --> 00:29:28,320
Speaker 1: Of like, Twitch probably doesn't want to just ban all

550
00:29:28,360 --> 00:29:31,440
Speaker 1: these people because there's tons of money to be made

551
00:29:31,480 --> 00:29:32,600
Speaker 1: from these people.

552
00:29:32,880 --> 00:29:37,760
Speaker 2: Certainly checks out with what we've seen just across tech

553
00:29:37,800 --> 00:29:42,040
Speaker 2: companies in general. And also you know the fact that

554
00:29:42,760 --> 00:29:46,959
Speaker 2: Twitch is the platform on which this all of this happens, right,

555
00:29:46,960 --> 00:29:49,040
Speaker 2: It's like it's a platform and it's also kind of

556
00:29:49,040 --> 00:29:55,480
Speaker 2: an ecosystem where these creators make their living and fans

557
00:29:55,600 --> 00:29:59,240
Speaker 2: and viewers. I think many, not all, but like many

558
00:29:59,360 --> 00:30:03,680
Speaker 2: spend a lot of their time watching streamers on this platform.

559
00:30:04,120 --> 00:30:08,840
Speaker 2: And so it's both like sort of tempting to call

560
00:30:08,840 --> 00:30:12,080
Speaker 2: it like a decentralized ecosystem where there are creators and

561
00:30:12,200 --> 00:30:15,440
Speaker 2: viewers and money flowing back and forth, but also like

562
00:30:15,640 --> 00:30:18,479
Speaker 2: very not decentralized, and there's really like one you know,

563
00:30:18,600 --> 00:30:22,320
Speaker 2: Twitch like runs the whole thing, And what a lot

564
00:30:22,320 --> 00:30:26,760
Speaker 2: of power to have over a community like this.

565
00:30:31,360 --> 00:30:45,640
Speaker 1: More after a quick break, let's get right back into it.

566
00:30:48,720 --> 00:30:52,080
Speaker 1: So open Ai just released their web browser called Atlas,

567
00:30:52,440 --> 00:30:55,280
Speaker 1: and I've been reading into it. I've not used it myself.

568
00:30:55,840 --> 00:30:58,400
Speaker 1: I'll get to why in a moment, but I think

569
00:30:58,480 --> 00:31:01,720
Speaker 1: folks should be a little bit wary of at List

570
00:31:01,800 --> 00:31:06,120
Speaker 1: because Atlas sounds to me like a privacy nightmare, even

571
00:31:06,160 --> 00:31:09,320
Speaker 1: in a landscape where a lot of browsers are bad

572
00:31:09,400 --> 00:31:11,480
Speaker 1: on privacy. According to a piece I read in the

573
00:31:11,480 --> 00:31:14,520
Speaker 1: Post called shashypt just came out with its own web browser.

574
00:31:14,680 --> 00:31:18,000
Speaker 1: Use it with caution. The browser from open ai out

575
00:31:18,080 --> 00:31:22,280
Speaker 1: surveils even Google Chrome, and that is saying something. It

576
00:31:22,280 --> 00:31:25,240
Speaker 1: doesn't just log which websites you visit. It also stores

577
00:31:25,600 --> 00:31:28,480
Speaker 1: quote memories of what you look at and what you

578
00:31:28,560 --> 00:31:31,560
Speaker 1: do on those sites. It can even grab control of

579
00:31:31,600 --> 00:31:35,880
Speaker 1: your mouth and browse for you. Yikes. Atlas works to

580
00:31:35,960 --> 00:31:39,080
Speaker 1: learn a ton about you if you grant permission during setup,

581
00:31:39,280 --> 00:31:41,880
Speaker 1: the browser build a trove of memories about the sites

582
00:31:41,920 --> 00:31:45,120
Speaker 1: that you visit and surfaces them when you need them,

583
00:31:45,320 --> 00:31:47,960
Speaker 1: so you could tell at Liss, for instance, open the

584
00:31:47,960 --> 00:31:50,800
Speaker 1: Halloween decorations I was looking at last week in some tabs,

585
00:31:51,080 --> 00:31:54,480
Speaker 1: and it could do it. Creepy. Atlass remembers not just

586
00:31:54,520 --> 00:31:58,720
Speaker 1: website addresses, but quote facts and insights from the sites themselves,

587
00:31:58,760 --> 00:32:01,400
Speaker 1: based on summaries of the content open ai makes on

588
00:32:01,480 --> 00:32:04,440
Speaker 1: its own surfers. So for instance, it might remember that

589
00:32:04,480 --> 00:32:06,640
Speaker 1: you have a trip coming up and that you prefer

590
00:32:06,800 --> 00:32:10,480
Speaker 1: Delta airlines and use Google Calendars, so these memories would

591
00:32:10,480 --> 00:32:14,360
Speaker 1: shape your experience across the browser. CHATGBT then tailor's its

592
00:32:14,400 --> 00:32:17,600
Speaker 1: responses to your Atlas memories in future chats, and the

593
00:32:17,640 --> 00:32:20,840
Speaker 1: browser's home screen offers personalized suggestions of things that you

594
00:32:20,840 --> 00:32:24,040
Speaker 1: should do next, such as find a vegetarian recipe. So,

595
00:32:24,240 --> 00:32:27,280
Speaker 1: if you are cool with trading this amount of your

596
00:32:27,320 --> 00:32:30,600
Speaker 1: privacy and data security for convenience, maybe all of this

597
00:32:30,640 --> 00:32:32,600
Speaker 1: doesn't sound so bad. Maybe you're like, oh, it will

598
00:32:32,640 --> 00:32:35,000
Speaker 1: be convenient for my browser to know that I only

599
00:32:35,040 --> 00:32:37,720
Speaker 1: fly Delta and surface me Delta flights only.

600
00:32:37,800 --> 00:32:40,800
Speaker 2: Right, I was just talking about digital nihilism. Sure, let's go.

601
00:32:42,800 --> 00:32:45,080
Speaker 1: I mean, on the one hand, I hear it, right,

602
00:32:45,120 --> 00:32:48,440
Speaker 1: I hear that how it would be very convenient.

603
00:32:48,440 --> 00:32:52,360
Speaker 2: These things would be super convenient. But at what cost?

604
00:32:52,480 --> 00:32:55,840
Speaker 1: Bridget I mean, is it really that much we're convenient?

605
00:32:56,080 --> 00:32:59,440
Speaker 1: Is it that hard to just go to Google flights

606
00:32:59,480 --> 00:33:02,120
Speaker 1: and then pick out the Delta flights yourself? Is it

607
00:33:02,560 --> 00:33:04,360
Speaker 1: really that big of a convenience?

608
00:33:04,440 --> 00:33:07,560
Speaker 2: Oh my god, I already looked up these Halloween decorations

609
00:33:07,640 --> 00:33:11,360
Speaker 2: last week. I'm supposed to do it again. Now, what

610
00:33:11,400 --> 00:33:12,360
Speaker 2: are computers for?

611
00:33:12,840 --> 00:33:15,200
Speaker 1: Like, even in these examples that they're giving us, where

612
00:33:15,240 --> 00:33:18,600
Speaker 1: clearly this is open aiyes, rosy version of it, where

613
00:33:18,600 --> 00:33:20,280
Speaker 1: it's going to save you so much time and effort.

614
00:33:20,320 --> 00:33:22,160
Speaker 1: It doesn't really sound like it saves you that much time.

615
00:33:22,200 --> 00:33:24,360
Speaker 1: I mean, we're ultimately just talking about getting keys on

616
00:33:24,360 --> 00:33:26,240
Speaker 1: a keyboard here and clicking a mouth.

617
00:33:26,640 --> 00:33:29,840
Speaker 2: Yeah. But anyway, so it would be convenient. So is

618
00:33:29,840 --> 00:33:32,160
Speaker 2: that the whole thing, just like the convenience of privacy?

619
00:33:32,560 --> 00:33:35,280
Speaker 1: Yeah, just can end the seglement right there. That's it.

620
00:33:35,400 --> 00:33:38,280
Speaker 1: Nothing else to say. Oh wait, only it does not

621
00:33:38,480 --> 00:33:41,560
Speaker 1: take much to imagine the kind of memories that you

622
00:33:41,640 --> 00:33:45,160
Speaker 1: might not want your search engine remembering about you forever.

623
00:33:45,640 --> 00:33:49,960
Speaker 1: And I gotta say this level of surveillance and remembering

624
00:33:50,200 --> 00:33:53,520
Speaker 1: is particularly fraught in our current social and political climate.

625
00:33:53,560 --> 00:33:56,720
Speaker 1: For instance, when Jeffrey Fowler from The Post asked open

626
00:33:56,760 --> 00:33:59,320
Speaker 1: ai if governments would be able to ask open ai

627
00:33:59,480 --> 00:34:02,840
Speaker 1: to hand over people's browsing data and memories, or what

628
00:34:02,960 --> 00:34:05,800
Speaker 1: happens if they're researching activities that are not legal in

629
00:34:05,840 --> 00:34:10,400
Speaker 1: certain states, such as abortion, open ai did not immediately

630
00:34:10,480 --> 00:34:14,279
Speaker 1: reply to his question, which, Yeah, for as convenient as

631
00:34:14,320 --> 00:34:17,239
Speaker 1: it would be to not have to sort through all

632
00:34:17,280 --> 00:34:19,799
Speaker 1: the non delta flights that are being serviced for me,

633
00:34:20,280 --> 00:34:21,759
Speaker 1: this feels bad to me.

634
00:34:22,480 --> 00:34:27,080
Speaker 2: Yeah, you could also imagine a lot of things beyond

635
00:34:27,280 --> 00:34:31,920
Speaker 2: abortion that you might not want the Trump administration knowing

636
00:34:32,080 --> 00:34:35,160
Speaker 2: about you engaging, and certainly abortion is high the list.

637
00:34:35,239 --> 00:34:35,640
Speaker 1: But like.

638
00:34:37,360 --> 00:34:42,359
Speaker 2: Who you voted for, what kind of questions you might

639
00:34:42,400 --> 00:34:47,920
Speaker 2: have about the Trump administration? Who knows what these creeps

640
00:34:48,160 --> 00:34:51,800
Speaker 2: might want to know about us? And I have seen

641
00:34:52,040 --> 00:34:55,680
Speaker 2: very little evidence to make me believe that open AI

642
00:34:55,920 --> 00:34:58,760
Speaker 2: or any other big tech company would not just happily

643
00:34:58,800 --> 00:34:59,399
Speaker 2: hand it over.

644
00:34:59,600 --> 00:35:02,920
Speaker 1: So that's bad. But there is one good thing that

645
00:35:03,000 --> 00:35:05,160
Speaker 1: has come from this, and this is a review of

646
00:35:05,200 --> 00:35:09,919
Speaker 1: Atlas from AI security pioneer does zd Susanna Cox? Can

647
00:35:10,000 --> 00:35:11,759
Speaker 1: I read this to you? Because it is phenomenal?

648
00:35:12,120 --> 00:35:12,760
Speaker 2: Yes? Please?

649
00:35:13,160 --> 00:35:15,920
Speaker 1: Do you love how much memory Chrome uses? But we

650
00:35:15,960 --> 00:35:19,200
Speaker 1: should spy on you more? And also as a bonus,

651
00:35:19,360 --> 00:35:23,240
Speaker 1: came with multiple security back doors. Well, great news. Open

652
00:35:23,280 --> 00:35:27,040
Speaker 1: ais new browser promises to deliver on all three. It's

653
00:35:27,040 --> 00:35:30,520
Speaker 1: called Atlas, and it finally answers the question can a

654
00:35:30,560 --> 00:35:35,000
Speaker 1: browser be even more insecure, privacy violating and bloated? Yes,

655
00:35:35,239 --> 00:35:38,800
Speaker 1: now it can. We've all been there reading the stupid

656
00:35:38,880 --> 00:35:42,840
Speaker 1: Internet with our boring eyeholes, using our own brains like

657
00:35:42,880 --> 00:35:46,239
Speaker 1: a bunch of literal primates, and thought there has to

658
00:35:46,320 --> 00:35:50,399
Speaker 1: be a better way. Well, now there is wish your

659
00:35:50,440 --> 00:35:54,719
Speaker 1: financial information was guarded with all the prompt injectable security

660
00:35:54,800 --> 00:35:59,480
Speaker 1: of AI agents. Guess what Atlas can make purchases? Want

661
00:35:59,480 --> 00:36:02,360
Speaker 1: to browser that reads everything you do and reports it

662
00:36:02,400 --> 00:36:05,480
Speaker 1: to big tech slash the government slash Sam Altman's a

663
00:36:05,520 --> 00:36:08,840
Speaker 1: personal media bank. Now you can have it all in Atlas,

664
00:36:08,880 --> 00:36:13,240
Speaker 1: the AI powered browser. Simply turn over all your financial information,

665
00:36:13,400 --> 00:36:16,200
Speaker 1: browsing habits, and details of your personal life to a

666
00:36:16,320 --> 00:36:19,600
Speaker 1: big company and start to feel smarter slash more productive,

667
00:36:19,600 --> 00:36:24,040
Speaker 1: slash cooler, slash more attractive, slash wealthier right away. Wow.

668
00:36:24,280 --> 00:36:25,000
Speaker 1: Thanks AI.

669
00:36:26,120 --> 00:36:27,799
Speaker 2: Wow that was art. That was really good.

670
00:36:28,120 --> 00:36:30,520
Speaker 1: So at least we got that review out of the

671
00:36:30,560 --> 00:36:31,000
Speaker 1: whole thing.

672
00:36:32,000 --> 00:36:32,839
Speaker 2: It's probably worth it.

673
00:36:33,200 --> 00:36:34,560
Speaker 1: I don't know if you know this about me, but

674
00:36:34,800 --> 00:36:38,120
Speaker 1: I really dig this report that comes out of UCLA

675
00:36:38,280 --> 00:36:42,759
Speaker 1: Center for Scholars and Storytellers every year called Teens and Screens.

676
00:36:43,200 --> 00:36:45,480
Speaker 1: It's one of the dorkiest things about me that when

677
00:36:45,520 --> 00:36:48,520
Speaker 1: this report comes out I comb through it. I just

678
00:36:48,520 --> 00:36:52,160
Speaker 1: find it so interesting. And basically this report offers insights

679
00:36:52,200 --> 00:36:56,200
Speaker 1: into the taste and preferences of young media consumers.

680
00:36:56,520 --> 00:36:59,759
Speaker 2: Oh that's really interesting. Why do you like this? Particular one.

681
00:36:59,800 --> 00:37:04,040
Speaker 1: So I just think that young media consumers, they can

682
00:37:04,080 --> 00:37:06,279
Speaker 1: really tell us a lot about where we're headed. And

683
00:37:06,320 --> 00:37:08,759
Speaker 1: they're also a niche, a niche group that I think

684
00:37:08,800 --> 00:37:11,440
Speaker 1: is very easy to not listen to, and so they'll

685
00:37:11,440 --> 00:37:14,840
Speaker 1: be telling us, Hey, we want XYZ, and it's just

686
00:37:14,960 --> 00:37:17,359
Speaker 1: very easy for people to be like, you don't want X,

687
00:37:17,400 --> 00:37:20,120
Speaker 1: you want Y. So something about this report, I just

688
00:37:20,160 --> 00:37:22,399
Speaker 1: think it really tells us a lot about what young

689
00:37:22,440 --> 00:37:25,680
Speaker 1: people are feeling and thinking about media, as opposed to

690
00:37:26,400 --> 00:37:29,120
Speaker 1: listing to people talk over them about what they want

691
00:37:29,160 --> 00:37:33,560
Speaker 1: from media. This year's report is titled get Real Relatability

692
00:37:33,600 --> 00:37:36,959
Speaker 1: on Demand because the finding show that teens and youth

693
00:37:37,280 --> 00:37:42,800
Speaker 1: are really craving things like relatability and authentic representations across

694
00:37:42,880 --> 00:37:45,840
Speaker 1: metrics as it pertains to media right now. So, the

695
00:37:45,880 --> 00:37:48,919
Speaker 1: study surveyed fifteen hundred Americans between the ages of ten

696
00:37:49,000 --> 00:37:51,719
Speaker 1: and twenty four about their media consumption, habits, and preferences.

697
00:37:51,800 --> 00:37:54,840
Speaker 1: According to Variety, the sample was proportionally reflective of the

698
00:37:54,920 --> 00:38:00,600
Speaker 1: United States gen Z population in terms of racial, ethnic, gender, sexuality, ability, economic,

699
00:38:00,640 --> 00:38:01,880
Speaker 1: and geographic diversity.

700
00:38:02,880 --> 00:38:08,239
Speaker 2: That is pretty cool. Uh So, how are young Americans

701
00:38:08,280 --> 00:38:09,239
Speaker 2: consuming media?

702
00:38:09,280 --> 00:38:12,319
Speaker 1: Well, I would say This was probably the most surprising

703
00:38:12,440 --> 00:38:16,240
Speaker 1: bit from the study, which is that surprisingly, young people

704
00:38:16,280 --> 00:38:19,360
Speaker 1: are not completely done with traditional media. I would have

705
00:38:19,400 --> 00:38:21,799
Speaker 1: thought that young people are only getting their media from

706
00:38:21,920 --> 00:38:24,880
Speaker 1: social media. Not the case. According to this study, fifty

707
00:38:24,960 --> 00:38:28,120
Speaker 1: three percent of young Americans discuss movies and television shows

708
00:38:28,120 --> 00:38:30,879
Speaker 1: with friends more than they discuss content that they watch

709
00:38:30,920 --> 00:38:33,759
Speaker 1: on social media, which really surprised me because I would

710
00:38:33,760 --> 00:38:36,760
Speaker 1: be thinking, Oh, what young people are consuming television and movies.

711
00:38:36,800 --> 00:38:40,080
Speaker 1: They're all this watching, They're all this watching with TikTok's right. No,

712
00:38:40,320 --> 00:38:44,040
Speaker 1: not so. They also really like animation, which I of

713
00:38:44,040 --> 00:38:47,960
Speaker 1: course love because I love animation. Gen Z shows a

714
00:38:48,000 --> 00:38:51,320
Speaker 1: distinct preference for animation. The percentage of adolescents who prefer

715
00:38:51,400 --> 00:38:54,560
Speaker 1: animated content over live action rose from forty two percent

716
00:38:54,640 --> 00:38:57,600
Speaker 1: last year to forty eight point five percent this year.

717
00:38:57,600 --> 00:39:00,640
Speaker 1: And that is not just for younger teens. Really, forty

718
00:39:00,640 --> 00:39:03,920
Speaker 1: eight percent of respondents aged nineteen to twenty four also

719
00:39:04,000 --> 00:39:07,880
Speaker 1: preferred animation. I I'm so curious what that is about.

720
00:39:08,239 --> 00:39:11,200
Speaker 1: I would say I also have a slight preference toward

721
00:39:11,239 --> 00:39:15,400
Speaker 1: animated content. I wish I could say what that was about.

722
00:39:15,440 --> 00:39:17,560
Speaker 1: I don't really know what it's about. With me, and

723
00:39:17,600 --> 00:39:20,680
Speaker 1: I'm so curious what this is about for the younger

724
00:39:20,719 --> 00:39:21,520
Speaker 1: folks surveyed.

725
00:39:22,000 --> 00:39:25,400
Speaker 2: Yeah, it's a good question. I also have always really

726
00:39:25,680 --> 00:39:29,200
Speaker 2: liked animation, and it's tempting the things that maybe the

727
00:39:29,239 --> 00:39:31,959
Speaker 2: young people are just like coming around to finally see

728
00:39:32,000 --> 00:39:34,879
Speaker 2: things the way that I do. But I suspect that's

729
00:39:35,000 --> 00:39:37,279
Speaker 2: probably not what's actually happening here.

730
00:39:37,840 --> 00:39:39,919
Speaker 1: You want to know something else young people are into?

731
00:39:40,160 --> 00:39:40,719
Speaker 1: According to this.

732
00:39:40,719 --> 00:39:43,720
Speaker 2: Study, Uh, what else are they into?

733
00:39:44,040 --> 00:39:47,240
Speaker 1: It's so wholesome just going to see movies with their friends.

734
00:39:47,680 --> 00:39:50,480
Speaker 1: Going to see a new movie in theaters would be

735
00:39:50,600 --> 00:39:54,080
Speaker 1: the top ranked weekend activity for a second year in

736
00:39:54,120 --> 00:39:56,960
Speaker 1: a row if cost were not a factor. And teens

737
00:39:56,960 --> 00:39:58,799
Speaker 1: are saying they would rather go see a new movie

738
00:39:58,800 --> 00:40:01,520
Speaker 1: in the theater with their friends over playing video games

739
00:40:01,560 --> 00:40:03,520
Speaker 1: with friends or going to concerts with friends.

740
00:40:03,719 --> 00:40:05,719
Speaker 2: Oh, they want to go see a movie. I get it.

741
00:40:05,760 --> 00:40:08,200
Speaker 2: Going to see a movie. Uh remember just a couple

742
00:40:08,200 --> 00:40:10,160
Speaker 2: of years ago, people were like, no one's ever gonna

743
00:40:10,200 --> 00:40:13,960
Speaker 2: go to the movies again. The theaters are dying. Seems

744
00:40:13,960 --> 00:40:15,000
Speaker 2: like that's not really happening.

745
00:40:15,320 --> 00:40:19,000
Speaker 1: In our episode with Stacy Spikes, the founder of movie Pass,

746
00:40:19,239 --> 00:40:21,680
Speaker 1: he dropped a surprising stat on me that going to

747
00:40:21,680 --> 00:40:24,600
Speaker 1: see movies in theater is still the number one leisure

748
00:40:24,640 --> 00:40:28,759
Speaker 1: activity of Americans, which again I also hear things like, oh,

749
00:40:28,880 --> 00:40:31,520
Speaker 1: scening is gonna kill movie theaters. Nobody's gonna go to

750
00:40:31,520 --> 00:40:34,120
Speaker 1: the movies anymore. I just went to see an theatrical

751
00:40:34,480 --> 00:40:37,279
Speaker 1: release of that new Paul Thomas Anderson movie One Battle

752
00:40:37,320 --> 00:40:39,680
Speaker 1: after Another. I went on an off time, I went

753
00:40:39,800 --> 00:40:44,720
Speaker 1: during I went weekday Mattine, completely packed, house full theater.

754
00:40:45,080 --> 00:40:47,640
Speaker 1: So from my total experiences, people are still going to

755
00:40:47,640 --> 00:40:49,239
Speaker 1: the movies, and the teens agree with me.

756
00:40:49,400 --> 00:40:51,359
Speaker 2: Oh you saw what a Battle after Another? I've seen

757
00:40:52,080 --> 00:40:54,760
Speaker 2: so many people talking about that. What did you think?

758
00:40:55,280 --> 00:40:57,839
Speaker 1: Hey, this is future bridget So just real quick, Mike

759
00:40:57,880 --> 00:41:01,239
Speaker 1: and I have a very quick spoiler free discussion of

760
00:41:01,280 --> 00:41:04,000
Speaker 1: the movie One Battle after Another. Here we share a

761
00:41:04,000 --> 00:41:07,759
Speaker 1: few basic overarching comments about the movie, but no real spoilers,

762
00:41:07,920 --> 00:41:09,759
Speaker 1: but just an FYI after trying to go win one

763
00:41:09,880 --> 00:41:12,840
Speaker 1: hundred percent fresh before seeing the movie. Okay, here we go.

764
00:41:13,440 --> 00:41:17,520
Speaker 1: So I love Paul Thomas Anderson. Boogie Knights is my

765
00:41:17,560 --> 00:41:19,480
Speaker 1: favorite movie of all time. If anybody asked me what

766
00:41:19,560 --> 00:41:21,399
Speaker 1: my favorite movie is, I will probably say Boogie Knights.

767
00:41:21,400 --> 00:41:25,359
Speaker 1: It's a masterpiece. So Paul Thomas Anderson is my guy.

768
00:41:25,600 --> 00:41:30,560
Speaker 1: I love him. I really liked one battle after another.

769
00:41:30,600 --> 00:41:34,080
Speaker 1: I thought it was a perfectly enjoyable movie. I thought

770
00:41:34,080 --> 00:41:36,200
Speaker 1: it was a good time. It was a long movie,

771
00:41:36,200 --> 00:41:39,879
Speaker 1: but it was one of those movies where you're never

772
00:41:40,760 --> 00:41:42,279
Speaker 1: You're never looking at your watch thinking how long I

773
00:41:42,280 --> 00:41:45,759
Speaker 1: have I've been in the theater. I don't know if

774
00:41:45,840 --> 00:41:50,000
Speaker 1: I would say how I felt about the movie matches

775
00:41:50,520 --> 00:41:53,640
Speaker 1: the hype that I feel like it got, and I

776
00:41:53,640 --> 00:41:55,600
Speaker 1: don't want to give anything away for folks who haven't

777
00:41:55,600 --> 00:41:58,640
Speaker 1: seen it. I have big Let's just say, I have

778
00:41:58,680 --> 00:42:03,320
Speaker 1: big questions about some of the way that the movie

779
00:42:03,880 --> 00:42:08,239
Speaker 1: deals with race, particularly black women. I think I don't

780
00:42:08,360 --> 00:42:10,880
Speaker 1: if folks haven't seen it. If you've seen it, I

781
00:42:10,920 --> 00:42:12,799
Speaker 1: want to talk about it with you, because a lot

782
00:42:12,800 --> 00:42:14,719
Speaker 1: of my friends have not seen it. There's one and

783
00:42:14,719 --> 00:42:15,840
Speaker 1: I don't want to give it a thing away, So

784
00:42:16,120 --> 00:42:18,399
Speaker 1: I'm speaking very cryptically and people people who haven't seen

785
00:42:18,400 --> 00:42:20,479
Speaker 1: it or like this is boring as hell. But there's

786
00:42:20,520 --> 00:42:23,000
Speaker 1: one particular character, and if you've seen the movie, you

787
00:42:23,000 --> 00:42:25,480
Speaker 1: know exactly who I'm talking about that I feel like

788
00:42:25,560 --> 00:42:29,120
Speaker 1: we are not really let into what the fuck is

789
00:42:29,160 --> 00:42:31,759
Speaker 1: going on with her? And why? And I feel like

790
00:42:32,040 --> 00:42:35,080
Speaker 1: I have a lot of questions And I don't think

791
00:42:35,080 --> 00:42:38,080
Speaker 1: it was the most loving depiction of black women I've

792
00:42:38,080 --> 00:42:40,319
Speaker 1: ever seen on screen. I'll put it that way. What

793
00:42:40,360 --> 00:42:42,240
Speaker 1: did you think about it? Uh?

794
00:42:42,280 --> 00:42:45,400
Speaker 2: Well, yeah, I also don't want to like surprise spoil

795
00:42:45,440 --> 00:42:48,279
Speaker 2: people who haven't seen it yet. I agree it was

796
00:42:48,360 --> 00:42:50,520
Speaker 2: It was a fun movie. It was funny, It was

797
00:42:50,560 --> 00:42:54,279
Speaker 2: well paced, It was like really well acted, just a

798
00:42:54,320 --> 00:42:58,560
Speaker 2: bunch of like great actors doing a phenomenal job with it.

799
00:42:59,280 --> 00:43:01,319
Speaker 2: If I were to criticize it, I would say that,

800
00:43:01,600 --> 00:43:06,200
Speaker 2: like it deals with some themes that feel like very

801
00:43:06,760 --> 00:43:10,120
Speaker 2: relevant right now. Uh, and I think that's part of

802
00:43:10,160 --> 00:43:12,520
Speaker 2: what's contributing to the hype. But I feel like it

803
00:43:12,560 --> 00:43:16,840
Speaker 2: deals with them kind of superficially. And uh, you know,

804
00:43:16,920 --> 00:43:19,799
Speaker 2: I after I left the theater, I was like, that

805
00:43:19,880 --> 00:43:22,759
Speaker 2: was fun. But then thinking back on it, I was like, well,

806
00:43:22,760 --> 00:43:24,920
Speaker 2: why did that character do that? Like, what was that

807
00:43:25,040 --> 00:43:26,600
Speaker 2: character's motivation?

808
00:43:26,760 --> 00:43:26,960
Speaker 1: Why?

809
00:43:28,600 --> 00:43:30,040
Speaker 2: And why is it important? Why do I as a

810
00:43:30,080 --> 00:43:35,919
Speaker 2: viewer like care about them? I Yeah, that was kind

811
00:43:35,920 --> 00:43:41,120
Speaker 2: of my impression was it was a fun, good action movie.

812
00:43:42,920 --> 00:43:46,440
Speaker 2: A lot to like about it, but not like it

813
00:43:46,480 --> 00:43:47,400
Speaker 2: wasn't a deep thinker.

814
00:43:47,560 --> 00:43:49,680
Speaker 1: Maybe because you can sneak in a recap episode and

815
00:43:49,680 --> 00:43:51,640
Speaker 1: we'll make it fit the text theme or fuck it,

816
00:43:51,680 --> 00:43:53,600
Speaker 1: maybe a we'll just have a one off, like I

817
00:43:53,640 --> 00:43:56,640
Speaker 1: want to talk about this movie episode and I'm owed

818
00:43:56,719 --> 00:43:59,239
Speaker 1: that I've been doing this over five years.

819
00:43:59,239 --> 00:44:02,600
Speaker 2: That can Oh that all right, Well, don't don't turn

820
00:44:02,680 --> 00:44:05,439
Speaker 2: against the little structures that we have here.

821
00:44:07,239 --> 00:44:09,279
Speaker 1: Oh you know that if you let me, it'll just

822
00:44:09,400 --> 00:44:13,920
Speaker 1: be like a straight You know, we rewatched Missus Doubt

823
00:44:13,920 --> 00:44:15,399
Speaker 1: Fire the other day and I was like, this might

824
00:44:15,400 --> 00:44:17,799
Speaker 1: make a good carecap. You were like, what about this

825
00:44:17,840 --> 00:44:21,080
Speaker 1: is technology? It's like nothing isn't an interesting movie.

826
00:44:21,320 --> 00:44:23,080
Speaker 2: Yeah, we have to draw the lines somewhere.

827
00:44:23,480 --> 00:44:28,520
Speaker 1: Okay, So back to the teens. Teens are really craving

828
00:44:28,800 --> 00:44:32,600
Speaker 1: relatable media and relatable stories right now. Thirty two percent

829
00:44:32,680 --> 00:44:36,600
Speaker 1: that they want to see relatable stories on screen more

830
00:44:36,680 --> 00:44:39,320
Speaker 1: so than in fantasy or aspirational stories. They want to

831
00:44:39,360 --> 00:44:43,000
Speaker 1: see stories with quote people with lives like mine. I

832
00:44:43,080 --> 00:44:46,799
Speaker 1: have to imagine that's why super wealthy, out of touch

833
00:44:46,840 --> 00:44:49,560
Speaker 1: celebrities is just not clicking with people anymore, particularly used

834
00:44:49,800 --> 00:44:51,840
Speaker 1: people like Kim Kardashian. I think that like in this

835
00:44:51,920 --> 00:44:54,319
Speaker 1: day and age, nobody really wants to see that, even

836
00:44:54,360 --> 00:44:57,160
Speaker 1: if you at one point you could connect with that Aspirationally,

837
00:44:57,760 --> 00:45:00,760
Speaker 1: young people are really not trying to see people living

838
00:45:00,760 --> 00:45:04,239
Speaker 1: these aspirational, out of touch stories. What they want is

839
00:45:04,280 --> 00:45:07,200
Speaker 1: people who have relatable lives and relatable stories and lives

840
00:45:07,280 --> 00:45:10,520
Speaker 1: like theirs. The fourteen to twenty four demographic, they show

841
00:45:10,600 --> 00:45:15,400
Speaker 1: significant preferences for stories about friendship. Specifically, almost sixty percent

842
00:45:15,440 --> 00:45:17,560
Speaker 1: say that they want to see more content where the

843
00:45:17,600 --> 00:45:20,680
Speaker 1: central relationship are friendship, and they are a little less

844
00:45:20,719 --> 00:45:25,839
Speaker 1: interested in romantic relationships on screen. Fifty four point nine

845
00:45:25,880 --> 00:45:28,839
Speaker 1: percent want to see more different gender friendships on screen,

846
00:45:28,880 --> 00:45:31,920
Speaker 1: while forty nine percent want to see same gender friendships,

847
00:45:32,239 --> 00:45:34,640
Speaker 1: and specifically, they want to see friendships where they have

848
00:45:34,760 --> 00:45:39,320
Speaker 1: healthy conflicts resolution. I really do feel like young people

849
00:45:39,320 --> 00:45:42,239
Speaker 1: are telling us something about about their lives and what

850
00:45:42,280 --> 00:45:44,640
Speaker 1: they want their lives and relationships to be like. They

851
00:45:44,640 --> 00:45:48,960
Speaker 1: want they want friendship and healthy friendships at the core

852
00:45:49,080 --> 00:45:52,239
Speaker 1: of their lives, and they want to see friends have conflict,

853
00:45:52,440 --> 00:45:54,920
Speaker 1: but where that conflict that when it does come up,

854
00:45:54,960 --> 00:45:57,279
Speaker 1: it's resolved in a healthy manner. And part of me

855
00:45:57,400 --> 00:46:00,440
Speaker 1: wonders if that's a reaction to our our current online

856
00:46:00,480 --> 00:46:03,359
Speaker 1: ecosystem where it seems like nothing could ever be be

857
00:46:03,680 --> 00:46:05,960
Speaker 1: no life could ever be managed in a healthy in

858
00:46:06,040 --> 00:46:06,719
Speaker 1: a healthy way.

859
00:46:07,160 --> 00:46:10,240
Speaker 2: Yeah, I'm sure there's a lot of very interesting stuff

860
00:46:10,239 --> 00:46:13,279
Speaker 2: to dig into there about Like, you know, one of

861
00:46:13,360 --> 00:46:17,680
Speaker 2: the functions that entertainment has served for years and years

862
00:46:17,800 --> 00:46:22,040
Speaker 2: is to show people something aspirational, right, Like, it's the

863
00:46:22,040 --> 00:46:24,399
Speaker 2: stuff that people want to see in entertainment. It's something

864
00:46:24,400 --> 00:46:27,760
Speaker 2: that they aspire to. And it sounds like for many

865
00:46:27,880 --> 00:46:33,480
Speaker 2: of the young people in this survey for the traditional

866
00:46:33,600 --> 00:46:37,759
Speaker 2: ideas of aspirational lifestyles of celebrities and wealthy people are

867
00:46:37,760 --> 00:46:40,880
Speaker 2: not resonating, and what they do aspire to is like

868
00:46:40,960 --> 00:46:45,799
Speaker 2: healthy friendships and healthy conflict resolution. Maybe because it's like

869
00:46:45,960 --> 00:46:48,800
Speaker 2: kind of scarce these days, or at least seems pretty scarce.

870
00:46:49,160 --> 00:46:50,719
Speaker 2: I think that's got to be part of it.

871
00:46:50,840 --> 00:46:54,759
Speaker 1: And they're really not interested in things like sex or

872
00:46:55,120 --> 00:46:59,960
Speaker 1: romantic relationships being depicted on screen. Romance actually ranked third

873
00:47:00,200 --> 00:47:02,560
Speaker 1: to last on the list of topics that you want

874
00:47:02,600 --> 00:47:05,680
Speaker 1: to see explored on screens, and sixty point nine percent

875
00:47:05,719 --> 00:47:09,359
Speaker 1: said that they want to see romantic relationship depicted as

876
00:47:09,480 --> 00:47:12,480
Speaker 1: quote more about the friendship between the couple than sex.

877
00:47:12,760 --> 00:47:15,080
Speaker 1: Forty eight point four percent said that there is too

878
00:47:15,160 --> 00:47:18,240
Speaker 1: much sex and sexual content in TV and movies. Toxic

879
00:47:18,320 --> 00:47:22,320
Speaker 1: relationships and love triangles also ranked among the most tiresome

880
00:47:22,480 --> 00:47:27,000
Speaker 1: or uninteresting tropes for young viewers. I know that there

881
00:47:27,000 --> 00:47:31,000
Speaker 1: has been a ton of talk about gen Z being

882
00:47:31,160 --> 00:47:33,880
Speaker 1: prudes or prudish or having an issue with sex, but

883
00:47:33,920 --> 00:47:36,279
Speaker 1: I'm sure some of that is true, and I want

884
00:47:36,320 --> 00:47:38,239
Speaker 1: to dig into that, but it does seem like what

885
00:47:38,280 --> 00:47:42,120
Speaker 1: they're telling us is that they want friendships, healthy friendships

886
00:47:42,120 --> 00:47:46,200
Speaker 1: with healthy conflict resolution at the heart, and they want genuine,

887
00:47:46,239 --> 00:47:49,160
Speaker 1: real connection. And I don't know, Maybe it's not that

888
00:47:49,320 --> 00:47:52,160
Speaker 1: gen Z is prudish. Maybe they're just tired of seeing

889
00:47:52,200 --> 00:47:56,560
Speaker 1: intimacy without intimacy right. Like what they're asking for is

890
00:47:56,600 --> 00:48:00,120
Speaker 1: not less love, but maybe like a different kind of

891
00:48:00,160 --> 00:48:03,160
Speaker 1: love that feels grounded in connection and friendship and care,

892
00:48:03,560 --> 00:48:06,600
Speaker 1: not just chemistry. And I say this as someone who

893
00:48:06,680 --> 00:48:09,600
Speaker 1: is fully like a horny person who is only really

894
00:48:09,600 --> 00:48:13,840
Speaker 1: interested in horny content. But the results of this survey

895
00:48:13,880 --> 00:48:16,000
Speaker 1: when it comes to wanting to see sex on screen,

896
00:48:16,280 --> 00:48:18,520
Speaker 1: could not have been more different than I felt. When

897
00:48:18,520 --> 00:48:20,520
Speaker 1: I was young, all I wanted to do was like

898
00:48:20,640 --> 00:48:24,000
Speaker 1: watch horny people on screen when I was a young person,

899
00:48:24,160 --> 00:48:27,359
Speaker 1: and that has not changed. So I have to say that.

900
00:48:27,440 --> 00:48:30,280
Speaker 1: But I also get where they're coming from. Of saying, hey,

901
00:48:30,600 --> 00:48:34,520
Speaker 1: I can get sexy content anywhere. I want to see real,

902
00:48:34,760 --> 00:48:39,360
Speaker 1: healthy media relationships and friendships on screen, not just horny content.

903
00:48:39,800 --> 00:48:44,480
Speaker 2: It makes sense. I mean sex is ubiquitous on the internet, right,

904
00:48:44,520 --> 00:48:47,840
Speaker 2: You're always like two clicks away, maybe three.

905
00:48:48,000 --> 00:48:50,360
Speaker 1: It's true. And again, when I was in high school,

906
00:48:50,400 --> 00:48:53,120
Speaker 1: my favorite film was The Talented Mister Ripley, which is

907
00:48:53,160 --> 00:48:56,759
Speaker 1: a film about horny hot people having horny hot antics.

908
00:48:57,120 --> 00:49:00,280
Speaker 1: But I can absolutely understand where these young people are

909
00:49:00,320 --> 00:49:00,839
Speaker 1: coming from.

910
00:49:01,040 --> 00:49:05,520
Speaker 2: Who would you say is the best friend in Talented

911
00:49:05,600 --> 00:49:06,320
Speaker 2: Mister Ripley.

912
00:49:06,560 --> 00:49:09,440
Speaker 1: Oh that's easy. Freddie played by the late Philip timour Hoffman.

913
00:49:09,520 --> 00:49:12,080
Speaker 1: May he rest in peace. Also, he's a real show

914
00:49:12,160 --> 00:49:15,920
Speaker 1: stealer in that movie. Every scene that he's that he's in,

915
00:49:16,080 --> 00:49:18,279
Speaker 1: he's like popping off the screen. He's one of my

916
00:49:18,320 --> 00:49:20,520
Speaker 1: favorite actors. Also fantastic and Billion Knights by the way,

917
00:49:20,520 --> 00:49:23,040
Speaker 1: a little callback. But yeah, if there was, if there were,

918
00:49:23,080 --> 00:49:26,239
Speaker 1: if there was a best friend in that movie, I

919
00:49:26,239 --> 00:49:28,840
Speaker 1: think you would have to go to Freddie. But even

920
00:49:28,920 --> 00:49:32,000
Speaker 1: Freddy is a hornball. The first time that you see Freddy,

921
00:49:32,280 --> 00:49:34,680
Speaker 1: the first he's it's It's one of my favorite character

922
00:49:34,800 --> 00:49:38,360
Speaker 1: being introduced in a movie ever. He's driving his tiny

923
00:49:38,400 --> 00:49:42,480
Speaker 1: supports car through a plaza in Italy Park someplace he's

924
00:49:42,480 --> 00:49:44,600
Speaker 1: not supposed to park, and then gets out and then

925
00:49:45,040 --> 00:49:47,640
Speaker 1: sleers on a woman walking by and says, don't you

926
00:49:47,680 --> 00:49:52,680
Speaker 1: want to fuck every woman you see? Just once? Bravo?

927
00:49:52,840 --> 00:49:53,840
Speaker 1: May he rest in peace?

928
00:49:54,040 --> 00:49:57,120
Speaker 2: Yeah? Interesting choice for best friend because he's pretty like

929
00:49:57,400 --> 00:50:01,360
Speaker 2: self motivated. He I don't think that he it is

930
00:50:01,440 --> 00:50:04,520
Speaker 2: motivated by any particular love of Dicky. He just hates Tom.

931
00:50:04,840 --> 00:50:09,120
Speaker 1: Yes, he just hates Tom. I think that when I

932
00:50:09,239 --> 00:50:12,319
Speaker 1: was growing up there was an intersection of being a

933
00:50:12,360 --> 00:50:17,560
Speaker 1: theater kid, a computer kid, and horny because when I

934
00:50:17,600 --> 00:50:19,680
Speaker 1: was growing up, there was another movie that came out

935
00:50:19,680 --> 00:50:22,720
Speaker 1: when I was just leaving high school called Closer, another

936
00:50:22,880 --> 00:50:24,920
Speaker 1: Jude Law movie now that i'm thinking about it, and

937
00:50:24,960 --> 00:50:29,160
Speaker 1: it was all about these grown up, sexy adults in

938
00:50:29,239 --> 00:50:33,400
Speaker 1: London having horny, sexy misunderstandings. All these movies that I

939
00:50:33,440 --> 00:50:35,720
Speaker 1: was when I was watching when I was young. Closer

940
00:50:35,760 --> 00:50:37,520
Speaker 1: I'm not even sure was a good movie. I think

941
00:50:37,560 --> 00:50:39,479
Speaker 1: I was just horny.

942
00:50:39,560 --> 00:50:42,879
Speaker 2: It also had a kick ass soundtrack, it did did?

943
00:50:42,920 --> 00:50:45,279
Speaker 1: I had it on CD? Man, I was if you

944
00:50:45,880 --> 00:50:50,080
Speaker 1: if you were a nerdy person who was falling in

945
00:50:50,160 --> 00:50:53,600
Speaker 1: the falling deep into the world of like fantasy through film,

946
00:50:53,680 --> 00:50:55,480
Speaker 1: what were the movies that you were obsessed with when

947
00:50:55,520 --> 00:50:57,279
Speaker 1: you were coming up? I need to know this. I'm sure.

948
00:50:57,320 --> 00:50:58,600
Speaker 1: I'm sure we have some overlap.

949
00:50:58,640 --> 00:51:01,080
Speaker 2: Do do you have any movie? Tank Girl?

950
00:51:01,960 --> 00:51:05,799
Speaker 1: Oh? My god, who do you think you're talking to? Man?

951
00:51:05,920 --> 00:51:09,240
Speaker 2: I remember, like me and a couple of my friends

952
00:51:10,160 --> 00:51:12,680
Speaker 2: we were having a sleepover at my friend's house. Like

953
00:51:12,719 --> 00:51:15,160
Speaker 2: that's how old we were. We were not driving, like

954
00:51:15,160 --> 00:51:17,600
Speaker 2: like our moms had dropped us off, and we went

955
00:51:17,680 --> 00:51:20,960
Speaker 2: to the video store. We rented Tank Girl and we

956
00:51:21,000 --> 00:51:24,960
Speaker 2: watched it like three times in a row.

957
00:51:25,440 --> 00:51:28,400
Speaker 1: The old heads. No, it was all about jet Girl. Actually,

958
00:51:28,840 --> 00:51:31,000
Speaker 1: oh yes, she was remember a tank Girl's friend.

959
00:51:31,080 --> 00:51:37,240
Speaker 2: Mm hmmm, I sure do friend. She was a good friend.

960
00:51:38,280 --> 00:51:41,839
Speaker 1: Yeah, gen Z would love that movie. It is It's

961
00:51:41,880 --> 00:51:46,000
Speaker 1: a it's an exploration of complex friendship on screen. We

962
00:51:46,160 --> 00:51:46,920
Speaker 1: need more of that.

963
00:51:47,360 --> 00:51:49,799
Speaker 2: Yeah, they probably would. They probably like that a lot

964
00:51:49,840 --> 00:51:52,680
Speaker 2: better than telling mister Ripley. They'd probably make them uncomfortable or.

965
00:51:52,760 --> 00:51:55,320
Speaker 1: I mean, even when I was reading this this survey,

966
00:51:55,760 --> 00:51:58,840
Speaker 1: I was like, Okay, well, youth don't like you know,

967
00:52:00,040 --> 00:52:03,360
Speaker 1: love triangles and toxic love and sex on screen, but

968
00:52:03,719 --> 00:52:07,960
Speaker 1: the terrible sort of reimagining of coal to mister Ripley Saltburn,

969
00:52:08,360 --> 00:52:12,200
Speaker 1: which I actually do kind of love. And there were

970
00:52:12,320 --> 00:52:15,040
Speaker 1: movies that were super horny that I think we're popular

971
00:52:15,080 --> 00:52:17,640
Speaker 1: with youth challengers. It's another one. So I don't know,

972
00:52:17,640 --> 00:52:19,840
Speaker 1: I will be curious to put that in conversation with

973
00:52:19,920 --> 00:52:22,040
Speaker 1: the survey of what do the youth want? What are

974
00:52:22,040 --> 00:52:22,439
Speaker 1: they into?

975
00:52:22,719 --> 00:52:26,120
Speaker 2: Yeah, and you know, as a science social scientist, I

976
00:52:26,400 --> 00:52:30,200
Speaker 2: like hate to burst the bubble and rain on the parade.

977
00:52:30,239 --> 00:52:34,279
Speaker 2: But like sometimes sometimes kids lie on surveys.

978
00:52:35,640 --> 00:52:37,760
Speaker 1: So kids are like, I would never want to see

979
00:52:37,800 --> 00:52:41,319
Speaker 1: sex on screen? Are you crazy? Yeah? Who would want

980
00:52:41,360 --> 00:52:41,799
Speaker 1: such a thing?

981
00:52:41,920 --> 00:52:44,360
Speaker 2: And maybe it's not lying, you know, but it's like

982
00:52:47,160 --> 00:52:50,520
Speaker 2: everybody has trouble with surveys, especially things about like preferences,

983
00:52:50,560 --> 00:52:53,440
Speaker 2: Like even adults, we don't know what we want. We

984
00:52:53,560 --> 00:52:57,960
Speaker 2: struggle to articulate what we want even if we know

985
00:52:58,000 --> 00:53:00,000
Speaker 2: what it is, and half the time we don't even

986
00:53:00,200 --> 00:53:03,280
Speaker 2: know what we want, and so asking like a fourteen

987
00:53:03,360 --> 00:53:06,000
Speaker 2: year old to report what kind of entertainment one. It's

988
00:53:06,040 --> 00:53:08,480
Speaker 2: super valuable to do And I no way mean to

989
00:53:08,520 --> 00:53:13,160
Speaker 2: diminish the surveys like so wonderful to like actually have

990
00:53:13,400 --> 00:53:17,799
Speaker 2: data on what they say. Uh, but like we do

991
00:53:17,840 --> 00:53:19,600
Speaker 2: need to interpret it with like a little bit of

992
00:53:19,600 --> 00:53:22,279
Speaker 2: a grain of salt that like, perhaps it is not

993
00:53:22,640 --> 00:53:26,919
Speaker 2: a clear window into the mind of the youth, and

994
00:53:27,719 --> 00:53:29,759
Speaker 2: in fact that there probably is a little bit of

995
00:53:29,840 --> 00:53:33,319
Speaker 2: like aspirational filtering in the way that they respond on

996
00:53:33,320 --> 00:53:34,200
Speaker 2: a survey like this.

997
00:53:34,719 --> 00:53:37,239
Speaker 1: There's a podcast that I love called uh Yeah, Dude

998
00:53:37,280 --> 00:53:40,440
Speaker 1: where they read surveys and they're always so funny and

999
00:53:40,960 --> 00:53:43,680
Speaker 1: the surveys. I always find this fascinating because it'll be

1000
00:53:43,880 --> 00:53:48,040
Speaker 1: it'll be the most basic question question on a survey.

1001
00:53:48,080 --> 00:53:55,720
Speaker 1: Do you own a toaster oven? No? Twenty percent? Yes,

1002
00:53:56,600 --> 00:53:59,440
Speaker 1: ten percent? I'm not sure. I can't answer. I don't know.

1003
00:54:00,320 --> 00:54:02,920
Speaker 1: Who are these people who's like, you know, they'll ask

1004
00:54:02,960 --> 00:54:07,200
Speaker 1: the most basic questions about their lives. Did you go

1005
00:54:07,239 --> 00:54:10,680
Speaker 1: out of the country this year? Yes? No, I couldn't

1006
00:54:10,719 --> 00:54:12,759
Speaker 1: say for sure. I don't know, Like what do you mean?

1007
00:54:14,120 --> 00:54:17,319
Speaker 1: What as as they ask somebody who designed surveys, what

1008
00:54:17,440 --> 00:54:18,200
Speaker 1: accounts for that.

1009
00:54:18,920 --> 00:54:21,239
Speaker 2: It could be any number of things, like maybe they

1010
00:54:21,280 --> 00:54:23,960
Speaker 2: genuinely don't know, maybe there's some cripple.

1011
00:54:24,000 --> 00:54:28,120
Speaker 1: Oh, they genuinely don't know. What are you talking about?

1012
00:54:28,640 --> 00:54:31,640
Speaker 2: All kinds of crazy shit happens in people's lives, right,

1013
00:54:31,760 --> 00:54:36,080
Speaker 2: Like you couldn't, especially when you're talking about population surveys

1014
00:54:36,080 --> 00:54:41,440
Speaker 2: where they sample like fifteen hundred people. All kinds of

1015
00:54:41,480 --> 00:54:44,920
Speaker 2: crazy shit is happening in that sample of people's lives.

1016
00:54:44,920 --> 00:54:47,080
Speaker 2: And so like some people be probably get some weird

1017
00:54:47,120 --> 00:54:51,480
Speaker 2: stuff going on that. You know, maybe they inherited a

1018
00:54:51,560 --> 00:54:54,640
Speaker 2: storage locker which may or may not contain a toaster oven,

1019
00:54:54,800 --> 00:54:57,680
Speaker 2: and so they don't know whether what is in there. Uh,

1020
00:54:58,080 --> 00:55:00,400
Speaker 2: maybe they just don't feel like answering the question. But

1021
00:55:00,440 --> 00:55:03,040
Speaker 2: it is important to include that when you're when you're

1022
00:55:03,080 --> 00:55:06,240
Speaker 2: designing the survey as the researcher, it's important to include

1023
00:55:06,280 --> 00:55:10,480
Speaker 2: that kind of option because otherwise, if you force those

1024
00:55:10,520 --> 00:55:13,799
Speaker 2: people to respond one of the other ways, uh, you're

1025
00:55:13,840 --> 00:55:16,320
Speaker 2: just kind of like gunking it up. If they truly

1026
00:55:16,360 --> 00:55:18,799
Speaker 2: don't know or like can't give a straight answer for

1027
00:55:18,840 --> 00:55:19,839
Speaker 2: whatever reason.

1028
00:55:20,040 --> 00:55:23,120
Speaker 1: Well, don't gunk up your surveys. People Words to live

1029
00:55:23,160 --> 00:55:26,560
Speaker 1: by Mike, Thank you for being here. Where can folks

1030
00:55:26,640 --> 00:55:28,440
Speaker 1: keep in touch with our podcast?

1031
00:55:28,840 --> 00:55:33,120
Speaker 2: You can leave comments right on Spotify. Maybe comment about

1032
00:55:33,120 --> 00:55:35,239
Speaker 2: what you think about their new policy about ice right

1033
00:55:35,239 --> 00:55:38,640
Speaker 2: there in the comments on Spotify, or what you think

1034
00:55:38,640 --> 00:55:44,040
Speaker 2: about our comments on the topic. You can write anything

1035
00:55:44,040 --> 00:55:46,440
Speaker 2: you want in the Spotify comments. You can also.

1036
00:55:46,239 --> 00:55:47,359
Speaker 1: Email read them all.

1037
00:55:47,560 --> 00:55:50,920
Speaker 2: She does uh. You can email us at Hello at

1038
00:55:50,920 --> 00:55:54,520
Speaker 2: tangote dot com. We're gonna do that mail bag episode

1039
00:55:55,080 --> 00:55:57,920
Speaker 2: pretty soon. The official window has closed, but if you

1040
00:55:57,960 --> 00:56:00,000
Speaker 2: email us something really good we can You can probably

1041
00:56:00,080 --> 00:56:03,400
Speaker 2: works about sneak it in. You can follow Bridget on

1042
00:56:03,600 --> 00:56:08,080
Speaker 2: TikTok and Instagram at Bridget Marie in DC, and you

1043
00:56:08,080 --> 00:56:10,759
Speaker 2: can follow the show on YouTube at There Are No

1044
00:56:10,840 --> 00:56:15,320
Speaker 2: Girls on the Internet and we look forward to interacting.

1045
00:56:15,360 --> 00:56:15,839
Speaker 2: We did there.

1046
00:56:16,280 --> 00:56:18,080
Speaker 1: Thanks so much for being here, Mike, Thanks to all

1047
00:56:18,120 --> 00:56:19,560
Speaker 1: of you for listening, and I will see you on

1048
00:56:19,600 --> 00:56:31,279
Speaker 1: the Internet. Got a story about an interesting thing in tech,

1049
00:56:31,440 --> 00:56:33,319
Speaker 1: or just want to say hi. You can reach us

1050
00:56:33,360 --> 00:56:35,719
Speaker 1: at Hello at tangody dot com. You can also find

1051
00:56:35,719 --> 00:56:38,600
Speaker 1: transcripts for today's episode at tenggody dot com. There Are

1052
00:56:38,600 --> 00:56:40,759
Speaker 1: No Girls on the Internet was created by me Bridget Todd.

1053
00:56:41,160 --> 00:56:44,560
Speaker 1: It's a production of iHeartRadio and unbossed creative Jonathan Strickland

1054
00:56:44,560 --> 00:56:47,319
Speaker 1: as our executive producer. Tari Harrison is our producer and

1055
00:56:47,400 --> 00:56:51,160
Speaker 1: sound engineer. Michael Amato is our contributing producer. I'm your host,

1056
00:56:51,160 --> 00:56:53,960
Speaker 1: Bridget Todd. If you want to help us grow, rate

1057
00:56:54,000 --> 00:56:57,759
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1058
00:56:57,840 --> 00:57:00,520
Speaker 1: check out the iHeartRadio app Apple Podcaste you get your

1059
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Speaker 1: podcasts