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Speaker 1: So last week we hit pause on our usual Friday

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Speaker 1: news wound up so we could dig into a different story,

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Speaker 1: and that is what is being erased from Charlie Kirk's

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Speaker 1: legacy after his murder. That meant no news round up,

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Speaker 1: But don't worry, We've got it for you now. It

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Speaker 1: is a fun one, so please enjoy There Are No

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Speaker 1: Girls on the Internet as a production of iHeartRadio and

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Speaker 1: Unbossed Creative. I'm Bridget Todd and this is There Are

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

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Speaker 1: Girls on the Internet, where we explore the intersection of technology,

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

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Speaker 1: our weekly news roundup where we dig into some of

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Speaker 1: the stories that you might have missed so you don't

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Speaker 1: have to. I am so thrilled to be joined by

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Speaker 1: this week's a guest host, Ashley Ray, the host of

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Speaker 1: TV I Say, a podcast that I've loved for a

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Speaker 1: really long time. It was on hiatus, now it's back

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Speaker 1: on the Courier Network. Ashley, thank you so much for

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Speaker 1: being here, Thank you.

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Speaker 2: For having me. I am so excited and I'm a

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Speaker 2: big fan of you. Oh my goodness, Well this is great.

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Speaker 1: So you know this a tech podcast, but it's also

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Speaker 1: a culture podcast, which includes TV, which I watch a

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Speaker 1: lot of. For folks who don't know, Ashley has the

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Speaker 1: most correct television opinion out of anybody out there. So

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Speaker 1: if you disagree with Ashley, you're basically just wrong. You're

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Speaker 1: you're wrong about TV. I just finished listening to your

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Speaker 1: episode with Gibson John's, which was delightful. I will say, like,

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Speaker 1: something that I was so curious to ask you about

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Speaker 1: that episode is that you know, you were talking about

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Speaker 1: reality TV, which I'm obsessed with. I watch a lot

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Speaker 1: of it. Uh, and it sounded to me that you

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Speaker 1: both were kind of agreeing that you don't kind of

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Speaker 1: like get mixed up in the online fandoms. That is

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Speaker 1: something I've really noticed you. You can't just watch and

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Speaker 1: enjoy a show like Love Island. You like, like, let's

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Speaker 1: say that you have an inconsistent opinion about somebody on

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Speaker 1: Love Island. There's this like rabid fan based dominated in

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Speaker 1: the conversation. Oh, you just stay out of it.

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Speaker 2: I mean I got called out for that very reason.

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Speaker 3: Uh.

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Speaker 2: This last season of Love Island, I was a big

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Speaker 2: O'landria fan. I loved her throughout the season. I was

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Speaker 2: rooting for my girl. I wanted her to leave Taylor

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Speaker 2: the whole time, and when you know, it was that

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Speaker 2: frustrating we when she just like kind of wouldn't do

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Speaker 2: it and her friends were like, no, you should try

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Speaker 2: to get it, and I was like, I'm so frustrated.

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Speaker 2: You need to play the game differently and go for

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Speaker 2: what you want. Girl, Like, I'm so tired of this.

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Speaker 2: And people were like, how dare you criticize her? Weren't

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Speaker 2: didn't you just say you liked her and now you're

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Speaker 2: criticizing her? And I was like, so you can like

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Speaker 2: someone and also not like everything that they do and

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Speaker 2: and then yeah, and people were like, I don't understand.

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Speaker 2: And then I was happy when she got with Nick,

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Speaker 2: and people were like, how can you be happy when

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Speaker 2: you were just criticizing her?

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Speaker 1: And I'm like, because she did so she went for

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Speaker 1: what she because she did what she wanted.

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Speaker 2: And it's it's just wild, like they're so it's stand

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Speaker 2: up all of it. I don't quite get. There's some

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Speaker 2: docs out about it, actually, if you want to check

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Speaker 2: about Stan on Paramount Plus. People really really intensely love

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Speaker 2: to love someone and reality stars, I guess are the

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Speaker 2: perfect target.

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Speaker 1: I've been a reality TV fan for a very long time.

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Speaker 1: You know, some of the shows that I have watched,

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Speaker 1: I have a very long relationships with the I'm thinking

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Speaker 1: about shows like OG Real Houseives of New York. Yeah,

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Speaker 1: I feel like the stan standem online fandom stuff. I

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Speaker 1: feel like that is so that it's gotten so much

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Speaker 1: more toxic and so much louder, And it didn't used

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Speaker 1: to be like that back in the day. You would

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Speaker 1: read what Kelly Bensimone had to say on the Bravo blog, right,

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Speaker 1: but it didn't really necessarily go like you didn't have

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Speaker 1: people watching, engaging in this level of fandom online around

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Speaker 1: it and looking at your every move.

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Speaker 2: I just think Love Island is the sort of perfect

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Speaker 2: example of how bad this just ecosystem has gotten, where

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Speaker 2: they went out and looked for things in these people

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Speaker 2: people's lives because they didn't like them, just so they

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Speaker 2: could pull that out and cancel them. People were calling

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Speaker 2: ICE on someone in the cast family, like people were

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Speaker 2: calling CPS on Hood's daughter and her and her baby daddy.

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Speaker 2: Like that's where I just I don't understand. I also

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Speaker 2: have been watching reality TV since the Osbourne since Simple

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Speaker 2: you know, since a simple life, Like we didn't do this,

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Speaker 2: Like I was never watching the Osbourne's and I was like,

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Speaker 2: I need to know Ozzy's every thought and who he's

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Speaker 2: friends with, and I need to follow him on every channel.

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Speaker 2: And now it's the expectation is like they need to

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Speaker 2: immediately leave the show and they need to be on

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Speaker 2: Snapchat and this and this, and I should be able

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Speaker 2: to know at all times what events they're going to.

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Speaker 2: Are they still dating this person? And I don't know

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Speaker 2: exactly why people think they have that level of access,

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Speaker 2: or why they deserve it, or why they think it's

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Speaker 2: mandatory if you're on television. But I don't think that

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Speaker 2: we can demand that of people just because they go

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Speaker 2: on a reality show.

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Speaker 1: No, And it also just makes it less fun. Yeah,

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Speaker 1: I want to watch a Philly fun show. I don't

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Speaker 1: want to have everything this person has ever done and said, yeah,

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Speaker 1: dragged up on social media and then be expected to

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Speaker 1: litigate it myself. But like watching the TV show.

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Speaker 2: Yeah, like it's a political statement. If I like watched

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Speaker 2: Love Island and left at something Austin said. I don't

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Speaker 2: like Austin, but he can say something funny. It's entertainment,

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Speaker 2: and so I like with ninety Day Fiance, I try

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Speaker 2: to separate like the real happenings and I just watched

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Speaker 2: the show. I don't like pay attention to the gossip

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Speaker 2: of the fans and what everyone's arguing.

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Speaker 1: I just I try.

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Speaker 2: I just put my faith in the narrative that I'm

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Speaker 2: presented by producers.

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Speaker 1: Yes, I mean I have noticed, even beyond the fandoms,

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Speaker 1: especially on Bravo, some of the shows have gotten quite dark.

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Speaker 1: You know. I'm thinking about some of the some of

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Speaker 1: Brinn's storyline from the last season of Rony, which was

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Speaker 1: a mess. Some of the stuff, I don't know if

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Speaker 1: you watch Potomac, some of the stuff with that Mia

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Speaker 1: the Valley is quite dark. Like I had to sort

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Speaker 1: of check out of it for a while because I

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Speaker 1: was like, well, I don't want to watch like like

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Speaker 1: a family go through something that seems quite real.

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Speaker 2: Even like Below Deck got really dark, Like, yeah, I

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Speaker 2: don't know what was up with Bravo or I don't

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Speaker 2: know what was kind of going around the Bravo universe

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Speaker 2: at the time, but like even Below Deck, I think

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Speaker 2: it was blow Deck. Mediterranean had this odd season where

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Speaker 2: like two people were kicked off of the crew because

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Speaker 2: they like assaulted other people on the crew, and everyone

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Speaker 2: was just like should we still be doing Below Deck?

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Speaker 2: And they kind of were like, we need to redo it,

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Speaker 2: like we just need to change everything, like we need

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Speaker 2: to make it fun again, like let's get some fun,

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Speaker 2: happy people in the crew.

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Speaker 1: And I don't.

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Speaker 2: I think obviously if everything is the pandemic and you know,

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Speaker 2: but I think there was something about that moment and

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Speaker 2: people being more online where I feel like in reality

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Speaker 2: TV people.

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Speaker 1: Share more now.

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Speaker 2: I think there was some expectation before of like this

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Speaker 2: is fake or like we're making a storyline, and now

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Speaker 2: people are like, well, you know, during COVID, I was fine,

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Speaker 2: like having a live log of my life, So why

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Speaker 2: would I make things up? Like yeah, I'll share these

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Speaker 2: kind of deep personal things. And I guess it's great TV.

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Speaker 2: Like I do think reality TV now feels more authentic

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Speaker 2: in a way, especially the stuff like ninety Day Fiance.

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Speaker 2: But I guess that is encouraging people to go, yeah,

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Speaker 2: you share everything, so I should have complete access to

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Speaker 2: you. You should be on like you know, TikTok live all

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Speaker 2: the time whenever I want to see you.

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Speaker 1: Yeah, I remember back in the day. I didn't watch

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Speaker 1: OC but like the stuff with Taylor Armstrong, there was

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Speaker 1: like darkness there. But looking back now, it's clear we

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Speaker 1: weren't the audience was not being told everything, and there

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Speaker 1: was still this attitude of like, oh this, we need

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Speaker 1: to make this seem a little more polished than it

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Speaker 1: actually is. And I think today that they would they

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Speaker 1: would they would handle that in a completely different way.

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Speaker 2: Oh yeah, yeah, I yeah, I will say I do

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Speaker 2: I give respect to reality TV produced They I don't

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Speaker 2: even respect, but I do think that they have put

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Speaker 2: more focus on like mental health and just trying to

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Speaker 2: think of, oh right, we're putting these people on TV

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Speaker 2: and how they appear could have consequences, and we're a

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Speaker 2: big part of how they appear and how those narratives

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Speaker 2: are made.

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Speaker 1: I hate to keep going back.

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Speaker 2: To Love Island, but it was you know, a lot

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Speaker 2: of what like Olandria face where they show kind of

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Speaker 2: at some points made her seem like a bully her

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Speaker 2: and Shelley, and you had BuzzFeed like posting memes about

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Speaker 2: like punching Shelley in the face and stuff, and it's like,

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Speaker 2: you know, yeah, and it's like producers are feeding into

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Speaker 2: that and they had to like kind of change the narrative.

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Speaker 2: So I do think reality producers now are like, yeah,

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Speaker 2: let's handle things with care.

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Speaker 1: I will say, I'm sure people listening are like, I'm

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Speaker 1: tuning in to hear tech takes, not reality TV takes.

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Speaker 1: But I do feel like being a longtime scholar of

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Speaker 1: reality TV, I think it helps me understand and explain

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Speaker 1: a lot where we're at politically and social Trump he

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Speaker 1: honestly got his start as a reality TV star. He like,

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Speaker 1: if you understand the medium of reality TV, if you

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Speaker 1: understand how villains work on reality TV, I think it

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Speaker 1: can be helpful to understanding this our current political.

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Speaker 2: Moment, our current political moment, and moment in entertainment. I

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Speaker 2: think if you want to understand why kids are watching

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Speaker 2: kick Now instead of television, why people prefer to watch

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Speaker 2: a show on YouTube versus you know, traditional streaming, A

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Speaker 2: lot of that has to do with reality TV and

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Speaker 2: kids who've just grown up on that and they are

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Speaker 2: used to it, and not only to the point where

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Speaker 2: it's the entertainment they're accustomed to, but it's the entertainment

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Speaker 2: that they want to make and know how to make.

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Speaker 2: Like there are people that are so young, and I

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Speaker 2: engage with them on TikTok and I'm like, oh.

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Speaker 1: My gosh, you would be perfect on real.

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Speaker 2: Healthwives, Like how like you know how to do this,

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Speaker 2: you know how to craft a narrative out of like

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Speaker 2: your life or whatever is happening. And I think, you know,

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Speaker 2: as those kids grow up and start making media, I

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Speaker 2: think we're going to see them really just doing more

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Speaker 2: with like reality and it blending the line between that

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Speaker 2: and documentary. There is a documentary that just came out

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Speaker 2: called Thirstrap on Paramount Plus. It's about this TikTok influencer

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Speaker 2: name like Whitey. He was like a white guy who

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Speaker 2: was very attractive, and he winked to some song by

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Speaker 2: Rod Stewart. I don't know, there's like a different internet

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Speaker 2: for white people that goes viral. I didn't know this guy,

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Speaker 2: but all of these like middle aged women became obsessed

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Speaker 2: with him and started sending him thousands and thousands of dollars,

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Speaker 2: and he basically dropped out of school to make this

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Speaker 2: his career and slowly realized like, oh, this attention economy

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Speaker 2: cannot last for forever. It has these negative impacts on

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Speaker 2: my mental health and my psyche and also it does

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Speaker 2: a horrible impact on these people who demand constant access

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Speaker 2: to me, and you know, start doxing his family when

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Speaker 2: he makes them mad because they don't like that he

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Speaker 2: had a girlfriend. And so I think, you know, if

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Speaker 2: you want to understand why it seems like people are

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Speaker 2: just so intense about everything, now, I think reality TV

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Speaker 2: it's the answer.

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Speaker 1: I need to watch this. Your laugh is so recommendation

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Speaker 1: was a good one. I hadn't even heard of it.

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Speaker 1: That what is it? Furry police?

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Speaker 2: No, yes, unmasking a murder? Yes, furry detectives.

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Speaker 1: That everybody should be talking about that documentary.

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Speaker 2: Why isn't everyone talking about it?

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Speaker 1: I am like, this is it's the wildest thing I've seen.

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Speaker 2: Every person I introduced to it is like, first of all,

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Speaker 2: why did you do that?

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Speaker 1: I have nightmares now?

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Speaker 2: And it's so well made, it's so well done, and

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Speaker 2: it's a story that no one knows about. And you

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Speaker 2: think like, oh, we've gotten to the worst part. And

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Speaker 2: then they're like.

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Speaker 1: So there's two more episodes and you're just.

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Speaker 2: Like, how, how does it get worse than this? And

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Speaker 2: it does and then it does.

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Speaker 1: Okay, So this is actually a great segue into what

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Speaker 1: I wanted to talk about first, which is everything happening

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Speaker 1: with CBS. I will give a little bit of backstory

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Speaker 1: to sort of set it up. So a few months ago,

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Speaker 1: CBS settled a lawsuit with Trump over allegedly a deceptively

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Speaker 1: edited interview of then presidential candidate Kamala Harris. So we

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Speaker 1: know Trump is not We'll say Trump has a specific

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Speaker 1: kind of relationship to the truth. But this is what

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Speaker 1: the suit alleged. His suit alleged that a sixty minutes

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Speaker 1: interview with Harris was deceptively edited to portray her in

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Speaker 1: a more favorable light. So specifically, the claim was that

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Speaker 1: there were these different versions of Harris's response to a

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Speaker 1: question that were used in different broadcasts, like a shorter, clearer,

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Speaker 1: more favorable version on face doination and then a more

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Speaker 1: kind of rambling, less polished, full version that was in

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Speaker 1: a sixty minutes broadcast. I have heard both versions, and

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Speaker 1: it is incredibly clear to me that the version in

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Speaker 1: question that he is upset about is like just edited

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Speaker 1: for clarity, like they've edited it for time because it's

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Speaker 1: a shorter segment. It's like very very clear to me

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Speaker 1: that's what's going on. So you're a podcast. You work

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Speaker 1: in media, you know this is a pretty cut. Like

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Speaker 1: I like, the podcast that you're listening to right now

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Speaker 1: is not raw audio. It is edited for clarity. I

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Speaker 1: don't want to I don't want to burst anybody's bubble.

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Speaker 1: But if media, it's edited. Yeah, you don't want to

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Speaker 1: hear us going uh uh uh. You don't need that,

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Speaker 1: so they cut it out.

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Speaker 2: I don't really understand why he's upset because I feel

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Speaker 2: like the same. It's not like the message becomes different.

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Speaker 2: It's I don't think she looks much better in the

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Speaker 2: other one. It's just quicker. So yeah, I didn't get it.

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Speaker 2: I'll see the big deal you and me both.

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Speaker 1: But Trump's argument was essentially that CBS editing these interviews

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Speaker 1: was election interference. Sued CBS. CBS settled and agreed to

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Speaker 1: donate sixteen million dollars to CBS's parent company, not to

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Speaker 1: Trump himself, but to his sort of presidential library fund.

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Speaker 1: Out of this came in agreement that CBS and sixty

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Speaker 1: Minutes in the future will release transcripts of interviews with

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Speaker 1: US presidential candidates after they air, with allowance for reactions

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Speaker 1: for like legal or national security concerns. Typically, I do

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Speaker 1: feel like a company only will settle like this if

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Speaker 1: they feel like they might lose. But I think that Trump.

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Speaker 1: I think that with CBS. Honestly, I think that they

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Speaker 1: were like, you know, this is a way to curry

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Speaker 1: favor with the new administration. I think from the perspective

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Speaker 1: of a CBS executive, it's like, oh, a win win,

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Speaker 1: we get to avoid this what could be a nasty

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Speaker 1: fight with a vengeful administration, and then we could hand

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Speaker 1: that administration like a pr win kiss the ring, easyps right.

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Speaker 2: Yeah, they already sacrificed Stephen Colbert as his alter. There's

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Speaker 2: already talk of them wanting to buy Warner Brothers Discovery

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Speaker 2: today that Paramount Help just today, Yeah, yeah, just today

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Speaker 2: that came out that this is the next move. They're

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Speaker 2: just buying it all up to make it cable again.

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Speaker 2: So I think them knowing that it's going to get pushedback,

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Speaker 2: that would just be such a large conglomerate. I think

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Speaker 2: they're trying to do whatever they can to abuse this

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Speaker 2: administration so they can I don't know, buy Peacock in

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Speaker 2: every single streaming platform. I don't I don't know why

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Speaker 2: they want to do that, but yeah, I don't know.

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Speaker 1: What the endgame is either, but I will say like

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Speaker 1: they really did a lot, I think to curry favor

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Speaker 1: with the Trump administration. They appointed a bias monitor, and

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Speaker 1: I think that just goes to show like there is

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Speaker 1: no end to like when you give an inch, they

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Speaker 1: take many many miles, there is no end to what

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Speaker 1: they take. And so CBS already has all of this

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Speaker 1: unprecedented oversight from the federal government to make sure that

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Speaker 1: they're not being I don't know, biased against Trump for

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Speaker 1: mean to Trump. So it is against this backdrump enter.

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Speaker 1: The United States Secretary of Homeland Security, Christine Home, she

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Speaker 1: did an interview with CBS and that she did not

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Speaker 1: like how that interview was edited after the fact. So

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Speaker 1: after the interview, she tweeted, this morning, I joined CBS

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Speaker 1: to report the facts of kilmar Garcia. Instead, CBS shamefully

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Speaker 1: edited the interview to whitewash the truth about this MS

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Speaker 1: thirteen gang member and the threat he poses to American

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Speaker 1: public safety. Now, mind you, when she tweeted this, CBS

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Speaker 1: had already posted both the full transcript and the full

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Speaker 1: recording of that interview. So it is sort of like, well,

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Speaker 1: I don't know just it just it just seems like, yeah,

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Speaker 1: what else do you want at this point.

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Speaker 2: They're never They're never going.

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Speaker 1: To be happy. It's just they'll never be happy.

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Speaker 2: If they can find something to pick at to say

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Speaker 2: I'm being victimized, they will. That's what they want, is

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Speaker 2: that ability to say you're targeting us.

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Speaker 1: It's unfair for us.

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Speaker 2: So even if you know these networks do everything they want,

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Speaker 2: they're still going to turn around and be like, oh, yeah,

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Speaker 2: well that Salas Park episode was rude, and so actually, no,

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Speaker 2: we're not going to approve this.

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Speaker 1: You know, it's they're never going to be.

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Speaker 2: On your side.

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Speaker 1: You can't truly buy them. Yes, exactly so. And again,

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Speaker 1: as we were talking about earlier, it is just so

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Speaker 1: common for networks to do this with lengthy interviews. It

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Speaker 1: is not unusual, it's it's totally commonplace. And again the

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Speaker 1: substance of the complaint doesn't even really stand because it's like, Okay, well,

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Speaker 1: somebody could just go watch the full interview. Honestly, when

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Speaker 1: you look at what they chose to edit so in

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Speaker 1: the interview that upset her so much, she's talking about

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Speaker 1: kilmar Garcia. Kill mar Garcia of course, with the man

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Speaker 1: who was sent to immigration prison in El Salvador because

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Speaker 1: of what was widely reported to be an administrative error.

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Speaker 1: She accused him of essentially sex crimes against a child.

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Speaker 1: He has not been charged with this, and as far

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Speaker 1: as I know, there was no evidence of this. Besides noo,

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Speaker 1: I'm saying the Department of Justice is like looking into it.

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Speaker 1: So CBS, I think kind of completely correctly made the

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Speaker 1: correct call. They're like, oh, we can't just.

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Speaker 2: Air belamatory live. Yeah, Like we don't want to get

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Speaker 2: sued for libel like that is real.

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Speaker 1: Yeah, And so I don't blame CBS for this at all.

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Speaker 1: And to be honest with you, I feel like they're

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Speaker 1: almost doing Christy a favor, because when you say something

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Speaker 1: that is wild and also potentially legally actionable, editing that

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Speaker 1: out is a favor. I almost thought they did her

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Speaker 1: a favor by not allowing her most salacious, baseless, inappropriate

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Speaker 1: accusation just stand. In that way, they almost are trying

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Speaker 1: to make her look I think, I think anything more sane.

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Speaker 2: Yeah, And that's the thing is that she doesn't want

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Speaker 2: to look sane. She wants that clip that's gonna go

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Speaker 2: viral and like get people angry, So that's what it is.

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Speaker 2: At the end of the day, it doesn't matter if

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Speaker 2: it's a lie or not true or unfounded. She dropped

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Speaker 2: that because she knew, Oh, this is what people are

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Speaker 2: going to freak out over and say, like, oh, she's

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Speaker 2: saying she's making things up, and that's what she wanted,

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Speaker 2: and she didn't get that moment, so she had to

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Speaker 2: turn it around and say, you know, CBS is editing

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Speaker 2: me and whitewashing what I had to say.

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Speaker 1: Exactly, and I guess I don't feel like journalists are

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Speaker 1: required to repeat on authoritarian administration's baseless smears, and at

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Speaker 1: the end of the day, like journalists do have an

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Speaker 1: ethical obligation to not just let anybody say anything on

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Speaker 1: their platform. However, because CBS is run by people who

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Speaker 1: are really trying to curry favor with this administration, they

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Speaker 1: announce that they are no longer doing any editing of

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Speaker 1: interviews like this. So just days after these complaints, CBS

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Speaker 1: News said that they would no longer allow editing of

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Speaker 1: its guests words on the Sunday Morning Public Affairs show,

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Speaker 1: So going forward, they're only going to do broadcast live

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Speaker 1: or live to tape meeting that guest statements could not

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Speaker 1: be edited subject to legal or national security restrictions. Phbeus

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Speaker 1: if this change was made in response to audience feedback,

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Speaker 1: which I guess you mean, like an audience of.

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Speaker 2: Ones, you know, you know, trum In Trevid Gnome just

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Speaker 2: like saying to me, I do like this honestly, because

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Speaker 2: it feels a bit sarcastic and sasse on CBS's part,

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Speaker 2: because I do think this is going to come back

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Speaker 2: to bite a politician in the ass. I they're they're

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Speaker 2: gonna be like, oh right. There was a reason why

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Speaker 2: we wanted things edited, and we wanted to have a

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Speaker 2: second to collect our thoughts.

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Speaker 1: Uh.

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Speaker 2: So that's what I'm excited for. Someone is gonna say

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Speaker 2: something really dumb.

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Speaker 1: This is a low stakes podcast, and it is lightly

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Speaker 1: edited for clarity. If you're talking about important global politics

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Speaker 1: stuff with big implications, you're stressed out. If you're in

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Speaker 1: this administration, maybe you've had a couple drinks, you know

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Speaker 1: what I'm saying, and the lights are on you. We're

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Speaker 1: about to get some gas. That's a great point. Yeah, yeah,

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Speaker 1: it's gonna be good. So okay, I wanted to include

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Speaker 1: this story because there's another sort of substory in this

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Speaker 1: that it's my own little personal thing. So I have

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Speaker 1: a little bit of a bone to pick with CBS, obviously,

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Speaker 1: and I live in DC. We've been dealing with the

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Speaker 1: entire you know, takeover of our police force and surge

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Speaker 1: of a National Guard in my city. President Trump was

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Speaker 1: trying to make a big show of how safe DC

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Speaker 1: is and how he's finally cleaned up the city, YadA, YadA.

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Speaker 1: So he was making a big show of going to

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Speaker 1: dinner at a seafood restaurant downtown. When he got there,

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Speaker 1: the activist group Code Pink had people in the restaurant

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Speaker 1: to protest. They screamed, they called him hitler. It was

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Speaker 1: a great clip. However, when CBS News reported on it,

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Speaker 1: their headline was quote when President Trump ada or Washington

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Speaker 1: restaurant to promote his federal law enforcement surge of the

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Speaker 1: nation's capital, he was greeted by protesters inside but cheers outside.

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Speaker 1: So I'm gonna play you a clip, Ashley, and this

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Speaker 1: is a clip of I'll put it in the show notes.

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Speaker 1: It is Trump walking into the restaurant. It is very

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Speaker 1: clearly outside. I'm gonna play you the clip, and you

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Speaker 1: tell me what you hear. Yeah, so that is the

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00:22:00,080 --> 00:22:04,280
Speaker 1: dynamic that CBS News described as cheers from outside. I

434
00:22:04,320 --> 00:22:07,320
Speaker 1: don't know what ye Ashley, would you give it time

435
00:22:07,400 --> 00:22:08,640
Speaker 1: about as cheers?

436
00:22:09,119 --> 00:22:11,200
Speaker 2: I know, I gotta give that to the booze.

437
00:22:11,240 --> 00:22:12,440
Speaker 1: The booze won that one.

438
00:22:13,200 --> 00:22:17,880
Speaker 2: Uh and people swearing there were some wooze but they

439
00:22:17,880 --> 00:22:19,080
Speaker 2: were drowned out by the booze.

440
00:22:19,600 --> 00:22:21,880
Speaker 1: Yeah, I feel like a more honest way to say

441
00:22:21,920 --> 00:22:25,919
Speaker 1: it was cheers and jeers something, But just to say that,

442
00:22:26,000 --> 00:22:30,120
Speaker 1: oh the it clearly sort of suggests that the people

443
00:22:30,240 --> 00:22:32,920
Speaker 1: outside on the street love them, and there was echoes

444
00:22:32,960 --> 00:22:36,560
Speaker 1: of cheer. But those activists inside they've always got They're

445
00:22:36,600 --> 00:22:38,119
Speaker 1: always complaining about something.

446
00:22:38,440 --> 00:22:41,359
Speaker 2: Yeah, you know, they're just people. They always making a

447
00:22:41,440 --> 00:22:43,639
Speaker 2: rock case. And but everybody outside was like, yeah, we

448
00:22:43,680 --> 00:22:47,720
Speaker 2: love this Trump. So yeah, yeah, I just CBS, what

449
00:22:47,760 --> 00:22:48,240
Speaker 2: are you doing?

450
00:22:48,680 --> 00:22:50,280
Speaker 1: What are you doing? Get it together?

451
00:22:51,080 --> 00:23:07,360
Speaker 3: Yeah, let's take a quick break at our back.

452
00:23:09,720 --> 00:23:12,439
Speaker 1: Okay. So I also wanted to talk about kind of

453
00:23:12,480 --> 00:23:16,320
Speaker 1: a darker story, which is the well dark, but I

454
00:23:16,320 --> 00:23:18,719
Speaker 1: guess it is a story about justice being served. So

455
00:23:18,960 --> 00:23:22,199
Speaker 1: I'll let y'all be the judge. So this website, this

456
00:23:22,280 --> 00:23:25,840
Speaker 1: porn website, girls do porn the creator of this site

457
00:23:25,840 --> 00:23:27,919
Speaker 1: has sort of been He was on the LAMB for

458
00:23:27,960 --> 00:23:31,520
Speaker 1: a while, he was on the FBI's most wanted list,

459
00:23:31,560 --> 00:23:33,480
Speaker 1: and this week he was sentenced to you might have

460
00:23:33,560 --> 00:23:36,840
Speaker 1: seen a documentary about it. If yeah, Ye're gonna say, yeah,

461
00:23:37,000 --> 00:23:39,640
Speaker 1: I've definitely seen this doccumentary yet, so you know all

462
00:23:39,680 --> 00:23:43,679
Speaker 1: about it. Michael James Pratt, the creator of the California

463
00:23:43,680 --> 00:23:46,880
Speaker 1: based porn website Girls Do Porn, was sentenced to twenty

464
00:23:46,920 --> 00:23:50,000
Speaker 1: seven years in federal prison for sex trafficking after pleading

465
00:23:50,000 --> 00:23:54,399
Speaker 1: guilty for using force, fraud and coercion to recruit hundreds

466
00:23:54,440 --> 00:23:57,080
Speaker 1: of women, many of whom were in their late teens,

467
00:23:57,119 --> 00:24:00,560
Speaker 1: for adult videos. He was sentenced on one charge of

468
00:24:00,600 --> 00:24:03,120
Speaker 1: sex trafficking in my force fraud or coersion, and one

469
00:24:03,200 --> 00:24:07,240
Speaker 1: kind of conspiracy to commit the same crime. So I've

470
00:24:07,240 --> 00:24:11,720
Speaker 1: also the documentary is quite good. It does really make

471
00:24:11,800 --> 00:24:15,040
Speaker 1: clear how long of a thing this has been. That

472
00:24:15,040 --> 00:24:18,000
Speaker 1: that documentary came out years ago, the site was online

473
00:24:18,160 --> 00:24:21,159
Speaker 1: even earlier than that, and only this week was he

474
00:24:21,200 --> 00:24:22,200
Speaker 1: actually sentenced.

475
00:24:22,560 --> 00:24:26,040
Speaker 2: Yeah, I did not know that there was more happening

476
00:24:26,080 --> 00:24:28,360
Speaker 2: with it, Like it feels like it was so long ago.

477
00:24:28,400 --> 00:24:30,800
Speaker 2: I was like, oh, right, how did that documentary and like,

478
00:24:30,840 --> 00:24:32,680
Speaker 2: did anything actually happen to him?

479
00:24:33,160 --> 00:24:36,320
Speaker 1: I guess not until now. Yeah, so it's been kind

480
00:24:36,320 --> 00:24:38,360
Speaker 1: of a long time coming. Pratt was on the FBI's

481
00:24:38,400 --> 00:24:40,680
Speaker 1: ten most wanted list when he was arrested in Madrid

482
00:24:40,680 --> 00:24:43,760
Speaker 1: and twenty twenty two, three years after he fled while

483
00:24:43,760 --> 00:24:47,600
Speaker 1: awaiting sex trafficking charges. The San Diego Union Tribune reports

484
00:24:47,640 --> 00:24:49,920
Speaker 1: that Pratt, a forty two year old New Zealand citizen,

485
00:24:50,200 --> 00:24:52,879
Speaker 1: admitted in a plea ar agreement earlier this year that

486
00:24:53,000 --> 00:24:56,280
Speaker 1: between twenty twelve and twenty nineteen he conspired to traffic

487
00:24:56,359 --> 00:24:59,040
Speaker 1: fifteen victims. The authorities have said that as a tiny

488
00:24:59,119 --> 00:25:02,880
Speaker 1: fraction of the app actual victims of the conspiracy, it

489
00:25:02,920 --> 00:25:06,560
Speaker 1: is a dizzying array of crimes. Basically the site it

490
00:25:06,640 --> 00:25:10,439
Speaker 1: just sounds so awful and scammy. Basically, these are not

491
00:25:10,640 --> 00:25:15,159
Speaker 1: women who were consensually wanting to be and signed up

492
00:25:15,200 --> 00:25:18,800
Speaker 1: to be involved in making consensual adult content. This was

493
00:25:18,840 --> 00:25:22,080
Speaker 1: back when I guess you might call it casting couch

494
00:25:22,320 --> 00:25:26,200
Speaker 1: porn was like a growing thing online, and so he

495
00:25:26,240 --> 00:25:29,879
Speaker 1: had this entire very gross, not to mention illegal scheme

496
00:25:29,920 --> 00:25:33,199
Speaker 1: worked up where him and his team would post what

497
00:25:33,320 --> 00:25:37,280
Speaker 1: seemed like legitimate modeling ads on Craigslist that women looking

498
00:25:37,320 --> 00:25:40,000
Speaker 1: for modeling work would apply to. Then they would pay

499
00:25:40,160 --> 00:25:43,800
Speaker 1: other women to kind of act as references to vouch

500
00:25:43,880 --> 00:25:46,680
Speaker 1: for this company and say, oh, it's a legitimate modeling company.

501
00:25:47,119 --> 00:25:49,400
Speaker 1: I had a good experience with them. You can trust them.

502
00:25:49,920 --> 00:25:52,040
Speaker 1: So when I was in college, I did a little

503
00:25:52,040 --> 00:25:54,040
Speaker 1: bit of modeling on the side for extra cash. And

504
00:25:54,240 --> 00:25:58,199
Speaker 1: I will say the it is a space rife with

505
00:25:58,640 --> 00:26:01,520
Speaker 1: all kinds of gross stuff. But what is a it's

506
00:26:01,560 --> 00:26:03,320
Speaker 1: it's not it's not a great space, I'll say. And

507
00:26:03,359 --> 00:26:06,200
Speaker 1: you you're when you're wading through it, it's like scam, scam, scam,

508
00:26:06,240 --> 00:26:07,000
Speaker 1: scam scam.

509
00:26:07,080 --> 00:26:07,359
Speaker 4: Yeah.

510
00:26:07,480 --> 00:26:11,280
Speaker 2: My my brother is a model and he lives in Texas.

511
00:26:11,359 --> 00:26:12,879
Speaker 2: One time he was like, Hey, I'm gonna be in

512
00:26:13,000 --> 00:26:14,440
Speaker 2: La I'm shooting. I'm a model.

513
00:26:14,480 --> 00:26:16,639
Speaker 1: I'm gonna do this thing with an agency. I was like,

514
00:26:16,680 --> 00:26:18,760
Speaker 1: what are you talking. He's like, yeahs the underwear thing.

515
00:26:18,800 --> 00:26:19,919
Speaker 1: I'll be in a warehouse.

516
00:26:19,960 --> 00:26:23,960
Speaker 2: And I was like, oh no, no, no, no, no, baby, yes,

517
00:26:24,400 --> 00:26:25,080
Speaker 2: mendis ad.

518
00:26:25,119 --> 00:26:27,280
Speaker 1: It was legit. It was okay, it was legit. It

519
00:26:27,359 --> 00:26:28,720
Speaker 1: was legit. It was legit.

520
00:26:28,880 --> 00:26:30,879
Speaker 2: But I was like, no, I'm coming with you, I

521
00:26:30,960 --> 00:26:32,360
Speaker 2: will be checking this out good.

522
00:26:32,560 --> 00:26:34,280
Speaker 1: Yeah, that was always my thing. I would always show

523
00:26:34,359 --> 00:26:36,800
Speaker 1: up with that with a male friend or you know,

524
00:26:37,000 --> 00:26:38,679
Speaker 1: that was always the way that I played it. But

525
00:26:39,119 --> 00:26:41,960
Speaker 1: so even for a space that is rife with those

526
00:26:42,080 --> 00:26:45,679
Speaker 1: kinds of gross things, what is described here I have

527
00:26:45,760 --> 00:26:48,360
Speaker 1: never encountered and had not heard of. So when these

528
00:26:48,400 --> 00:26:50,520
Speaker 1: women would would you know, think like, okay, this is

529
00:26:50,520 --> 00:26:53,959
Speaker 1: a legit, legitimate modeling gig. They would show up and

530
00:26:54,000 --> 00:26:56,719
Speaker 1: then they would be asked to do sex acts on camera,

531
00:26:56,800 --> 00:26:59,640
Speaker 1: but often would be applied with drugs or alcohol, made

532
00:26:59,680 --> 00:27:02,080
Speaker 1: to through a contract that they were unable to read,

533
00:27:02,560 --> 00:27:05,359
Speaker 1: or even verbally promised that the videos would not be

534
00:27:05,440 --> 00:27:08,120
Speaker 1: published in the US, only abroad, or it would only

535
00:27:08,160 --> 00:27:11,000
Speaker 1: be sold for like private buyers, so it won't be

536
00:27:11,320 --> 00:27:14,960
Speaker 1: widely distributed. So they would knowingly lie to these women

537
00:27:15,760 --> 00:27:20,320
Speaker 1: and with the understanding that these videos were going to

538
00:27:20,359 --> 00:27:23,600
Speaker 1: be distributed widely and essentially blow up their lives. I

539
00:27:23,640 --> 00:27:26,120
Speaker 1: found this to be really interesting. According to people who

540
00:27:26,119 --> 00:27:28,800
Speaker 1: were in on this scheme, fifty percent of the women

541
00:27:28,920 --> 00:27:31,679
Speaker 1: were not even paid the amount they were promised. So

542
00:27:31,720 --> 00:27:34,320
Speaker 1: after all of that, you know, you're already kind of

543
00:27:34,400 --> 00:27:37,639
Speaker 1: being forced into doing something that you don't want to

544
00:27:37,680 --> 00:27:39,919
Speaker 1: do via lies. They're like, oh, we're going to give

545
00:27:39,920 --> 00:27:41,720
Speaker 1: you two thousand dollars and they didn't even get that.

546
00:27:41,800 --> 00:27:43,840
Speaker 1: So I don't think I don't need to tell anybody.

547
00:27:43,880 --> 00:27:45,480
Speaker 1: All of this is not only just like scam me,

548
00:27:45,520 --> 00:27:48,840
Speaker 1: it is also illegal. And the sex acts that they

549
00:27:48,880 --> 00:27:51,720
Speaker 1: would do once the women you know, agreed to do

550
00:27:51,800 --> 00:27:55,119
Speaker 1: this were often violent and sometimes according to the Department

551
00:27:55,160 --> 00:27:57,760
Speaker 1: of Justice Criminal Right, it was it was assault. It

552
00:27:57,840 --> 00:28:01,120
Speaker 1: was like very violent. The Sandy Jago Union Tribune has

553
00:28:01,160 --> 00:28:04,679
Speaker 1: some really in depth reporting about the sentencing and how

554
00:28:05,119 --> 00:28:08,439
Speaker 1: Pratt was made to listen to his victims and like

555
00:28:08,520 --> 00:28:10,440
Speaker 1: face them for the first time. A lot of these

556
00:28:10,440 --> 00:28:13,240
Speaker 1: women have been trying to communicate with him for years,

557
00:28:13,240 --> 00:28:15,760
Speaker 1: saying take this down, take this down, and he would

558
00:28:15,760 --> 00:28:19,600
Speaker 1: just ignore them and eventually skip the country. So him

559
00:28:19,720 --> 00:28:22,560
Speaker 1: having to actually listen to what they had to say

560
00:28:22,600 --> 00:28:25,520
Speaker 1: for the first time I think was really resonant. Their

561
00:28:25,600 --> 00:28:29,399
Speaker 1: testimony is horrifying. They talk about how after the footage

562
00:28:29,480 --> 00:28:33,160
Speaker 1: was released online, their personal contact information would also be released,

563
00:28:33,160 --> 00:28:36,160
Speaker 1: so they would face harassment. They would, you know, face consequence.

564
00:28:36,200 --> 00:28:38,480
Speaker 1: People would send videos to their friends and family, they'd

565
00:28:38,480 --> 00:28:41,000
Speaker 1: be fired from jobs, kicked out of school, lots of

566
00:28:41,040 --> 00:28:45,040
Speaker 1: addiction issues, lots of suicidal ideation. So when these victims

567
00:28:45,560 --> 00:28:48,440
Speaker 1: faced him in court and spoke to him, you really

568
00:28:48,520 --> 00:28:51,080
Speaker 1: kind of hear how important of a moment it was

569
00:28:51,120 --> 00:28:52,840
Speaker 1: for them. A couple of things that they said. One

570
00:28:52,920 --> 00:28:56,000
Speaker 1: victim said, we meet again, but this time it is

571
00:28:56,080 --> 00:28:58,880
Speaker 1: you who cannot leave. Another said, we are not here

572
00:28:58,920 --> 00:29:02,680
Speaker 1: for forgiveness, we are here for justice. I won. One

573
00:29:02,680 --> 00:29:05,000
Speaker 1: woman told Pratt, turning to speak directly to him, I

574
00:29:05,000 --> 00:29:07,880
Speaker 1: am not your victim. I am your reckoning. So these

575
00:29:07,880 --> 00:29:10,680
Speaker 1: were women who were, like I am waited so long.

576
00:29:10,800 --> 00:29:12,720
Speaker 1: Like I definitely think people should read the article because

577
00:29:12,760 --> 00:29:15,800
Speaker 1: they talk about how these women almost formed a sisterhood

578
00:29:15,800 --> 00:29:19,520
Speaker 1: because they were so like they had to band together

579
00:29:19,640 --> 00:29:20,520
Speaker 1: to take down this too.

580
00:29:20,600 --> 00:29:22,600
Speaker 2: Yeah, so even to get to get any of this

581
00:29:22,680 --> 00:29:25,360
Speaker 2: to happen, to get anyone in law enforcement to pay

582
00:29:25,360 --> 00:29:27,440
Speaker 2: attention and to take it seriously.

583
00:29:27,400 --> 00:29:30,880
Speaker 1: It's so sad how long this took. And I found

584
00:29:30,880 --> 00:29:32,880
Speaker 1: this bit to be really interesting from the piece. One

585
00:29:32,960 --> 00:29:35,600
Speaker 1: victim told the judge that Pratt provided her a cake

586
00:29:35,720 --> 00:29:37,600
Speaker 1: on the day that she shot her video because it

587
00:29:37,640 --> 00:29:41,120
Speaker 1: was her eighteenth birthday. Another woman identified in the fleet agreement,

588
00:29:41,120 --> 00:29:43,800
Speaker 1: as victim number one was also eighteen years old when

589
00:29:43,840 --> 00:29:46,280
Speaker 1: Pratt quote rushed her through a contract and did not

590
00:29:46,320 --> 00:29:48,760
Speaker 1: provide her with a copy. He paid her two thousand

591
00:29:48,840 --> 00:29:51,000
Speaker 1: dollars and then ignored her please to pick the video

592
00:29:51,040 --> 00:29:54,400
Speaker 1: down when it posted online. That woman said in court

593
00:29:54,480 --> 00:29:57,080
Speaker 1: that she has since graduated from Princeton University and now

594
00:29:57,160 --> 00:29:59,800
Speaker 1: works with the tech industry and has become a specialist

595
00:29:59,800 --> 00:30:03,120
Speaker 1: and helping people send takedown notices to websites.

596
00:30:03,560 --> 00:30:05,520
Speaker 2: You know what, I follow her.

597
00:30:05,640 --> 00:30:08,840
Speaker 1: I follow her. Really, she's very cool. She's very cool.

598
00:30:10,840 --> 00:30:15,280
Speaker 2: Because people like, like misogynists are always like, you know,

599
00:30:16,360 --> 00:30:18,560
Speaker 2: some rich guy probably paid for you to go to Princeton,

600
00:30:18,640 --> 00:30:20,440
Speaker 2: like you're a sugar baby. And she's like, uh no,

601
00:30:20,480 --> 00:30:22,760
Speaker 2: actually I had a fluorized scholarship and I do this.

602
00:30:23,080 --> 00:30:26,760
Speaker 2: And she's just such a badass who is not afraid

603
00:30:26,920 --> 00:30:30,040
Speaker 2: to shoot back at these people who, you know, think

604
00:30:30,080 --> 00:30:33,000
Speaker 2: that they can control women with online manipulation.

605
00:30:33,560 --> 00:30:36,400
Speaker 1: Yes, And I think your point is such a good

606
00:30:36,400 --> 00:30:40,320
Speaker 1: one because I think for so long the implication was

607
00:30:40,360 --> 00:30:43,160
Speaker 1: that these women don't deserve respect. They did it, They

608
00:30:43,200 --> 00:30:45,800
Speaker 1: did it to themselves, exactly when we actually look at

609
00:30:45,800 --> 00:30:48,360
Speaker 1: the charges and what they is what the government says

610
00:30:48,400 --> 00:30:51,440
Speaker 1: he did. They didn't do anything themselves. They showed up

611
00:30:51,440 --> 00:30:53,600
Speaker 1: thinking there was going to be a modeling gig and

612
00:30:53,640 --> 00:30:56,240
Speaker 1: they were clearly lied to and manipulated, And so it's

613
00:30:56,240 --> 00:30:58,160
Speaker 1: I don't think it's fair to be like, oh, well,

614
00:30:58,680 --> 00:31:00,440
Speaker 1: you brought this on yourself, didn't know this was going

615
00:31:00,480 --> 00:31:02,320
Speaker 1: to happen, whatever, whatever, whatever, And it said, well, if

616
00:31:02,320 --> 00:31:03,920
Speaker 1: I brought it on myself, he wouldn't have had to

617
00:31:03,960 --> 00:31:05,200
Speaker 1: lie to me to get me to do it. He

618
00:31:05,200 --> 00:31:07,640
Speaker 1: wouldn't have to manipulate me to get me to agree to.

619
00:31:07,600 --> 00:31:08,560
Speaker 2: It, to get me to agree.

620
00:31:08,560 --> 00:31:12,200
Speaker 1: But it's coercion exactly. And I remember kind of a

621
00:31:12,240 --> 00:31:15,080
Speaker 1: while ago, I think it was two young women who

622
00:31:15,120 --> 00:31:18,920
Speaker 1: were contestants in the Miss Teen USA pageant. They were

623
00:31:18,920 --> 00:31:20,760
Speaker 1: they it was revealed that they were both on this

624
00:31:20,960 --> 00:31:23,320
Speaker 1: site and they had to drop out of the pageant,

625
00:31:23,320 --> 00:31:26,240
Speaker 1: and I remember thinking, you know, all these years later,

626
00:31:26,680 --> 00:31:29,400
Speaker 1: clearly they were not the one who did something wrong.

627
00:31:29,640 --> 00:31:29,720
Speaker 3: It.

628
00:31:29,920 --> 00:31:33,560
Speaker 1: Yeah, where where was the Where was the smoke back then?

629
00:31:33,960 --> 00:31:35,880
Speaker 1: I know we had a very different culture for women then,

630
00:31:35,880 --> 00:31:37,560
Speaker 1: but where was the smoke for the person who was

631
00:31:37,760 --> 00:31:42,440
Speaker 1: breaking the law and trafficking women to enrich himself by

632
00:31:42,600 --> 00:31:44,120
Speaker 1: victimizing and manipulating them.

633
00:31:44,600 --> 00:31:47,040
Speaker 2: Yeah, that just as the time was not part of

634
00:31:47,040 --> 00:31:50,280
Speaker 2: the narrative. Nobody thought about like you know, revenge, peorn

635
00:31:50,320 --> 00:31:53,440
Speaker 2: laws or oh the guy is doing something wrong here too.

636
00:31:53,600 --> 00:31:56,400
Speaker 2: It was just Oh, it's her fault she did, you know,

637
00:31:56,520 --> 00:31:58,560
Speaker 2: she's their own fault. She didn't want this, she shouldn't

638
00:31:58,560 --> 00:32:02,160
Speaker 2: have taken nude, she shouldn't have done this. And now

639
00:32:02,320 --> 00:32:05,480
Speaker 2: at least kind of people are like, oh, you're committing

640
00:32:05,520 --> 00:32:06,520
Speaker 2: a criminal act like this.

641
00:32:07,280 --> 00:32:10,440
Speaker 1: It's just so intrusive.

642
00:32:10,600 --> 00:32:12,640
Speaker 2: I like, more than like just, I just think it's

643
00:32:12,640 --> 00:32:14,680
Speaker 2: one of the worst things that can happen to someone,

644
00:32:14,760 --> 00:32:16,840
Speaker 2: even if it's someone like I don't like, you know,

645
00:32:16,960 --> 00:32:20,440
Speaker 2: like Kendall from Love Island his nudes were leaked by

646
00:32:20,480 --> 00:32:24,000
Speaker 2: someone and you just felt awful for him, Like, it

647
00:32:24,600 --> 00:32:28,360
Speaker 2: just is such a horrible invasion of privacy and to

648
00:32:28,400 --> 00:32:31,960
Speaker 2: see someone not only do that because they want to

649
00:32:32,080 --> 00:32:34,680
Speaker 2: humiliate these women, but also because they're making so much

650
00:32:34,720 --> 00:32:36,480
Speaker 2: money off of it, and they know they can get

651
00:32:36,480 --> 00:32:38,360
Speaker 2: away with it because there are other horrible.

652
00:32:38,000 --> 00:32:42,120
Speaker 1: Men who want this content. Yeah, I just this is

653
00:32:42,160 --> 00:32:44,800
Speaker 1: so mad. Yeah, you put that really well. I mean,

654
00:32:45,280 --> 00:32:47,280
Speaker 1: it's something I think about a lot is and I

655
00:32:47,600 --> 00:32:50,240
Speaker 1: know that you remember this, but in the in the

656
00:32:50,400 --> 00:32:55,200
Speaker 1: twenty tens, the iCloud photo hacks where all of these

657
00:32:55,640 --> 00:33:00,400
Speaker 1: celebrity intimate content got hacked and put online, and you know,

658
00:33:00,560 --> 00:33:03,680
Speaker 1: I remember so clearly the conversation that we had about

659
00:33:03,680 --> 00:33:06,880
Speaker 1: that when it happened was exactly what you just articulated, that, oh,

660
00:33:06,920 --> 00:33:09,840
Speaker 1: they shouldn't have taken the pictures. I remember the it

661
00:33:09,880 --> 00:33:12,800
Speaker 1: was a the tech columnist at the New York Times.

662
00:33:12,920 --> 00:33:16,360
Speaker 1: His advice to Starlits was don't take nude pictures. And

663
00:33:16,520 --> 00:33:19,200
Speaker 1: here we are, you know, ten years later, and we

664
00:33:19,240 --> 00:33:22,600
Speaker 1: really see how none of that met the moment because

665
00:33:22,640 --> 00:33:26,120
Speaker 1: now we have AI enabled deep fakes, where all you

666
00:33:26,120 --> 00:33:28,120
Speaker 1: have to do is exist. You don't do anything. You

667
00:33:28,120 --> 00:33:31,080
Speaker 1: could just be existing in like fully clothed it get

668
00:33:31,160 --> 00:33:34,480
Speaker 1: clothed existing, and then you actually have to have that part.

669
00:33:34,560 --> 00:33:38,560
Speaker 2: You can just have a personality, and then Meta will

670
00:33:38,600 --> 00:33:41,240
Speaker 2: take you and turn you into a sexual chatbop so

671
00:33:41,280 --> 00:33:45,440
Speaker 2: exactly as they did with Taylor Swift. And it's just

672
00:33:46,360 --> 00:33:48,600
Speaker 2: I don't know, I think, you know, years from now,

673
00:33:48,720 --> 00:33:51,480
Speaker 2: like as we've had this kind of reckoning with revenge porn,

674
00:33:51,480 --> 00:33:53,720
Speaker 2: I think eventually we're going to be like, oh, right,

675
00:33:53,960 --> 00:33:56,160
Speaker 2: that was a horrible invasion of privacy that we were

676
00:33:56,160 --> 00:34:00,160
Speaker 2: just taking people's photos and making AI images and voices.

677
00:33:59,800 --> 00:34:03,000
Speaker 1: Out of them. I think it was some brand just

678
00:34:03,040 --> 00:34:03,920
Speaker 1: recently got in trouble.

679
00:34:04,320 --> 00:34:06,760
Speaker 2: I think it was maybe Fashion Nova, but they like

680
00:34:07,160 --> 00:34:10,120
Speaker 2: clearly were using AI to make their model things. And

681
00:34:10,160 --> 00:34:13,520
Speaker 2: then the AI pulled in Luigi Mangioni's face. Yes it

682
00:34:13,640 --> 00:34:17,719
Speaker 2: was she am yeah, she and yeah, and everyone's like yeah, yeah,

683
00:34:17,760 --> 00:34:19,200
Speaker 2: that's a problem.

684
00:34:20,600 --> 00:34:21,000
Speaker 3: Yeah.

685
00:34:21,040 --> 00:34:24,040
Speaker 1: And I wish I could rewind back to those moments

686
00:34:24,440 --> 00:34:28,160
Speaker 1: like the iCloud photo hacks and force us to have

687
00:34:28,200 --> 00:34:33,400
Speaker 1: a different conversation because about digital consent, about consent more generally,

688
00:34:33,440 --> 00:34:38,440
Speaker 1: about not just putting the onus on the victim, the

689
00:34:38,560 --> 00:34:41,800
Speaker 1: or people to not do things in order to protect themselves.

690
00:34:41,840 --> 00:34:43,959
Speaker 1: I wish we could have had a different conversation because

691
00:34:43,960 --> 00:34:45,839
Speaker 1: I feel like where we are today in twenty twenty

692
00:34:45,920 --> 00:34:47,800
Speaker 1: five might be different. And it's a shame that we

693
00:34:47,880 --> 00:34:49,680
Speaker 1: had all of these big moments where we could have

694
00:34:49,760 --> 00:34:52,320
Speaker 1: had that conversation and work to create a different culture,

695
00:34:52,320 --> 00:34:52,960
Speaker 1: and we didn't.

696
00:34:53,280 --> 00:34:55,879
Speaker 2: Yeah, and now we're still dealing with it.

697
00:34:56,400 --> 00:34:58,960
Speaker 1: And you mentioned a good point of how this is,

698
00:34:59,280 --> 00:35:04,279
Speaker 1: you knowuative these videos from Girls to Porn, they were

699
00:35:04,320 --> 00:35:07,480
Speaker 1: distributed to other porn sites like bigger porn sites and

700
00:35:07,520 --> 00:35:10,880
Speaker 1: tube sites like Pornhub. According to the Daily Dot, Girls

701
00:35:10,920 --> 00:35:14,160
Speaker 1: Do Porn videos were viewed over eight hundred million times

702
00:35:14,200 --> 00:35:17,280
Speaker 1: on these websites, including roughly six hundred and eighty million

703
00:35:17,360 --> 00:35:20,640
Speaker 1: views on Pornhub, where Girls du Porn was among the

704
00:35:20,680 --> 00:35:24,520
Speaker 1: top twenty most viewed channels. And it just sounds like

705
00:35:24,920 --> 00:35:29,040
Speaker 1: even after Pornhub officially cut ties with the Girls Dow

706
00:35:29,120 --> 00:35:31,600
Speaker 1: porn channel in twenty nineteen, it just sounds like they

707
00:35:31,600 --> 00:35:35,200
Speaker 1: were very, very slow to take these videos down. It

708
00:35:35,239 --> 00:35:38,800
Speaker 1: took like reporting, it took I think Motherboard wrote a

709
00:35:38,800 --> 00:35:41,080
Speaker 1: big piece about it, like it took a lot of

710
00:35:41,200 --> 00:35:45,320
Speaker 1: cajoling to just simply take down these videos that I

711
00:35:45,360 --> 00:35:47,960
Speaker 1: think it sounds like Pornhub knew were made under very

712
00:35:47,960 --> 00:35:51,480
Speaker 1: sketchy circumstances. Yeah, and they're making money off of it,

713
00:35:51,600 --> 00:35:51,799
Speaker 1: you know.

714
00:35:52,040 --> 00:35:54,600
Speaker 2: I think they also have faced some lawsuits because they,

715
00:35:55,040 --> 00:35:57,640
Speaker 2: you know, had videos up that were people who were

716
00:35:57,680 --> 00:36:01,279
Speaker 2: under age or trafficked, and they get those reports and

717
00:36:01,480 --> 00:36:03,879
Speaker 2: just kind of were ignoring them until it was like, oh,

718
00:36:03,920 --> 00:36:05,520
Speaker 2: we have to take it down like the cops are

719
00:36:05,560 --> 00:36:07,000
Speaker 2: getting involved, Like okay.

720
00:36:07,360 --> 00:36:10,600
Speaker 1: So that's exactly why this whole girl's due porn thing

721
00:36:10,920 --> 00:36:14,200
Speaker 1: currently is also implicating porn Hub. In twenty twenty three,

722
00:36:14,280 --> 00:36:16,719
Speaker 1: Pornhub's parent company agreed to pay more than one point

723
00:36:16,800 --> 00:36:19,520
Speaker 1: eight million dollars to resolve a criminal probe alleging that

724
00:36:19,560 --> 00:36:22,760
Speaker 1: it profited from sex trafficking through its hosting of girls

725
00:36:22,800 --> 00:36:25,320
Speaker 1: do porn videos. More than one hundred and twenty women

726
00:36:25,360 --> 00:36:27,920
Speaker 1: have sued porn Hub's parent company in San Diego in

727
00:36:28,000 --> 00:36:32,160
Speaker 1: federal court a legend pornhub illegally published sex trafficking videos.

728
00:36:32,440 --> 00:36:34,919
Speaker 1: Pornhub's parent company settled the first of those suits under

729
00:36:35,000 --> 00:36:38,000
Speaker 1: terms that were not disclosed. So exactly what you just

730
00:36:38,080 --> 00:36:42,520
Speaker 1: said that you know, this wasn't just one small site

731
00:36:42,680 --> 00:36:46,440
Speaker 1: that was profiting off of this. They were profiting off

732
00:36:46,440 --> 00:36:50,680
Speaker 1: of it, but then those videos were then enriching like

733
00:36:50,840 --> 00:36:54,560
Speaker 1: much bigger porn sites like Pornhub, and so yeah, it's

734
00:36:54,640 --> 00:36:59,320
Speaker 1: just a scheme where mostly men who run these sites

735
00:36:59,360 --> 00:37:02,040
Speaker 1: get rich off of the manipulation of women, and then

736
00:37:02,080 --> 00:37:04,960
Speaker 1: it's the women who also face this added burden of

737
00:37:05,360 --> 00:37:08,759
Speaker 1: public scorn, public scrutiny. Why did you take the pictures,

738
00:37:08,800 --> 00:37:11,640
Speaker 1: getting kicked out of school, getting online, harassed, all of that.

739
00:37:12,040 --> 00:37:14,880
Speaker 2: Yeah, and then you know, like it reaches the Andrew

740
00:37:14,880 --> 00:37:17,279
Speaker 2: Tate level where then they have the women who are

741
00:37:17,280 --> 00:37:20,080
Speaker 2: making these videos, and then they use that to blackmail

742
00:37:20,160 --> 00:37:23,319
Speaker 2: them into making more content, and then they blackmail the

743
00:37:23,360 --> 00:37:27,399
Speaker 2: women into scamming other men so that they can get

744
00:37:27,440 --> 00:37:28,040
Speaker 2: more money.

745
00:37:28,120 --> 00:37:32,319
Speaker 1: And yeah, yeah, just a whole pyramid scheme built on

746
00:37:32,400 --> 00:37:49,160
Speaker 1: manipulation and sex crimes. More after a quick break, let's

747
00:37:49,160 --> 00:37:52,919
Speaker 1: get right back into it. Okay, So I have one

748
00:37:53,040 --> 00:37:55,040
Speaker 1: other story I wanted to talk to you about, which

749
00:37:55,080 --> 00:37:57,600
Speaker 1: I feel like as a podcaster, as somebody who cares

750
00:37:57,640 --> 00:38:00,000
Speaker 1: about media, I know I have a lot of thought abot,

751
00:38:00,200 --> 00:38:03,160
Speaker 1: but you might as well. Did you hear about this

752
00:38:04,160 --> 00:38:09,960
Speaker 1: AI based podcast startup? Yes, yeah, I heard? Okay, So,

753
00:38:10,440 --> 00:38:13,560
Speaker 1: as a podcaster, I am often asked if I'm worried

754
00:38:13,560 --> 00:38:17,839
Speaker 1: about AI, specifically AI taking the jobs of human podcasters

755
00:38:17,880 --> 00:38:20,440
Speaker 1: like you and me, Ashley, and so I'm asked this

756
00:38:20,560 --> 00:38:22,680
Speaker 1: so much that I've actually turned the answer to this

757
00:38:22,760 --> 00:38:26,279
Speaker 1: question into one of my signature public talks about the

758
00:38:26,320 --> 00:38:29,800
Speaker 1: intersection of creativity and technology. If you follow the conversation

759
00:38:29,840 --> 00:38:33,560
Speaker 1: around AI, oftentimes that conversation is like, Oh, what's going

760
00:38:33,640 --> 00:38:36,799
Speaker 1: to happen to human creative professionals, human podcasters. Huve been screenwriters,

761
00:38:36,800 --> 00:38:40,080
Speaker 1: Wh've been artists, animators, musicians, whatever. Anyway, so I spend

762
00:38:40,120 --> 00:38:42,440
Speaker 1: a lot of time thinking about this, talking about this.

763
00:38:42,560 --> 00:38:45,840
Speaker 1: So I was not terribly shocked to read that Hollywood

764
00:38:45,880 --> 00:38:49,040
Speaker 1: Reporter piece about Inception Point AI, this company that is

765
00:38:49,120 --> 00:38:52,240
Speaker 1: trying to build a stable of AI talent to host

766
00:38:52,320 --> 00:38:57,560
Speaker 1: podcasts and eventually become broader influencers across social media, literature,

767
00:38:57,840 --> 00:39:01,399
Speaker 1: and more. The Hollywood Reporter report, it's amid the high

768
00:39:01,400 --> 00:39:04,200
Speaker 1: cost of producing narrative podcasts and price the short term

769
00:39:04,239 --> 00:39:07,600
Speaker 1: contracts for popular hosts. The idea here is being able

770
00:39:07,600 --> 00:39:10,839
Speaker 1: to own scale and control the talent unlike those off

771
00:39:10,880 --> 00:39:14,080
Speaker 1: the cuff humans, and produce shows at a minimal cost.

772
00:39:14,440 --> 00:39:16,560
Speaker 1: We believe that in the near future, half the people

773
00:39:16,560 --> 00:39:19,040
Speaker 1: on the planet will be AI, and we're the company

774
00:39:19,040 --> 00:39:21,320
Speaker 1: that's bringing those people to life. This is from CEO

775
00:39:21,480 --> 00:39:24,880
Speaker 1: Janine Wright, who previously was the chief operating officer of

776
00:39:24,920 --> 00:39:28,600
Speaker 1: podcasting at Wondery, which has recently had to be organized

777
00:39:28,680 --> 00:39:32,040
Speaker 1: under the changing podcast landscape following a series of questionable

778
00:39:32,080 --> 00:39:33,560
Speaker 1: business decisions.

779
00:39:33,000 --> 00:39:37,160
Speaker 2: Decisions and layoffs and yeah, all of it, all of it.

780
00:39:37,160 --> 00:39:41,080
Speaker 2: It's making a lot of sense now. I I don't

781
00:39:41,560 --> 00:39:44,880
Speaker 2: know who hears the statistic like we think fifty percent

782
00:39:44,920 --> 00:39:47,480
Speaker 2: of the people will be AI, and gets excited about that,

783
00:39:47,600 --> 00:39:51,239
Speaker 2: Like what you're excited about? Like dead Internet? You want

784
00:39:51,280 --> 00:39:54,439
Speaker 2: to be chatting with thoughts? I That alone is something

785
00:39:54,480 --> 00:39:59,560
Speaker 2: I just don't get, you know, I don't Apparently they

786
00:39:59,600 --> 00:40:02,560
Speaker 2: already getting like ten million downloads a month or something,

787
00:40:03,560 --> 00:40:07,120
Speaker 2: And I truly just believe it's other robots listening to it,

788
00:40:07,120 --> 00:40:09,080
Speaker 2: that they are just their spot farms that are just

789
00:40:09,120 --> 00:40:11,879
Speaker 2: listening to the AI, and we're just creating a whole

790
00:40:11,880 --> 00:40:14,560
Speaker 2: other ecosystem where AI is just talking and listening to AI.

791
00:40:15,680 --> 00:40:17,239
Speaker 1: Because who wants that?

792
00:40:17,320 --> 00:40:19,759
Speaker 2: Like you listen to your favorite comedians probably you know

793
00:40:19,760 --> 00:40:20,880
Speaker 2: people who are experts.

794
00:40:21,440 --> 00:40:23,839
Speaker 1: Who needs that?

795
00:40:24,000 --> 00:40:28,239
Speaker 2: And I think anytime a place has tried the online

796
00:40:28,280 --> 00:40:32,560
Speaker 2: influencer thing, it just hasn't worked. Little Mikayla is an

797
00:40:32,560 --> 00:40:35,680
Speaker 2: example of that just really failing, because at the end

798
00:40:35,680 --> 00:40:37,560
Speaker 2: of the day, people want an influencer who is a

799
00:40:37,600 --> 00:40:38,319
Speaker 2: real human being.

800
00:40:39,239 --> 00:40:43,480
Speaker 1: Yeah. I remember when she had a scandal where it

801
00:40:43,520 --> 00:40:47,480
Speaker 1: was like, oh, I've been sexually harassed and it's like

802
00:40:47,520 --> 00:40:49,760
Speaker 1: you're not a real personally talk.

803
00:40:49,760 --> 00:40:52,319
Speaker 2: About like yep, and then they did it they where

804
00:40:52,320 --> 00:40:54,879
Speaker 2: they like had her get cancer so that and then

805
00:40:54,920 --> 00:40:57,360
Speaker 2: like she disappeared and people just kind of forgot.

806
00:40:57,400 --> 00:41:01,560
Speaker 1: So yeah, it is why the stuff that people think

807
00:41:01,760 --> 00:41:04,560
Speaker 1: people want, you know. And so you were talking about

808
00:41:04,719 --> 00:41:08,080
Speaker 1: some of the numbers behind this AI podcasting company. So

809
00:41:08,440 --> 00:41:10,759
Speaker 1: the company says that it is able to produce each

810
00:41:10,840 --> 00:41:14,440
Speaker 1: episode for one dollar or less, depending on length and complexity,

811
00:41:14,760 --> 00:41:18,520
Speaker 1: and then attach programmatic advertising to it. According to them,

812
00:41:18,560 --> 00:41:20,760
Speaker 1: this means that if twenty people listen to that episode,

813
00:41:20,800 --> 00:41:23,560
Speaker 1: the company has made a profit on the episode without

814
00:41:23,800 --> 00:41:26,480
Speaker 1: factoring in overhead. So they also said that they have

815
00:41:26,760 --> 00:41:30,800
Speaker 1: five thousand shows already across its Quiet Please podcast network,

816
00:41:30,960 --> 00:41:33,239
Speaker 1: and they are able to produce three thousand episodes a week.

817
00:41:33,520 --> 00:41:35,759
Speaker 1: You were right. Collectively, the network says that they have

818
00:41:35,840 --> 00:41:39,399
Speaker 1: seen ten million downloads since since September twenty twenty three.

819
00:41:39,760 --> 00:41:41,960
Speaker 1: It takes about an hour to create an episode from

820
00:41:41,960 --> 00:41:43,600
Speaker 1: coming up to the idea to getting it out to

821
00:41:43,640 --> 00:41:46,759
Speaker 1: the world, and they have about fifty AI personalities that

822
00:41:46,800 --> 00:41:51,320
Speaker 1: they've created, including food expert Claire Delish Gardner and nature

823
00:41:51,400 --> 00:41:56,320
Speaker 1: expert Nigel Thistledown and Alie Bennett, who covers offbeat sports. Obviously,

824
00:41:56,400 --> 00:41:59,200
Speaker 1: none of those people are real. Claire Delish is not

825
00:41:59,280 --> 00:42:01,160
Speaker 1: a real cookie an expert. I don't know if I

826
00:42:01,200 --> 00:42:01,680
Speaker 1: need to say that.

827
00:42:01,920 --> 00:42:05,400
Speaker 2: Nigel Thistle, that's a name from That is Bridgerton. That

828
00:42:05,440 --> 00:42:06,560
Speaker 2: is the name from Bridgton.

829
00:42:08,000 --> 00:42:11,280
Speaker 1: So you might be thinking, who in the hell wants

830
00:42:11,280 --> 00:42:13,640
Speaker 1: to listen to this? If you're thinking that, I am

831
00:42:13,640 --> 00:42:16,560
Speaker 1: sorry to tell you you're an idiot who was also lazy

832
00:42:16,600 --> 00:42:18,279
Speaker 1: and you don't know what you want. That is from

833
00:42:18,320 --> 00:42:21,279
Speaker 1: the founder who said quote. I think that people who

834
00:42:21,280 --> 00:42:24,880
Speaker 1: are still referring to AI generated content as AI slop

835
00:42:25,160 --> 00:42:27,839
Speaker 1: are probably lazy luddites because there's a lot of good

836
00:42:27,920 --> 00:42:29,560
Speaker 1: stuff out there. So you don't know. If you're thinking

837
00:42:29,560 --> 00:42:31,560
Speaker 1: I don't want to listen to that, you're a moron

838
00:42:31,560 --> 00:42:32,560
Speaker 1: who is also lazy.

839
00:42:32,840 --> 00:42:35,319
Speaker 2: According to the CEO, if you don't want to listen

840
00:42:35,400 --> 00:42:38,640
Speaker 2: to a robot that's making things up from scanning Google,

841
00:42:39,440 --> 00:42:40,239
Speaker 2: you're lazy.

842
00:42:41,120 --> 00:42:44,319
Speaker 1: Yes. So I saw this headline everywhere and there were

843
00:42:44,320 --> 00:42:45,839
Speaker 1: a couple of pieces that I just want to make

844
00:42:45,840 --> 00:42:48,960
Speaker 1: sure it get included to like one. It sounds like

845
00:42:49,160 --> 00:42:53,640
Speaker 1: right now they're focused on making podcasts out of just

846
00:42:53,840 --> 00:42:56,520
Speaker 1: basic information that you could find anywhere online, but you

847
00:42:56,600 --> 00:42:59,080
Speaker 1: might want it in podcast form. We might make a

848
00:42:59,120 --> 00:43:02,080
Speaker 1: pollen podcast that eighty fifty people listen to, but I'm

849
00:43:02,120 --> 00:43:05,200
Speaker 1: already at unit profitability on that, so maybe I can

850
00:43:05,239 --> 00:43:09,200
Speaker 1: make five hundred pollen report podcasts, she said. So it

851
00:43:09,360 --> 00:43:13,239
Speaker 1: sounds like what they're saying is that right now, like

852
00:43:13,280 --> 00:43:15,200
Speaker 1: you might want to get the weather report in a

853
00:43:15,719 --> 00:43:20,239
Speaker 1: hyper personalized podcast. So I can understand that as a

854
00:43:20,320 --> 00:43:23,120
Speaker 1: completely different use case from the kind of podcast that

855
00:43:23,160 --> 00:43:27,040
Speaker 1: you and I make that take research and human ingenuity

856
00:43:27,080 --> 00:43:30,319
Speaker 1: and human compassion and human connection. I guess if it.

857
00:43:30,320 --> 00:43:32,360
Speaker 2: Was like a robot that like went through my emails

858
00:43:32,360 --> 00:43:34,399
Speaker 2: and messages and then it was just a podcast where

859
00:43:34,400 --> 00:43:38,040
Speaker 2: it's like telling me about things I need to know personally,

860
00:43:38,840 --> 00:43:41,120
Speaker 2: but that's just like Siri, I guess.

861
00:43:40,880 --> 00:43:44,120
Speaker 1: Like yeah, And I guess, I mean, I almost feel

862
00:43:44,120 --> 00:43:46,040
Speaker 1: like if they were, if they were going to do that,

863
00:43:46,560 --> 00:43:48,960
Speaker 1: I would want them. But that's not a podcast, you know.

864
00:43:50,040 --> 00:43:52,600
Speaker 1: But I when I listen to my voicemails that somebody

865
00:43:52,600 --> 00:43:54,040
Speaker 1: has left just for me, I wouldn't call that a

866
00:43:54,440 --> 00:43:58,200
Speaker 1: like words have meetings and podcasts mean something. And yeah,

867
00:43:58,239 --> 00:44:02,600
Speaker 1: I do think a big part of podcasting is the

868
00:44:03,360 --> 00:44:05,600
Speaker 1: kind of community aspect of it. You know, when I

869
00:44:05,640 --> 00:44:08,279
Speaker 1: when I listen to the episode of of the of

870
00:44:08,320 --> 00:44:12,040
Speaker 1: Your podcast with Gibson John's, I scrolled down to the

871
00:44:12,080 --> 00:44:14,200
Speaker 1: Spotify comments because I wanted to see what people were

872
00:44:14,200 --> 00:44:17,480
Speaker 1: saying about it. Right, part of it is talking about

873
00:44:17,560 --> 00:44:20,040
Speaker 1: what other listeners are thinking about it and what their responding,

874
00:44:20,239 --> 00:44:22,040
Speaker 1: what their response is to it. I don't see how

875
00:44:22,080 --> 00:44:25,000
Speaker 1: a hyper personal podcast, it is only listened to by

876
00:44:25,000 --> 00:44:27,080
Speaker 1: a handful of people in the country, is going to

877
00:44:27,120 --> 00:44:27,640
Speaker 1: provide that.

878
00:44:27,960 --> 00:44:30,840
Speaker 2: Yeah, you're not creating a community, and that's what people

879
00:44:30,840 --> 00:44:33,239
Speaker 2: want when they really get into podcasts. They love they

880
00:44:33,280 --> 00:44:36,560
Speaker 2: love the community, like you said, the friends, the just

881
00:44:37,200 --> 00:44:40,600
Speaker 2: I think it's also seeing a podcaster grow, right, like

882
00:44:40,680 --> 00:44:43,640
Speaker 2: when you look at people who if you look at

883
00:44:43,640 --> 00:44:46,520
Speaker 2: people who love the podcast like come Town and now

884
00:44:46,560 --> 00:44:48,919
Speaker 2: you have like Adam Friedlander and he's, you know, doing

885
00:44:48,960 --> 00:44:53,360
Speaker 2: these huge YouTube interviews with like big people, starv ros

886
00:44:53,480 --> 00:44:56,279
Speaker 2: is blowing up everywhere and just having I love him.

887
00:44:56,280 --> 00:44:58,120
Speaker 1: I have such a I love frush on him.

888
00:44:59,000 --> 00:45:01,879
Speaker 2: He's so so funny and he's like I love seeing

889
00:45:01,960 --> 00:45:04,040
Speaker 2: him blow up and it's like, oh my gosh, I

890
00:45:04,120 --> 00:45:06,800
Speaker 2: remember when he was like on this little tiny podcast,

891
00:45:06,880 --> 00:45:09,839
Speaker 2: like making weird jokes with his friends. So you don't

892
00:45:09,840 --> 00:45:14,520
Speaker 2: get that obviously with AI robot podcasts.

893
00:45:14,920 --> 00:45:17,880
Speaker 1: Yeah, in the article, the people who run this company

894
00:45:17,880 --> 00:45:19,520
Speaker 1: were saying, oh, well, we're not gonna have it so

895
00:45:19,560 --> 00:45:22,640
Speaker 1: that people like, we're intentionally don't want people to create

896
00:45:23,080 --> 00:45:25,719
Speaker 1: connections with our AI host. So we're going we're not

897
00:45:25,760 --> 00:45:28,120
Speaker 1: coming up with the backstories for them. And I was

898
00:45:28,160 --> 00:45:30,400
Speaker 1: sort of thinking that is sort of the fun of

899
00:45:30,680 --> 00:45:32,919
Speaker 1: listening to a PA. I'm sure there are people who

900
00:45:33,960 --> 00:45:35,640
Speaker 1: don't care about what I have to say but have

901
00:45:35,640 --> 00:45:37,200
Speaker 1: been listening to me for a while and it's like, oh,

902
00:45:37,239 --> 00:45:39,000
Speaker 1: I just want to I just want to hear her

903
00:45:39,040 --> 00:45:40,879
Speaker 1: thoughts on stuff. And I didn't even watch that movie

904
00:45:40,920 --> 00:45:43,360
Speaker 1: She's talk like I half of the stuff that you

905
00:45:43,400 --> 00:45:45,160
Speaker 1: all talked about in that episode, I don't watch it.

906
00:45:45,239 --> 00:45:48,120
Speaker 1: I don't watch TLC. Not not to say that I'm

907
00:45:48,120 --> 00:45:50,320
Speaker 1: better than you, cause I watch Bravo. No you are,

908
00:45:50,400 --> 00:45:53,560
Speaker 1: you are, But you know, like, I just enjoy your

909
00:45:53,640 --> 00:45:55,040
Speaker 1: voice and I want to hear what you have to say,

910
00:45:55,040 --> 00:45:58,160
Speaker 1: and I don't think that that can be replicated with AI.

911
00:45:58,280 --> 00:46:01,640
Speaker 1: That is that that human aspect of it is integral

912
00:46:01,680 --> 00:46:04,040
Speaker 1: to the the why we make anything.

913
00:46:04,280 --> 00:46:07,279
Speaker 2: Yeah, and those the background stories of who someone is.

914
00:46:07,320 --> 00:46:12,000
Speaker 2: My absolute favorite podcast moment is Caleb Harron. I'm like,

915
00:46:12,000 --> 00:46:15,040
Speaker 2: I feel like everyone knows this, him asking a guy

916
00:46:15,040 --> 00:46:18,000
Speaker 2: in his class what Colonne he's using, and he like

917
00:46:18,040 --> 00:46:20,640
Speaker 2: does the whole story of like just him causing me,

918
00:46:20,760 --> 00:46:22,279
Speaker 2: like son Connor.

919
00:46:22,640 --> 00:46:23,520
Speaker 1: Well cologne is it that?

920
00:46:23,960 --> 00:46:25,840
Speaker 2: And it's so funny and it's just like one of

921
00:46:25,840 --> 00:46:28,319
Speaker 2: those things that's the story from his life that just

922
00:46:28,400 --> 00:46:29,520
Speaker 2: came up in conversation.

923
00:46:30,040 --> 00:46:32,560
Speaker 4: He said, Cooper, what clone do you wear? And he said, oh,

924
00:46:32,640 --> 00:46:33,839
Speaker 4: I don't. It's just like I don't know. My mom

925
00:46:33,840 --> 00:46:35,520
Speaker 4: gets it for me. And I go, oh yeah, but

926
00:46:35,560 --> 00:46:38,080
Speaker 4: like what is he goes, I really don't know. My

927
00:46:38,080 --> 00:46:39,720
Speaker 4: mom gets it from me. And during this whole assembly,

928
00:46:39,840 --> 00:46:41,000
Speaker 4: I just keep every time I get a chance to

929
00:46:41,040 --> 00:46:42,319
Speaker 4: being like Cooper, do you even know like what the

930
00:46:42,320 --> 00:46:43,160
Speaker 4: bottle is?

931
00:46:43,920 --> 00:46:44,160
Speaker 3: You know?

932
00:46:45,440 --> 00:46:47,200
Speaker 4: But I'm trying to play it cool, so I'm like, yeah,

933
00:46:47,200 --> 00:46:51,719
Speaker 4: I almost wonder what the shaped the bottle and he's like, oh, yeah,

934
00:46:51,719 --> 00:46:52,320
Speaker 4: I don't remember.

935
00:46:52,360 --> 00:46:52,520
Speaker 3: Man.

936
00:46:52,680 --> 00:46:55,440
Speaker 4: Literally, I'm being so persistent because when finally I go,

937
00:46:55,680 --> 00:46:58,319
Speaker 4: I need to know what colone you are, and he goes,

938
00:46:58,360 --> 00:46:59,960
Speaker 4: if I tell you, will you just stop talking to me?

939
00:47:00,040 --> 00:47:01,840
Speaker 1: And I and I go, Can I go?

940
00:47:01,880 --> 00:47:05,279
Speaker 4: And I go? Yeah. It was like scenic the way

941
00:47:05,320 --> 00:47:06,760
Speaker 4: it played. It liked be like talking to the boys

942
00:47:06,760 --> 00:47:08,280
Speaker 4: and cutting up. And then there was a moment's silence.

943
00:47:08,280 --> 00:47:14,680
Speaker 4: I'd be like, anyway, Coop, I'm having to repeat Cooper Cooper,

944
00:47:16,520 --> 00:47:19,280
Speaker 4: like I was saying, No, you guys are so bunny Cooper,

945
00:47:19,320 --> 00:47:20,160
Speaker 4: what color is the box?

946
00:47:20,200 --> 00:47:20,680
Speaker 1: It comes up?

947
00:47:22,080 --> 00:47:23,880
Speaker 2: Sorry, you're not getting that from an AI robot.

948
00:47:23,960 --> 00:47:27,839
Speaker 1: They can't do it. And that's the best part of podcasting,

949
00:47:27,880 --> 00:47:31,000
Speaker 1: those little moments where you could never have scripted it

950
00:47:31,040 --> 00:47:32,759
Speaker 1: if you tried. You could never You could never have

951
00:47:32,960 --> 00:47:36,040
Speaker 1: have planned it out if you tried. Yeah, that's what

952
00:47:36,080 --> 00:47:38,439
Speaker 1: you that's what you tune into podcasts for. You never

953
00:47:38,520 --> 00:47:40,120
Speaker 1: know what's gonna happen when you put on the mic.

954
00:47:40,160 --> 00:47:43,520
Speaker 1: And if we if we give that over to AI,

955
00:47:44,120 --> 00:47:47,120
Speaker 1: and not just AI, but a certain kind of like

956
00:47:47,280 --> 00:47:50,360
Speaker 1: tech company suit. I don't know. I just I thought

957
00:47:50,400 --> 00:47:54,240
Speaker 1: reading this, this this Holly. Everything she said was gross. Yes,

958
00:47:54,520 --> 00:47:56,880
Speaker 1: And there was a time where you would be embarrassed

959
00:47:56,880 --> 00:47:59,680
Speaker 1: to have your name attached to something like this, and

960
00:47:59,719 --> 00:48:02,960
Speaker 1: so I don't like the idea that it's not just

961
00:48:03,040 --> 00:48:06,839
Speaker 1: the AI, it's people who talk about creativity in this

962
00:48:06,920 --> 00:48:09,720
Speaker 1: particular kind of way, like oh, well, we're already getting

963
00:48:09,760 --> 00:48:11,360
Speaker 1: ten ten million downloads and.

964
00:48:11,400 --> 00:48:13,520
Speaker 2: Da da da dah, and yeah it's hitting my KPIs

965
00:48:13,600 --> 00:48:16,279
Speaker 2: and the ROI and it's crazy, like, oh, I.

966
00:48:17,960 --> 00:48:22,200
Speaker 1: Hate the idea that the people in charge of things

967
00:48:22,480 --> 00:48:25,120
Speaker 1: sometimes hate the thing that they're in charge of it.

968
00:48:25,239 --> 00:48:27,200
Speaker 1: You don't, you don't even love it, you don't even

969
00:48:27,280 --> 00:48:27,600
Speaker 1: like it.

970
00:48:28,239 --> 00:48:30,600
Speaker 2: Yeah, it's just there's a job for you. You like people

971
00:48:30,600 --> 00:48:33,560
Speaker 2: who make TV. Now, it's so many industries are just

972
00:48:33,680 --> 00:48:36,759
Speaker 2: people who are able to grow a profit, and that's

973
00:48:36,800 --> 00:48:37,719
Speaker 2: why they have their job.

974
00:48:38,760 --> 00:48:40,600
Speaker 1: I'm sure they answer is yes, But do you watch

975
00:48:40,600 --> 00:48:44,160
Speaker 1: the Studio? Oh? Yeah, I love the Studio me too.

976
00:48:44,520 --> 00:48:46,759
Speaker 1: I think I think I watched it because of your recommendation.

977
00:48:47,200 --> 00:48:50,160
Speaker 1: It was so good. I feel like that that show

978
00:48:50,200 --> 00:48:51,880
Speaker 1: if you're not watching The Studio on Apple TV. I

979
00:48:52,239 --> 00:48:54,000
Speaker 1: just finished it and I loved it. But I think

980
00:48:54,000 --> 00:48:57,040
Speaker 1: that that show gets it right of the difference between

981
00:48:57,040 --> 00:48:59,279
Speaker 1: somebody who just I love movies. I've always wanted to

982
00:48:59,320 --> 00:49:01,320
Speaker 1: work for movies. Do you you? And then when he's

983
00:49:01,800 --> 00:49:04,759
Speaker 1: taken a pee at the Oscars next to the head

984
00:49:04,800 --> 00:49:05,480
Speaker 1: of Netflix.

985
00:49:05,680 --> 00:49:08,319
Speaker 2: Yeah, just Ted Cerana Sorondos has said many times like

986
00:49:08,400 --> 00:49:10,320
Speaker 2: I am not a movie person, I'm a tech person.

987
00:49:10,840 --> 00:49:14,160
Speaker 1: Yeah, and that's where the industry is now. I guess

988
00:49:14,560 --> 00:49:17,200
Speaker 1: it just makes me very sad. And I will say, like,

989
00:49:17,520 --> 00:49:19,560
Speaker 1: as somebody who knows quite a bit about tech and

990
00:49:19,600 --> 00:49:21,120
Speaker 1: the people who make it and how they talk about

991
00:49:21,120 --> 00:49:25,040
Speaker 1: their own technology, reading that Hollywood reporter piece to me,

992
00:49:25,680 --> 00:49:27,759
Speaker 1: a couple of pieces still gets about to me. One

993
00:49:27,880 --> 00:49:31,160
Speaker 1: the fact that clearly humans are still required to make

994
00:49:31,239 --> 00:49:34,800
Speaker 1: this thing exists. You know, the episodes are built by AI,

995
00:49:35,040 --> 00:49:38,319
Speaker 1: but it's humans who are doing everything else. And so

996
00:49:38,719 --> 00:49:40,680
Speaker 1: the idea, I think that we are being sold, this

997
00:49:40,800 --> 00:49:44,040
Speaker 1: idea that you can have a soup to nuts podcast

998
00:49:44,160 --> 00:49:47,440
Speaker 1: with no human interference, and the article does not suggest that.

999
00:49:47,480 --> 00:49:48,920
Speaker 1: It's like, oh no, it's humans in every step of

1000
00:49:48,960 --> 00:49:53,080
Speaker 1: the way, and those humans are currently unpaid. The startup

1001
00:49:53,120 --> 00:49:56,400
Speaker 1: is currently bootstrapped and employees are not yet salaried, but

1002
00:49:56,520 --> 00:50:00,319
Speaker 1: the company will will soon seek outside funding. So I

1003
00:50:00,320 --> 00:50:03,360
Speaker 1: feel like the big headline that I think being missed

1004
00:50:03,360 --> 00:50:06,279
Speaker 1: in this whole operation is that it's profitable because people

1005
00:50:06,320 --> 00:50:08,040
Speaker 1: are not being paid or not making money.

1006
00:50:08,440 --> 00:50:10,600
Speaker 2: Yeah, because there are people in and they're not being

1007
00:50:10,640 --> 00:50:12,360
Speaker 2: paid and so it's profitable.

1008
00:50:12,520 --> 00:50:14,719
Speaker 1: Yeah, it's the whole AI boat.

1009
00:50:14,760 --> 00:50:16,680
Speaker 2: That's what it all feels like, like a bubble that's going

1010
00:50:16,719 --> 00:50:19,960
Speaker 2: to burst because it's something where like when Chad GBT

1011
00:50:20,280 --> 00:50:22,960
Speaker 2: was really just like a bunch of people in India

1012
00:50:23,000 --> 00:50:26,359
Speaker 2: who would like google things really quickly for you and

1013
00:50:26,400 --> 00:50:29,000
Speaker 2: then as soon as the you know, curtain lifts, I'm

1014
00:50:29,120 --> 00:50:31,160
Speaker 2: hoping that every the whole bubble just pops.

1015
00:50:31,760 --> 00:50:33,960
Speaker 1: We have been sort of talking about it a bit.

1016
00:50:34,080 --> 00:50:38,040
Speaker 1: I think it's coming. I think people are less interested

1017
00:50:38,200 --> 00:50:42,719
Speaker 1: in AI and their commercial products, people are using AI less.

1018
00:50:43,560 --> 00:50:45,880
Speaker 1: I just think we are starting to see the writing

1019
00:50:45,920 --> 00:50:48,719
Speaker 1: on the wall that yeah, maybe this this technology is

1020
00:50:48,760 --> 00:50:51,439
Speaker 1: not all it's cracked up to be. And I think

1021
00:50:51,480 --> 00:50:56,160
Speaker 1: that the article about this AI podcast scheme to me,

1022
00:50:56,360 --> 00:50:59,280
Speaker 1: it read the whole thing read like a please fund

1023
00:50:59,360 --> 00:51:02,160
Speaker 1: us advertise, like, oh, you know we're seeking funding on that.

1024
00:51:02,680 --> 00:51:04,640
Speaker 2: You know, all things seem like and it's so lubinan

1025
00:51:04,719 --> 00:51:06,759
Speaker 2: and so profitable and you listen, you want to get

1026
00:51:06,800 --> 00:51:09,840
Speaker 2: on the ground floor of this, and it's I think

1027
00:51:10,040 --> 00:51:12,200
Speaker 2: that's just someone who knows this is disgusting and who

1028
00:51:12,200 --> 00:51:16,000
Speaker 2: has probably gotten no traction among like actual podcasters or

1029
00:51:16,040 --> 00:51:16,800
Speaker 2: in the industry.

1030
00:51:17,480 --> 00:51:21,480
Speaker 1: Yeah, and you know, coming talking to somebody who writes

1031
00:51:21,719 --> 00:51:26,080
Speaker 1: phenomenally well about culture and television and art and media,

1032
00:51:26,960 --> 00:51:30,000
Speaker 1: A robot can never replace you, Ashley, like I can

1033
00:51:30,080 --> 00:51:33,040
Speaker 1: never have you. A robot could never have the kind

1034
00:51:33,040 --> 00:51:36,719
Speaker 1: of nuanced, layered opinions that you have about media. A

1035
00:51:36,800 --> 00:51:40,720
Speaker 1: robot like you, you approach everything as such a rich text.

1036
00:51:40,760 --> 00:51:43,640
Speaker 1: It doesn't matter if it's you know, a fit like

1037
00:51:44,040 --> 00:51:46,520
Speaker 1: an indie film, an a twenty four film or something

1038
00:51:46,680 --> 00:51:50,759
Speaker 1: or ninety day fiance brings such a like nuance to it,

1039
00:51:50,840 --> 00:51:53,759
Speaker 1: and a robot couldn't do that. I wouldn't even to

1040
00:51:54,200 --> 00:51:57,719
Speaker 1: listen to it. Try. Yeah, exactly. You know who wants that.

1041
00:51:57,760 --> 00:52:00,400
Speaker 2: We want to hear the people we love want to

1042
00:52:00,440 --> 00:52:02,640
Speaker 2: hear their brains think of weird things.

1043
00:52:02,719 --> 00:52:05,720
Speaker 1: That's the fun of it. That is the fun of it. Actually,

1044
00:52:05,719 --> 00:52:08,200
Speaker 1: I have to put you on the spot. You ended

1045
00:52:08,239 --> 00:52:11,640
Speaker 1: your episode with like rapid fire TV homework. I gotta

1046
00:52:11,680 --> 00:52:13,640
Speaker 1: watch the show Smoke, which I have not watched. With

1047
00:52:13,640 --> 00:52:15,200
Speaker 1: the way that you described it made me want to

1048
00:52:15,239 --> 00:52:17,000
Speaker 1: watch it. What are you watching right now that you

1049
00:52:17,000 --> 00:52:18,040
Speaker 1: think people should know about.

1050
00:52:18,560 --> 00:52:24,759
Speaker 2: Oh oh, there's so many good ones right now, but

1051
00:52:25,360 --> 00:52:28,719
Speaker 2: I'll keep it techy for you. There's a new show

1052
00:52:28,719 --> 00:52:32,880
Speaker 2: that just debuted. It's called The tech Bro Murders. Each

1053
00:52:32,920 --> 00:52:38,320
Speaker 2: episode looks at a murder in the Bay Area basically,

1054
00:52:38,920 --> 00:52:41,120
Speaker 2: and is usually someone who's like, oh, they worked at

1055
00:52:41,160 --> 00:52:42,520
Speaker 2: Facebook and then they lost their mind.

1056
00:52:43,880 --> 00:52:46,360
Speaker 1: So so far, it's like, I think a few episodes

1057
00:52:46,360 --> 00:52:50,080
Speaker 1: out right now. It's pretty good docuseries. It's a docu.

1058
00:52:50,160 --> 00:52:56,000
Speaker 2: It's a docuseries that's out weekly, and it's it's yeah,

1059
00:52:56,040 --> 00:52:58,719
Speaker 2: it's like wow, Yeah, drinking red bull all day and

1060
00:52:58,800 --> 00:53:01,040
Speaker 2: not sleeping isn't good for your mental health and we'll

1061
00:53:01,040 --> 00:53:02,719
Speaker 2: make you do wild things.

1062
00:53:03,239 --> 00:53:07,200
Speaker 1: Who knew? Okay, that's going on my list. Yeah, where

1063
00:53:07,320 --> 00:53:10,720
Speaker 1: can folks follow you listen to your podcast? Well, it's

1064
00:53:10,760 --> 00:53:12,800
Speaker 1: a career. And also I don't want to embarrass you,

1065
00:53:12,880 --> 00:53:16,279
Speaker 1: but I went to the courier, I guess like a

1066
00:53:16,400 --> 00:53:18,480
Speaker 1: launch aman in DC, and I chatted with your boss

1067
00:53:18,520 --> 00:53:21,160
Speaker 1: and he had such glowing things to say about you,

1068
00:53:21,239 --> 00:53:24,040
Speaker 1: Like he was like, it's such a brilliant podcast and

1069
00:53:24,080 --> 00:53:25,600
Speaker 1: we're so lucky to get it. And I was like,

1070
00:53:25,600 --> 00:53:28,480
Speaker 1: you sure are better say that. I was supposed to

1071
00:53:28,560 --> 00:53:30,799
Speaker 1: be that and I couldn't. I had to stay back

1072
00:53:30,840 --> 00:53:32,839
Speaker 1: and help my mom with some stuff. But I was like, oh,

1073
00:53:32,960 --> 00:53:33,480
Speaker 1: I want.

1074
00:53:33,280 --> 00:53:36,000
Speaker 2: To be in DC and like, but they're amazing there.

1075
00:53:36,239 --> 00:53:38,920
Speaker 2: You can listen to my podcast wherever you do podcasts.

1076
00:53:38,960 --> 00:53:43,480
Speaker 2: It's also on YouTube on Courreer's channel or at YouTube

1077
00:53:43,520 --> 00:53:45,920
Speaker 2: dot com at tv I Say, and you can follow

1078
00:53:45,960 --> 00:53:47,760
Speaker 2: me at the Ashley Ray Everywhere.

1079
00:53:48,320 --> 00:53:50,920
Speaker 1: And you can follow me at Bridget Marine DC, on

1080
00:53:50,960 --> 00:53:53,360
Speaker 1: TikTok and Instagram, and on YouTube at There Are No

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Speaker 1: Girls on the Internet, Ashley. Thank you so much for

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Speaker 1: being here. Thanks to all of you for listening. I

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Speaker 1: will see you on the Internet. Got a story about

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Speaker 1: an interesting thing in tech, or just want to say hi?

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Speaker 1: You can read us at Hello at tangody dot com.

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Speaker 1: You can also find transcripts for today's episode at tengody

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Speaker 1: dot com. There Are No Girls on the Internet was

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Speaker 1: created by me Bridget tod It's a production of iHeartRadio,

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Speaker 1: an unbossed creative. Jonathan Strickland is our executive producer. Tarry

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Speaker 1: Harrison is our producer and sound engineer. Michael Almato is

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Speaker 1: our contributing producer. I'm your host, Bridget Todd. If you

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Speaker 1: want to help us grow, rate and review us on

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Speaker 1: Apple podcasts. For more podcasts from iHeartRadio, check out the

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Speaker 1: iHeartRadio app, Apple Podcasts, or wherever you get your podcasts.