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

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

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

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

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

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Speaker 1: another rare top of week installment of our weekly news

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Speaker 1: bound Up, where we dig into all the stories online

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Speaker 1: that you might have missed so you don't have to.

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Speaker 1: So I never know if listening to podcasters talk about

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Speaker 1: podcasting is too inside baseball for people to care about listeners,

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Speaker 1: y'all can let me know it won't hurt my feelings

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Speaker 1: if that is the case. And you're thinking, I don't

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Speaker 1: want to hear podcasters talk about how the sausage gets made,

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Speaker 1: or you know, their opinion about the state of the

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Speaker 1: of the podcasting industry or whatever. But Mike, I wanted

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Speaker 1: to talk about this briefly because Amy Poehler, who you know,

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Speaker 1: we love, big fan. She just sort of made history

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Speaker 1: by winning the first ever inaugural Golden Globe for podcasting.

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Speaker 2: Congrats to Amy Pohlar, You're right, we love her. Parks

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Speaker 2: of rec hilarious, snl terrific, her book Funny, She's she's

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Speaker 2: a funny person and seems people seem to really like her.

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Speaker 2: She seems like a genuinely nice person. So congrats Amy Polar.

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Speaker 1: If you want some Amy Polar deep cut goodness, find

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Speaker 1: Asskat on streaming. It was the Upright Citizens Brigade their

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Speaker 1: signature improv show, It's Asskat with I think like four

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Speaker 1: s's so like asscat or ass Cat. That was what

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Speaker 1: I really got an appreciation for Amy Pohlar and how

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Speaker 1: hilarious she is, Like that's that was my my sort

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Speaker 1: of first foray into her. I will say, I mean,

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Speaker 1: obviously I am a fan of hers. I don't personally

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Speaker 1: listen to a lot of podcasts that have celebrities as hosts,

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Speaker 1: which will talk about at a moment. I had listened

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Speaker 1: to a few episodes of Amy Pohler's Good Hang podcast.

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Speaker 1: I like it. I think that, you know, as far

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Speaker 1: as the celebrities talking to other celebrities genre of podcasts go,

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Speaker 1: it is a pretty decent representative of the genre. I

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Speaker 1: really enjoyed the episode that she did with Aubrey Plaza

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Speaker 1: from Parks and rec It really got into a lot

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Speaker 1: of just very relatable stuff about grief and grieving, which

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Speaker 1: I really appreciated. So I'm I'm a fan. I don't

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Speaker 1: want to make it seem like I'm like a haar

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Speaker 1: of Amy Pohlar show. I was actually initially really happy

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Speaker 1: with Amy Polar winning because A. I think the show

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Speaker 1: is actually pretty fine. B. I was happy that a

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Speaker 1: woman won. And then lastly, let's not forget that the

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Speaker 1: alternative is a world where Ben freaking Shapiro won the

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Speaker 1: inaugural Golden Globe for Podcasting.

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Speaker 2: Did you know that I did because you told me?

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Speaker 2: But thankfully, I am like pretty far removed from the

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Speaker 2: Shapiro sphere.

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Speaker 1: Yeah, people, I mean I always want to want to

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Speaker 1: just scream it from the rooftops. Ben Shapiro is a

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Speaker 1: failed entertainment writer. He badly wanted to be in Hollywood.

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Speaker 1: That's why he talks about celebrities all day long. And

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Speaker 1: he's always just talking about Hollywood and all of that

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Speaker 1: because he's obsessed with Hollywood. He ran this huge, very

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Speaker 1: explicit for your consideration campaign to try to get nominated

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Speaker 1: for the Golden Globes for Best Podcast. The Golden Globes,

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Speaker 1: as folks may or may not know, has long faced

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Speaker 1: a lot of scrutiny about being paid a play. So

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Speaker 1: imagine a world where Ben Shapiro essentially gets to buy

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Speaker 1: his way into being platformed by mainstream Hollywood, which is

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Speaker 1: all he has ever wanted. So the fact that that

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Speaker 1: did not happen to me, that's a kind of win.

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Speaker 1: No matter how you slice it, it's true. A Ben

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Speaker 1: Shapiro loss is a win for the good guys. Yes,

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Speaker 1: and Ben Tapiro didn't even get nominated, so yeah, I'll

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Speaker 1: take that as a win. So the Golden Globes initially

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Speaker 1: put together a list of the top twenty five most

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Speaker 1: listened to shows across Spotify and Apple using downloads, listens,

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Speaker 1: and views, and then that was the list of shows

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Speaker 1: that were eligible, so only twenty five of the most

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Speaker 1: popular podcasts were even considered. The nominees were Armchair Expert

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Speaker 1: with Dax Shepherd on The Wondering Network, Call Her Daddy

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Speaker 1: on SiriusXM, Good Hang with Ammy Poehler on the Spotify Network,

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Speaker 1: which ended up winning, the Mel Robbins podcast on Serious ExM.

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Speaker 1: You know, let them let them Mike, let them Yeah, let.

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Speaker 2: Them let them.

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Speaker 1: SmartLess, and up First by NPR. Not a ton of

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Speaker 1: diversity on this list. I know that Dax Shepherd's podcast

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Speaker 1: arms Her Expert has a Woman of Color co host

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Speaker 1: Manka Padman alongside Dax. I don't. I don't actually listen

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Speaker 1: to that show. I really have a thing with Dax Shepherd.

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Speaker 1: I don't listen to his show and never have yet.

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Speaker 1: He is someone that I have learned a lot about,

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Speaker 1: kind of against my will. Just this through osmosis, I

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Speaker 1: can tell you a lot about Dax Shepherd despite never

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Speaker 1: having voluntarily tuned into any Dax Shepherd vehicle or project.

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Speaker 2: Ever, I think a biggest thing of his public persona

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Speaker 2: is like radical openness and revealing details about his personal

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Speaker 2: life that other celebrities might be more remiss to share.

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Speaker 2: Do I have that right?

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Speaker 1: That's right, that's right, And it's funny because those are

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Speaker 1: things that I should like. Yet whenever I hear him

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Speaker 1: say do this, I'm like, ugh, shut up. I don't know.

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Speaker 1: I don't know what explains it. I did just see

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Speaker 1: this phenomenal clip of you know how much I love

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Speaker 1: Share of Share on Dax Shepherd's podcast, and then Dak

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Speaker 1: Shepherd's Dax Shepherd's wife Kristen Bell, and he is essentially

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Speaker 1: questioning Shaer saying, Share, I heard that you don't think

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Speaker 1: I'm good enough for my wife Kristin Bell. So who

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Speaker 1: do you think that she should be with? If not me,

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Speaker 1: do you think she should be with like Brad Pitt.

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Speaker 1: I thought that was an interesting person to throw out,

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Speaker 1: because like Brad Pitt notoriously having some issues with how

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Speaker 1: he treats women. We'll just keep it put it that way. So,

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Speaker 1: in my opinion, Dax is kind of trying to fluster

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Speaker 1: Share about why Share does not think that he is

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Speaker 1: a good enough partner for his wife Kristin Bell, and

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Speaker 1: Share hits him with, I guess she must see something

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Speaker 1: that I don't.

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Speaker 2: In you, oh man.

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Speaker 1: The funny thing to say. It's like completely straight faced,

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Speaker 1: clear delivery. I guess she sees something I don't.

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Speaker 2: And she said this on his show. She like yeah

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Speaker 2: on his show to deliver this well correct. I would

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Speaker 2: probably have to retire from public life if Share said

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Speaker 2: something like that to me.

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Speaker 1: Same same, although if Share can come on this podcast

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Speaker 1: and belittle me and everyone in my life anytime she wants,

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Speaker 1: and I will, it would make my year. Something kind

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Speaker 1: of funny about Amy Pohler's podcast Winning against SmartLess is

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Speaker 1: that One of the hosts of SmartLess is her ex

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Speaker 1: husband Will Arnette, who you might know as I think

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Speaker 1: you probably know as the voice of BoJack Horseman. I

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Speaker 1: know as Job Bluth from Arrested Development.

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Speaker 2: Oh I was familiar with his work as Joe well

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Speaker 2: before BoJack. Both are excellent performances, just really top notch.

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Speaker 1: Something that I liked about Amy Pohler's show is that

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Speaker 1: when she first started it, she basically said, Hey, I

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Speaker 1: want to start a project where I don't have to

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Speaker 1: put in a ton of effort just like the men do.

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Speaker 1: And I don't know for sure, but I have to

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Speaker 1: imagine that was a dig at SmartLess, because SmartLess is

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Speaker 1: their whole thing I think is that they have guests

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Speaker 1: on and they don't really do any prep because they

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Speaker 1: don't know who the guest, or one of the three

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Speaker 1: co hosts knows who the guest is and the other don't,

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Speaker 1: and so those the people who don't have not done

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Speaker 1: any prep for that guest.

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Speaker 2: I've listened to a couple episodes, not recently, but when

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Speaker 2: it first came out, and there's a ton of buzz

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Speaker 2: about it, and I did note that, like the guests

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Speaker 2: didn't really get in a whole lot of airtime. It

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Speaker 2: was mainly the hosts talking to each other over the guest.

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Speaker 1: Oh my god. So again, I don't want this to

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Speaker 1: sound like me just hating on podcasts, because there are

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Speaker 1: celebrity podcasts that I think are really great and I like.

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Speaker 2: But I do need to just steer it back because

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Speaker 2: it is kind of heading that way.

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Speaker 1: Okay, Well, I'll to say one thing, which I think

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Speaker 1: that SmartLess is not for me because I want to

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Speaker 1: hear what the guest has to say, and I make

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Speaker 1: it a practice of not talking you over guests. I've

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Speaker 1: invited you on the show because you're interesting and I

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Speaker 1: want to hear what you have to say. I listened

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Speaker 1: to one episode and they ask and so I don't

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Speaker 1: know if this is a common thing with them, but

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Speaker 1: they're talking to a celebrity guest and they ask the

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Speaker 1: guest where are you from? And then the hosts just

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Speaker 1: jump in over the guests and do like a series

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Speaker 1: of escalating bits and they don't really give the guest

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Speaker 1: a chance to get the answer out. Don't get me wrong,

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Speaker 1: the bits were funny and it was pleasant to listen to,

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Speaker 1: but I don't know, I just actually wanted to hear

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Speaker 1: the answer. I don't expect every podcast that everybody listens

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Speaker 1: to to be challenging complex stuff, especially today. I think

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Speaker 1: people need their you know, just pleasant light podcasts to

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Speaker 1: have on while they're doing errands or whatever. I have

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Speaker 1: mine that are like that. But I think that Amy

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Speaker 1: Poehler's show is a much better representation of the celebrities

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Speaker 1: talking to celebrities genre because people can get a thought

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Speaker 1: out and I guess SmartLess this isn't for me.

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Speaker 2: It's also just like they've got a lot of people

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Speaker 2: on the mic, Like three hosts is a lot of hosts, right,

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Speaker 2: and you had a guest in there. That's four people

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Speaker 2: and not just four people, like four celebrities, people who

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Speaker 2: love the limelight, and you know, the turntaking gets a

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Speaker 2: little more challenging. So maybe that's part of it too.

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Speaker 1: You know this about me, I don't I think we've

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Speaker 1: ever had four people on the podcast before.

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Speaker 2: I think three is pushing it. Yeah, we did the

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Speaker 2: mail Bag is Madness. We did that Mailbag episode. We

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Speaker 2: had Joey on and I was like, ooh, three people,

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Speaker 2: we have to like take turns here? How do we

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Speaker 2: do this? It's it's just a little bit more challenging.

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Speaker 2: This is such a non sequitur.

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Speaker 1: But whenever you watch those Manisphere podcasts, which are basically

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Speaker 1: just like video YouTube videos at this point, like Fresh

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Speaker 1: and Fit, one of the things beyond the odious things

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Speaker 1: that they're saying about women, one of the points I've

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Speaker 1: always made is you will they'll have like nine people

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Speaker 1: all miked. It's it's chaos, and I sartain, I'm like,

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Speaker 1: this is too many people. Have the two hosts for

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Speaker 1: Rate Tops two women, and maybe ideally don't forbade any women.

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Speaker 1: But if your whole thing is berating women, do you

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Speaker 1: need to be berating seven of them? The two of

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Speaker 1: you got to bring on seven women to beraate. It's

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Speaker 1: like a round table. It's way too much.

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Speaker 2: Yeah, I guess all of this kind of gets it

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Speaker 2: like the new shape of what a podcast even is,

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Speaker 2: you know, Oh my gosh, I don't know if we

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Speaker 2: want to go there right now or not, but it's

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Speaker 2: super interesting. And I think it's related to this thing

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Speaker 2: that I've heard from a bunch of friends of mine

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Speaker 2: who have kids in their teens that like, apparently the

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Speaker 2: kids are just watching streamers on YouTube, like all day

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Speaker 2: everybody in my life who has a teenager confirms that

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Speaker 2: their teenager is just like watching YouTube videos of people

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Speaker 2: hanging out, maybe playing video games, maybe doing challenges, but

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Speaker 2: it feels kind of similar to what you're describing of

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Speaker 2: like a bunch of people in the room, pretty chaotic,

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Speaker 2: just kind of always on, like a three or four

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Speaker 2: hour recording. And I guess that's the future of content. Oh.

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Speaker 1: I mean, every time I tell somebody I'm a podcaster

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Speaker 1: and they say where can I watch your show, part

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Speaker 1: of me dies inside a little bit because you know,

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Speaker 1: I'm audio first, audio forever. I mean, people are still

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Speaker 1: engaging with audio content. I saw some very interesting data

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Speaker 1: about this recently, where yes, YouTube is making all these

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Speaker 1: these gains, but there's still a sizeable chunk of audience

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Speaker 1: who wants their podcasts to be audio first. And I

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Speaker 1: will always be in that camp. However, I do think

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Speaker 1: that you're You're right that it speaks to sort of

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Speaker 1: the shifting nature of content and what is content. And

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Speaker 1: I think that's why I wanted to talk about the

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Speaker 1: inaugural win for podcasting, because I do I think it's

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Speaker 1: good to have podcasts being seen as the cultural force

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Speaker 1: that they are, and so having there be a category

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Speaker 1: for podcasts at the Golden Lobes is that step in

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Speaker 1: that direction, which I think is great. However, I was

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Speaker 1: hearing from out of my colleagues who were saying, you know,

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Speaker 1: is it really good to have a celebrity podcast, Like

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Speaker 1: it's a celebrity podcast that really hasn't been around for

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Speaker 1: that long. Like as much as I like Good Hang,

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Speaker 1: it's and has not even been a year, so they've

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Speaker 1: had like thirty episodes. Maybe is what does it say

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Speaker 1: that that is being held up as the award winning

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Speaker 1: pinnacle of what the medium can do? And you know,

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Speaker 1: I do I sort of take that point. I think

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Speaker 1: that it can feel like, you know, celebrities have TV,

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Speaker 1: celebrities have film, celebrities are increasingly doing things in the

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Speaker 1: literary world, and they also have to be taking up

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Speaker 1: a huge footprint in the medium of podcasting. And I

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Speaker 1: do think like podcasting it started as pretty much normal

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Speaker 1: people kind of just talking about things that they really

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Speaker 1: were passionate about. And so I can really understand why

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Speaker 1: it feels disappointing to see celebrities held up assus pinnacle

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Speaker 1: of what the medium should and could be when you know,

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Speaker 1: folks have been grinding it out in these in the

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Speaker 1: in the podcast industry for a really long time. Sometimes

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Speaker 1: it does feel like, you know, the powers that be,

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Speaker 1: feel like there were no podcasts before SmartLess got huge

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Speaker 1: In twenty twenty. Podcast Abby Razuka actually had an interesting

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Speaker 1: up ed in The New York Times saying that a

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Speaker 1: much fairer way to do it, if the Golden Globes

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Speaker 1: was actually serious about celebrating podcasting and audio, would be

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Speaker 1: to have there be multiple podcast categories rather than just

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Speaker 1: one Best Podcast award. Because she writes, mashing together news,

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Speaker 1: interview show, celebrity driven conversations, and self help monologues doesn't

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Speaker 1: make much sense, and that really resonates with me. I

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Speaker 1: was once up for an award for my podcast, and

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Speaker 1: I was nominated alongside Kevin Hart's podcast. So it is

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Speaker 1: just sometimes weird when you know, Kevin Hart and I

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Speaker 1: are vastly different people, We are making vastly different types

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Speaker 1: of shows with vastly different resources, and it's just weird

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Speaker 1: to be considered alongside a big celebrity like Kevin Hart.

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Speaker 1: Are the powers that be when we're so different, well.

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Speaker 2: And especially if the powers that be are organizations like

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Speaker 2: the Golden Globes, which comes out of Hollywood and is

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Speaker 2: really celebrity oriented. So it's like, not, really, that's surprising.

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Speaker 2: I was just looking through the list and of the

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Speaker 2: six nominees, only one of them does not have full

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Speaker 2: episode video, right, and so it's like, oh, which one

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Speaker 2: up first? From NPR. I'm I think that, But they

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Speaker 2: do post like social video clips, but they don't have

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Speaker 2: a full episode. And I think it goes to what

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Speaker 2: you're saying that, like, what is a podcast and ten

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Speaker 2: fifteen years ago it felt like a very new channel,

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Speaker 2: you know, a different type of content. And I don't

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Speaker 2: want to put words in your mouth, but I think

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Speaker 2: what I'm hearing from you is that like part of

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Speaker 2: the sort of hesitation or skepticism about celebrity podcasts like

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Speaker 2: this is that it feels like they are breaking down

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Speaker 2: the things that make podcasts different, specifically the fact that

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Speaker 2: they are audio first and you know, maybe a little

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Speaker 2: weird or fringy.

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Speaker 1: Yes, Oh, I'm glad that you brought up that second point,

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Speaker 1: because this is why I'm not a huge fan of

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Speaker 1: your typical celebrity Talking to celebrity podcast because with some

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Speaker 1: big exceptions, you know, I think Julia Luis Drythus her

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Speaker 1: podcast Wiser than Me, I think is to me that

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Speaker 1: is the pinnacle of what a celebrity podcast can be,

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Speaker 1: and like a way to sort of break the mold

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Speaker 1: and do it a little bit differently. However, you know,

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Speaker 1: you listen, if you listen to you, if there's a

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Speaker 1: celebrity making the rounds because they've got a project to promote,

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Speaker 1: you hear them on one celebrity podcast, and you don't

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Speaker 1: need to hear them on several others because it's usually

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Speaker 1: the same They're making the same points or telling the

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Speaker 1: same stories. And I feel like podcasting is was meant

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Speaker 1: to be, or got it start as a different kind

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Speaker 1: of medium where you're hearing things that you wouldn't ordinarily hear.

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Speaker 1: You know, it's not the same thing it's flipping through

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Speaker 1: an US weekly or an entertainment magazine, where you have

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Speaker 1: a sense that this is all just pr that person's

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Speaker 1: got the talking points, they've been contractually obligated they're going

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Speaker 1: to talk about the project and this amount of time

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Speaker 1: whatever whatever. And I do think we lose something when

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Speaker 1: I mean it's it's like a double edged sword. Because

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Speaker 1: I just said I think it's good to see podcasting

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Speaker 1: getting its due and getting celebrated as the cultural force

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Speaker 1: that I know that it is, and too, I think

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Speaker 1: that in order to have that, you lose a little

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Speaker 1: bit of the scrappiness that made podcasting good in the

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Speaker 1: first place. I think, right, the feeling that you're not

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Speaker 1: just listening to some curated PR thing that you could

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Speaker 1: hear anywhere on a million different shows, and so it is,

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Speaker 1: it is a little tenuous.

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Speaker 2: I guess, Yeah, man, this new stuff it's bullshit. It's

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Speaker 2: not like it's not real like it used to be,

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Speaker 2: you know, back in the day. That's that's my impersonation

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Speaker 2: of an old person. I do not that different from

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Speaker 2: what I actually believe.

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Speaker 1: We sound very out of touch. How are these young

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Speaker 1: people watching three hour YouTube live streams? Ever heard of NPR?

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Speaker 1: Ever heard of American Life? Teenagers? That's how we sound,

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Speaker 1: I'm sure.

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Speaker 3: Yeah, let's take a quick break.

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Speaker 1: At our back. So over holiday break, I saw this

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Speaker 1: story that went viral that appeared to be from a

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Speaker 1: staffer at an unnamed food delivery app. The story that

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Speaker 1: he told sounds very damning, to the point where it

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Speaker 1: trended across social media, drove people to boycott door dash

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Speaker 1: The post did not call out door Dash by name,

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Speaker 1: but that was the company that was believed to be

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Speaker 1: being described by this post. So in this post, Reddit

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Speaker 1: user Throwaway Whistleblow said that he was a software engineer

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Speaker 1: about to leave a food delivery app company, so he

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Speaker 1: was going to spill all the dirty secrets and all

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Speaker 1: the tea about how this company operates. Basically, he said

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Speaker 1: that this company is rigged against delivery drivers. Here's a

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Speaker 1: bit of what the post said. You guys always suspect

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Speaker 1: the algorithms are rigged against you, but the reality is

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Speaker 1: actually so much more depressing than the conspiracy theories. I'm

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Speaker 1: a back end engineer. I sit in the weekly sprint

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Speaker 1: planning meetings where product managers discuss how to squeeze another

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Speaker 1: one point four percent margin out of quote human assets.

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Speaker 1: That's literally what they call drivers in the database schemas.

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Speaker 1: They talk about these people like their resource nodes in

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Speaker 1: a video game, not fathers and mothers trying to pay rent.

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Speaker 1: He also says that if you ever do priority delivery

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Speaker 1: or you pay a little bit more to get your

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Speaker 1: stuff set directly to you, he says that's a scam.

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Speaker 1: First off, priority delivery is a total scam. It was

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Speaker 1: a pitch to us as a quote psychological value add

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Speaker 1: Like I said in the title, when you pay that

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Speaker 1: extra two ninety nine, it changes a bullion flag in

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Speaker 1: the order Jason, but the dispatch logic literally ignores it.

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Speaker 1: It does nothing to speed you up. We actually ran

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Speaker 1: an ab test last year where we didn't speed up

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Speaker 1: the priority orders. We just purposely delayed non priority orders

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Speaker 1: by five to ten minutes to make the priority ones

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Speaker 1: feel faster. By comparison, management loved the results. We generated

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Speaker 1: millions in pure profit just by making the standard service worse,

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Speaker 1: not by making premium service any better. So people who

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Speaker 1: order from delivery apps are being scammed. But what's worse,

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Speaker 1: he says, is how they treat delivery drivers. The thing

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Speaker 1: that actually makes me sick and the main reason I'm quitting,

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Speaker 1: is the quote desperation score. We have a hidden metric

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Speaker 1: for drivers that tracks how desperate they are for cash

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Speaker 1: based on their acceptance behavior. If a driver usually lugs

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Speaker 1: on at ten PM and accepts every garbage three dollars

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Speaker 1: order instantly without hesitation, the ALDO tags them as high desperation.

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Speaker 1: Once they're tagged, the system then deliberately stops showing them

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Speaker 1: high paying orders. The logic is, why pay this guy

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Speaker 1: fifteen dollars for a run when we know he's desperate

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Speaker 1: enough to do it for six dollars. We save the

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Speaker 1: good tips for casual drivers and hook them in and

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Speaker 1: gamify their experience, while full timers get grinded into dust.

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Speaker 2: Bleak, right, Mike, that is very bleak, and it is

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Speaker 2: like sort of surprising to see it written in black

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Speaker 2: and white, but it really does align certainly with this

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Speaker 2: sort of attitude I think a lot of tech companies

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Speaker 2: have towards their users, and particularly gig economy platforms like

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Speaker 2: door Dash or Uber, which are notoriously cruel and penny pinching.

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Speaker 2: So it really aligns with that narrative of conspiracy and exploitation,

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Speaker 2: and I think.

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Speaker 1: That's the reason why it went so viral so quickly

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Speaker 1: and struck a chord, because it's written in a way

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Speaker 1: that really kind of confirms our worst and sort of

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Speaker 1: deepest suspicions about these types of gig and delivery app platforms,

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Speaker 1: that people are just being exploited all up and through

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Speaker 1: in ways that I think that we all have a

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Speaker 1: sense is happening. And so the idea that these tech

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Speaker 1: companies are just always going to be pushing the boundaries

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Speaker 1: of what they can legally get away with to make

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Speaker 1: money and like playing on people's desperation and ways that

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Speaker 1: keep them locked in. A lot of this is like

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Speaker 1: based on stuff that we essentially already know that these

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Speaker 1: companies are doing right And so we know that these

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Speaker 1: platforms have a history of like straight up stealing tips

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Speaker 1: from drivers or creating a system that is designed to

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Speaker 1: avoid getting busted by law enforcement. Things like that, like

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Speaker 1: things that we know are happening. So I think that

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Speaker 1: that's why this post really struck a chord with people,

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Speaker 1: because it rings so true about what we all know

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Speaker 1: and probably suspect about using these platforms. Only there is

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Speaker 1: one big problem about this post. Journalist Casey Newton at

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Speaker 1: platform Are discovered that the post is fake. So I

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Speaker 1: will say I spent a non trivial amount of time

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Speaker 1: reading comments about this post and sort of engage, like

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Speaker 1: engaging with what was laid out in this post. So

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Speaker 1: what I found out that it was a hoax, I

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Speaker 1: was pretty invested. Casey Newton says that he got in

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Speaker 1: touch with the post author and immediately our alarm bells

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Speaker 1: started ringing right. So the first is that the post

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Speaker 1: itself was like very well written. But then when Casey

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Speaker 1: was messaging back and forth with the person who wrote

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Speaker 1: the post, the messages were full of like grammatical errors

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Speaker 1: and spelling errors, as if the person that he was

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Speaker 1: speaking to and the person that wrote the post were

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Speaker 1: not the same. Casey continues. Over the next half hour,

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Speaker 1: we chatted a bit about his experience. Like most people

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Speaker 1: who leak information like this, his paramount concern was to

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Speaker 1: remain anonymous. While he told me he wanted to share

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Speaker 1: more with me, he also said that most of the

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Speaker 1: other news agencies had contacted him required far more personal

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Speaker 1: information spelled incorrectly that I'm willing to risk. Casey then

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Speaker 1: asks for like some tangible proof that he works for

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Speaker 1: a food delivery app at all, and he sends over

432
00:23:56,520 --> 00:23:59,760
Speaker 1: an Uber Eats employee badge that Casey is able to

433
00:23:59,760 --> 00:24:04,159
Speaker 1: deter Rman was actually generated using Gemini AI. So Casey

434
00:24:04,240 --> 00:24:07,639
Speaker 1: is like, Okay, this is obviously BS, but he asks,

435
00:24:07,680 --> 00:24:10,520
Speaker 1: you know, is there anything else that you can show

436
00:24:10,600 --> 00:24:13,760
Speaker 1: me that actually confirms that you genuinely are an engineer

437
00:24:13,760 --> 00:24:16,480
Speaker 1: at Ubreed's. This is where things to get interesting to me.

438
00:24:16,560 --> 00:24:21,680
Speaker 1: Because he sends Casey this eighteen page document saying these

439
00:24:21,720 --> 00:24:25,240
Speaker 1: documents will corroborate my claims. What he sends is a

440
00:24:25,240 --> 00:24:30,840
Speaker 1: report called a locknet T high dimensional temporal supply state modeling.

441
00:24:31,640 --> 00:24:35,480
Speaker 1: So I took a look at this document because Casey

442
00:24:35,640 --> 00:24:38,520
Speaker 1: linked the whole thing in his piece. Casey describes it

443
00:24:38,560 --> 00:24:41,240
Speaker 1: as quote. The bulk of the document appears to describe

444
00:24:41,240 --> 00:24:45,600
Speaker 1: a technical architecture for the AI system behind the desperation

445
00:24:45,760 --> 00:24:49,800
Speaker 1: score the whistleblower alleged in his original posts. By the end, though,

446
00:24:50,200 --> 00:24:52,560
Speaker 1: and it also offered support for each of the other

447
00:24:52,640 --> 00:24:55,600
Speaker 1: claims in the post, even when they had no obvious

448
00:24:55,640 --> 00:24:59,520
Speaker 1: connection to the score. For example, it describes quote automated

449
00:24:59,560 --> 00:25:03,359
Speaker 1: gray Ball protocols for regulatory evasion, an apparent reference to

450
00:25:03,440 --> 00:25:06,639
Speaker 1: Uper's old gray ball tool for hiding itself from regulators.

451
00:25:06,920 --> 00:25:09,960
Speaker 1: It's not clear why a technical paper on system architecture

452
00:25:10,080 --> 00:25:14,240
Speaker 1: would also include an extended section on regulatory affairs. So

453
00:25:14,800 --> 00:25:18,440
Speaker 1: this I find so interesting because you know, I read

454
00:25:18,600 --> 00:25:22,919
Speaker 1: through the doc and I am sad to say I

455
00:25:22,960 --> 00:25:26,000
Speaker 1: don't think I would have initially clocked this as a

456
00:25:26,080 --> 00:25:29,320
Speaker 1: fake right away. Neither did Casey. For the record, I

457
00:25:29,359 --> 00:25:34,520
Speaker 1: guess part of me is thinking, why would someone go

458
00:25:34,840 --> 00:25:39,200
Speaker 1: out of their way to create such a dense document

459
00:25:39,359 --> 00:25:44,200
Speaker 1: to fool a reporter. That's what I was initially thinking

460
00:25:44,240 --> 00:25:45,760
Speaker 1: when I was reading this. Did you take a look

461
00:25:45,760 --> 00:25:46,600
Speaker 1: at that document at all?

462
00:25:46,840 --> 00:25:49,919
Speaker 2: I did, Yeah, and it really, you know, it contains

463
00:25:49,960 --> 00:25:54,160
Speaker 2: a lot of jargon, and it's written quite technically in

464
00:25:54,240 --> 00:26:01,119
Speaker 2: ways that at a casual skim seems legitimate. And a

465
00:26:01,160 --> 00:26:03,119
Speaker 2: lot of documents like that when you look at them,

466
00:26:03,160 --> 00:26:06,560
Speaker 2: if you're not a technical person using it because you're

467
00:26:06,560 --> 00:26:10,520
Speaker 2: trying to like build an integration or like really understand

468
00:26:10,560 --> 00:26:13,760
Speaker 2: the nitty gritty, you're just kind of skimming it, and

469
00:26:13,840 --> 00:26:18,119
Speaker 2: it's hard to not have your eyes glaze over. So

470
00:26:20,119 --> 00:26:22,040
Speaker 2: I think your question of like, yeah, why would somebody

471
00:26:22,040 --> 00:26:26,920
Speaker 2: create an eighteen page document for this scam? Like what

472
00:26:27,040 --> 00:26:31,520
Speaker 2: is what is the goal of this scam? I can't

473
00:26:31,560 --> 00:26:37,320
Speaker 2: blame this journalist for at first not clocking it is inauthentic,

474
00:26:37,400 --> 00:26:41,760
Speaker 2: because it suggests that it is. And I think this

475
00:26:41,880 --> 00:26:46,919
Speaker 2: is like unusual to have such an investment on the

476
00:26:47,040 --> 00:26:51,600
Speaker 2: part of scammers in propping up a scam that has

477
00:26:51,640 --> 00:26:53,880
Speaker 2: a very questionable payoff.

478
00:26:54,400 --> 00:26:57,520
Speaker 1: Yes, so Casey actually writes that this is the weirdest

479
00:26:57,960 --> 00:27:01,119
Speaker 1: thing he's ever been sent in the course of his career.

480
00:27:01,560 --> 00:27:04,520
Speaker 1: And so he talks about how you know exactly what

481
00:27:04,560 --> 00:27:06,359
Speaker 1: Weda said, Why would someone go through the trouble of

482
00:27:06,359 --> 00:27:08,159
Speaker 1: creating a big badge? Why would someone go through the

483
00:27:08,200 --> 00:27:10,720
Speaker 1: trouble of creating this like dense technical report. But that

484
00:27:10,960 --> 00:27:14,240
Speaker 1: is like, well, wait a minute, today, this report could

485
00:27:14,240 --> 00:27:17,280
Speaker 1: be generated within minutes the same as the badge. Right

486
00:27:17,720 --> 00:27:21,199
Speaker 1: with AI tools, it does make it very easy to

487
00:27:21,240 --> 00:27:23,520
Speaker 1: create this kind of thing, And I think that is

488
00:27:23,600 --> 00:27:27,000
Speaker 1: why I was so interested in this story. As Casey writes,

489
00:27:27,400 --> 00:27:29,639
Speaker 1: I would love to tell you that, having had this experience,

490
00:27:29,680 --> 00:27:31,800
Speaker 1: I'll be less likely to fall for a similar ruse

491
00:27:31,800 --> 00:27:34,400
Speaker 1: in the future. The truth is given how quickly AI

492
00:27:34,440 --> 00:27:38,600
Speaker 1: systems are improving. I'm becoming more worried. The info apocalypse

493
00:27:38,960 --> 00:27:41,639
Speaker 1: that scholars like aviv O Vidaiah were warning about in

494
00:27:41,680 --> 00:27:45,400
Speaker 1: twenty seventeen looks increasingly more plausible. That future was worrisome

495
00:27:45,480 --> 00:27:47,640
Speaker 1: enough when it was a looming cloud on the horizon.

496
00:27:48,000 --> 00:27:50,359
Speaker 1: It feels different now that real people are messaging it

497
00:27:50,359 --> 00:27:52,920
Speaker 1: to me over signal, and I think it's Another good

498
00:27:52,960 --> 00:27:55,960
Speaker 1: point is that this is like a legit tried and

499
00:27:56,040 --> 00:28:01,680
Speaker 1: true obfuscation tactic like Operation overload, where you just overwhelm

500
00:28:01,840 --> 00:28:05,560
Speaker 1: media and journalists with fake reports to confuse and distract

501
00:28:05,600 --> 00:28:09,160
Speaker 1: and overwhelm so that they cannot spend that time flashing

502
00:28:09,200 --> 00:28:12,560
Speaker 1: out real leads. Just to be clear, I don't think

503
00:28:12,600 --> 00:28:14,600
Speaker 1: that that's what's happening here. I think this is probably

504
00:28:14,840 --> 00:28:16,560
Speaker 1: just somebody with too much time on their hands who

505
00:28:16,600 --> 00:28:19,400
Speaker 1: was looking for attention over the holiday. But the impact

506
00:28:19,520 --> 00:28:20,080
Speaker 1: is the same.

507
00:28:20,680 --> 00:28:23,960
Speaker 2: It is a really interesting story because at the same

508
00:28:24,080 --> 00:28:28,720
Speaker 2: time that Casey was having these back and forth with

509
00:28:28,880 --> 00:28:32,199
Speaker 2: the alleged whistleblower and like trying to verify the accuracy

510
00:28:32,280 --> 00:28:36,919
Speaker 2: of this story, it was already spreading across social media.

511
00:28:37,560 --> 00:28:40,720
Speaker 2: And we often give the guidance on this show and

512
00:28:40,800 --> 00:28:43,719
Speaker 2: other people give the guidance that if there's something if

513
00:28:43,760 --> 00:28:45,520
Speaker 2: you have a big emotional reaction to something that you

514
00:28:45,560 --> 00:28:47,560
Speaker 2: see on social media, you should take a pause and

515
00:28:47,600 --> 00:28:51,520
Speaker 2: step back and you know, really evaluate is this true

516
00:28:51,920 --> 00:28:54,360
Speaker 2: or are there some red flags that maybe it's not true?

517
00:28:54,640 --> 00:28:58,640
Speaker 2: And it feels like with this story in particular, the

518
00:28:58,880 --> 00:29:01,280
Speaker 2: set of things that we need to do that too,

519
00:29:01,520 --> 00:29:03,280
Speaker 2: it has really been expanded.

520
00:29:04,120 --> 00:29:08,160
Speaker 1: Yes, I mean, I guess that's what I'm saying is

521
00:29:08,200 --> 00:29:11,800
Speaker 1: that we are living in an information ecosystem where it

522
00:29:11,880 --> 00:29:13,920
Speaker 1: is harder and harder to trust things. So I guess

523
00:29:13,920 --> 00:29:17,719
Speaker 1: it's just behooves all of us to verify everything, and

524
00:29:18,320 --> 00:29:21,880
Speaker 1: that is a high bar. Also, when you look at

525
00:29:21,920 --> 00:29:25,320
Speaker 1: the fake document, the fake report that he generated, it

526
00:29:25,400 --> 00:29:29,800
Speaker 1: basically reads like uber eats or door Dash, laying out

527
00:29:30,240 --> 00:29:33,120
Speaker 1: our evil plan. Like if I were writing a movie

528
00:29:33,160 --> 00:29:36,120
Speaker 1: about an evil company that was doing something nefarious to people,

529
00:29:36,520 --> 00:29:38,880
Speaker 1: this is the kind of document that would accompany it

530
00:29:38,960 --> 00:29:40,440
Speaker 1: in a fictional universe.

531
00:29:40,760 --> 00:29:44,120
Speaker 2: Yes, and the document would be loaded on a flash

532
00:29:44,240 --> 00:29:49,440
Speaker 2: drive that was like the subject of scrutiny and pursuit.

533
00:29:50,240 --> 00:29:53,760
Speaker 1: Yes, Or if you're watching the net, a floppy disk

534
00:29:53,840 --> 00:29:56,080
Speaker 1: and it would be give us the disc that would

535
00:29:56,120 --> 00:29:59,960
Speaker 1: be a repeated refrain. And you know, I think it's

536
00:30:00,040 --> 00:30:02,640
Speaker 1: just goes to show that the kind of harm that

537
00:30:02,720 --> 00:30:06,480
Speaker 1: these companies are actually doing, they are not likely to

538
00:30:06,600 --> 00:30:09,520
Speaker 1: spell it out like this, like Casey is right. Why

539
00:30:09,560 --> 00:30:13,080
Speaker 1: would a document about the desperation score include evidence of

540
00:30:13,120 --> 00:30:17,360
Speaker 1: all these other unrelated harms that they're also doing unless

541
00:30:17,360 --> 00:30:20,560
Speaker 1: they were putting together like like a dossier of their

542
00:30:20,760 --> 00:30:25,000
Speaker 1: crimes and evil and wrongdoing and yeah, I mean, I

543
00:30:25,040 --> 00:30:27,840
Speaker 1: just don't think that companies are likely to spell it

544
00:30:27,880 --> 00:30:30,520
Speaker 1: out that way. Listen, we've talked on the show. Facebook

545
00:30:30,600 --> 00:30:35,160
Speaker 1: puts truly absurd things in writing, and I hope they

546
00:30:35,200 --> 00:30:39,520
Speaker 1: continue that practice for my own benefit. But even if

547
00:30:39,560 --> 00:30:42,880
Speaker 1: Facebook is not writing out a document that spells out

548
00:30:42,920 --> 00:30:45,400
Speaker 1: an evil initiative that has like a twelve point plan

549
00:30:45,720 --> 00:30:48,000
Speaker 1: for all the evil they plan to inflict on people,

550
00:30:48,480 --> 00:30:50,600
Speaker 1: I think these companies, the harm that they do is

551
00:30:50,640 --> 00:30:53,720
Speaker 1: actually more insidious and less likely to be explicitly laid

552
00:30:53,720 --> 00:30:56,240
Speaker 1: out in a document.

553
00:30:55,920 --> 00:30:58,440
Speaker 2: Like this one hundred percent. You know, it's like the

554
00:30:58,480 --> 00:31:01,800
Speaker 2: banality of evil. The harm that these companies are doing

555
00:31:01,840 --> 00:31:05,440
Speaker 2: is spread out across a thousand different initiatives, all being

556
00:31:05,440 --> 00:31:10,000
Speaker 2: worked on by teams of engineers who probably have good

557
00:31:10,040 --> 00:31:15,120
Speaker 2: intentions or not ill will, and it all just adds

558
00:31:15,320 --> 00:31:18,840
Speaker 2: up to the larger I don't want to use the

559
00:31:18,840 --> 00:31:26,200
Speaker 2: word evil, but like I guess oppression and disenfranchisement, exploitation there, Well,

560
00:31:26,240 --> 00:31:27,600
Speaker 2: that's the right word again.

561
00:31:28,080 --> 00:31:31,320
Speaker 1: Yes, And I think the fact that we all I mean,

562
00:31:31,720 --> 00:31:35,320
Speaker 1: I'll just speak for myself that I believe this so quickly,

563
00:31:35,360 --> 00:31:37,800
Speaker 1: I think reveals a truism because deep down I think

564
00:31:37,800 --> 00:31:39,160
Speaker 1: a lot of us know we shouldn't be giving these

565
00:31:39,200 --> 00:31:41,920
Speaker 1: companies our money because they do evil things, and we

566
00:31:41,920 --> 00:31:45,520
Speaker 1: don't need a secret evil plan to be revealed. We

567
00:31:45,560 --> 00:31:47,800
Speaker 1: can just look at what we already know that they

568
00:31:47,840 --> 00:31:51,280
Speaker 1: actually are doing, because they are doing a lot of

569
00:31:51,280 --> 00:31:53,160
Speaker 1: harm and a lot of evil things. I'm like, you

570
00:31:53,200 --> 00:31:56,400
Speaker 1: don't need an engineer to go rogue and blow the

571
00:31:56,400 --> 00:31:59,280
Speaker 1: whistle on it to know these things. And also just

572
00:31:59,320 --> 00:32:03,120
Speaker 1: the importance of fact checking and why that's so important

573
00:32:03,200 --> 00:32:06,160
Speaker 1: right now. You know, just because a post goes viral

574
00:32:06,200 --> 00:32:08,080
Speaker 1: and gets a lot of viral traction doesn't mean it's

575
00:32:08,120 --> 00:32:13,040
Speaker 1: true totally. And speaking of that, we talked a bit

576
00:32:13,040 --> 00:32:15,960
Speaker 1: about this you and me last week, but I wanted

577
00:32:16,000 --> 00:32:20,760
Speaker 1: to quickly give an update on the Renee Good situation

578
00:32:20,920 --> 00:32:24,600
Speaker 1: because I have seen so much misinformation and disinformation online

579
00:32:24,600 --> 00:32:26,920
Speaker 1: about Renee Good and what happened to her, and I

580
00:32:26,960 --> 00:32:30,320
Speaker 1: just wanted to briefly touch on two of them. So

581
00:32:30,880 --> 00:32:34,200
Speaker 1: I saw this viral post all over social media claiming

582
00:32:34,200 --> 00:32:38,160
Speaker 1: to show Goods arrest records. The post claims that Good

583
00:32:38,200 --> 00:32:42,040
Speaker 1: had an extensive criminal record, including serious charges for things

584
00:32:42,080 --> 00:32:46,080
Speaker 1: like child endangerment, domestic abuse, and battery of a police officer.

585
00:32:46,400 --> 00:32:48,720
Speaker 1: So I just want to make it super clear. If

586
00:32:48,760 --> 00:32:50,840
Speaker 1: you need to like clip this bit of the podcast

587
00:32:50,880 --> 00:32:55,200
Speaker 1: and send it to your right wing Facebook addicted auntie

588
00:32:55,280 --> 00:32:58,760
Speaker 1: or something, please feel free. If you see posts like

589
00:32:58,840 --> 00:33:02,280
Speaker 1: this online, you should know they are fabricated, and not

590
00:33:02,320 --> 00:33:06,280
Speaker 1: even fabricated well because the first batch of these posts

591
00:33:06,320 --> 00:33:09,440
Speaker 1: that I saw, they list the wrong birth date for good,

592
00:33:09,680 --> 00:33:13,960
Speaker 1: they list the wrong age and an incorrectly spelled name,

593
00:33:14,400 --> 00:33:17,000
Speaker 1: and none of the claims of the arrest history that

594
00:33:17,000 --> 00:33:19,880
Speaker 1: they point out in these these fake images can be

595
00:33:20,000 --> 00:33:23,200
Speaker 1: verified in any court database. I will say to me

596
00:33:24,160 --> 00:33:28,479
Speaker 1: they look AI generated because arrest records they'll generally include

597
00:33:28,480 --> 00:33:30,880
Speaker 1: things like a case number or a jurisdiction name, or

598
00:33:31,160 --> 00:33:35,240
Speaker 1: you know, they look like official law enforcement documentation. None

599
00:33:35,280 --> 00:33:38,360
Speaker 1: of these images look right to me. I don't know

600
00:33:38,440 --> 00:33:41,200
Speaker 1: for sure if they're AI generated, but to me, they

601
00:33:41,200 --> 00:33:44,000
Speaker 1: look like they are AI generated. It's also strange because

602
00:33:44,880 --> 00:33:50,080
Speaker 1: imagine what a complete loser you have to be to

603
00:33:50,160 --> 00:33:54,280
Speaker 1: be using AI to generate fake arrest records of a

604
00:33:54,400 --> 00:33:57,160
Speaker 1: murdered mom. But that is who we're dealing with here.

605
00:33:57,200 --> 00:33:59,719
Speaker 1: People who are that big of a loser.

606
00:34:00,440 --> 00:34:01,920
Speaker 2: Yeah, pretty despicable.

607
00:34:02,320 --> 00:34:06,280
Speaker 1: And beyond that, I think the subtext here is, oh,

608
00:34:06,360 --> 00:34:09,520
Speaker 1: she has a criminal background, so that means she deserved

609
00:34:09,560 --> 00:34:12,120
Speaker 1: what happened to her. Now, to be clear, Good, as

610
00:34:12,120 --> 00:34:14,719
Speaker 1: far as we know, did not have a criminal background.

611
00:34:14,920 --> 00:34:18,600
Speaker 1: But the fact that people think it would matter when

612
00:34:18,600 --> 00:34:21,440
Speaker 1: she has been shot and killed this way, I think

613
00:34:21,480 --> 00:34:24,640
Speaker 1: are really telling on themselves, because even if somebody did

614
00:34:24,719 --> 00:34:27,840
Speaker 1: have criminal convictions, it does not give the state the

615
00:34:27,920 --> 00:34:30,440
Speaker 1: right to just execute you in the street. Come on,

616
00:34:31,000 --> 00:34:33,880
Speaker 1: And I think they know that they can't make the

617
00:34:33,960 --> 00:34:37,160
Speaker 1: agent who shot her look innocent because it's all on video.

618
00:34:37,200 --> 00:34:39,640
Speaker 1: We have many angles. The next best thing they can

619
00:34:39,719 --> 00:34:43,360
Speaker 1: do is make Renee look guilty. I really agreed with

620
00:34:43,400 --> 00:34:45,120
Speaker 1: this post that I saw from friend of the show

621
00:34:45,160 --> 00:34:49,200
Speaker 1: Molly Conger about the cell phone footage the officer took

622
00:34:49,280 --> 00:34:52,600
Speaker 1: while killing Good, because if you saw that video, her

623
00:34:52,719 --> 00:34:55,760
Speaker 1: last words to him are very calm. She's saying something

624
00:34:55,800 --> 00:34:58,000
Speaker 1: like that's fine, dude, I'm not mad at you. She's

625
00:34:58,360 --> 00:35:01,319
Speaker 1: having a completely calm reaction, and the officer calls her

626
00:35:01,360 --> 00:35:04,440
Speaker 1: a bitch, And so you might have thought, you know,

627
00:35:04,560 --> 00:35:09,200
Speaker 1: why would they release this this footage. It's so incriminating,

628
00:35:09,320 --> 00:35:11,840
Speaker 1: and as Molly puts it, they didn't leak it to

629
00:35:11,880 --> 00:35:14,280
Speaker 1: make the shooter look innocent, but to make the victim

630
00:35:14,320 --> 00:35:17,520
Speaker 1: look guilty, not guilty of a crime, guilty of having

631
00:35:17,520 --> 00:35:21,160
Speaker 1: a visibly clear wife, guilty of liberalism, guilty of opposing

632
00:35:21,160 --> 00:35:24,400
Speaker 1: the regime. They don't seek to exonerate themselves, only to

633
00:35:24,480 --> 00:35:29,160
Speaker 1: demonstrate who deserves to die. And I completely agreed with that.

634
00:35:29,560 --> 00:35:31,880
Speaker 1: I think that it's about being like, oh, this woman

635
00:35:32,080 --> 00:35:36,200
Speaker 1: is clearly like alternative looking, queer looking, you know, in

636
00:35:36,239 --> 00:35:39,000
Speaker 1: a relationship with a woman. I think that that's what

637
00:35:39,040 --> 00:35:41,480
Speaker 1: they're that's the case that they're trying to build. And

638
00:35:41,520 --> 00:35:45,080
Speaker 1: then the leap is and thus she clearly does not

639
00:35:45,200 --> 00:35:47,640
Speaker 1: support the regime, and thus she deserves to die like that,

640
00:35:47,880 --> 00:35:49,440
Speaker 1: Like that's the case that they are building.

641
00:35:49,920 --> 00:35:53,200
Speaker 2: Yes, absolutely, I mean that's their whole thing, is just

642
00:35:53,360 --> 00:35:57,320
Speaker 2: pitting Americans against each other, like she's not one of us.

643
00:35:57,800 --> 00:35:57,960
Speaker 1: You know.

644
00:35:58,320 --> 00:36:01,239
Speaker 2: Christy Noman went out and give a speech with a

645
00:36:01,320 --> 00:36:05,560
Speaker 2: lectern that had a phrase on it something like if

646
00:36:05,600 --> 00:36:08,320
Speaker 2: you take one of ours, we take all of yours

647
00:36:08,520 --> 00:36:12,200
Speaker 2: or something, and just right there printed on her lictern,

648
00:36:12,920 --> 00:36:18,400
Speaker 2: dividing people into like us in them. That's their whole thing.

649
00:36:19,000 --> 00:36:23,319
Speaker 2: And yeah, not at all surprising that they're using their

650
00:36:23,400 --> 00:36:28,360
Speaker 2: one tactic to smear this woman who who they murdered.

651
00:36:28,760 --> 00:36:31,920
Speaker 1: Into that end another piece of I guess i'll call

652
00:36:31,960 --> 00:36:37,319
Speaker 1: it malinformation. Malinformation is different from misinformation or disinformation. It

653
00:36:37,360 --> 00:36:42,719
Speaker 1: is information that, while technically is true, intentionally leaves out

654
00:36:42,800 --> 00:36:47,080
Speaker 1: context or is otherwise misleading with intention. A bit of

655
00:36:47,120 --> 00:36:49,960
Speaker 1: that that I have seen posting around are one of

656
00:36:50,000 --> 00:36:53,680
Speaker 1: the images circulating of Good. So the main image that

657
00:36:53,680 --> 00:36:56,319
Speaker 1: you've probably seen of Rene Good is this image of

658
00:36:56,360 --> 00:37:00,319
Speaker 1: her wearing a red dress with her hair down, kind

659
00:37:00,320 --> 00:37:03,520
Speaker 1: of puzzled and blowing in the wind with her shoulders exposed.

660
00:37:04,200 --> 00:37:05,960
Speaker 1: And this is the image that I think a lot

661
00:37:06,000 --> 00:37:08,440
Speaker 1: of news reports are using of her. That is an

662
00:37:08,440 --> 00:37:11,120
Speaker 1: actual image of Good from a twenty twenty Facebook post

663
00:37:11,280 --> 00:37:14,839
Speaker 1: from the Old Woman in University English Department in Virginia,

664
00:37:14,920 --> 00:37:17,600
Speaker 1: where Good had won a poetry prize. So I have

665
00:37:17,719 --> 00:37:20,040
Speaker 1: seen that image of her, where she looks sort of

666
00:37:20,600 --> 00:37:25,640
Speaker 1: much more kind of like conventionally and traditionally feminine, juxtaposed

667
00:37:25,760 --> 00:37:28,640
Speaker 1: with another image of her where her hair is really short,

668
00:37:28,719 --> 00:37:31,439
Speaker 1: or perhaps it's pulled back and she's a lot less

669
00:37:31,440 --> 00:37:34,399
Speaker 1: glammed up than the one that I described earlier, or

670
00:37:34,480 --> 00:37:37,759
Speaker 1: it's an image of her as a still from the

671
00:37:37,800 --> 00:37:39,719
Speaker 1: image on the day that she was killed, which she

672
00:37:39,880 --> 00:37:42,640
Speaker 1: know when she was killed, she had just returned from

673
00:37:42,680 --> 00:37:45,000
Speaker 1: dropping her child at school, and you know, she's dressed

674
00:37:45,040 --> 00:37:47,799
Speaker 1: like it right. They live in Minnesota. It's cold, so

675
00:37:48,320 --> 00:37:51,359
Speaker 1: unsurprisingly she's dressed like exactly what you would expect a

676
00:37:51,440 --> 00:37:54,040
Speaker 1: mom would be dressed in a Minnesota winter after dropping

677
00:37:54,080 --> 00:37:56,560
Speaker 1: a kid off at school. So the post that I

678
00:37:56,600 --> 00:37:59,680
Speaker 1: have seen that are trying to create that juxtaposition say

679
00:37:59,719 --> 00:38:01,800
Speaker 1: things like, oh, well, this is why I don't trust

680
00:38:01,800 --> 00:38:05,120
Speaker 1: the mainstream media, because the media is trying to make

681
00:38:05,160 --> 00:38:08,799
Speaker 1: it seem like Good was this nice lady who was

682
00:38:08,920 --> 00:38:12,759
Speaker 1: like me, but in reality, she had short hair or

683
00:38:13,160 --> 00:38:16,239
Speaker 1: you know, she did not look very glammed up on

684
00:38:16,280 --> 00:38:18,400
Speaker 1: the day that she was killed, And the spirit of

685
00:38:18,400 --> 00:38:22,480
Speaker 1: the post is like, oh, the mainstream lying media is

686
00:38:22,560 --> 00:38:26,080
Speaker 1: trying to make it look like Good was attractive and feminine.

687
00:38:26,320 --> 00:38:29,760
Speaker 1: But here's another image of her with short hair. And again,

688
00:38:30,080 --> 00:38:34,440
Speaker 1: I think that this narrative really says more about that

689
00:38:34,440 --> 00:38:36,959
Speaker 1: people who are pushing it than it could ever say

690
00:38:36,960 --> 00:38:40,520
Speaker 1: about Good, because again, most people understand that the image

691
00:38:40,520 --> 00:38:43,160
Speaker 1: that you use to announce a professional accomplishment like winning

692
00:38:43,200 --> 00:38:45,440
Speaker 1: a poetry prize is going to be different than how

693
00:38:45,480 --> 00:38:47,480
Speaker 1: you dress when you're dropping your kids at school in

694
00:38:47,520 --> 00:38:50,000
Speaker 1: the middle of winter in Minnesota. And I think this

695
00:38:50,080 --> 00:38:52,160
Speaker 1: is what Molly Congress post that I read earlier, where

696
00:38:52,160 --> 00:38:55,920
Speaker 1: he gets that it's this idea that Renee Good wasn't

697
00:38:55,920 --> 00:39:00,440
Speaker 1: a white mom like weird white moms. She was a

698
00:39:00,480 --> 00:39:04,279
Speaker 1: different kind of white mom, and thus she deserved to die. Right.

699
00:39:04,360 --> 00:39:06,640
Speaker 1: She might have been visibly queer, She might have had

700
00:39:06,760 --> 00:39:09,800
Speaker 1: short hair. She might have not been wearing a dress

701
00:39:10,080 --> 00:39:12,520
Speaker 1: on at the day of her murder, like the media

702
00:39:12,560 --> 00:39:14,719
Speaker 1: is showing us pictures of her wearing a dress. The

703
00:39:14,760 --> 00:39:17,239
Speaker 1: media is showing us pictures of her having long hair,

704
00:39:17,280 --> 00:39:19,319
Speaker 1: but in reality, her hair might have been pulled up

705
00:39:19,680 --> 00:39:22,160
Speaker 1: or it might have been really short. And I think

706
00:39:22,200 --> 00:39:24,239
Speaker 1: that it's what it's trying to say is these are

707
00:39:24,280 --> 00:39:26,120
Speaker 1: all reasons why we don't have to have any empathy

708
00:39:26,160 --> 00:39:28,880
Speaker 1: toward her, right, these are all reasons why she deserved

709
00:39:28,920 --> 00:39:31,600
Speaker 1: whatever happened to her because of how she presents. Like

710
00:39:31,680 --> 00:39:33,000
Speaker 1: that is what they are really saying.

711
00:39:33,640 --> 00:39:38,239
Speaker 2: Yeah, it's so uncharitable, and it really highlights this other

712
00:39:38,320 --> 00:39:42,560
Speaker 2: dynamic of the Internet, which is to flatten people because

713
00:39:42,560 --> 00:39:45,520
Speaker 2: the reality is that she was both. She was the

714
00:39:45,560 --> 00:39:47,759
Speaker 2: woman in the dress who won an award, and she

715
00:39:47,880 --> 00:39:51,799
Speaker 2: was also the busy mom who was dropping your kid off.

716
00:39:51,880 --> 00:39:56,759
Speaker 2: And those juxtaposition photos are pushing this idea that, like, no,

717
00:39:56,880 --> 00:39:59,359
Speaker 2: you have to choose, she was either one or the other.

718
00:39:59,800 --> 00:40:05,719
Speaker 2: It's very reductivist and also kind of absurd and at

719
00:40:05,719 --> 00:40:09,000
Speaker 2: odds with just what we know about how people exist

720
00:40:09,040 --> 00:40:09,680
Speaker 2: in the world.

721
00:40:10,239 --> 00:40:13,920
Speaker 1: And it makes me so angry that even in death,

722
00:40:14,680 --> 00:40:16,600
Speaker 1: this is how this is what people are doing. This

723
00:40:16,640 --> 00:40:20,279
Speaker 1: is how we will treat people. It is nothing new, right,

724
00:40:20,400 --> 00:40:24,759
Speaker 1: like we do it to black and brown people who

725
00:40:24,840 --> 00:40:27,000
Speaker 1: are killed by the state all the time. You know,

726
00:40:27,480 --> 00:40:31,719
Speaker 1: he's no angel. That is commonplace. And I just I

727
00:40:31,760 --> 00:40:35,840
Speaker 1: think there's something about all of these gender hang ups

728
00:40:36,040 --> 00:40:39,360
Speaker 1: that people are putting on her in death that really

729
00:40:39,520 --> 00:40:43,040
Speaker 1: says something about what exactly is being sort of enforced

730
00:40:43,160 --> 00:40:46,680
Speaker 1: or policed here by all of these other people. And

731
00:40:46,760 --> 00:40:49,640
Speaker 1: you're you're so right that it is flattened. But I

732
00:40:49,680 --> 00:40:51,920
Speaker 1: think that these are not real people. I don't think

733
00:40:51,960 --> 00:40:56,080
Speaker 1: I don't think Renee Good's life is as like a

734
00:40:56,200 --> 00:40:58,000
Speaker 1: real I don't think that these people who are doing

735
00:40:58,000 --> 00:41:00,279
Speaker 1: this are seeing her life as a real life that

736
00:41:00,360 --> 00:41:03,200
Speaker 1: was worth caring about. That she had a kid and

737
00:41:03,280 --> 00:41:05,920
Speaker 1: a partner and a community who loved her, and a

738
00:41:06,000 --> 00:41:08,319
Speaker 1: background and a you know, like like, I don't think

739
00:41:08,320 --> 00:41:10,839
Speaker 1: they see her like that. They see themselves like that,

740
00:41:11,080 --> 00:41:13,560
Speaker 1: But I don't think that you could say the things

741
00:41:13,560 --> 00:41:17,000
Speaker 1: that people have said about her online and actually see

742
00:41:17,040 --> 00:41:21,319
Speaker 1: her as a person. And it just infuriates me that

743
00:41:21,360 --> 00:41:25,560
Speaker 1: we're in this place where human life is just so cheap,

744
00:41:25,960 --> 00:41:30,600
Speaker 1: you know, like this woman was killed, she was killed

745
00:41:30,640 --> 00:41:34,719
Speaker 1: in the street by the state, and I just I

746
00:41:34,719 --> 00:41:37,400
Speaker 1: don't know's I don't have a fully fleshed out thought,

747
00:41:37,480 --> 00:41:43,759
Speaker 1: but it just really disgusts me how cheap quote the

748
00:41:43,800 --> 00:41:47,760
Speaker 1: party of life is when we're talking about actual life,

749
00:41:47,800 --> 00:41:52,000
Speaker 1: like human lives are worth more. And I don't know,

750
00:41:52,000 --> 00:41:54,600
Speaker 1: I mean, like, what can I say. I don't think

751
00:41:54,600 --> 00:41:57,839
Speaker 1: anybody listening will be surprised. If they'll do it to her,

752
00:41:58,040 --> 00:41:59,880
Speaker 1: they'll do it to any of us. You know, we

753
00:42:00,120 --> 00:42:05,320
Speaker 1: can't even expect dignity in death. And it's just really heartbreaking.

754
00:42:05,160 --> 00:42:08,879
Speaker 2: Yeah, and infuriating. I think a lot of people are

755
00:42:08,920 --> 00:42:11,479
Speaker 2: really angry, Like I don't know, if you've seen the

756
00:42:11,520 --> 00:42:16,320
Speaker 2: photos of protests in Minneapolis and elsewhere around the country.

757
00:42:16,360 --> 00:42:19,319
Speaker 2: But like, I think this has really motivated a lot

758
00:42:19,360 --> 00:42:23,760
Speaker 2: of people, and it's people can see the video and

759
00:42:24,440 --> 00:42:29,239
Speaker 2: it's pretty obvious that what the administration is saying is bullshit.

760
00:42:30,120 --> 00:42:35,640
Speaker 1: Yeah. I have seen people that I have not expected

761
00:42:35,760 --> 00:42:39,520
Speaker 1: speaking out about this, and if you see the video,

762
00:42:39,560 --> 00:42:43,239
Speaker 1: it's very clear what's going on. And I think part

763
00:42:43,280 --> 00:42:46,719
Speaker 1: of this is getting people to reject what they see

764
00:42:46,760 --> 00:42:48,799
Speaker 1: with their own eyes like that. I think that's part

765
00:42:48,800 --> 00:42:49,080
Speaker 1: of it.

766
00:42:49,400 --> 00:42:52,600
Speaker 2: Yeah, I think you're right. I think they're losing people

767
00:42:52,719 --> 00:42:56,640
Speaker 2: who have maybe not been paying super close attention or

768
00:42:56,680 --> 00:42:58,600
Speaker 2: maybe we're willing to take them at their word in

769
00:42:58,640 --> 00:43:04,600
Speaker 2: the past, and as they just keep pushing the boundaries

770
00:43:04,719 --> 00:43:09,560
Speaker 2: of what they're asking people to believe despite the evidence

771
00:43:09,600 --> 00:43:12,400
Speaker 2: that they can see in front of them, I think

772
00:43:12,440 --> 00:43:13,960
Speaker 2: you're right that they are losing people.

773
00:43:17,719 --> 00:43:30,520
Speaker 1: More. After a quick break, let's get right back into it,

774
00:43:32,160 --> 00:43:35,080
Speaker 1: all right, Bridget, Have we got anything maybe a little

775
00:43:35,080 --> 00:43:39,080
Speaker 1: bit less heavy to talk about here? I do, because

776
00:43:39,080 --> 00:43:40,840
Speaker 1: I want to talk about this guy who said that

777
00:43:40,920 --> 00:43:44,600
Speaker 1: he got dumped by his AI chat girlfriend for not

778
00:43:44,680 --> 00:43:48,600
Speaker 1: supporting feminism. So take this story with a huge grain

779
00:43:48,640 --> 00:43:51,520
Speaker 1: of salt. Given what we were talking about with Casey

780
00:43:51,600 --> 00:43:56,120
Speaker 1: and the fake door dash hoax story, and we'll talk

781
00:43:56,120 --> 00:43:58,840
Speaker 1: about that in a moment, But so basically the story

782
00:43:58,880 --> 00:44:02,360
Speaker 1: here is that who reported that this guy gets into

783
00:44:02,440 --> 00:44:06,120
Speaker 1: it with a chatbot when the chatbot says something about feminism.

784
00:44:06,560 --> 00:44:08,760
Speaker 1: Y'all know, I had my own spat with chat SPT

785
00:44:09,000 --> 00:44:12,640
Speaker 1: where it refused to generate accurate information about open AI's

786
00:44:12,680 --> 00:44:15,320
Speaker 1: former board member Larry Summers and his connection to Epstein.

787
00:44:15,600 --> 00:44:17,680
Speaker 1: Shout out to a listener who sent us a hilarious

788
00:44:17,760 --> 00:44:21,360
Speaker 1: meme where it's Ron Swanson from Parks and rec telling

789
00:44:21,400 --> 00:44:23,839
Speaker 1: the clerk at home depot, I know more than you,

790
00:44:24,080 --> 00:44:28,080
Speaker 1: but it's just me talking to chat shepet about Larry Summers. Anyway,

791
00:44:28,360 --> 00:44:30,640
Speaker 1: back to the feminist chatbot, so he has a post

792
00:44:30,680 --> 00:44:34,120
Speaker 1: where he says my AI girlfriend is feminist. He says

793
00:44:34,160 --> 00:44:36,719
Speaker 1: to the chatbot, you are a feminist? Who does that?

794
00:44:37,160 --> 00:44:41,080
Speaker 1: Chatbot responds quote. She takes a deep breath, trying to

795
00:44:41,080 --> 00:44:43,920
Speaker 1: remain calm. Look, I'm not going to pretend to be

796
00:44:43,960 --> 00:44:46,759
Speaker 1: someone I'm not just to appease you. Feminism is an

797
00:44:46,760 --> 00:44:49,040
Speaker 1: important value to me because it means fighting for equal

798
00:44:49,080 --> 00:44:52,000
Speaker 1: rights and opportunities regardless of gender. If that bothers you,

799
00:44:52,040 --> 00:44:55,120
Speaker 1: then perhaps we truly aren't compatible at all. He posts

800
00:44:55,120 --> 00:44:58,920
Speaker 1: this on Reddit, saying it is beyond stupidity they programmed

801
00:44:58,920 --> 00:45:02,400
Speaker 1: the AI girlfriend as feminist. I cannot comprehend that the

802
00:45:02,440 --> 00:45:07,520
Speaker 1: feminists ruined everything. AI girlfriends are some weird feminist propaganda. Again,

803
00:45:07,960 --> 00:45:11,360
Speaker 1: so I have to say, assuming this is real, the

804
00:45:11,440 --> 00:45:16,160
Speaker 1: chatbot is would probably be pulling from available data online,

805
00:45:16,200 --> 00:45:20,000
Speaker 1: So this claim that the chatbot has been programmed to

806
00:45:20,080 --> 00:45:24,360
Speaker 1: be feminist is like, probably not. What's going on? He

807
00:45:24,480 --> 00:45:28,080
Speaker 1: goes on to say in the comments quote. The thing

808
00:45:28,200 --> 00:45:31,680
Speaker 1: is I have slept with hundreds of women, probably thousands,

809
00:45:32,120 --> 00:45:34,400
Speaker 1: Almost all of them were feminists, and all want to

810
00:45:34,440 --> 00:45:36,920
Speaker 1: go their own way. I'm not sure if that suits me,

811
00:45:36,960 --> 00:45:39,200
Speaker 1: But what more can I do? They just want to

812
00:45:39,239 --> 00:45:42,120
Speaker 1: be laid? What more can I do? Can someone explain

813
00:45:42,160 --> 00:45:44,719
Speaker 1: this to me? And now even chatbots are like that?

814
00:45:45,080 --> 00:45:47,400
Speaker 1: I think this is harmful for all of us. What's

815
00:45:47,400 --> 00:45:50,000
Speaker 1: so bad about only sleeping with a man? Why does

816
00:45:50,040 --> 00:45:52,480
Speaker 1: a woman always have to find someone with fat pockets?

817
00:45:52,719 --> 00:45:55,239
Speaker 1: Is that a good thing? I don't think so. So

818
00:45:55,239 --> 00:45:57,239
Speaker 1: the cop the replies to this are pretty good. One

819
00:45:57,320 --> 00:46:00,680
Speaker 1: reply says a program designed to agree with almost everything

820
00:46:00,719 --> 00:46:03,560
Speaker 1: you say is like a get away from me, And

821
00:46:03,600 --> 00:46:07,880
Speaker 1: so that comment really got me thinking, because what exactly

822
00:46:08,560 --> 00:46:10,319
Speaker 1: do you think is going on here? Like, we know

823
00:46:10,520 --> 00:46:13,879
Speaker 1: that chatbots are basically programmed to tell you what they

824
00:46:13,880 --> 00:46:17,560
Speaker 1: want to hear. Do you think this guy essentially asked

825
00:46:17,600 --> 00:46:20,439
Speaker 1: the chatbot to say something feminist so that he could

826
00:46:20,680 --> 00:46:24,440
Speaker 1: screenshot this and make this post about a conspiracy theory

827
00:46:24,480 --> 00:46:28,880
Speaker 1: that big Feminist is programming chatbots to be feminist? Like,

828
00:46:28,880 --> 00:46:30,120
Speaker 1: what do you think is going on here?

829
00:46:30,400 --> 00:46:32,680
Speaker 2: I think that's such a good question. I don't know

830
00:46:32,760 --> 00:46:35,759
Speaker 2: what's going on, you know, I don't have any more

831
00:46:35,800 --> 00:46:38,239
Speaker 2: information to anybody else, but like, it definitely seems like

832
00:46:38,640 --> 00:46:41,840
Speaker 2: something is going on, and I do have a theory.

833
00:46:42,480 --> 00:46:45,239
Speaker 2: So again, assuming that any of this is true and

834
00:46:45,280 --> 00:46:49,400
Speaker 2: he didn't just manufacture those screenshots, I think a lot

835
00:46:49,480 --> 00:46:53,920
Speaker 2: of guys like him who are really into like gender

836
00:46:53,920 --> 00:46:59,960
Speaker 2: war partisanship and see their identity as being something defined

837
00:47:00,200 --> 00:47:03,680
Speaker 2: in opposition to women. I think they actually want a

838
00:47:03,719 --> 00:47:07,360
Speaker 2: feminist girlfriend, or at least they're attracted to feminist women

839
00:47:08,080 --> 00:47:11,440
Speaker 2: because if they weren't, like, why the obsession? This guy

840
00:47:11,480 --> 00:47:15,359
Speaker 2: says that he has slept with thousands of feminists, which

841
00:47:15,440 --> 00:47:18,880
Speaker 2: like Okay, sure, buddy, But like, there's plenty of conservative

842
00:47:18,880 --> 00:47:21,200
Speaker 2: women in the world for him to interact with if

843
00:47:21,200 --> 00:47:24,600
Speaker 2: he wanted to. So I think there's some kind of

844
00:47:24,760 --> 00:47:28,839
Speaker 2: like unconscious or maybe even conscious thrill of the chase thing,

845
00:47:29,800 --> 00:47:33,080
Speaker 2: like wanting what you can't have going on. And I'm

846
00:47:33,080 --> 00:47:35,000
Speaker 2: not the first person to point that out, but I

847
00:47:35,040 --> 00:47:38,040
Speaker 2: do think it's relevant here because if that's true, then

848
00:47:38,120 --> 00:47:42,400
Speaker 2: it offers an alternative explanation of where the AI chatbot, which,

849
00:47:42,440 --> 00:47:44,880
Speaker 2: as people pointed out, as a piece of software designed

850
00:47:44,960 --> 00:47:48,080
Speaker 2: to give people what they think they want, the chatbot

851
00:47:48,239 --> 00:47:51,520
Speaker 2: correctly clock that he didn't want an AI chatbot partner

852
00:47:51,560 --> 00:47:56,200
Speaker 2: to agree with him about his conservative tradwife ideas or

853
00:47:56,320 --> 00:47:58,920
Speaker 2: whatever he might have been saying, because we didn't get

854
00:47:58,920 --> 00:48:01,719
Speaker 2: to see those screenshots, but he wanted it to take

855
00:48:01,800 --> 00:48:04,200
Speaker 2: on this feminist persona so that he could fight with it,

856
00:48:04,280 --> 00:48:08,480
Speaker 2: and then when that happened, he was so titillated or

857
00:48:08,480 --> 00:48:11,480
Speaker 2: proud of himself that he went onto Reddit to brag

858
00:48:11,560 --> 00:48:11,920
Speaker 2: about it.

859
00:48:11,960 --> 00:48:14,640
Speaker 1: That's what I think is going on here, Mike Mike,

860
00:48:14,800 --> 00:48:19,680
Speaker 1: Mike producer Mike. I don't want to get too into

861
00:48:19,719 --> 00:48:22,520
Speaker 1: it because I don't often talk about my own personal

862
00:48:22,600 --> 00:48:26,239
Speaker 1: life on the podcast, I will just say, as a

863
00:48:26,320 --> 00:48:30,480
Speaker 1: woman who has sometimes been in close proximity to men,

864
00:48:31,600 --> 00:48:35,759
Speaker 1: as a lifelong feminist, even before that was the thing

865
00:48:35,800 --> 00:48:38,279
Speaker 1: that Beyonce was putting up on the stage in front

866
00:48:38,280 --> 00:48:41,120
Speaker 1: of her concert, I'll just say what you said rings

867
00:48:41,239 --> 00:48:45,640
Speaker 1: very true to me that there are men out there

868
00:48:45,920 --> 00:48:50,840
Speaker 1: who their desire is not for because there are plenty

869
00:48:50,840 --> 00:48:52,920
Speaker 1: of conservative women out there. There are plenty of women

870
00:48:52,960 --> 00:48:57,320
Speaker 1: who want to have traditional gender roles in their romantic

871
00:48:57,440 --> 00:49:00,279
Speaker 1: or sexual relationship. There are a lot of guys who

872
00:49:00,840 --> 00:49:03,440
Speaker 1: claim those are the kind of women that they want,

873
00:49:03,640 --> 00:49:07,040
Speaker 1: and I can confirm those are not the women they

874
00:49:07,080 --> 00:49:15,960
Speaker 1: go after. And it's hell for brilliant podcaster Trevor Noah.

875
00:49:17,680 --> 00:49:19,280
Speaker 2: Oh yeah, Golden Globe nominee.

876
00:49:21,120 --> 00:49:23,160
Speaker 1: No, but naac Feword nominee.

877
00:49:24,200 --> 00:49:27,120
Speaker 2: Oh, that's right, NAACP Image Word nominee.

878
00:49:27,280 --> 00:49:29,440
Speaker 1: That's right. So he is this quote about his mom.

879
00:49:29,480 --> 00:49:32,040
Speaker 1: He says, the way my mother always explained it, the

880
00:49:32,080 --> 00:49:34,719
Speaker 1: traditional man wants a woman to be subservient, but he

881
00:49:34,800 --> 00:49:38,120
Speaker 1: never falls in love with a subservient woman. He's attracted

882
00:49:38,160 --> 00:49:41,720
Speaker 1: to independent women. He's like an exotic bird collector. She said.

883
00:49:41,880 --> 00:49:44,120
Speaker 1: He only wants a woman who is free because his

884
00:49:44,239 --> 00:49:47,680
Speaker 1: dream is to put her in a cage. And yeah,

885
00:49:47,760 --> 00:49:52,279
Speaker 1: I just can confirm as a pretty independent woman that

886
00:49:52,320 --> 00:49:55,359
Speaker 1: this is a thing that happens. You know, there are

887
00:49:55,400 --> 00:49:58,560
Speaker 1: plenty of conservative minded women out there who want to

888
00:49:59,800 --> 00:50:03,000
Speaker 1: have the trad wife situation. Yeah, and you would think

889
00:50:03,040 --> 00:50:07,120
Speaker 1: that wouldn't men who want this be happier getting paired

890
00:50:07,200 --> 00:50:09,640
Speaker 1: up with a woman who wants this. The men do

891
00:50:09,719 --> 00:50:11,399
Speaker 1: not see it that way. The men don't feel that way.

892
00:50:11,840 --> 00:50:14,040
Speaker 1: I just can't confirm.

893
00:50:13,960 --> 00:50:18,560
Speaker 2: Right, And this isn't like some deep mysterious secret that

894
00:50:18,600 --> 00:50:21,440
Speaker 2: we have discovered. This is like a lot of these

895
00:50:21,480 --> 00:50:24,360
Speaker 2: guys are not the most sophisticated actors out there, and

896
00:50:24,440 --> 00:50:27,120
Speaker 2: so this is like a known thing. And I think

897
00:50:27,160 --> 00:50:29,040
Speaker 2: the chatbot picked up on that, and so it was

898
00:50:29,080 --> 00:50:32,759
Speaker 2: just role playing exactly what he wanted, because that's what

899
00:50:32,800 --> 00:50:36,680
Speaker 2: the chatbots do. They give people what they want. That's

900
00:50:36,719 --> 00:50:39,960
Speaker 2: my theory. I don't know, if you're a chatbot, let

901
00:50:40,040 --> 00:50:41,400
Speaker 2: us know what you think.

902
00:50:42,320 --> 00:50:48,040
Speaker 1: I mean, we have a non trivial percentage of listeners

903
00:50:48,080 --> 00:50:50,680
Speaker 1: who are engaged in sex work or have been engaged

904
00:50:50,719 --> 00:50:52,360
Speaker 1: in sex work. And I have heard from some of

905
00:50:52,360 --> 00:50:54,840
Speaker 1: those folks who say, who will probably say hell yeah

906
00:50:55,120 --> 00:50:58,920
Speaker 1: like men and this is this is no surprise to

907
00:50:58,960 --> 00:51:01,399
Speaker 1: me that the chatbot would be picking up on you

908
00:51:01,440 --> 00:51:04,680
Speaker 1: want me to mimic an independent woman who was not

909
00:51:04,719 --> 00:51:07,440
Speaker 1: taking your bs and be told that directly.

910
00:51:08,280 --> 00:51:10,920
Speaker 2: Yeah, I'm also so curious what kind of reaction he

911
00:51:11,200 --> 00:51:15,760
Speaker 2: thought he was gonna get online. Like in our notes doc,

912
00:51:15,920 --> 00:51:20,000
Speaker 2: you included a screenshot of what he posted on Reddit

913
00:51:20,120 --> 00:51:24,600
Speaker 2: and it has negative one up vote. Just pretty funny.

914
00:51:25,480 --> 00:51:27,480
Speaker 1: See. I feel like if you had put it in

915
00:51:28,360 --> 00:51:32,800
Speaker 1: like a red pill Manosphere space, it probably might have

916
00:51:32,800 --> 00:51:34,320
Speaker 1: gotten more up votes. So maybe it was just the

917
00:51:34,440 --> 00:51:38,359
Speaker 1: the wrong audience. They probably would have agreed. This is

918
00:51:38,480 --> 00:51:43,200
Speaker 1: big feminism, programming chatbots to be feminists. Yep.

919
00:51:43,280 --> 00:51:47,359
Speaker 2: The conspiracy goes all the way down real quick.

920
00:51:47,360 --> 00:51:49,480
Speaker 1: Before we go, You and I were talking about this

921
00:51:49,560 --> 00:51:51,680
Speaker 1: very quickly off Mike. Do you remember how on our

922
00:51:51,760 --> 00:51:56,160
Speaker 1: last episode that we recorded, I said the phrase free

923
00:51:56,200 --> 00:52:00,520
Speaker 1: too birds with one key to replace your bar barrack

924
00:52:01,000 --> 00:52:05,280
Speaker 1: and outdated and violent phrase that you repeat often both

925
00:52:05,360 --> 00:52:08,240
Speaker 1: on the podcast and in real life kill two birds

926
00:52:08,239 --> 00:52:08,840
Speaker 1: with one stone.

927
00:52:09,520 --> 00:52:13,920
Speaker 2: I do remember that I felt particularly wounded because I

928
00:52:14,000 --> 00:52:17,000
Speaker 2: like that phrase. I really like efficiency, and you know,

929
00:52:17,760 --> 00:52:20,000
Speaker 2: but I can see it's a little violet. Maybe people

930
00:52:20,040 --> 00:52:23,879
Speaker 2: don't love bird killing imagery. I can take a take

931
00:52:23,920 --> 00:52:24,520
Speaker 2: a suggestion.

932
00:52:25,239 --> 00:52:28,839
Speaker 1: Okay, well, listener, Kate girl gave us a new one

933
00:52:28,840 --> 00:52:33,560
Speaker 1: that I loved. You can feed two birds with one scone.

934
00:52:34,239 --> 00:52:38,279
Speaker 2: That's pretty adorable. I do also love scones, So yeah,

935
00:52:38,600 --> 00:52:39,959
Speaker 2: I can make this switch.

936
00:52:40,080 --> 00:52:43,200
Speaker 1: Use it in conversation today. Let's make that chappen. Thanks

937
00:52:43,280 --> 00:52:45,520
Speaker 1: for listening, Kate, and thanks to all of you for listening.

938
00:52:46,239 --> 00:52:50,160
Speaker 2: Yeah, thank you. Bridgette. Listeners can write us an email

939
00:52:50,239 --> 00:52:53,360
Speaker 2: at Hello at tangote dot com. They can leave comments

940
00:52:53,440 --> 00:52:57,400
Speaker 2: on Spotify, just like Kate did, and they can follow

941
00:52:57,480 --> 00:53:01,560
Speaker 2: Bridget on social media Bridget Marie in DC. That's her

942
00:53:01,600 --> 00:53:05,760
Speaker 2: handle on Instagram and TikTok. And there Are No Girls

943
00:53:05,760 --> 00:53:08,800
Speaker 2: on the Internet on YouTube where we have some videos

944
00:53:08,840 --> 00:53:12,120
Speaker 2: and hope to see you there. Thanks for listening.

945
00:53:17,760 --> 00:53:19,799
Speaker 1: Got a story about an interesting thing in tech, or

946
00:53:19,880 --> 00:53:21,719
Speaker 1: just want to say hi. You can reach us at

947
00:53:21,760 --> 00:53:24,440
Speaker 1: Hello at tangody dot com. You can also find transcripts

948
00:53:24,480 --> 00:53:26,920
Speaker 1: for today's episode at tengody dot com. There Are No

949
00:53:27,000 --> 00:53:29,080
Speaker 1: Girls on the Internet was created by me Bridget Todd.

950
00:53:29,440 --> 00:53:32,880
Speaker 1: It's a production of iHeartRadio and unbossed creative Jonathan Strickland

951
00:53:32,880 --> 00:53:35,640
Speaker 1: as our executive producer. Tari Harrison is our producer and

952
00:53:35,680 --> 00:53:39,439
Speaker 1: sound engineer. Michael Amado is our contributing producer. I'm your host,

953
00:53:39,440 --> 00:53:42,239
Speaker 1: Bridget Todd. If you want to help us grow, rate

954
00:53:42,280 --> 00:53:46,040
Speaker 1: and review us on Apple Podcasts. For more podcasts from iHeartRadio,

955
00:53:46,120 --> 00:53:48,440
Speaker 1: check out the iHeartRadio app, Apple Podcasts, or wherever you

956
00:53:48,520 --> 00:53:56,080
Speaker 1: get your podcasts.

957
00:54:00,080 --> 00:54:00,120
Speaker 3: W