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

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

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

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Speaker 1: another installment of our weekly news roundup, where we round

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Speaker 1: up all the stories at the intersection of text culture

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Speaker 1: and social media that you might have missed so you

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Speaker 1: don't have to. And I am thrilled to be joined

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Speaker 1: once again by my producer Mike.

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Speaker 2: Mike, thank you for being.

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Speaker 3: Here, Bridget, Thank you for having me.

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Speaker 1: I wanted to first give a little bit of an

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Speaker 1: update on a story that you and I covered together

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Speaker 1: earlier this week about this wave of AI generated videos

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Speaker 1: that are flooding TikTok right now that feature black people,

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Speaker 1: typically black women, but some black men in these very

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Speaker 1: over the top exaggerated racist ways, to the point where

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Speaker 1: I argue they are essentially the new iteration of minstrel

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Speaker 1: shows for the digital age. Minstrel shows were an incredibly

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Speaker 1: popular form of entertainment in the nineteenth century where mostly

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Speaker 1: white performers would wear black faces to make fun of

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Speaker 1: black people and portray us as stupid and lazy. But

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Speaker 1: they weren't just entertainment. They were also a way to

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Speaker 1: affirm a political and social climate hostile to black folks

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Speaker 1: after slavery. This is essentially what these videos on TikTok

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Speaker 1: are doing, just using AI and we made that episode.

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Speaker 2: We recorded it about a week ago on Saturday.

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Speaker 1: It has not even been a full week, and already

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Speaker 1: I have seen more and more iterations of these videos

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Speaker 1: on TikTok. One of the hallmarks of these videos that

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Speaker 1: we talked about in that episode, it was really a

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Speaker 1: deep dive into where these AI generated videos are coming from,

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Speaker 1: the historical context of them, the technological context of how

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Speaker 1: they're being made, and what they say about our culture today.

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Speaker 1: One of the points that we made is that AI

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Speaker 1: generated content oftentimes gets more and more extreme, and so

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Speaker 1: in just a week's time, I feel like the version

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Speaker 1: of these videos we were talking about has already kind

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Speaker 1: of been cranked to eleven in terms of the extremest

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Speaker 1: qualities that I am seeing in this kind of content.

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Speaker 3: Yeah, one hundred percent. After we recorded that episode, over

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Speaker 3: the weekend, I created a TikTok account, which is not

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Speaker 3: something that I'd had before, and my for you page

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Speaker 3: was just filled with those kinds of videos and the

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Speaker 3: ones that are there today. I looked just a couple

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Speaker 3: of hours ago before we got on the mic. Here,

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Speaker 3: there's so much more extreme than the ones that you

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Speaker 3: and I were concerned about just like four or five

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Speaker 3: days ago. So just more extreme. And they, like to

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Speaker 3: be clear, they started off extreme and now they're like

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Speaker 3: so over the top. I can't believe that I'm looking

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Speaker 3: at a mainstream social media platform like TikTok and not

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Speaker 3: some kind of like video powered four cham. No.

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Speaker 1: Absolutely, So there's one in particular that I saw that

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Speaker 1: I want to talk about now because it honestly, not

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Speaker 1: only has the racism been to eleven, but it's gotten

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Speaker 1: even more horrifying, if you can believe that. So one

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Speaker 1: of the types of these videos that we talked about

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Speaker 1: in that episode was what people are calling slave talk,

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Speaker 1: which kind of shows these AI generated enslaved people imagined

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Speaker 1: if they had social media and they were vlogging their

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Speaker 1: experiences on the plantation. And so that is bad enough,

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Speaker 1: end of sentence.

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Speaker 3: However, the premise is insane to begin.

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Speaker 2: It's noxious, it's it's terrible.

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Speaker 3: Noxious is a better word.

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Speaker 1: They have found a way to make it even worse

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Speaker 1: because I saw videos where someone was making these slave

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Speaker 1: talk Ai generated TikTok videos that showed an enslaved person,

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Speaker 1: you know, using AI to demonstrate what their time on

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Speaker 1: a plantation might have been like and having them say, oh,

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Speaker 1: slavery wasn't that bad. I don't actually mind it. Being

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Speaker 1: an enslaved person on a plantation isn't so bad.

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Speaker 2: But they were.

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Speaker 1: Utilizing TikTok shop to use these videos to sell a

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Speaker 1: solar powered sun hat that has fans built into it,

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Speaker 1: and so it was a an enslaved person being like, oh,

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Speaker 1: my time on the plantation is hard, but this hat

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Speaker 1: has made it so much easier. Go to my TikTok

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Speaker 1: shop and my bio to buy this hat. Now, I

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Speaker 1: will say enough people complained about this video that TikTok.

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Speaker 2: Took it down, so it's no longer available.

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Speaker 1: They're no longer selling this hat using a borrent AI

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Speaker 1: generated enslaved people fan fiction.

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Speaker 2: However, this is what I mean.

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Speaker 1: I feel like this really, even just in a few

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Speaker 1: days since we made that episode about this kind of content.

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Speaker 1: The fact that somebody would put this kind of content

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Speaker 1: on TikTok and then utilize this kind of content to

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Speaker 1: sell a sun hat really just says a lot I mean,

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Speaker 1: I guess I gotta say I'm happy TikTok took it down.

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Speaker 1: But when I talked about the ways that I thought

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Speaker 1: that this kind of content really said something about how

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Speaker 1: extreme we are willing to go and how AI really

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Speaker 1: allows us to do that at scale. When I made

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Speaker 1: this episode, I didn't think it was gonna get this dark,

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Speaker 1: and it got so dark, so fast.

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Speaker 3: So dark, so fast, And something about using the technology

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Speaker 3: to sell such a pedestrian item as like a solar

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Speaker 3: powered sun hat with a little fan on it is

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Speaker 3: like extra dark like it. It's so almost benign, built

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Speaker 3: on such an absurd marketing premise that it's actually like

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Speaker 3: unimaginably dark.

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Speaker 1: It's pretty bad, and there's just something so dystopian about it.

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Speaker 1: And you know, I made this point in our deep

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Speaker 1: dive about this kind of content that it really bothers

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Speaker 1: me that this is the use case for AI. We're

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Speaker 1: being told that this is the lynch pin of our economy.

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Speaker 1: It's going to be so important, it's going to transform everything,

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Speaker 1: and the way that we are seeing it used is

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Speaker 1: just so small and gross, making what I would argue

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Speaker 1: is AI generated minstrel show content to sell cheap junk

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Speaker 1: on TikTok shop.

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Speaker 2: I just we're cooked. It's just it's bad. I will say.

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Speaker 1: One pivot of this kind of content that I did

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Speaker 1: see is in the wake of the news that the

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Speaker 1: United States bombed Iran. So most of the content that

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Speaker 1: we talked about in that episode was about how black

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Speaker 1: women were being shown in this incredibly negative, racist, stereotypical

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Speaker 1: light where we were aggressive, violent, loud, ghetto, all of that.

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Speaker 1: But in the wake of the bonding of Iran, this

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Speaker 1: video went viral that showed what I would have thought

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Speaker 1: was an obviously AI generated black woman who is supposed

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Speaker 1: to be a soldier who is celebrating this bombing with

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Speaker 1: her other soldiers, and she's saying, you know, blessed, she's

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Speaker 1: saying what sounds like a Bible quote, like are the peacemakers?

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Speaker 2: It's paker.

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Speaker 1: And so even though so much of the AI generated

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Speaker 1: content that is flooding TikTok that depicts black women now

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Speaker 1: is negative, what's also interesting to me is that I

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Speaker 1: don't think it's a coincidence that they chose an AI

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Speaker 1: generated black woman to be in this video. You know

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Speaker 1: that I think is pretty clearly AI generated propaganda to

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Speaker 1: make the United States like really excited about the fact

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Speaker 1: that we bombed Aron. And when I saw this video,

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Speaker 1: I thought, well, this is obviously AI. Like, no one's

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Speaker 1: gonna think this is a real person. I have to

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Speaker 1: kind of say, like, the comments of that video surprised

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Speaker 1: me in that the majority of the comments that I

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Speaker 1: saw did not seem to clock that it was AI,

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Speaker 1: and it just I mean, I'm not saying anything groundbreaking here,

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Speaker 1: but the ability of AI to generate propaganda at scale

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Speaker 1: it was worrisome. And I think especially using black women

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Speaker 1: as a symbol of that. I mean, I think whoever

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Speaker 1: created that video chose to have it centered on an

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Speaker 1: AI generated black women because I do think there's a

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Speaker 1: narrative that people trust black women when it comes to

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Speaker 1: political happenings, you know, the phrase of like trust black women.

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Speaker 1: And so I think that it's interesting how black womanhood

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Speaker 1: is used as this easy shorthand when you want to

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Speaker 1: get people on your side of your propaganda video. But

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Speaker 1: also you could swipe up and you could see a

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Speaker 1: video of that same black woman being depicted in an

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Speaker 1: incredibly demeaning, disrespectful, racist way. And so it's like, I mean,

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Speaker 1: when do we get to have our humanity depicted. It's

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Speaker 1: either we're this mouthpiece for like jingoistic, raw, raw American sentiment,

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Speaker 1: or we're being compared two animals in racist AI generated content.

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Speaker 2: There's no in between.

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Speaker 1: No one is showing our actual humanity and any of

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Speaker 1: this content.

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Speaker 2: I guess as we I'll.

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Speaker 3: Say, like when we first recorded that episode about a

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Speaker 3: week ago, it was a problem that people were noticing

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Speaker 3: and talking about, like, hey, what's up with all these

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Speaker 3: strange videos? And it's only gotten worse. It is interesting,

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Speaker 3: and you know, commendable that TikTok is taking some of

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Speaker 3: these videos down, but you know that's not a solution

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Speaker 3: on the same scale of the people who are putting

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Speaker 3: these videos up, right, Like, if there are a thousand

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Speaker 3: people creating a thousand of these videos every hour and

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Speaker 3: putting them up, and only the most popular generate enough

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Speaker 3: reaction and calls to take them down, the TikTok or

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Speaker 3: whatever platform actually takes action, that's not effective solution. So

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Speaker 3: it's gonna be really interesting to see what happens with this,

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Speaker 3: Like it can't just keep going on and getting worse.

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Speaker 3: Something's got to break.

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Speaker 1: When I was doing platform accountability work and like working

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Speaker 1: with the leaders at platforms like TikTok, who I used

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Speaker 1: to sit down with regularly, some of the folks who

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Speaker 1: worked there about their content moderation policies. That was the

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Speaker 1: biggest frustration was that it was like playing whack a mole,

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Speaker 1: and there's no whack a mole strategy.

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Speaker 2: Like I thought we were doing good work, and I'm.

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Speaker 1: Proud of the work that we did, but you know,

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Speaker 1: one comes down to come up in its place, and

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Speaker 1: so yeah, I think that it really demonstrates the need

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Speaker 1: for TikTok to do something meaningful if they want to

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Speaker 1: get a handle of this kind of content on their platform.

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Speaker 1: And we have already seen this in the last few days,

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Speaker 1: how much this content has taken off, become more extreme,

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Speaker 1: become more racist, and really ratcheted up what they're doing.

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Speaker 1: So I think it's great TikTok took this on video down,

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Speaker 1: but that's certainly not going to be enough to turn

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Speaker 1: the tides of this.

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Speaker 3: Yeah, and we see that all over the place, and

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Speaker 3: not just this category of videos, but like health misinformation,

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Speaker 3: vaccine misinformation, political attacks, like whack a mole is not

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Speaker 3: an effective solution. Whackable is like one step removed from

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Speaker 3: just giving.

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Speaker 1: Up, so we will put the link to our deep

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Speaker 1: dive into AI generated digital blackface. I guess menstreal content

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Speaker 1: on TikTok in the show notes.

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Speaker 2: Check it out. I'm pretty proud of that episode, so

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Speaker 2: if you haven't listened to it, please.

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

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Speaker 3: Now that we've got the easy breezy banter out of

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Speaker 3: the way, should we get into the news round up?

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Speaker 1: Well, I mean, speaking of horrible, noxious things, we have

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Speaker 1: to talk about the absolutely enraging tragedy that happened last

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Speaker 1: week in Minnesota, where Minnesota House Speaker Melissa Hortman and

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Speaker 1: her husband Mark were gunned down in what authorities say

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Speaker 1: was a politically motivated killing. She will lie in state

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Speaker 1: at the Capitol Rotunda this week, a day ahead of

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Speaker 1: their funeral. I mean, reading this was sort of like

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Speaker 1: the saddest milestone, the saddest, most enraging milestone I've ever read.

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Speaker 1: Hortman will be the first woman and one of fewer

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Speaker 1: than twenty Minnesotans accorded the honor of lying in state

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Speaker 1: at the state Capitol rotunda, Which something about that really

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Speaker 1: horrified me. That in order to be the first woman

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Speaker 1: to have this honor, she has to be murdered in

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Speaker 1: an act of political violence.

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Speaker 2: It makes me sick.

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Speaker 1: And I know this happened a bit ago, but I'm

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Speaker 1: still in I'm still as enraged about this as I

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Speaker 1: was when I heard about it, And I think one

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Speaker 1: of the reasons why I'm so enraged is how quickly

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Speaker 1: we moved on from this story. You know, I would

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Speaker 1: argue that a story in which a person committed what

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Speaker 1: it's pretty obviously an act of like political violence and

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Speaker 1: terror on multiple elected officials, the fact that that was

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Speaker 1: like over and done within a few days just does

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Speaker 1: not sit right with me. And it really just reminded

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Speaker 1: me how much the mainstream media really just allows right

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Speaker 1: wing influencers to set the agenda.

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Speaker 2: They Once it.

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Speaker 1: Was clear the attacker was a Trump supporter, the right

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Speaker 1: wing media stopped talking about it because for a while

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Speaker 1: they were like, the shooter was a Democrat, he was

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Speaker 1: a leftist, da da, da da. Once they couldn't say

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Speaker 1: that anymore, they just stopped talking about it. And I'm

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Speaker 1: enraged at the way that the legacy media really just

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Speaker 1: followed suit. You know, I knew I wanted to talk

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Speaker 1: about this story today, so I was searching for some

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Speaker 1: of the news articles written about it, and most of

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Speaker 1: the most recent coverage I saw was either from local

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Speaker 1: Minnesota press or global.

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Speaker 2: Press, and I thought, how shameful, Like, aren't we ashamed?

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Speaker 3: Yeah, it is shameful, like you say it. Really, I

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Speaker 3: think demonstrates how thoroughly right ring influencers are just able

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Speaker 3: to control the media right now, this isn't a story

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Speaker 3: that serves them any longer. They thought it was for

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Speaker 3: a little while. Senator Mike Lee made some really regretful

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Speaker 3: comments in the immedia aftermath of the killings, But after

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Speaker 3: it became clear that this was not a flattering story

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Speaker 3: for the right, they just stopped talking about it. And

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Speaker 3: once they stopped talking about it, apparently everybody else did too,

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Speaker 3: and the media did as well. It really feels like

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Speaker 3: a pretty major event we should be talking about. We

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Speaker 3: just we aren't used to political leaders being killed in America.

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Speaker 3: It's a very unusual and scary story, and the way

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Speaker 3: that the media has so quickly moved on makes it

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Speaker 3: seem normal and almost expected in ways that that are wrong.

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Speaker 3: I think every American should be like really upset and

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Speaker 3: concerned and horrified.

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Speaker 2: I mean, I think you said it.

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Speaker 1: I think that's the point of why you move on

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Speaker 1: to normalize it, to normalize political violence against people who

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Speaker 1: don't go along with the status quo. And I think

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Speaker 1: in some of the commentary that folks have said, you know,

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Speaker 1: Lee included, I think that's I think that's what they're

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Speaker 1: trying to do. They're trying to say this is this

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Speaker 1: is people should expect this. And you know, we've seen

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Speaker 1: other and officials talk about how they were afraid to

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Speaker 1: speak up, afraid to go against Trump. I think this

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Speaker 1: is what they're afraid of, you know. And I think

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Speaker 1: the fact that the media is essentially their doing that

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Speaker 1: dirty work for them by saying, you're right, we will

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Speaker 1: normalize it by not acting like it's a big deal,

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Speaker 1: and then it becomes not a big deal. I think

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Speaker 1: that you're right that this is a different kind of

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Speaker 1: thing than what we're used to seeing in the United States.

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Speaker 1: And the way that we have just so quickly moved

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Speaker 1: into like, oh, this is normal. Maybe it'll get three

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Speaker 1: days of coarbadge and we'll move on to something else,

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Speaker 1: really is telling. And it also sparked another thing that

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Speaker 1: we talk about on the show a lot, which is

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Speaker 1: just you know, I was really thrilled to see that

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Speaker 1: Mamdanie won the election in New York, and I'm sad

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Speaker 1: to say that one of my first thoughts was, I

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Speaker 1: hope he has security. I hope he's safe.

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Speaker 4: Like it.

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Speaker 1: That's not the way it should be. We should not

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Speaker 1: be worried about the safety of elected officials and political

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Speaker 1: leaders in this way. And one of the reasons I

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Speaker 1: was interested in talking about this on the podcast is

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Speaker 1: because it really brings up an issue that we talk

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Speaker 1: quite a bit about, and that is just the way

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Speaker 1: that our data privacy, or lack thereof in this country

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Speaker 1: really does pose a specific threat to women and other

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Speaker 1: marginalized folks in politics, in government, in just like local

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Speaker 1: civic spaces, because how did the attacker find the lawmaker's address?

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Speaker 1: Super shady data broker sites that in the United States,

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Speaker 1: because of our lack of any kind of meaningful, functional

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Speaker 1: data privacy laws, allow for anybody's information to be easily

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Speaker 1: found online.

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Speaker 2: If you're willing to pay for it.

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Speaker 1: MSNBC published the items the attacker had on him during

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Speaker 1: the murders, and it included photos of a notepad that

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Speaker 1: they found in his car.

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Speaker 2: With a long list of people search sites.

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Speaker 1: Where anybody can basically use them to find the home

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Speaker 1: address of anybody in the US. So we know that

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Speaker 1: before he killed Hortman and her husband, he shot State

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Speaker 1: Senator John Hoffman and his wife, Vette Hoffman first. So

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Speaker 1: the gunman had notebooks in his car containing the names

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Speaker 1: of more than forty five Minnesota state and federal public officials,

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Speaker 1: including Hortman's name and her home address. Senator Ron Wyden

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Speaker 1: said in a statement, the accused Minneapolis assassin allegedly use

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Speaker 1: data brokers as a key part of this plot to

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Speaker 1: track down and murder Democratic lawmakers. Congress doesn't need any

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Speaker 1: more proof that people are being killed based on data

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Speaker 1: for sale to anyone with a credit card. Every single

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Speaker 1: American safety is at risk until Congress cracks down on

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Speaker 1: this sleazy industry. And he is one one hundred percent correct.

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Speaker 3: Yeah, And I mean, this is the most egregious possible

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Speaker 3: example of the sort of harms that can happen when

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Speaker 3: everybody's personal information is just out on the internet available.

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Speaker 3: But there are so many other harms that are more

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Speaker 3: common than murder, like people have their identities stolen, people

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Speaker 3: get harassed, things that happen so much more routinely that

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Speaker 3: do not reach this level. It shouldn't take like highly

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Speaker 3: visible public figures getting murdered to call attention to this

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Speaker 3: very serious problem. And yet here we are.

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Speaker 1: No and this is not even the first time that

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Speaker 1: this kind of thing has happened. So back in twenty twenty,

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Speaker 1: this men's rights activist used information that he got from

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Speaker 1: a data broker site to find esther Salsa, a judge

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Speaker 1: who was appointed by Obama who had dismissed his lawsuit

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Speaker 1: challenging the men's only draft. He used that information that

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Speaker 1: he got from a data broker site to find and

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Speaker 1: break into her home and shoot and kill her child.

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Speaker 1: And So, if you're listening and you're thinking, well, certainly

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Speaker 1: my home address is not on one of these shady

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Speaker 1: ass sites. I have never put my address on the internet.

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Speaker 1: I am so sorry to be the one to tell

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Speaker 1: you this. Your address is probably on the internet, because

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Speaker 1: how do people's information get on these shady ass sites.

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Speaker 1: We did a whole episode about this with the founder

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Speaker 1: of an organization that helps women avoid doxing, which we'll

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Speaker 1: link to in the show notes. But if you've ever voted,

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Speaker 1: if you've ever had the utilities turned on in your home,

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Speaker 1: If you've ever paid a parking ticket, odds are your

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Speaker 1: information is available for purchase online. And here's the kicker,

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Speaker 1: it might have even been put there for sale by

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Speaker 1: your local government.

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Speaker 2: It is a travesty.

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Speaker 1: It is disgusting, it is disgraceful, and it is dangerous.

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Speaker 1: So the police found the list of eleven data brokers

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Speaker 1: in the suv driven by the man who murdered the

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Speaker 1: Minnesota state representative and her husband, and the list naming

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Speaker 1: the data brokers also included notations about which sites were

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Speaker 1: free to use and how much information they require to

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Speaker 1: obtain detailed data about the individuals being searched. This is

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Speaker 1: according to an FBI affidated So yeah, he just basically

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Speaker 1: was able to go on to these shady people finder

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Speaker 1: data broker sites to find this information and it led

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Speaker 1: to two people being murdered. This is just so interest

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Speaker 1: I mean, the lack of us having any kind of

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Speaker 1: meaningful data privacy laws in this country can lead to

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Speaker 1: people literally getting murdered.

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Speaker 3: I think part of what makes this so maddening for

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Speaker 3: me is just the fact that it doesn't need to

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Speaker 3: be this way. As a society, we could choose to

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Speaker 3: put privacy protections in place, but we don't. We've allowed

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Speaker 3: tech companies and scammers who profit off of selling these

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Speaker 3: data brokerage services, We've allowed them to convince us that

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Speaker 3: it's already too late. We should just abandon the idea

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Speaker 3: of privacy entirely and embrace digital nihilism. And I hear

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Speaker 3: a lot of people of like my parents' generation, espouse

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Speaker 3: these like digital nihilist ideas like oh, my data is

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Speaker 3: out there, what does it matter? Blah blah blah, But

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Speaker 3: it does, you know, Like either that or the idea

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Speaker 3: that any attempts to protect privacy would be some version

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Speaker 3: of overbearing regulation. It's going to stifle industry. But that's

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Speaker 3: all nonsense, Like all of it. We don't have to

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Speaker 3: choose between privacy and innovation. Lawmakers and regulators could put

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Speaker 3: in place workable solutions to protect privacy, but for various reasons,

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Speaker 3: we just choose not to.

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Speaker 2: We don't have to live like this, like we deserve better.

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Speaker 3: Yeah, we do, we deserve better. And they're like in Europe,

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Speaker 3: they don't live like this. And people in California have

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Speaker 3: protections that the rest of us don't have, Like there

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Speaker 3: are models it's it's a choice to live like this.

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Speaker 1: Melissa Wrtman and her husband should still be alive. We

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Speaker 1: don't have to live like this. We shouldn't have to

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Speaker 1: live like this.

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Speaker 4: Let's take a quick break. That are back.

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Speaker 1: Well, speaking of ways that we deserve better and ways

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Speaker 1: that we don't have to live like this.

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Speaker 2: Did you see.

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Speaker 1: RFK Junior's big plug about wearables.

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Speaker 3: He plugs the stupidest shit. Everything that comes out of

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Speaker 3: his mouth is so stupid.

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Speaker 2: But yes, okay. So, Health Secretary Robert F.

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Speaker 1: Kennedy Junior announced one of the largest HHS campaigns in

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Speaker 1: history his words to encourage the use of wearables to

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Speaker 1: track health conditions. You might be asking, what's the wearable

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Speaker 1: things like the Aura ring, the fitbit, rings, bands, watches,

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Speaker 1: and even clothes that use tech to track human vital signs.

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Speaker 1: It can track how many steps you take, your heart rate,

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Speaker 1: how many calories you've burned, all that kind of stuff.

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Speaker 1: So RFK Junior said that Americans buying wearables are one

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Speaker 1: of the keys to his plan to making America healthy again.

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Speaker 1: He said, we think that wearables are a key to

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Speaker 1: the MAHA agenda making America healthy again. My vision is

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Speaker 1: that every American is wearing a wearable within four years,

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Speaker 1: they can see what food is doing to their glucose levels,

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Speaker 1: their heart rates, and a number of other metrics as

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Speaker 1: they eat it. He also tweeted that wearables put the

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Speaker 1: power of health back in the hands of the American people.

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Speaker 1: This is horseshit. This is absolute horseshit. And I say

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Speaker 1: this as somebody who wears a wearable. I went through

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Speaker 1: quite a lot of trouble to find one that I

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Speaker 1: felt like was like the least smart, like the you know,

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Speaker 1: if you've got a smartphone, like the opposite, the dumbest

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Speaker 1: wearable I could find.

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Speaker 2: But so I'm not anti wearable, but this idea that

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Speaker 2: he could gut public health infrastructure and then move that

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Speaker 2: responsibility onto individuals by saying it's our responsibility to buy

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Speaker 2: a consumer product that monitors our health is absolute horseshit

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Speaker 2: for so many reasons. First, philosophically, the idea that health

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Speaker 2: and optimizing one's health means giving hundreds of dollars to

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Speaker 2: a private company for them to be able to access

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Speaker 2: sensitive information about your body and your health is absurd.

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Speaker 2: I am not buying that.

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Speaker 1: And so what these products sometimes monitor is so much

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Speaker 1: more than like your heartbeat and how much you sleep.

437
00:25:12,720 --> 00:25:15,679
Speaker 1: Kevin Johnson, the CEO of security testing and consulting at

438
00:25:15,680 --> 00:25:18,600
Speaker 1: the company Secure Lab, said, we are not just talking

439
00:25:18,600 --> 00:25:21,560
Speaker 1: about heartbeat. We're not just talking about your sleep schedule.

440
00:25:21,760 --> 00:25:24,480
Speaker 1: We're talking about your location. We're talking about most of

441
00:25:24,520 --> 00:25:26,560
Speaker 1: these apps tie into your contacts.

442
00:25:26,680 --> 00:25:26,840
Speaker 2: Right.

443
00:25:26,880 --> 00:25:29,639
Speaker 1: So the fact that RFK Junior is saying that we

444
00:25:29,640 --> 00:25:33,240
Speaker 1: should all be moving toward wearing wearables in four years

445
00:25:33,280 --> 00:25:35,080
Speaker 1: and that that is going to be the ideal way

446
00:25:35,080 --> 00:25:37,160
Speaker 1: for us to keep to take charge of our health,

447
00:25:37,720 --> 00:25:41,080
Speaker 1: I completely reject the idea that giving more of my

448
00:25:41,240 --> 00:25:45,080
Speaker 1: private intimate information to private companies and paying for the

449
00:25:45,160 --> 00:25:49,919
Speaker 1: pleasure is me taking charge of my health and physically,

450
00:25:50,280 --> 00:25:54,320
Speaker 1: wearables are notoriously not reliable. Seenet did a study and

451
00:25:54,359 --> 00:25:57,439
Speaker 1: found that even good wearables, wearables that you that like

452
00:25:57,840 --> 00:26:00,600
Speaker 1: have a pretty good reputation, are often an accurate. So

453
00:26:01,280 --> 00:26:04,800
Speaker 1: if you're trying to like casually measure your sleep, casually

454
00:26:04,920 --> 00:26:08,480
Speaker 1: measure your steps, casually measure how much calories you've burned

455
00:26:08,480 --> 00:26:11,080
Speaker 1: in a day, fine, But if you are relying on

456
00:26:11,119 --> 00:26:15,080
Speaker 1: a wearable in lieu of actual access to medical testing

457
00:26:15,320 --> 00:26:19,960
Speaker 1: or information to meetingfullet monitor your health. No, bad, it

458
00:26:20,000 --> 00:26:22,520
Speaker 1: does not work that way. Wearables and the kind of

459
00:26:22,560 --> 00:26:25,359
Speaker 1: information they provide is simply not a substitute for a

460
00:26:25,480 --> 00:26:27,160
Speaker 1: robust public health infrastructure.

461
00:26:27,440 --> 00:26:32,720
Speaker 3: Yeah, not even close. It's it's such a joke. I mean,

462
00:26:32,800 --> 00:26:36,240
Speaker 3: it falls apart in multiple ways. For one, his whole

463
00:26:36,240 --> 00:26:39,280
Speaker 3: thing is like make America healthy again, where he wants

464
00:26:39,359 --> 00:26:43,720
Speaker 3: to return us to some previous state of the glory

465
00:26:43,800 --> 00:26:49,520
Speaker 3: days of health where people wearing wearables during that previous heyday. No,

466
00:26:49,920 --> 00:26:54,160
Speaker 3: not at all. His big initiatives are like getting people

467
00:26:54,200 --> 00:27:00,320
Speaker 3: to not take vaccines, getting people to eat more beef

468
00:27:00,400 --> 00:27:06,360
Speaker 3: tallow and less seed oils, stuff like that. Like, none

469
00:27:06,400 --> 00:27:12,040
Speaker 3: of these things are gonna be immediately visible by wearables.

470
00:27:13,720 --> 00:27:16,640
Speaker 3: Like wearables are fine, they're great. You know people who

471
00:27:16,880 --> 00:27:22,480
Speaker 3: like them. I use one. It provides interesting information, but

472
00:27:22,720 --> 00:27:29,200
Speaker 3: it's not gonna be the like huge difference maker in

473
00:27:29,840 --> 00:27:33,360
Speaker 3: the health of a national public population.

474
00:27:33,800 --> 00:27:36,560
Speaker 1: Honestly, it's not even worth me going down. That's sort

475
00:27:36,600 --> 00:27:41,439
Speaker 1: of like it's so ridiculous on its face that getting

476
00:27:41,440 --> 00:27:43,720
Speaker 1: into the specifics of how it won't work and why

477
00:27:43,760 --> 00:27:45,560
Speaker 1: it doesn't make sense, it's almost not even worth it.

478
00:27:45,720 --> 00:27:48,960
Speaker 3: Yes, that is exactly right. That's how, unfortunately, how we

479
00:27:49,000 --> 00:27:51,080
Speaker 3: have to treat everything that comes out of his idiot

480
00:27:51,160 --> 00:27:54,439
Speaker 3: mouth like. He just says such stupid stuff all the time.

481
00:27:54,960 --> 00:27:58,840
Speaker 3: At first, I thought he was just like a dumb person,

482
00:27:59,400 --> 00:28:01,040
Speaker 3: but I no longer think that. I think he knows

483
00:28:01,040 --> 00:28:05,639
Speaker 3: exactly what he's doing. I think he is performing the

484
00:28:05,800 --> 00:28:13,440
Speaker 3: like shapes of an informed health official, knowing full well

485
00:28:13,640 --> 00:28:17,240
Speaker 3: that he is doing so deceptively. His references don't back

486
00:28:17,320 --> 00:28:20,200
Speaker 3: up what he says. He talks in the same meeting.

487
00:28:20,240 --> 00:28:24,359
Speaker 3: He'll talk about like demanding a gold standard for vaccines,

488
00:28:24,960 --> 00:28:27,359
Speaker 3: and then he'll turn around and pull up a bunch

489
00:28:27,359 --> 00:28:30,920
Speaker 3: of bullshit about wearables when like, there's no evidence that

490
00:28:31,280 --> 00:28:35,200
Speaker 3: where you know, putting on wearables is going to improve

491
00:28:35,320 --> 00:28:39,320
Speaker 3: the overall health of some population. There's zero evidence for that.

492
00:28:39,880 --> 00:28:43,400
Speaker 3: He's just all over the place. And you're absolutely right

493
00:28:43,440 --> 00:28:46,440
Speaker 3: that like engaging in his the things he says in

494
00:28:46,480 --> 00:28:50,080
Speaker 3: good faith is a losing battle. It's like more whackable.

495
00:28:50,320 --> 00:28:52,240
Speaker 3: Everything that comes out of his mouth is a goddamn

496
00:28:52,240 --> 00:28:53,400
Speaker 3: mold that needs to be whacked.

497
00:28:53,840 --> 00:28:56,960
Speaker 1: So I absolutely agree with you that I don't think

498
00:28:57,000 --> 00:28:58,960
Speaker 1: he's just like a stupid person. I think, you know,

499
00:28:59,000 --> 00:29:00,920
Speaker 1: with exactly what he's doing, and this is all part

500
00:29:00,920 --> 00:29:03,840
Speaker 1: of a larger agenda. There is a very good episode

501
00:29:03,880 --> 00:29:07,360
Speaker 1: of one of my favorite podcasts, Conspiratuality. It's at like

502
00:29:07,880 --> 00:29:10,360
Speaker 1: I don't recommend other podcasts a ton on this show,

503
00:29:10,400 --> 00:29:12,200
Speaker 1: but like, I will put the episode in the show

504
00:29:12,200 --> 00:29:14,120
Speaker 1: notes because you need to hear it.

505
00:29:14,120 --> 00:29:16,440
Speaker 2: It's it's so fascinating if you care about this stuff.

506
00:29:16,600 --> 00:29:19,440
Speaker 1: But basically, they were saying that make America Healthy Again

507
00:29:19,600 --> 00:29:23,520
Speaker 1: is really all about shifting public health from a public

508
00:29:23,600 --> 00:29:27,800
Speaker 1: concern to a private concern that will be managed by

509
00:29:27,800 --> 00:29:32,160
Speaker 1: a network of loosely slash, if at all regulated private companies,

510
00:29:32,200 --> 00:29:36,560
Speaker 1: whether it's bogus health testing companies or supplement companies and

511
00:29:36,680 --> 00:29:39,440
Speaker 1: now wearable. So I listened to that episode maybe a

512
00:29:39,440 --> 00:29:41,080
Speaker 1: month ago, and I was like, that makes so much sense.

513
00:29:41,080 --> 00:29:43,720
Speaker 1: That makes so much sense. And then lo and behold

514
00:29:43,760 --> 00:29:46,720
Speaker 1: today he's like, oh, you know, everybody should be buying wearables.

515
00:29:46,720 --> 00:29:49,200
Speaker 1: In four years time, every American should have a wearable.

516
00:29:49,240 --> 00:29:52,000
Speaker 1: That's really what that's all it takes to make America

517
00:29:52,040 --> 00:29:56,320
Speaker 1: healthy again. And oftentimes we'll have these make America Healthy

518
00:29:56,320 --> 00:30:00,240
Speaker 1: Again influencers getting a cut of that, because so those

519
00:30:00,280 --> 00:30:03,280
Speaker 1: influencers are now legitimately in the administration. So then you

520
00:30:03,280 --> 00:30:06,120
Speaker 1: have situations like the administration's nominee for source in general,

521
00:30:06,520 --> 00:30:10,040
Speaker 1: doctor Casey Means, who co founded a glucose monitoring company

522
00:30:10,080 --> 00:30:12,680
Speaker 1: called Levels and sells a monitoring app as well as

523
00:30:12,720 --> 00:30:14,600
Speaker 1: other kind of bullshit wellness products.

524
00:30:14,640 --> 00:30:18,200
Speaker 2: And so that the whole idea is shifting the.

525
00:30:18,160 --> 00:30:22,160
Speaker 1: Burden and responsibility of health from the public sphere to

526
00:30:22,320 --> 00:30:26,000
Speaker 1: the private sphere selling. Instead of there being okay, like

527
00:30:26,520 --> 00:30:30,640
Speaker 1: robust access to public health, robust access to healthcare, all

528
00:30:30,680 --> 00:30:31,440
Speaker 1: of that, it's.

529
00:30:31,320 --> 00:30:33,440
Speaker 2: Like no, no, no, no scam.

530
00:30:33,080 --> 00:30:37,000
Speaker 1: Testing that is loosely regulated and supplements and also we'll

531
00:30:37,000 --> 00:30:38,880
Speaker 1: make a little cut of that on the side.

532
00:30:39,200 --> 00:30:45,440
Speaker 3: Yeah. And also these grifters have built their whole enterprise

533
00:30:46,120 --> 00:30:51,560
Speaker 3: on a foundation of like flooding the zone with scammy

534
00:30:51,960 --> 00:30:57,240
Speaker 3: information on social media and just overwhelming people with information

535
00:30:57,680 --> 00:31:04,200
Speaker 3: and like shiny statistics and stories that like feel one

536
00:31:04,280 --> 00:31:07,480
Speaker 3: way even though maybe they don't like actually connect to

537
00:31:08,000 --> 00:31:12,880
Speaker 3: a deeper impact in terms of health. And the thing

538
00:31:12,920 --> 00:31:16,280
Speaker 3: with wearables is that they produce a ton of information

539
00:31:17,040 --> 00:31:21,320
Speaker 3: and it's it truly is a way of taking that

540
00:31:21,520 --> 00:31:27,840
Speaker 3: scammy online universe of health misinformation that has so enriched

541
00:31:28,480 --> 00:31:36,280
Speaker 3: these scammy people like RFK Junior and the whole network

542
00:31:36,440 --> 00:31:41,480
Speaker 3: of his friends who sell supplements online. It takes that

543
00:31:41,600 --> 00:31:49,240
Speaker 3: whole scammy information ecosystem and moves it offline onto people's

544
00:31:49,280 --> 00:31:53,880
Speaker 3: bodies and into people's health. And it's bad. It's bad.

545
00:31:53,920 --> 00:31:54,719
Speaker 3: We shouldn't do that.

546
00:31:55,080 --> 00:31:57,600
Speaker 1: And there's been so much research in reporting about the

547
00:31:57,600 --> 00:32:00,480
Speaker 1: fact that wearables and again I'm not anti way. I

548
00:32:00,520 --> 00:32:02,480
Speaker 1: were one of myself to measure my physical activity in

549
00:32:02,520 --> 00:32:05,520
Speaker 1: my sleep, Like I'm not anti wearable, but there's so

550
00:32:05,720 --> 00:32:09,680
Speaker 1: much research about the fact that simply having more access

551
00:32:09,760 --> 00:32:14,280
Speaker 1: to information about your vitals and your physicality does not

552
00:32:14,320 --> 00:32:16,720
Speaker 1: actually make you healthier, and in some ways that level

553
00:32:16,720 --> 00:32:21,160
Speaker 1: of surveillance actually might be perhaps counterintuitively, making you less healthy, right,

554
00:32:21,200 --> 00:32:24,080
Speaker 1: And so more information is not always the thing that

555
00:32:24,120 --> 00:32:29,560
Speaker 1: makes you healthier. And again, even if it were, wearables

556
00:32:29,800 --> 00:32:33,360
Speaker 1: are not a substitute for having access to healthcare, being

557
00:32:33,360 --> 00:32:35,720
Speaker 1: able to see a doctor, being able to get actual

558
00:32:36,240 --> 00:32:38,720
Speaker 1: medical tests from a doctor, not some sort of scam

559
00:32:38,760 --> 00:32:42,040
Speaker 1: testing company, actual public health infrastructure.

560
00:32:42,280 --> 00:32:43,600
Speaker 2: And I just hate the way that.

561
00:32:43,520 --> 00:32:46,920
Speaker 1: This has turned our health into just another thing these

562
00:32:46,960 --> 00:32:49,120
Speaker 1: people can grift off, another way to scam.

563
00:32:49,240 --> 00:32:53,000
Speaker 3: Yeah, I'm not anti wearable either. I think wearables are

564
00:32:53,280 --> 00:32:58,720
Speaker 3: very valuable. And you know, if he were out there

565
00:32:58,760 --> 00:33:02,800
Speaker 3: talking about the importance of wearables as part of a

566
00:33:02,880 --> 00:33:10,920
Speaker 3: new initiative that's going to connect people and their bioinformatics

567
00:33:11,160 --> 00:33:17,840
Speaker 3: with healthcare providers who will monitor them and help them

568
00:33:17,960 --> 00:33:20,720
Speaker 3: make health decisions as part of some sort of cohesive

569
00:33:21,360 --> 00:33:24,560
Speaker 3: plan to improve people's health, that would be something I

570
00:33:24,560 --> 00:33:29,800
Speaker 3: would want to listen to. But there's no there's none

571
00:33:29,840 --> 00:33:32,080
Speaker 3: of that follow through and what he talks about. He's

572
00:33:32,080 --> 00:33:35,080
Speaker 3: just like, oh, people should have wearables. That's where it

573
00:33:35,120 --> 00:33:36,520
Speaker 3: begins and ends.

574
00:33:37,160 --> 00:33:39,600
Speaker 2: Yeah, that's not a healthcare plan.

575
00:33:39,760 --> 00:33:43,160
Speaker 3: No, it's not. That's a plan to sell stuff.

576
00:33:43,680 --> 00:33:46,640
Speaker 1: Speaking of selling stuff, I have to talk about this

577
00:33:46,720 --> 00:33:49,760
Speaker 1: story about Mattel and open Ai. So are you ready

578
00:33:49,760 --> 00:33:52,760
Speaker 1: for an AI enabled Barbie Doll or an AI enabled

579
00:33:52,760 --> 00:33:56,360
Speaker 1: Hot Wheels car? Because Mattel, a toy maker behind Barbie's

580
00:33:56,440 --> 00:33:59,440
Speaker 1: and Hot Wheels, announced a partnership with open Ai that

581
00:33:59,480 --> 00:34:03,080
Speaker 1: would result in AI products marketed to kids. So to

582
00:34:03,120 --> 00:34:06,240
Speaker 1: be clear, we don't fully know what the product will

583
00:34:06,240 --> 00:34:08,480
Speaker 1: be just yet. They're keeping it pretty tight lipped, but

584
00:34:08,560 --> 00:34:09,239
Speaker 1: I already.

585
00:34:08,960 --> 00:34:10,440
Speaker 2: Can tell you this. I hate it.

586
00:34:11,160 --> 00:34:14,279
Speaker 1: One anonymous source told Axios that Mattel's plans for the

587
00:34:14,280 --> 00:34:16,760
Speaker 1: AI partnership are still in early stages, so we'll probably

588
00:34:16,800 --> 00:34:19,920
Speaker 1: no more soon. That source also said that the first

589
00:34:19,920 --> 00:34:22,680
Speaker 1: product would probably not be marketed to kids who are

590
00:34:22,800 --> 00:34:25,919
Speaker 1: under thirteen. You're probably thinking, Oh, they probably don't want

591
00:34:26,000 --> 00:34:29,120
Speaker 1: harmful AI impacting very young kids.

592
00:34:29,280 --> 00:34:30,200
Speaker 2: Don't get too excited.

593
00:34:30,480 --> 00:34:34,600
Speaker 1: They're probably capping it at kids thirteen and up because

594
00:34:34,600 --> 00:34:37,840
Speaker 1: of open AIS age restrictions on its API, which prohibits

595
00:34:37,960 --> 00:34:39,440
Speaker 1: users under the age of thirteen.

596
00:34:40,040 --> 00:34:42,759
Speaker 3: Yeah. And also, just because the company says that they're

597
00:34:42,800 --> 00:34:45,799
Speaker 3: not going to explicitly market some product to kids, that

598
00:34:45,840 --> 00:34:49,680
Speaker 3: doesn't mean that kids still won't see that marketing and

599
00:34:49,800 --> 00:34:54,560
Speaker 3: still want that product and obtain and use that product.

600
00:34:54,920 --> 00:34:59,240
Speaker 3: For example, vapes are illegal to market to kids anybody

601
00:34:59,360 --> 00:35:03,040
Speaker 3: under the age of twenty one, and yet many high

602
00:35:03,080 --> 00:35:07,320
Speaker 3: schoolers still use them. The most recent national survey suggested

603
00:35:07,400 --> 00:35:12,319
Speaker 3: that eight percent of high schoolers are using them regularly. Right,

604
00:35:12,400 --> 00:35:16,440
Speaker 3: and so, just because something's not going to be marketed

605
00:35:16,440 --> 00:35:22,520
Speaker 3: to kids, that is no guarantee that kids won't use it,

606
00:35:22,600 --> 00:35:23,480
Speaker 3: not by a long shot.

607
00:35:23,560 --> 00:35:24,840
Speaker 2: Yeah, exactly, so.

608
00:35:24,960 --> 00:35:28,520
Speaker 1: Ours Technica spoke to Public Citizens co president Robert Weissman,

609
00:35:28,880 --> 00:35:31,520
Speaker 1: who really laid out just how potentially.

610
00:35:31,160 --> 00:35:32,480
Speaker 2: Harmful this could be to kids.

611
00:35:32,480 --> 00:35:35,279
Speaker 1: He said, Buttel should announce immediately that it will not

612
00:35:35,360 --> 00:35:39,080
Speaker 1: incorporate AI technology into children's toys. Children do not have

613
00:35:39,160 --> 00:35:42,400
Speaker 1: the cognitive capacity to distinguish fully between reality and play.

614
00:35:42,680 --> 00:35:46,080
Speaker 1: Mattel should not leverage its trust with parents to conduct

615
00:35:46,080 --> 00:35:49,160
Speaker 1: a reckless social experiment on our young children by selling

616
00:35:49,200 --> 00:35:53,480
Speaker 1: toys that incorporate AI. So, when asked about the specifics

617
00:35:53,520 --> 00:35:56,000
Speaker 1: of what this toy might be like, both Mattel and

618
00:35:56,080 --> 00:35:59,200
Speaker 1: open Ai were like, it's gonna be fine, trust us,

619
00:35:59,400 --> 00:35:59,959
Speaker 1: it's all good.

620
00:36:00,040 --> 00:36:01,040
Speaker 2: Don't worry about it. We got it.

621
00:36:01,080 --> 00:36:02,759
Speaker 3: Oh, why are we even talking about this? Then they

622
00:36:02,800 --> 00:36:04,040
Speaker 3: said I was gonna be fine.

623
00:36:04,400 --> 00:36:06,960
Speaker 2: Yeah, they were like, it's we got this, don't even worry.

624
00:36:06,760 --> 00:36:08,000
Speaker 3: All right, next story.

625
00:36:08,280 --> 00:36:11,640
Speaker 1: They both put out statements where they really glossed over everything,

626
00:36:11,840 --> 00:36:13,800
Speaker 1: And I will say the statements kind of like said

627
00:36:13,800 --> 00:36:16,759
Speaker 1: the right words to signal that they're like, don't want

628
00:36:16,800 --> 00:36:19,320
Speaker 1: to harm kids with this product. What's also funny to

629
00:36:19,360 --> 00:36:22,560
Speaker 1: me is like, how nothing the state the statement from

630
00:36:22,560 --> 00:36:25,520
Speaker 1: open ai is. So in their statement they promise quote

631
00:36:25,760 --> 00:36:29,359
Speaker 1: to bring a new dimension of AI powered innovation and

632
00:36:29,480 --> 00:36:32,560
Speaker 1: magic to Mattel's iconic brands.

633
00:36:32,320 --> 00:36:34,719
Speaker 2: Like I'm sorry, what does that mean? Like what does

634
00:36:34,760 --> 00:36:37,239
Speaker 2: that mean? Like? Like break that down for me?

635
00:36:37,520 --> 00:36:41,680
Speaker 1: It really is giving, Like it's Barbie but now she

636
00:36:41,760 --> 00:36:45,239
Speaker 1: harnesses the power of AI or like but now she's

637
00:36:45,239 --> 00:36:45,879
Speaker 1: got a new hat.

638
00:36:46,000 --> 00:36:46,160
Speaker 2: You know.

639
00:36:46,200 --> 00:36:48,400
Speaker 1: It's just it just it doesn't It's like it says

640
00:36:48,400 --> 00:36:50,359
Speaker 1: a lot while saying nothing. I guess is what I'm

641
00:36:50,360 --> 00:36:50,919
Speaker 1: trying to say.

642
00:36:51,520 --> 00:36:55,400
Speaker 3: Yeah, so curious what sort of toys are you gonna

643
00:36:55,480 --> 00:37:00,480
Speaker 3: roll off? The MATEL assembly line? Empowered with AI whatever

644
00:37:00,520 --> 00:37:01,040
Speaker 3: that means?

645
00:37:01,320 --> 00:37:01,640
Speaker 2: My god.

646
00:37:01,680 --> 00:37:04,240
Speaker 1: When I was a kid, my brother had a Teddy rubskin,

647
00:37:04,800 --> 00:37:10,120
Speaker 1: which was the most terrifying item, probably of my entire childhood.

648
00:37:10,640 --> 00:37:11,640
Speaker 2: I don't know if people have.

649
00:37:11,600 --> 00:37:14,560
Speaker 1: Ever experienced teddy rubskin, but it was this nightmare bear

650
00:37:15,200 --> 00:37:17,160
Speaker 1: that I think. You would put a tape. He had

651
00:37:17,160 --> 00:37:19,480
Speaker 1: a tape cassette in his stomach, and you will put

652
00:37:19,480 --> 00:37:22,360
Speaker 1: a tape in it, and the tape would play, and

653
00:37:22,440 --> 00:37:26,200
Speaker 1: his mouth would move in this very natural, animatronic way,

654
00:37:26,239 --> 00:37:27,920
Speaker 1: and then it would play the tape as if he

655
00:37:28,040 --> 00:37:31,040
Speaker 1: was speaking, but it was you could tell he wasn't

656
00:37:31,040 --> 00:37:34,040
Speaker 1: speaking well because it's like a stuffed animal, but also

657
00:37:34,160 --> 00:37:37,160
Speaker 1: like it didn't sync up right, like I didn't look right.

658
00:37:37,560 --> 00:37:41,160
Speaker 1: And my brother loved this fucking thing and it was terrifying.

659
00:37:41,360 --> 00:37:43,480
Speaker 1: And I just hope for the sake of the next

660
00:37:43,520 --> 00:37:47,120
Speaker 1: generation that they're not making some sort of AI enabled

661
00:37:47,280 --> 00:37:50,480
Speaker 1: nightmare style Teddy reubskin where the mouth moves but it's

662
00:37:50,520 --> 00:37:55,279
Speaker 1: fucking open AI chat gpt my god, nightmares just.

663
00:37:55,200 --> 00:38:00,799
Speaker 3: Like feeding you sycophantic like narcissism fuel about how much

664
00:38:00,840 --> 00:38:02,360
Speaker 3: smarter you are than all the rest.

665
00:38:02,640 --> 00:38:04,400
Speaker 1: Oh my god, I mean have you seen that? I mean,

666
00:38:04,560 --> 00:38:06,200
Speaker 1: I know you've seen it because we saw it together

667
00:38:06,480 --> 00:38:10,880
Speaker 1: the movie Megan. Yeah, so I love Megan and it

668
00:38:11,000 --> 00:38:13,919
Speaker 1: was probably the most fun I had at the.

669
00:38:13,800 --> 00:38:16,200
Speaker 2: Theater of whatever year it came out. If you have

670
00:38:16,239 --> 00:38:18,360
Speaker 2: not seen Megan, it's about.

671
00:38:17,960 --> 00:38:22,280
Speaker 1: A toy like like a doll that is for kids,

672
00:38:22,280 --> 00:38:24,280
Speaker 1: that is like AI enabled.

673
00:38:23,800 --> 00:38:27,360
Speaker 3: But like it's basically this story. Yes, it's basically.

674
00:38:26,920 --> 00:38:31,160
Speaker 1: This story, but also Meghan is cunty, like she like

675
00:38:31,160 --> 00:38:38,080
Speaker 1: like it like it's this story, but she serves cunt Also,

676
00:38:38,080 --> 00:38:40,279
Speaker 1: they're making a sequel, by the way, oh, which I

677
00:38:40,320 --> 00:38:42,399
Speaker 1: will be like first in line to see.

678
00:38:42,440 --> 00:38:44,120
Speaker 2: I'm sure. I'm sure it's gonna be terrible, but I'll

679
00:38:44,120 --> 00:38:45,080
Speaker 2: be first some line to see.

680
00:38:45,480 --> 00:38:48,600
Speaker 1: But I mean so that I'm glad you compared this

681
00:38:48,640 --> 00:38:51,440
Speaker 1: because it is very I think that the movie Megan

682
00:38:52,080 --> 00:38:54,920
Speaker 1: does present a version of like what this might be like,

683
00:38:54,960 --> 00:38:57,880
Speaker 1: because we know so much about the ways that AI

684
00:38:58,040 --> 00:39:01,440
Speaker 1: can be unsafe for adult even let alone for kids.

685
00:39:01,760 --> 00:39:04,160
Speaker 1: Adam Dodge, the founder of a digital safety company that

686
00:39:04,200 --> 00:39:07,920
Speaker 1: prevents cyber abuse called n TAB, pointed to a lawsuit

687
00:39:07,920 --> 00:39:11,160
Speaker 1: where a grieving mom alleged her son died by suicide

688
00:39:11,280 --> 00:39:16,480
Speaker 1: after interacting with hyperrealistic chatbots. He said, AI is unpredictable, sycophantic,

689
00:39:16,560 --> 00:39:18,960
Speaker 1: and addictive. I don't want to be posting a year

690
00:39:19,000 --> 00:39:22,280
Speaker 1: from now about how a hot Wheels car encourage self harm,

691
00:39:22,480 --> 00:39:25,160
Speaker 1: or that children are in committed romantic relationships with their

692
00:39:25,200 --> 00:39:29,760
Speaker 1: AI barbies, like come on a hot Wheels that convinces

693
00:39:29,800 --> 00:39:31,120
Speaker 1: a kid to self harm?

694
00:39:31,200 --> 00:39:31,759
Speaker 2: No, thank you.

695
00:39:32,120 --> 00:39:36,640
Speaker 3: Yeah, it sounds ridiculous, but it's not outside the realm

696
00:39:36,680 --> 00:39:41,319
Speaker 3: of possibility, right because it's people who are on the

697
00:39:41,480 --> 00:39:47,400
Speaker 3: borderline of the danger zone already who are most susceptible

698
00:39:47,440 --> 00:39:51,000
Speaker 3: to being harmed by products like this. And I think

699
00:39:51,080 --> 00:39:55,800
Speaker 3: everybody should be concerned about privacy. Hopefully Mattel is gonna

700
00:39:55,800 --> 00:40:00,080
Speaker 3: build in some good privacy protocols into their AI toy

701
00:40:00,320 --> 00:40:02,600
Speaker 3: I guess I don't know. I would think long and

702
00:40:02,640 --> 00:40:06,000
Speaker 3: hard about buying any of them for any kids in

703
00:40:06,040 --> 00:40:11,000
Speaker 3: my life. But even aside from privacy, I think those

704
00:40:11,360 --> 00:40:20,400
Speaker 3: other harms are even scarier. Like encouraging suicides. Obviously about

705
00:40:20,480 --> 00:40:25,319
Speaker 3: the worst, but lllms are already causing mental problems for

706
00:40:25,440 --> 00:40:27,319
Speaker 3: a lot of people. You know. Last week on the

707
00:40:27,320 --> 00:40:31,480
Speaker 3: News Roundup, You and ed Zatron talked about chatbots falsely

708
00:40:31,520 --> 00:40:37,520
Speaker 3: claiming to have therapy degrees and doling out therapeutic advice inappropriately.

709
00:40:38,440 --> 00:40:41,279
Speaker 3: Who knows what harm might come from that. But there

710
00:40:41,320 --> 00:40:43,440
Speaker 3: was another story that came out about the same time

711
00:40:43,560 --> 00:40:46,439
Speaker 3: about last week about people just straight up losing their

712
00:40:46,520 --> 00:40:51,520
Speaker 3: minds on Reddit. The moderators of a pro AI subreddits

713
00:40:51,560 --> 00:40:54,440
Speaker 3: that they've had to ban over one hundred people who

714
00:40:54,560 --> 00:40:58,080
Speaker 3: kept spamming the subreddit with claims about how they created

715
00:40:58,520 --> 00:41:03,160
Speaker 3: a new form of God, are super intelligence. You know,

716
00:41:03,200 --> 00:41:07,560
Speaker 3: all that sycophantic behavior and flattery from the chatbots just

717
00:41:07,600 --> 00:41:11,040
Speaker 3: reflecting back what they what these people wanted to hear.

718
00:41:11,360 --> 00:41:15,719
Speaker 3: It broke their brains and matel thinks that they can

719
00:41:15,840 --> 00:41:19,520
Speaker 3: safely put that stuff in the hands of children. I

720
00:41:19,560 --> 00:41:23,880
Speaker 3: don't know. I just don't buy it, Like yikes.

721
00:41:23,400 --> 00:41:25,120
Speaker 1: Yeah, And I mean you make a good point, like

722
00:41:25,200 --> 00:41:29,400
Speaker 1: we don't even fully know how AI is impacting adults yet,

723
00:41:29,719 --> 00:41:33,080
Speaker 1: so why give why like encourage children.

724
00:41:32,920 --> 00:41:34,400
Speaker 2: To be to be mixed up with this?

725
00:41:35,040 --> 00:41:36,839
Speaker 1: And I do think it just I mean, we've talked

726
00:41:36,880 --> 00:41:40,040
Speaker 1: about this on the show before, but just the sort

727
00:41:40,080 --> 00:41:45,680
Speaker 1: of philosophical idea of shouldn't some things be protected like

728
00:41:45,880 --> 00:41:48,880
Speaker 1: children and play, I think that's really sacred. And the

729
00:41:48,920 --> 00:41:52,080
Speaker 1: fact that open AI sees this as just another thing

730
00:41:52,160 --> 00:41:56,160
Speaker 1: to harness, another way to you know, mine, Our kids

731
00:41:56,719 --> 00:42:01,040
Speaker 1: exploit their privacy, probably use whatever they're able to glean

732
00:42:01,120 --> 00:42:04,680
Speaker 1: from those experiences with children to train their AI further

733
00:42:05,360 --> 00:42:09,200
Speaker 1: for their own benefit. It just feels very exploitative. And

734
00:42:09,960 --> 00:42:13,000
Speaker 1: I mean I would have thought for a society that

735
00:42:13,880 --> 00:42:17,240
Speaker 1: so often gets up in arms about protecting the children,

736
00:42:17,280 --> 00:42:19,520
Speaker 1: and nobody wants to protect kids more than me, But

737
00:42:19,680 --> 00:42:23,080
Speaker 1: that is often our rally and cry when it comes

738
00:42:23,080 --> 00:42:25,120
Speaker 1: down to it, we fucking hate children. We don't want

739
00:42:25,120 --> 00:42:27,800
Speaker 1: to protect children. We will not do the bare minimum

740
00:42:27,800 --> 00:42:31,440
Speaker 1: to protect children. The bare minimum is don't let open

741
00:42:31,520 --> 00:42:35,640
Speaker 1: AI give them this technology that we already know harms

742
00:42:35,680 --> 00:42:39,280
Speaker 1: adults and we don't even know that the full scale

743
00:42:39,360 --> 00:42:41,759
Speaker 1: of that yet. That would be the very least we

744
00:42:41,760 --> 00:42:43,759
Speaker 1: could do, and we're not willing to do it. And

745
00:42:43,840 --> 00:42:47,600
Speaker 1: so yeah, I just I hate this. It reminds me

746
00:42:47,640 --> 00:42:50,520
Speaker 1: a lot of the way that we know that social

747
00:42:50,520 --> 00:42:54,319
Speaker 1: media platforms like Facebook knowingly harm kids, girls as young

748
00:42:54,320 --> 00:42:58,080
Speaker 1: as thirteen, and continue to knowingly do so because it

749
00:42:58,120 --> 00:42:59,080
Speaker 1: makes them a profit.

750
00:43:00,120 --> 00:43:00,359
Speaker 2: You know.

751
00:43:00,760 --> 00:43:04,799
Speaker 1: I wish that we had a country where tech companies

752
00:43:04,840 --> 00:43:08,800
Speaker 1: making more money was not as important as protecting our youth.

753
00:43:09,000 --> 00:43:10,280
Speaker 2: But we don't live in that country.

754
00:43:10,320 --> 00:43:13,640
Speaker 1: But we love to grandstand about protecting the kids when

755
00:43:13,640 --> 00:43:17,919
Speaker 1: it's convenient. When it's just the bare minimum, we do nothing.

756
00:43:22,320 --> 00:43:22,480
Speaker 3: More.

757
00:43:22,520 --> 00:43:38,320
Speaker 1: After a quick break, let's get right back into it, Okay,

758
00:43:38,400 --> 00:43:41,520
Speaker 1: So to say, really clear up top, this is one

759
00:43:41,520 --> 00:43:44,239
Speaker 1: of those stories where I suspect I know what's going on,

760
00:43:44,520 --> 00:43:45,400
Speaker 1: but I don't have any.

761
00:43:45,200 --> 00:43:46,200
Speaker 2: Hard proof yet.

762
00:43:46,360 --> 00:43:51,560
Speaker 1: So really I'm just asking questions about what the fuck.

763
00:43:51,360 --> 00:43:52,680
Speaker 2: Is going on here? You got me?

764
00:43:52,920 --> 00:43:57,400
Speaker 1: Yeah, And that is the question of is Spotify pushing

765
00:43:57,640 --> 00:44:02,280
Speaker 1: AI generated musicians who do not actually exist, but acting

766
00:44:02,480 --> 00:44:06,759
Speaker 1: as if they do exist, so I suspect, and lots

767
00:44:06,800 --> 00:44:09,520
Speaker 1: of the Internet suspects. The answer is yes. So we

768
00:44:09,560 --> 00:44:12,560
Speaker 1: have known that Spotify has had AI generated music on

769
00:44:12,600 --> 00:44:13,960
Speaker 1: their platform for a while.

770
00:44:14,320 --> 00:44:15,560
Speaker 2: It hasn't gotten much traction.

771
00:44:15,640 --> 00:44:17,520
Speaker 1: It's been kind of a quiet under the radar thing

772
00:44:17,560 --> 00:44:22,400
Speaker 1: because people don't really enjoy one hundred percent AI generated music. However,

773
00:44:23,160 --> 00:44:26,200
Speaker 1: now it might be that they are pushing this music

774
00:44:26,280 --> 00:44:29,400
Speaker 1: in some kind of shady ways while not disclosing that

775
00:44:29,440 --> 00:44:32,320
Speaker 1: it's entirely AI generated and these bands do not exist,

776
00:44:32,640 --> 00:44:36,400
Speaker 1: while kind of pretending like these bands are actually human.

777
00:44:36,880 --> 00:44:39,279
Speaker 1: So Paul Bender, who is the basis for the Australian

778
00:44:39,320 --> 00:44:42,360
Speaker 1: band Hiatus Coyote who I Love, released a new solo

779
00:44:42,400 --> 00:44:45,799
Speaker 1: project called the Sweet Enoughs and Spotify pushed his new

780
00:44:45,840 --> 00:44:47,759
Speaker 1: song to all of his followers. He has locked lots

781
00:44:47,760 --> 00:44:50,600
Speaker 1: of followers on Spotify. The Spotify was like, Hey, this band,

782
00:44:50,640 --> 00:44:52,480
Speaker 1: this guy who's in a band that you like, has

783
00:44:52,520 --> 00:44:53,720
Speaker 1: a new solo project.

784
00:44:53,760 --> 00:44:54,399
Speaker 2: Here's the song.

785
00:44:54,640 --> 00:44:58,000
Speaker 1: Only one problem, this was not his song and in

786
00:44:58,000 --> 00:45:01,080
Speaker 1: fact he never authorized this. He said it was some

787
00:45:01,120 --> 00:45:05,000
Speaker 1: of the most insanely clunky, amateurish, bizarre pieces of audio

788
00:45:05,080 --> 00:45:09,319
Speaker 1: I have ever experienced. And then it happened again the

789
00:45:09,360 --> 00:45:12,960
Speaker 1: next time with a distorted mumble wrap track that appeared

790
00:45:13,000 --> 00:45:16,720
Speaker 1: in his Spotify profile. That another which he said, quote

791
00:45:16,840 --> 00:45:21,400
Speaker 1: basically sounded like crazy Frog era eurotrash. Gotta say that

792
00:45:21,480 --> 00:45:24,600
Speaker 1: kind of makes me want to like it, doesn't make

793
00:45:24,680 --> 00:45:26,720
Speaker 1: me not want to hear it.

794
00:45:26,719 --> 00:45:29,480
Speaker 3: It's not a genre I'm familiar with, but I am

795
00:45:29,680 --> 00:45:30,880
Speaker 3: curious about.

796
00:45:30,680 --> 00:45:32,920
Speaker 1: Crazy Frog era euro trash is not a genre you're

797
00:45:32,920 --> 00:45:33,400
Speaker 1: familiar with.

798
00:45:33,640 --> 00:45:35,279
Speaker 3: Yeah, am I missing out?

799
00:45:35,360 --> 00:45:37,719
Speaker 1: I think you might be missing out. So in the

800
00:45:37,719 --> 00:45:41,840
Speaker 1: following days a fourth track surface. So he was convinced

801
00:45:42,040 --> 00:45:44,680
Speaker 1: these songs were AI generated and it made him realize

802
00:45:44,960 --> 00:45:47,440
Speaker 1: just how easy it was for platforms to do this,

803
00:45:47,600 --> 00:45:50,879
Speaker 1: to create AI generated music and then push it out

804
00:45:50,880 --> 00:45:55,480
Speaker 1: to his followers, lying saying that it was him. He

805
00:45:55,560 --> 00:45:58,000
Speaker 1: spoke to the Australian outlet ABC and said that he

806
00:45:58,080 --> 00:46:00,800
Speaker 1: realized how easy it was to create an AI generated

807
00:46:00,840 --> 00:46:03,880
Speaker 1: song and upload it to an artist's profile within ten minutes,

808
00:46:03,880 --> 00:46:07,080
Speaker 1: with zero hacking or authentication required. A track can be

809
00:46:07,120 --> 00:46:09,960
Speaker 1: cleared and approved to be released in five to seven days.

810
00:46:10,320 --> 00:46:13,760
Speaker 1: The whole process is effectively functioning on an honor based system,

811
00:46:13,920 --> 00:46:17,040
Speaker 1: which he says is incredibly inappropriate for the music industry

812
00:46:17,320 --> 00:46:21,080
Speaker 1: one of the slimiest, most parasitic places in the universe.

813
00:46:21,440 --> 00:46:24,480
Speaker 3: Yeah, that's wild. I had no idea that. So, like

814
00:46:24,600 --> 00:46:28,960
Speaker 3: I could just upload a track and be like, oh,

815
00:46:29,160 --> 00:46:32,320
Speaker 3: this is this is the new Ariana Grande track.

816
00:46:32,400 --> 00:46:34,520
Speaker 1: Yeah, you can upload a track of you like farting

817
00:46:34,520 --> 00:46:36,680
Speaker 1: into a microphone and being like, this is the new

818
00:46:36,680 --> 00:46:39,680
Speaker 1: AREAA just dropped it. Spotify will be like sure, okay, yeah,

819
00:46:40,280 --> 00:46:44,960
Speaker 1: that's according to Paul Bender, that's how it works. So

820
00:46:45,320 --> 00:46:46,560
Speaker 1: this was back in twenty twenty.

821
00:46:46,640 --> 00:46:46,799
Speaker 4: Right.

822
00:46:46,840 --> 00:46:48,759
Speaker 2: The story gets even weirder because.

823
00:46:48,520 --> 00:46:53,520
Speaker 1: Now we might have another AI generated band being populated

824
00:46:54,120 --> 00:46:57,239
Speaker 1: via Spotify's curated listener playlist.

825
00:46:57,600 --> 00:47:01,040
Speaker 2: The band is called the Velvet Sundown, which that name.

826
00:47:01,120 --> 00:47:05,360
Speaker 1: Kind of made me like something about it says a

827
00:47:05,640 --> 00:47:08,200
Speaker 1: the fact that it's like very close to the Velvet Underground,

828
00:47:08,400 --> 00:47:09,200
Speaker 1: like it just.

829
00:47:09,280 --> 00:47:11,719
Speaker 3: Oh, I went down the rabbit hole, Like there's this,

830
00:47:11,760 --> 00:47:14,640
Speaker 3: Oh my god, great story about this. I don't want

831
00:47:14,640 --> 00:47:16,000
Speaker 3: to like scoop your thunder here.

832
00:47:16,480 --> 00:47:16,879
Speaker 2: But so.

833
00:47:18,640 --> 00:47:22,400
Speaker 3: It's the name comes from this video game from like

834
00:47:22,440 --> 00:47:28,080
Speaker 3: ten years ago, this like super uh like low profile

835
00:47:28,239 --> 00:47:33,120
Speaker 3: video game there's released on Steam. It's like a murder

836
00:47:33,200 --> 00:47:36,719
Speaker 3: mystery on a cruise ship and the tagline is something

837
00:47:36,920 --> 00:47:40,600
Speaker 3: like like you can't trust anyone and nothing is what

838
00:47:40,680 --> 00:47:46,480
Speaker 3: it seems, and it's like, what a perfect name? AI bad?

839
00:47:46,880 --> 00:47:51,200
Speaker 2: This is some like taunting letters to the police. Oh J.

840
00:47:51,400 --> 00:47:55,200
Speaker 1: Simpson if publishing a book called if I didn't nonsense like,

841
00:47:55,719 --> 00:47:56,279
Speaker 1: this is some.

842
00:47:56,440 --> 00:47:58,440
Speaker 2: Like do you know what I'm saying?

843
00:47:58,760 --> 00:48:04,120
Speaker 3: Like, Yeah, listeners should really uh read that piece that

844
00:48:04,160 --> 00:48:07,480
Speaker 3: we're gonna link to about the Velvet Sundown. It is

845
00:48:07,560 --> 00:48:10,520
Speaker 3: truly an interesting, weird little rabbit hole.

846
00:48:10,600 --> 00:48:13,640
Speaker 1: Okay, so I did not find that information about the

847
00:48:13,640 --> 00:48:16,239
Speaker 1: band's name, but I did do a deep dive. I

848
00:48:16,280 --> 00:48:19,080
Speaker 1: am confident in saying that, in my opinion, this band

849
00:48:19,120 --> 00:48:21,719
Speaker 1: is AI generated. There is just there is not a

850
00:48:21,880 --> 00:48:25,879
Speaker 1: stitch of evidence that this band exists outside of music

851
00:48:25,920 --> 00:48:30,480
Speaker 1: streaming platforms. They say the names of the individual musicians

852
00:48:30,880 --> 00:48:31,719
Speaker 1: wink wink.

853
00:48:31,480 --> 00:48:33,319
Speaker 2: Who are in the band. Did a Google on them.

854
00:48:33,360 --> 00:48:35,439
Speaker 1: There's not a stitch of evidence that any of them

855
00:48:35,520 --> 00:48:39,160
Speaker 1: has existed ever online. I know that I sound like

856
00:48:39,239 --> 00:48:43,040
Speaker 1: freaking Charlie and always Sonny. There is no Pepe Sylvia.

857
00:48:43,200 --> 00:48:46,080
Speaker 1: But I am confident in saying that, in my opinion,

858
00:48:46,080 --> 00:48:48,799
Speaker 1: there is no Velvet Sundown. This band doesn't exist. All

859
00:48:48,840 --> 00:48:50,960
Speaker 1: of the names that they have given to us are

860
00:48:51,000 --> 00:48:53,399
Speaker 1: made up. All the images that they give us are

861
00:48:53,440 --> 00:48:55,520
Speaker 1: AI generated. Like, well, we'll put it in the show notes.

862
00:48:55,520 --> 00:48:57,520
Speaker 1: But like when you look at this image, it's like,

863
00:48:57,880 --> 00:48:59,239
Speaker 1: this is an AI generated band, Like.

864
00:48:59,320 --> 00:49:00,000
Speaker 2: These are not real people.

865
00:49:00,040 --> 00:49:02,319
Speaker 1: Well, now that doesn't necessarily mean the band is fake,

866
00:49:02,400 --> 00:49:04,600
Speaker 1: Like you bet this could be their like visual style.

867
00:49:05,120 --> 00:49:07,040
Speaker 1: This is an AI generated band. This band don't exist.

868
00:49:07,080 --> 00:49:09,520
Speaker 1: Like I'm I'm very confident in saying that. So, this

869
00:49:09,719 --> 00:49:12,120
Speaker 1: band has more than three hundred and twenty five thousand

870
00:49:12,480 --> 00:49:15,759
Speaker 1: monthly listeners on Spotify, and it's not only on Spotify,

871
00:49:15,800 --> 00:49:18,799
Speaker 1: it's also on Amazon Music, YouTube, and Deezer and other

872
00:49:18,840 --> 00:49:23,520
Speaker 1: streaming services. So questions about this band first gathered steam

873
00:49:23,560 --> 00:49:25,840
Speaker 1: on Reddit and then later TikTok, where people were wondering

874
00:49:25,880 --> 00:49:30,120
Speaker 1: why this random band with an obviously AI generated image

875
00:49:30,120 --> 00:49:32,839
Speaker 1: and zero footprint across all of social media was being

876
00:49:32,840 --> 00:49:37,640
Speaker 1: included on Spotify's curated playlists. So Velvet Sundown have one thousand,

877
00:49:37,719 --> 00:49:40,520
Speaker 1: five hundred and thirty three followers on Spotify, but three

878
00:49:40,640 --> 00:49:43,200
Speaker 1: hundred and twenty five thousand, three hundred and eighty eight

879
00:49:43,480 --> 00:49:47,279
Speaker 1: monthly listeners at the time of the publishing of this

880
00:49:47,360 --> 00:49:50,600
Speaker 1: like really good deep dive article on music Ally. Their

881
00:49:50,719 --> 00:49:53,960
Speaker 1: bio on Spotify includes a very glowing quote from the

882
00:49:54,040 --> 00:49:58,800
Speaker 1: music magazine Billboard saying, quote they sound like the memory

883
00:49:58,840 --> 00:50:02,360
Speaker 1: of something you never live and somehow make it feel real,

884
00:50:02,880 --> 00:50:08,000
Speaker 1: which a Google search suggest has never been published by Billboard. Ever,

885
00:50:08,520 --> 00:50:10,600
Speaker 1: that is a that is not a quote, it is

886
00:50:10,680 --> 00:50:10,920
Speaker 1: it is.

887
00:50:10,840 --> 00:50:13,640
Speaker 2: Attributed to Billboard. Billboard has never published it.

888
00:50:14,000 --> 00:50:17,080
Speaker 1: And also again like if you were making a fake band,

889
00:50:17,080 --> 00:50:18,640
Speaker 1: it's like, oh, they sound like a memory of putting

890
00:50:18,640 --> 00:50:20,680
Speaker 1: it doesn't exist, Like what are you trying to say?

891
00:50:20,880 --> 00:50:21,160
Speaker 2: I mean?

892
00:50:22,000 --> 00:50:25,799
Speaker 3: That is like the best description of AI I have

893
00:50:25,880 --> 00:50:26,480
Speaker 3: ever heard.

894
00:50:26,880 --> 00:50:28,480
Speaker 2: It really is. It absolutely is.

895
00:50:28,560 --> 00:50:32,400
Speaker 3: It feels like a memory of something that I never experienced.

896
00:50:31,800 --> 00:50:33,560
Speaker 2: But somehow it feels real.

897
00:50:33,719 --> 00:50:35,040
Speaker 3: It feels real, it feels right.

898
00:50:35,640 --> 00:50:38,319
Speaker 1: So their music is on the streaming platform Deezer, which

899
00:50:38,320 --> 00:50:40,880
Speaker 1: is actually kind of helpful because that service has been

900
00:50:40,920 --> 00:50:44,960
Speaker 1: developing technology to identify AI generated music and tag it publicly. So,

901
00:50:45,040 --> 00:50:48,360
Speaker 1: according to Deezer, some tracks on this album may have

902
00:50:48,440 --> 00:50:49,960
Speaker 1: been created using AI.

903
00:50:50,200 --> 00:50:51,400
Speaker 2: So for me, it's case.

904
00:50:51,239 --> 00:50:55,360
Speaker 1: Closed, right, So we'll link to this music Ally piece

905
00:50:55,400 --> 00:50:57,680
Speaker 1: that they did a very impressive deep dive and they

906
00:50:57,800 --> 00:51:02,120
Speaker 1: found that on Spotify. Essentially, these potentially AI generated bands

907
00:51:02,120 --> 00:51:05,160
Speaker 1: and songs are getting lots of play from being featured

908
00:51:05,200 --> 00:51:08,840
Speaker 1: on the Spotify playlists. If you use Spotify like I do,

909
00:51:08,920 --> 00:51:11,640
Speaker 1: you know, their playlists are a big part of the platform.

910
00:51:11,680 --> 00:51:13,359
Speaker 1: In my opinion, it's like the only thing that sets

911
00:51:13,400 --> 00:51:18,160
Speaker 1: it apart from other music streaming platforms. But essentially, this

912
00:51:18,320 --> 00:51:21,719
Speaker 1: fake band is being included on playlists that they have

913
00:51:22,200 --> 00:51:25,640
Speaker 1: really no business being on. For instance, a bunch of

914
00:51:25,719 --> 00:51:29,640
Speaker 1: their songs are on a Spotify playlist for the OC soundtrack.

915
00:51:29,680 --> 00:51:32,719
Speaker 1: Remember that Fox show Orange County, Oh My God, which,

916
00:51:32,760 --> 00:51:34,480
Speaker 1: by the way, I watched every episode I had, I

917
00:51:34,560 --> 00:51:37,040
Speaker 1: had the soundtrack. They put out a CD for that show.

918
00:51:37,040 --> 00:51:39,440
Speaker 1: That's that's how That's how deep into the trenches I.

919
00:51:39,480 --> 00:51:39,960
Speaker 2: Was into this.

920
00:51:40,280 --> 00:51:41,800
Speaker 1: So the OC was a show that was sort of

921
00:51:41,960 --> 00:51:45,000
Speaker 1: known for its music, and like big important moments of

922
00:51:45,080 --> 00:51:47,000
Speaker 1: the show would have like a song, and that song

923
00:51:47,040 --> 00:51:48,600
Speaker 1: would be like the hit song for the rest of

924
00:51:48,640 --> 00:51:51,200
Speaker 1: the week. So they have a playlist, it's like an

925
00:51:51,200 --> 00:51:54,120
Speaker 1: OC playlist which includes all of the famous musical moments

926
00:51:54,120 --> 00:51:58,880
Speaker 1: from the show like Phantom Planet, Image and Heap, Jeff Buckley, Oasis,

927
00:51:58,960 --> 00:52:03,440
Speaker 1: and in t two tracks from the Velvet Sundown, So

928
00:52:03,480 --> 00:52:06,520
Speaker 1: that means that thirteen point three percent of the entire

929
00:52:06,680 --> 00:52:11,600
Speaker 1: Orange County playlist on Spotify is Velvet Sundown, a potentially

930
00:52:11,719 --> 00:52:16,560
Speaker 1: AI generated band that don't exist, which is also especially impressive.

931
00:52:16,680 --> 00:52:19,280
Speaker 1: Music Ally points out given that the show The OC

932
00:52:19,640 --> 00:52:22,320
Speaker 1: ran from two thousand and three to two thousand and seven,

933
00:52:22,760 --> 00:52:26,480
Speaker 1: while both of the Velvet Sundowns albums today came out

934
00:52:26,480 --> 00:52:27,560
Speaker 1: in twenty twenty five.

935
00:52:27,960 --> 00:52:29,120
Speaker 2: That's suspicious.

936
00:52:29,840 --> 00:52:33,279
Speaker 3: It's one of the really interesting things that hit me

937
00:52:33,520 --> 00:52:35,920
Speaker 3: as I was like reading about this band, because like,

938
00:52:36,080 --> 00:52:39,360
Speaker 3: it's just a very interesting story, and it's nice that

939
00:52:39,400 --> 00:52:44,680
Speaker 3: it's not like fucking up kids or like destroying democracy,

940
00:52:44,760 --> 00:52:47,000
Speaker 3: So it feels like kind of a nice thing to

941
00:52:47,040 --> 00:52:50,760
Speaker 3: engage with. But I had never thought about the power

942
00:52:51,280 --> 00:52:56,080
Speaker 3: that the people who are creating these Spotify playlists have,

943
00:52:56,440 --> 00:53:01,360
Speaker 3: Like it's a lot of power to shape what music

944
00:53:01,400 --> 00:53:05,200
Speaker 3: people are listening to, and I guess I had just

945
00:53:05,239 --> 00:53:07,080
Speaker 3: never thought about it. I don't know, maybe I'm like

946
00:53:07,160 --> 00:53:09,319
Speaker 3: late to the party, but I kind of feel it's

947
00:53:09,320 --> 00:53:11,120
Speaker 3: like an under the radar thing.

948
00:53:11,360 --> 00:53:14,480
Speaker 1: Oh, there is absolutely a lot of power in terms

949
00:53:14,520 --> 00:53:17,640
Speaker 1: of curating these playlists. I've heard from bands who will

950
00:53:17,719 --> 00:53:19,799
Speaker 1: randomly get a song added to a playlist and it's

951
00:53:19,800 --> 00:53:24,000
Speaker 1: like that song represents like more streams than they've ever

952
00:53:24,040 --> 00:53:26,839
Speaker 1: had in their career. Now, Spotify don't have the best

953
00:53:26,840 --> 00:53:28,719
Speaker 1: reputation when it comes to paying artists, So I don't

954
00:53:28,719 --> 00:53:31,720
Speaker 1: know if that, like that probably translates to like, here's

955
00:53:31,760 --> 00:53:35,400
Speaker 1: two dollars because they don't pay artists very well. But

956
00:53:35,600 --> 00:53:38,680
Speaker 1: if this whole saga with this AI generated band that

957
00:53:38,719 --> 00:53:41,080
Speaker 1: doesn't really exist is to say anything about it, I

958
00:53:41,080 --> 00:53:43,200
Speaker 1: think Spotify is probably trying to find a way that

959
00:53:43,280 --> 00:53:48,399
Speaker 1: to see if they can sidestep human artists altogether. Right,

960
00:53:48,480 --> 00:53:51,759
Speaker 1: Like one little pesky thing about humans is that we

961
00:53:51,840 --> 00:53:53,800
Speaker 1: really do prefer to be paid for our work.

962
00:53:54,000 --> 00:53:56,440
Speaker 2: And so I think if they're like, how can.

963
00:53:56,360 --> 00:53:59,760
Speaker 1: We cut out this whole humans liking to be paid

964
00:53:59,800 --> 00:54:04,320
Speaker 1: for their labor thing? And obviously Spotify would be doubling

965
00:54:04,360 --> 00:54:07,759
Speaker 1: down on AI because they're CEO Daniel Eck just announced

966
00:54:07,760 --> 00:54:10,680
Speaker 1: a seven hundred and two million dollar investment in Helsing,

967
00:54:11,080 --> 00:54:14,160
Speaker 1: a German defense tech startup that develops military drones and

968
00:54:14,239 --> 00:54:18,600
Speaker 1: AI battlefield software. Which you know what I think when

969
00:54:18,600 --> 00:54:22,200
Speaker 1: I think like Spotify and like music and streaming.

970
00:54:22,880 --> 00:54:26,040
Speaker 2: I think military drones and AI battlefield software? Sure, who doesn't.

971
00:54:26,120 --> 00:54:28,880
Speaker 3: Why does everything have to be military drones?

972
00:54:30,360 --> 00:54:34,200
Speaker 1: Yes, everything is military drone. Everything is AI and military.

973
00:54:34,600 --> 00:54:38,440
Speaker 1: It went from everything's computer to everything is AI and

974
00:54:38,480 --> 00:54:39,400
Speaker 1: military drones.

975
00:54:39,920 --> 00:54:40,520
Speaker 3: God damn?

976
00:54:41,840 --> 00:54:47,000
Speaker 2: Okay, well, can will you indulge me? Can we talk

977
00:54:47,040 --> 00:54:48,360
Speaker 2: about Jeff Bezos' wedding?

978
00:54:49,000 --> 00:54:51,759
Speaker 3: Do you think he has some drones? Oh?

979
00:54:52,080 --> 00:54:54,040
Speaker 2: I haven't heard about any aboudy you know they're in

980
00:54:54,040 --> 00:54:54,800
Speaker 2: the mix somewhere.

981
00:54:54,880 --> 00:54:57,600
Speaker 3: All right, Yeah, let's talk about Jeff Bezos' wedding. Is

982
00:54:57,600 --> 00:55:00,080
Speaker 3: it going really well? Does everybody love it?

983
00:55:00,080 --> 00:55:00,479
Speaker 2: It's going?

984
00:55:01,400 --> 00:55:05,440
Speaker 1: I mean, I'm loving gawking at what a train wreck

985
00:55:05,640 --> 00:55:09,680
Speaker 1: it's been. So Jeff Bezos is marrying his partner Lauren

986
00:55:09,760 --> 00:55:13,080
Speaker 1: Sanchez this week, which, by the way, for people who

987
00:55:13,120 --> 00:55:16,080
Speaker 1: watch Housewives, you like, when I first saw her picture,

988
00:55:16,080 --> 00:55:19,279
Speaker 1: I was like, damn, is that Mia Thornton from Housewives?

989
00:55:19,560 --> 00:55:21,239
Speaker 1: Look up a picture of Mia Thornton and look up

990
00:55:21,239 --> 00:55:23,360
Speaker 1: a picture of Lauren Sanchez. They could be sisters. They

991
00:55:23,400 --> 00:55:24,280
Speaker 1: look so much alike.

992
00:55:24,440 --> 00:55:24,720
Speaker 3: Wow.

993
00:55:24,840 --> 00:55:27,239
Speaker 1: So the wedding is scheduled for June twenty six through

994
00:55:27,320 --> 00:55:29,160
Speaker 1: June twenty eighth in Venice, Italy.

995
00:55:29,640 --> 00:55:31,959
Speaker 2: So first of all, just.

996
00:55:31,920 --> 00:55:37,320
Speaker 1: Have to applaud what attacking menagerie of fucked up rich people.

997
00:55:37,520 --> 00:55:39,120
Speaker 2: Bullshit, this whole thing has been.

998
00:55:39,360 --> 00:55:44,800
Speaker 1: Like when you look through the way that Lauren Sanchez

999
00:55:44,840 --> 00:55:49,719
Speaker 1: and Jeff Bezos met, it's very scandalous when you look

1000
00:55:49,880 --> 00:55:53,200
Speaker 1: through who's coming to this wedding, Like, the whole thing

1001
00:55:53,320 --> 00:55:55,480
Speaker 1: is just a I mean, it should really put to

1002
00:55:55,560 --> 00:56:00,640
Speaker 1: bed that wealthy people like wealthy wealthy people have taste,

1003
00:56:01,400 --> 00:56:06,960
Speaker 1: have refinement, have poised, because this is the tackiest menagerie

1004
00:56:07,080 --> 00:56:11,200
Speaker 1: I have ever seen. And I love attacking menagerie. Like

1005
00:56:11,400 --> 00:56:13,399
Speaker 1: I'm not even saying this as somebody who's looking down

1006
00:56:13,440 --> 00:56:15,719
Speaker 1: on this, but even for me, I'm.

1007
00:56:15,520 --> 00:56:18,200
Speaker 2: Like, wow, these people are trash. And you know who

1008
00:56:18,280 --> 00:56:19,040
Speaker 2: else agrees with me?

1009
00:56:19,760 --> 00:56:23,120
Speaker 1: The entire country of Italy, because the Italians are celebrating

1010
00:56:23,120 --> 00:56:24,360
Speaker 1: these nuptials by.

1011
00:56:24,280 --> 00:56:27,120
Speaker 2: Welcoming them with waves of protests.

1012
00:56:27,280 --> 00:56:31,800
Speaker 3: Yeah, the Italians people known for rejecting all things tacky.

1013
00:56:32,920 --> 00:56:36,480
Speaker 1: I mean, do you know how tacky I mean as

1014
00:56:36,520 --> 00:56:39,000
Speaker 1: an Italian? Do you know how tacky you have to

1015
00:56:39,040 --> 00:56:40,719
Speaker 1: be for the Italians to be like you need to

1016
00:56:40,800 --> 00:56:44,160
Speaker 1: leave too much, it's too gaudy, it's too much.

1017
00:56:44,239 --> 00:56:46,759
Speaker 3: Yeah, it's too gaudy, it's too much. Yeah, as an

1018
00:56:46,760 --> 00:56:51,120
Speaker 3: Italian American, you have to really work to earn that.

1019
00:56:51,360 --> 00:56:54,880
Speaker 2: So activists with Green Peace, No Space for Bezos, and

1020
00:56:54,920 --> 00:56:57,640
Speaker 2: a UK based group called Everyone Hates Elon.

1021
00:57:00,360 --> 00:57:02,240
Speaker 3: It's a pretty good name for their group.

1022
00:57:04,000 --> 00:57:06,760
Speaker 1: They have all been making their displeasure known this week

1023
00:57:06,840 --> 00:57:09,680
Speaker 1: with a large banner unfrilled in Saint Mark's Square meeting

1024
00:57:10,000 --> 00:57:12,359
Speaker 1: if you can rent Venice for your wedding, you can

1025
00:57:12,400 --> 00:57:16,480
Speaker 1: pay more tax, which like fair point, like honestly, like

1026
00:57:17,160 --> 00:57:18,800
Speaker 1: I would love to see I would love to see

1027
00:57:18,800 --> 00:57:23,680
Speaker 1: somebody dispute that, like freaking yeah, understatement of the century.

1028
00:57:23,440 --> 00:57:26,200
Speaker 3: Like what if instead of renting Venice for their wedding,

1029
00:57:26,800 --> 00:57:28,800
Speaker 3: everybody in America got health insurance?

1030
00:57:29,200 --> 00:57:31,720
Speaker 1: Yeah, or like yeah, we eradicated poverty, we ere eradicated

1031
00:57:31,800 --> 00:57:34,960
Speaker 1: childhood poverty. So they actually have already had one victory,

1032
00:57:34,960 --> 00:57:37,600
Speaker 1: and that is forcing Bezos to change the venue for

1033
00:57:37,640 --> 00:57:37,959
Speaker 1: the wedding.

1034
00:57:38,040 --> 00:57:38,400
Speaker 2: Reception.

1035
00:57:38,680 --> 00:57:41,600
Speaker 1: Organizers for No Space for Bezos told the BBC that

1036
00:57:41,640 --> 00:57:44,080
Speaker 1: they had gotten the venue moved to the Venetian Arsenal

1037
00:57:44,360 --> 00:57:48,640
Speaker 1: after threatening to fill the canals with inflatable crocodiles, flamingos,

1038
00:57:48,880 --> 00:57:51,600
Speaker 1: ducks and unicorns so that none of their I don't know,

1039
00:57:51,760 --> 00:57:53,400
Speaker 1: superyachts or whatever could get through.

1040
00:57:53,640 --> 00:57:55,440
Speaker 3: I mean, I have to say that does actually sound

1041
00:57:56,120 --> 00:58:01,480
Speaker 3: kind of fun. Like maybe not the dream wedding that

1042
00:58:02,280 --> 00:58:07,360
Speaker 3: Bezos and his bride had imagined, but like I hope

1043
00:58:07,400 --> 00:58:10,680
Speaker 3: they still filled the canals with inflatable crocodiles, flamingos, ducks,

1044
00:58:10,680 --> 00:58:13,040
Speaker 3: and unicorns. I think that might be nice.

1045
00:58:13,480 --> 00:58:16,400
Speaker 1: Ooh, I almost wonder if we should do a deep

1046
00:58:16,480 --> 00:58:21,840
Speaker 1: dive into jeff Bezos and Lauren Sanchez's relationship because it's

1047
00:58:21,840 --> 00:58:24,520
Speaker 1: too I'll just say this for folks who for if

1048
00:58:24,560 --> 00:58:27,600
Speaker 1: you know, you know, it is so juicy, Like one

1049
00:58:27,600 --> 00:58:29,680
Speaker 1: of the juiciest bits of it is the fact that

1050
00:58:29,840 --> 00:58:31,919
Speaker 1: the text messages that they were sending to each other

1051
00:58:32,320 --> 00:58:36,920
Speaker 1: while jeff Bezos was fully married were published by the

1052
00:58:37,000 --> 00:58:40,880
Speaker 1: National Inquirer. And I mean it's like one of my like, like,

1053
00:58:40,920 --> 00:58:43,240
Speaker 1: it's a text that I have sent in Jess many times.

1054
00:58:43,680 --> 00:58:46,040
Speaker 1: I love you A live girl is one of the

1055
00:58:46,080 --> 00:58:48,840
Speaker 1: texts that Jeffrey Bezos sent to Lauren Sanchez when they

1056
00:58:48,840 --> 00:58:51,280
Speaker 1: were like running around behind their partners.

1057
00:58:51,360 --> 00:58:52,920
Speaker 3: Is back A live girl.

1058
00:58:53,280 --> 00:58:54,160
Speaker 2: A live girl.

1059
00:58:54,400 --> 00:58:58,640
Speaker 1: That also was like what for, what's the alternative, like

1060
00:58:58,800 --> 00:59:01,960
Speaker 1: what kind of girl you running around.

1061
00:59:01,680 --> 00:59:07,200
Speaker 2: With that were not alive? I have many questions.

1062
00:59:07,360 --> 00:59:09,040
Speaker 3: I guess it was a dig at his wife.

1063
00:59:09,760 --> 00:59:12,320
Speaker 1: I don't know, so, yeah, maybe we should do a

1064
00:59:12,400 --> 00:59:15,680
Speaker 1: full deep dive into their relationship because it's fascinating to me.

1065
00:59:16,200 --> 00:59:18,280
Speaker 2: But in getting them to move.

1066
00:59:18,160 --> 00:59:21,000
Speaker 1: Where their wedding was going to be, the group no

1067
00:59:21,080 --> 00:59:23,440
Speaker 1: Space for Bezo said, we are very proud of this.

1068
00:59:23,560 --> 00:59:27,120
Speaker 1: We are nobody's we have no money, nothing, and we're

1069
00:59:27,160 --> 00:59:29,480
Speaker 1: just citizens who started organizing and we managed to move

1070
00:59:29,520 --> 00:59:31,560
Speaker 1: one of the most powerful people in the world, all

1071
00:59:31,560 --> 00:59:35,160
Speaker 1: the billionaires out of the city. Now, a greenpeace organizer

1072
00:59:35,200 --> 00:59:37,680
Speaker 1: said that it wasn't so much about protesting these two

1073
00:59:37,800 --> 00:59:41,360
Speaker 1: specific people, but more what they represent. The riches live

1074
00:59:41,400 --> 00:59:44,360
Speaker 1: in excess while others endured the consequences of a climate

1075
00:59:44,400 --> 00:59:47,800
Speaker 1: emergency they did not create. And I have to say,

1076
00:59:48,080 --> 00:59:50,640
Speaker 1: living in DC, we were going through a historic heat

1077
00:59:50,680 --> 00:59:53,280
Speaker 1: wave and I did have this moment where I was

1078
00:59:53,320 --> 00:59:57,880
Speaker 1: reading about this wedding and the huge environmental impact that

1079
00:59:57,920 --> 01:00:00,960
Speaker 1: it is that it will definitely play and just like

1080
01:00:01,200 --> 01:00:04,880
Speaker 1: getting this note from my local government that was like, oh,

1081
01:00:04,920 --> 01:00:06,920
Speaker 1: in a heat wave, the best temperature to set your

1082
01:00:06,920 --> 01:00:09,360
Speaker 1: air conditioner at is seventy eight, and I was like,

1083
01:00:09,400 --> 01:00:12,200
Speaker 1: the hell you say seventy eight, my ass, And I

1084
01:00:12,280 --> 01:00:15,760
Speaker 1: just had this moment of like, why are brokies like

1085
01:00:15,840 --> 01:00:19,160
Speaker 1: me expected to sweat it up in our one bedroom

1086
01:00:19,160 --> 01:00:22,640
Speaker 1: apartments with our window units in our box fans while

1087
01:00:22,680 --> 01:00:26,440
Speaker 1: he's able to have this lavish, voluntary wedding display was

1088
01:00:26,480 --> 01:00:29,520
Speaker 1: seemingly no regard to how it might impact the climate.

1089
01:00:30,280 --> 01:00:32,920
Speaker 1: I will say I love how people are kind of

1090
01:00:33,680 --> 01:00:38,080
Speaker 1: generally protesting jeff Bezos, Like there are specific issues and

1091
01:00:38,120 --> 01:00:41,200
Speaker 1: specific groups that people are protesting about, like the environmental

1092
01:00:41,200 --> 01:00:44,840
Speaker 1: impact of this wedding, which is massive Bezos's aerospace investments,

1093
01:00:45,040 --> 01:00:49,640
Speaker 1: but also just like generally anti Bezos being in Italy

1094
01:00:49,680 --> 01:00:52,640
Speaker 1: and anti Bezos in general, just like we don't like him.

1095
01:00:52,680 --> 01:00:54,919
Speaker 1: I saw we were watching that video that was set

1096
01:00:54,920 --> 01:00:56,200
Speaker 1: to the Bo Burnham.

1097
01:00:55,800 --> 01:00:59,960
Speaker 2: Song Jeffrey Jeffrey Bezos.

1098
01:00:59,480 --> 01:01:01,960
Speaker 1: And it was a video of them just unfer like

1099
01:01:02,000 --> 01:01:04,400
Speaker 1: a massive banner that said Bezos with a big red

1100
01:01:04,680 --> 01:01:07,520
Speaker 1: X through it, just like we don't like him, Get

1101
01:01:07,600 --> 01:01:08,280
Speaker 1: him out of here.

1102
01:01:08,680 --> 01:01:12,000
Speaker 3: Yeah, there's even like the names of their organizations that

1103
01:01:12,040 --> 01:01:16,840
Speaker 3: you just read. It's funny how personal these protests are.

1104
01:01:17,760 --> 01:01:20,400
Speaker 3: It's just like we don't like him personally, we don't

1105
01:01:20,480 --> 01:01:24,520
Speaker 3: want him here, and I get it. I do hope

1106
01:01:24,560 --> 01:01:28,280
Speaker 3: that once the honeymoon is over that some of this

1107
01:01:28,440 --> 01:01:32,760
Speaker 3: energy gets channeled into like demanding change in the global

1108
01:01:32,800 --> 01:01:36,320
Speaker 3: system that just keeps funneling an ever increasing proportion of

1109
01:01:36,320 --> 01:01:38,560
Speaker 3: the world's wealth into the pockets of a handful of

1110
01:01:38,560 --> 01:01:41,960
Speaker 3: olive arts. Like I love seeing the signs and messaging

1111
01:01:42,160 --> 01:01:44,840
Speaker 3: that focus on taxes for that reason, you know, like

1112
01:01:45,240 --> 01:01:47,320
Speaker 3: the one that just says Bezos with a big X

1113
01:01:47,360 --> 01:01:51,480
Speaker 3: over it. I get it. That is satisfying, But like

1114
01:01:51,880 --> 01:01:56,360
Speaker 3: the ones that focus on taxes and climate feel like

1115
01:01:56,440 --> 01:02:01,000
Speaker 3: they had the more enduring political message, like forcing him

1116
01:02:01,000 --> 01:02:04,720
Speaker 3: to move his wedding to a more secure, yet still

1117
01:02:04,720 --> 01:02:08,400
Speaker 3: intensely opulent than you. It's a nice reminder that these

1118
01:02:08,440 --> 01:02:12,280
Speaker 3: people are not all powerful. But I think like a

1119
01:02:12,480 --> 01:02:16,680
Speaker 3: real victory for the people will be getting able to

1120
01:02:16,680 --> 01:02:18,120
Speaker 3: pay his fair share of taxes.

1121
01:02:18,360 --> 01:02:22,000
Speaker 1: So you don't think the slogan Bezos colon, we just

1122
01:02:22,000 --> 01:02:24,760
Speaker 1: don't like him, that's not compelling to you.

1123
01:02:26,160 --> 01:02:28,840
Speaker 3: It's a good start. I think it's a good start.

1124
01:02:29,360 --> 01:02:34,160
Speaker 3: It gets the people going. But we can't stop there.

1125
01:02:34,720 --> 01:02:40,680
Speaker 1: Bezos colon, He rubs us the wrong way. So the

1126
01:02:40,760 --> 01:02:45,800
Speaker 1: guests to this wedding include that people like Bill Gates, Oprah,

1127
01:02:45,880 --> 01:02:48,320
Speaker 1: of course Gail, She's never gonna like turn out an

1128
01:02:48,320 --> 01:02:53,640
Speaker 1: event like this, climate activist Leonardo DiCaprio, which like, come on,

1129
01:02:54,360 --> 01:02:58,560
Speaker 1: Barbara Streisan, Eva Longoria, Robert Pattinson, and Orlando Bloom.

1130
01:02:58,760 --> 01:03:00,840
Speaker 2: Allow me a quick diversion on our Lando Bloom. So,

1131
01:03:00,960 --> 01:03:02,160
Speaker 2: Orlando Bloom was.

1132
01:03:02,240 --> 01:03:05,560
Speaker 1: Very recently, up until recently, in a relationship with the

1133
01:03:05,600 --> 01:03:08,080
Speaker 1: singer Katy Perry. They have a child together, and I

1134
01:03:08,120 --> 01:03:11,760
Speaker 1: don't know who at the Daily Mail has it out

1135
01:03:11,800 --> 01:03:15,320
Speaker 1: for Katy Perry so much, because this Jeffrey Bezos wedding

1136
01:03:15,480 --> 01:03:17,880
Speaker 1: is really being used to highlight that her relationship with

1137
01:03:18,000 --> 01:03:20,600
Speaker 1: Orlando Bloom is over, and that the reason why that

1138
01:03:20,640 --> 01:03:24,280
Speaker 1: relationship is over is because of the fallout from her

1139
01:03:24,400 --> 01:03:29,160
Speaker 1: panned spaceflight that she took with Jeffrey Bezos. It honestly

1140
01:03:29,280 --> 01:03:31,600
Speaker 1: kind of sounds like Bezos is like ruining her life.

1141
01:03:31,760 --> 01:03:34,960
Speaker 1: This is how Ola Magazine reported on it. Katy Perry's

1142
01:03:35,000 --> 01:03:37,760
Speaker 1: recent journey to space may have only lasted eleven minutes,

1143
01:03:37,880 --> 01:03:40,280
Speaker 1: but the aftermath has gone on for much longer in

1144
01:03:40,320 --> 01:03:43,120
Speaker 1: her personal life. The pop Star and her longtime fiance

1145
01:03:43,320 --> 01:03:46,360
Speaker 1: Orlando Bloom are reportedly facing a rough patch after an

1146
01:03:46,560 --> 01:03:49,800
Speaker 1: explosive argument over her Blue Origin space flight.

1147
01:03:50,000 --> 01:03:52,000
Speaker 3: An explosive argument.

1148
01:03:52,240 --> 01:03:57,040
Speaker 1: So that spaceflight was poorly received, we'll say, And it

1149
01:03:57,120 --> 01:04:00,040
Speaker 1: sounds like, according to these like gossip rags, maybe she

1150
01:04:00,320 --> 01:04:04,320
Speaker 1: like ruined her marriage and now she's being publicly excluded

1151
01:04:04,360 --> 01:04:08,520
Speaker 1: from like the rich asshole spectacle wedding of the year.

1152
01:04:08,840 --> 01:04:10,720
Speaker 1: Like I know people that are down on Katie Perry

1153
01:04:10,760 --> 01:04:13,080
Speaker 1: right now for whatever reason, but it genuinely sounds like

1154
01:04:13,120 --> 01:04:14,600
Speaker 1: Jeff Bezos is ruining her life.

1155
01:04:14,720 --> 01:04:16,920
Speaker 3: Damn. So she wasn't even invited to the wedding. She

1156
01:04:17,360 --> 01:04:18,800
Speaker 3: got to go to space but then it was like

1157
01:04:18,840 --> 01:04:20,160
Speaker 3: you can't come to Venice.

1158
01:04:21,560 --> 01:04:23,680
Speaker 1: Yeah, I mean that would be another good deep dive

1159
01:04:23,800 --> 01:04:24,600
Speaker 1: is that space flight.

1160
01:04:24,720 --> 01:04:26,480
Speaker 2: I did read an article where she was like, oh,

1161
01:04:26,520 --> 01:04:27,080
Speaker 2: I wish that.

1162
01:04:27,440 --> 01:04:30,160
Speaker 1: I think our big mistake was letting the video footage

1163
01:04:30,160 --> 01:04:31,960
Speaker 1: that we took from the space flight go public.

1164
01:04:31,960 --> 01:04:33,520
Speaker 2: And it's like, well, literally, what did you think they

1165
01:04:33,560 --> 01:04:36,880
Speaker 2: were taking video footage for? Like what of course people

1166
01:04:36,880 --> 01:04:38,320
Speaker 2: were to see it? Like what did you say?

1167
01:04:39,600 --> 01:04:42,040
Speaker 3: Yeah, it wasn't like a scientific mission.

1168
01:04:42,160 --> 01:04:46,440
Speaker 1: So this wedding seems involved. Wired reports that exclusive private

1169
01:04:46,480 --> 01:04:49,640
Speaker 1: parties are playing at secret locations and smaller islands of

1170
01:04:49,640 --> 01:04:51,680
Speaker 1: the lagoon, and it's an event that will leave its

1171
01:04:51,720 --> 01:04:54,240
Speaker 1: mark on Venice, including in terms of the environmental impact

1172
01:04:54,280 --> 01:04:56,640
Speaker 1: and the possible inconvenience it could create for the city's

1173
01:04:56,680 --> 01:05:00,360
Speaker 1: transit infrastructure. Guests will arrive on eighty private j and

1174
01:05:00,400 --> 01:05:03,720
Speaker 1: travel aboard more than thirty already reserved water taxis, yachts

1175
01:05:03,720 --> 01:05:07,280
Speaker 1: and gondolas. According to some official sources, flights from New York,

1176
01:05:07,320 --> 01:05:10,479
Speaker 1: Los Angeles, London, Paris and Dubaier planned not to mention

1177
01:05:10,640 --> 01:05:15,000
Speaker 1: luxuryats coming to Venice, with moorings already planned between different points.

1178
01:05:15,640 --> 01:05:17,600
Speaker 2: So there is an argument.

1179
01:05:17,400 --> 01:05:22,800
Speaker 1: That this wedding could potentially help Venice's local economy conveniently enough.

1180
01:05:23,080 --> 01:05:26,520
Speaker 1: Can you guess where that has been reported that this

1181
01:05:26,600 --> 01:05:28,840
Speaker 1: wedding actually is a good thing because it's going to

1182
01:05:28,920 --> 01:05:30,760
Speaker 1: help support Venice's local economy.

1183
01:05:31,160 --> 01:05:36,320
Speaker 3: Uh. The Bezos Daily Newsletter, I.

1184
01:05:36,280 --> 01:05:39,360
Speaker 1: Mean essentially the Washington Post, which is owned by Bezos.

1185
01:05:39,720 --> 01:05:42,320
Speaker 1: The Post declared that about eighty percent of the produests

1186
01:05:42,320 --> 01:05:45,120
Speaker 1: and services come from local Venetian suppliers. I don't know

1187
01:05:45,120 --> 01:05:47,120
Speaker 1: if that's true, but it does kind of feel like

1188
01:05:47,240 --> 01:05:50,080
Speaker 1: me saying there are no girls on the internet reports

1189
01:05:50,120 --> 01:05:52,680
Speaker 1: that Bridget Todd is actually really nice to everybody all

1190
01:05:52,720 --> 01:05:55,200
Speaker 1: the time and is super smart, you know what I mean,

1191
01:05:55,400 --> 01:05:58,520
Speaker 1: Like convenient that the paper that you own says your

1192
01:05:58,560 --> 01:06:00,720
Speaker 1: wedding that everyone hates is actually good.

1193
01:06:01,040 --> 01:06:04,120
Speaker 3: It's nice. That's a you know, they've just got their

1194
01:06:04,160 --> 01:06:04,800
Speaker 3: own take.

1195
01:06:05,240 --> 01:06:08,760
Speaker 1: But in all the pictures of famous celebrities arriving at

1196
01:06:08,760 --> 01:06:11,320
Speaker 1: this wedding, I will say, like you can kind of

1197
01:06:11,360 --> 01:06:13,600
Speaker 1: see from the pictures that everybody is sort of like,

1198
01:06:13,720 --> 01:06:16,400
Speaker 1: oh why am I like, like like I don't know

1199
01:06:16,440 --> 01:06:18,520
Speaker 1: if I should be here. With the picture of Tom

1200
01:06:18,560 --> 01:06:21,920
Speaker 1: Brady with his like hat really low, Oprah doesn't look

1201
01:06:21,920 --> 01:06:24,400
Speaker 1: too like nobody looks thrilled to be going to this wedding,

1202
01:06:24,720 --> 01:06:28,080
Speaker 1: And it does sort of make me happy that people

1203
01:06:28,120 --> 01:06:33,200
Speaker 1: are really spotlighting the ways that you know, celebrity, even

1204
01:06:33,240 --> 01:06:37,040
Speaker 1: celebrities that say the right thing, sometimes really just care

1205
01:06:37,080 --> 01:06:40,760
Speaker 1: about other rich celebrities, like they don't really care about us.

1206
01:06:40,920 --> 01:06:44,640
Speaker 1: They will go to Jeffrey Bezos's wedding, you know, they

1207
01:06:44,640 --> 01:06:47,160
Speaker 1: will probably be at a table with Avanka Trump, who

1208
01:06:47,200 --> 01:06:49,920
Speaker 1: is definitely gonna be there. The Kardashians are going to

1209
01:06:50,000 --> 01:06:51,920
Speaker 1: be there, Like I don't know, I hope this is

1210
01:06:52,400 --> 01:06:56,280
Speaker 1: if that picture of Ellen DeGeneres from the Grammys with

1211
01:06:56,400 --> 01:06:59,920
Speaker 1: all of these different celebrities that like famous celebrity selfie,

1212
01:07:00,120 --> 01:07:04,520
Speaker 1: if that was the welcoming in of celebrity culture of

1213
01:07:04,560 --> 01:07:07,840
Speaker 1: the digital age. I hope the images of celebrity sort

1214
01:07:07,840 --> 01:07:12,160
Speaker 1: of shamefully attending this like spectacle, this like rich asshole

1215
01:07:12,200 --> 01:07:15,760
Speaker 1: sepectacle is the nail in the coffin of celebrity worship

1216
01:07:15,800 --> 01:07:18,480
Speaker 1: online because I do think that like the tides are

1217
01:07:18,520 --> 01:07:21,720
Speaker 1: turning because nobody wants to sit in their hot ass

1218
01:07:21,880 --> 01:07:25,160
Speaker 1: one bedroom apartment during a heat wave and watch Jeffrey

1219
01:07:25,200 --> 01:07:29,000
Speaker 1: Bezos yat in and fly in every celebrity on the

1220
01:07:29,040 --> 01:07:33,280
Speaker 1: planet for his taxi ass wedding. Well, Mike, thank you

1221
01:07:33,360 --> 01:07:35,880
Speaker 1: so much for being here. Be sure to follow us

1222
01:07:35,920 --> 01:07:38,480
Speaker 1: around the web. I'm on Instagram at Bridget Marie DC.

1223
01:07:38,720 --> 01:07:41,520
Speaker 1: I'm on TikTok at Bridget Marie and DC, and we're

1224
01:07:41,520 --> 01:07:45,080
Speaker 1: trying to grow our YouTube presents. So if you like YouTube,

1225
01:07:45,360 --> 01:07:47,080
Speaker 1: check us out there at there are no girls on

1226
01:07:47,120 --> 01:07:49,000
Speaker 1: the Internet. Thanks so much for listening, and I will

1227
01:07:49,000 --> 01:08:00,000
Speaker 1: see you on the internet if you're looking for waste.

1228
01:08:00,040 --> 01:08:02,240
Speaker 1: To support the show, check out our March store at

1229
01:08:02,240 --> 01:08:05,960
Speaker 1: tangody dot com Slash Store. Got a story about an

1230
01:08:06,000 --> 01:08:07,960
Speaker 1: interesting thing in tech, or just want to say hi?

1231
01:08:08,360 --> 01:08:10,560
Speaker 1: You can read us at Hello at tangody dot com.

1232
01:08:10,640 --> 01:08:12,800
Speaker 1: You can also find transcripts for today's episode at TENG

1233
01:08:12,840 --> 01:08:15,040
Speaker 1: Goody dot com. There Are No Girls on the Internet

1234
01:08:15,160 --> 01:08:17,439
Speaker 1: was created by me Bridget Tood. It's a production of

1235
01:08:17,520 --> 01:08:22,120
Speaker 1: iHeartRadio and Unbossed Creative edited by Joey pat Jonathan Strickland

1236
01:08:22,160 --> 01:08:24,840
Speaker 1: is our executive producer. Tari Harrison is our producer and

1237
01:08:24,920 --> 01:08:28,679
Speaker 1: sound engineer. Michael Almado is our contributing producer. I'm your host,

1238
01:08:28,680 --> 01:08:30,920
Speaker 1: Bridget Todd. If you want to help us grow, rate

1239
01:08:30,960 --> 01:08:31,599
Speaker 1: and review us.

1240
01:08:31,479 --> 01:08:32,440
Speaker 4: On Apple Podcasts.

1241
01:08:33,200 --> 01:08:36,000
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1242
01:08:36,040 --> 01:08:39,520
Speaker 1: Apple Podcasts, or wherever you get your podcasts