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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 Toad and this

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

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

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Speaker 1: is another installment of our weekly round up where we

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Speaker 1: dig into the stories that you might have missed on the.

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Speaker 2: Internet so you don't have to.

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Speaker 1: And I am thrilled to introduce to my guest co host,

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Speaker 1: Sammy Canter, founder of Girl and the Gub, one of

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Speaker 1: my favorite newsletters. Thank you so much for being here, Sammy.

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Speaker 3: Oh gosh, thank you so much for having me. I

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Speaker 3: am such a fangirl, so apologies in advance because you've

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Speaker 3: seen me fainting over here to be on the show Beyond.

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Speaker 3: I'm so excited to get into it all. So thank

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Speaker 3: you so much.

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Speaker 1: Oh my god, I'm very excited to have you on.

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Speaker 1: You're one of my favorite Instagram follows. You do something

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Speaker 1: that I think is really tough that I aspire to do,

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Speaker 1: where you find a good way of blending all of

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Speaker 1: the fun of social media with also the politics and

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Speaker 1: government stuff that people need to know about. I often

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Speaker 1: feel sort of torn between, well, I want my audience

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Speaker 1: to know about this, like they should know about this,

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Speaker 1: but I don't want to bore them with some like

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Speaker 1: government topic, even though it's very important. I feel that

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Speaker 1: you have really threaded the needle on social media of

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Speaker 1: how you do.

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Speaker 3: That, Honored. First of all, we'll be clipping that and

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Speaker 3: sending that to anyone I ever pitched for funding in

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Speaker 3: the Mirror and distant future all at the same time.

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Speaker 3: But yeah, it's an interesting way of looking at politics

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Speaker 3: or trying to really essentially get it in front of

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Speaker 3: new eyes. And we've learned anything from the last number

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Speaker 3: of years is that the old formula isn't working. And

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Speaker 3: what I really try and do, whether it's on and

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Speaker 3: star TikTok or YouTube shorts for God's sake, is figure

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Speaker 3: out a way that it slips into people's day to day.

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Speaker 3: They're radars what they're scrolling through anyways. So if that's

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Speaker 3: an esthetic outfit video that also happens to have three

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Speaker 3: news stories on it in a way that's explained how

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Speaker 3: we actually speak, then by all means I'll keep doing it.

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Speaker 1: Okay, Well, the first story I want to talk about

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Speaker 1: I think kind of fits in your wheelhouse, and that

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Speaker 1: is this story about influencer vibe theft.

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Speaker 2: So the question is.

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Speaker 1: Sort of, can someone sue you for copying your vibe

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Speaker 1: or aesthetic on social media?

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Speaker 2: Did you hear about this story?

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Speaker 3: I did, and so what was interesting is I heard

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Speaker 3: just sort of the general overview and the People article,

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Speaker 3: and I opened my phone maybe an hour before actually

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Speaker 3: we hopped on here, and the influencer that was suing

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Speaker 3: was giving her feel about like what actually went down

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Speaker 3: and how she felt like the People article and also

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Speaker 3: any other sources I covered it were like missing the

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Speaker 3: beef of it, the real, you know, the real core

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Speaker 3: of what the lawsuit was, which I found so interesting

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Speaker 3: and does also shows for that angle, like how sometimes

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Speaker 3: things can get lost in the social media sauce aka

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Speaker 3: Where's Nuance? Miss? Her Nuance is MII.

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Speaker 1: So in her TikTok, she says that this is so

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Speaker 1: much more than her trying to copyright a beage aesthetic,

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Speaker 1: and that the media is sort of failing to tell

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Speaker 1: this story accurately because they're focusing on the most ridiculous

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Speaker 1: parts of her suit, the beige esthetic part of it,

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Speaker 1: and ignoring the more interesting meatia aspects of it.

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Speaker 4: I believe her intention was to look so similar to

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Speaker 4: me and copy my post so similarly that she could

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Speaker 4: profit off my business. A lot of articles are claiming

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Speaker 4: I'm suing over a beige aesthetic. I have never claimed

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Speaker 4: to own beige, and I'm not suing anyone over a

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Speaker 4: color or a trend. The story the media is telling

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Speaker 4: is missing so many details, and the way that they're

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Speaker 4: spinning it to make it seem like it's all about

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Speaker 4: an aesthetic to create drama is misrepresenting the importance of

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Speaker 4: this case.

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Speaker 1: So the meat of what's happening here is that it's

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Speaker 1: a lawsuit where one influencer is not just saying that

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Speaker 1: somebody else is mimicking her aesthetic, which is this beige

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Speaker 1: neutral vibe, but going so much for to mimic many

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Speaker 1: aspects about her business as an influencer that she argues

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Speaker 1: is being done intentionally to create brand confusion and siphon

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Speaker 1: from her business as an influencer and content creator. So

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Speaker 1: while you might have heard of this lawsuit as oh,

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Speaker 1: this influencer thinks she owns the color page, and yeah,

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Speaker 1: that part of it might be kind of funny, the

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Speaker 1: issue is so much deeper than that. It goes to

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Speaker 1: can someone essentially steal the entire look and feel of

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Speaker 1: someone else's business, even if that business relies on visual

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Speaker 1: aspects or visual aesthetics that may not traditionally be able

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Speaker 1: to be copyrighted as somebody else's intellectual property. You know,

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Speaker 1: the way that somebody wears their hair or a specific

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Speaker 1: tattoo or a specific manicure, or a specific pose and photos.

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Speaker 1: So I loved both of these influencers up and it

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Speaker 1: is uncanny, like it goes so much further than just

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Speaker 1: a beige Look, here's what went down. Influencer Sydney Nicole

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Speaker 1: Gifford did a joint photo shoot with twenty twenty three

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Speaker 1: with another influencer named Alyssa Shiel.

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Speaker 2: After this shoot, Gifford's.

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Speaker 1: Claims that she'l started copying her sort of minimal page

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Speaker 1: aesthetic and specific poses in her content. But it's not

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Speaker 1: just about the specific pose, because when she would do that,

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Speaker 1: you know well worn influencer pose where you have the

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Speaker 1: phone in front of your face in a mirror, like

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Speaker 1: obscuring your face.

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Speaker 2: The two are essentially identical, like it is very.

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Speaker 1: Difficult to tell one influencer from the other. Like she's

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Speaker 1: not wrong, it is uncanny and if you're making money

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Speaker 1: from your look as an influencer, and somebody else comes

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Speaker 1: in and starts doing that exact same thing to maybe

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Speaker 1: create a little bit of brand confusion, I get it.

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Speaker 1: Then she'll blocked her on Instagram and TikTok. So Gifford

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Speaker 1: sent a season desist, and those season desists were ignored.

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Speaker 1: So Gifford filed a first of its kind federal lawsuit

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Speaker 1: in twenty twenty four a legend copyright infringement, trade dress misappropriation,

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Speaker 1: and unfair competition. So, like you, Sammy, I don't think

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Speaker 1: of this as like a frivolous lawsuit. Like I was like, oh,

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Speaker 1: this is deeply interesting because as we know, influencers and

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Speaker 1: content creators are a big part of the lifeblood of

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Speaker 1: like e commerce, and so it's like how brands like

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Speaker 1: Amazon become very economically successful. So we're not talking about

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Speaker 1: just like, oh, she stole my Look. What she's arguing

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Speaker 1: about is like a very suppicific kind of copyright and

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Speaker 1: IP dispute. And so it raised the question of can

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Speaker 1: somebody sue somebody for allegedly like stealing their their intellectual

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Speaker 1: property on social media, which again I just find super interesting.

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Speaker 1: According to the fashion law site Gifford's, who commands over

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Speaker 1: half a million followers across platforms like Instagram, TikTok, and Amazon.

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Speaker 1: Sorefront allegeds that Shield systematically replicated her content and online

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Speaker 1: persona to mislead followers and unfairly compete in the influencer

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Speaker 1: marketing space central to gifford suit, where her claims that

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Speaker 1: she'll copied her copyright protective images, mimics her appearance down

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Speaker 1: to specific physical traits such as a flower tattoo, and

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Speaker 1: appropriated the distinctive visual and stylistic elements associated with her brand.

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Speaker 1: So something I do find interesting about this is I

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Speaker 1: just I mean, I'm no attorney, but visually, when I

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Speaker 1: look at both women's content, there is definitely a lot

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Speaker 1: of aesthetic overlap happening. One of the women just recently

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Speaker 1: had a child, and I'm like, oh, well, luckily that

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Speaker 1: is one way to make like I was like, okay,

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Speaker 1: well that is a distinction because they're so otherwise, they're

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Speaker 1: so similar, And it makes me wonder like, in the

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Speaker 1: age of Instagram, is can anybody claim to sort of

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Speaker 1: be unique or distinct when so much of what gets

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Speaker 1: popular on Instagram because of algorithms is things that seem

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Speaker 1: like copies of each other, right, Like all of the

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Speaker 1: influencers use this specific pose. They're all they have a

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Speaker 1: specific manicure, a specific look. If both of these women

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Speaker 1: are sort of chilling for Amazon's Amazon clothing, like, it's

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Speaker 1: not surprising that they might have some overlap.

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Speaker 2: But the case actually was.

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Speaker 1: Essentially dropped with the influencer, the one accused of doing

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Speaker 1: the copying having to pay nothing. Is what seems like

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Speaker 1: that case might have answered that question of like, can

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Speaker 1: you copyright an aesthetic? I'm not an attorney, but it

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Speaker 1: seems like from this lawsuit answer is perhaps no.

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Speaker 3: Yeah. I think it's super interesting. And what the girl

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Speaker 3: that was suing said in her TikTok was that she

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Speaker 3: had to drop the case because it was getting incredibly expensive.

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Speaker 3: So had she had the funds, like, there may have

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Speaker 3: been a continued pursuit at least on some some frame here,

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Speaker 3: which I find interesting in and of itself. And I

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Speaker 3: do think that in the creator sphere too, money gets

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Speaker 3: in the way, Right we think about how many people

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Speaker 3: would actually sue if they had the money to do

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Speaker 3: so in terms of somebody copying them. We see it

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Speaker 3: with small brands all the time with big brands we have.

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Speaker 3: I don't know if you saw this, but there is

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Speaker 3: in the city this vintage brand called Kissing Cowboys and

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Speaker 3: it's run by also like an influencer, one of those

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Speaker 3: people that's just like at the cross section of many things.

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Speaker 3: And Brandy Melville allegedly copied her logo, which she went

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Speaker 3: on to say that is not just like a Canva

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Speaker 3: logo or something like that. Her and her sister handmade

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Speaker 3: the logo and the brand has copy and pasted the

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Speaker 3: exact logo onto a T shirt that they have god

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Speaker 3: knows how many skews of that they're selling in their stores.

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Speaker 3: And so you have a small creator that has a

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Speaker 3: small brand that doesn't have the funds most likely to

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Speaker 3: soon pursue this. So I think like that also is

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Speaker 3: particularly interesting to me because if creators had more monetary means,

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Speaker 3: and same with small brands, where would that really lie,

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Speaker 3: Like what would the decision process actually look like? For

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Speaker 3: that It just leaves me with more questions. But I

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Speaker 3: do think in this particular case, the thing that gave

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Speaker 3: me the ooh, that's a little creepy and maybe it's

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Speaker 3: almost like a different angle is seeing the matching tattoos,

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Speaker 3: the blocking almost the intentionality of it, because you also

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Speaker 3: go into that other frame, right, Like, if you are

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Speaker 3: an influencer, you expect people to copy you, right, That's

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Speaker 3: part of the job is to push people to do

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Speaker 3: things that you're doing, or to lead them to an

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Speaker 3: idea to consider it. So where does that start and

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Speaker 3: where does that end? I think there definitely is some

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Speaker 3: gray area when the person is starting to get exactly

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Speaker 3: the same as body or and copying things at the

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Speaker 3: same timetable. Yes, uh, that's where I'm like, ooh.

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Speaker 1: She alleges that she got she has a very distinctive

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Speaker 1: flower tattoo on her arm, and she says that this

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Speaker 1: influencer who was copying her got the same distinctive tattoo. Like,

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Speaker 1: first of all, that is commitment to the bit, that is,

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Speaker 1: you were really committing to copying this woman. But it

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Speaker 1: is uncanny and the fact that they're both specifically Amazon influencers,

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Speaker 1: they both do that Amazon storefront you know, hashtag Amazon finds.

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Speaker 1: She basically is arguing that she is intentionally trying to

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Speaker 1: confuse that what would be their audience market into like

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Speaker 1: associating the two, which I think is that's like a

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Speaker 1: very good point. So, reflecting on this, professor of law

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Speaker 1: and Media Faculey director of the Center for Law, Information

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Speaker 1: and Creativity Alexander Roberts wrote, surprising or not deeming any

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Speaker 1: of the Gifford's claims plausible for creators posting similar content

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Speaker 1: has serious implications thanks to the fact that influencer marketing

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Speaker 1: has become increasingly central to commerce and social media. Content

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Speaker 1: more broadly is built on trends and served to users

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Speaker 1: via algorithms, which are quick to push content related to

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Speaker 1: what users click and or linger on in their feeds.

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Speaker 1: Roberts asserts that intellectual property law has not traditionally protected

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Speaker 1: the way somebody styles their hair, makes up their face,

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Speaker 1: or decorates their home, whether or not those choices are

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Speaker 1: photographed and shared. And I do think it's sort of

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Speaker 1: I mean, had this case had had the influencer who

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Speaker 1: was making these these allegations been able to have the

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Speaker 1: funds to see this case through, it could have been

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Speaker 1: very different. But had it been, you know, had it

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Speaker 1: gone differently, this could have potentially like set an entire

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Speaker 1: new precedent when it comes to IP online, which I

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Speaker 1: just find fascinating.

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Speaker 3: Totally, which I do think could be troubling for the

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Speaker 3: larger creator industry because sometimes you don't understand that, like

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Speaker 3: you used x y Z sound and it came from

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Speaker 3: this person and they created a trend off of it,

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Speaker 3: Like where's the origins? It's really hard to trace that

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Speaker 3: origin of a trend, you know, a concept or whatnot.

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Speaker 3: So struggle.

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Speaker 5: Let's take a quick break at our back.

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Speaker 1: Okay, so I have to talk about this story with

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Speaker 1: Real Page really quickly. I will say right off the top,

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Speaker 1: it doesn't really fit with the content that we usually do.

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Speaker 1: But Real Page is my personal like I think of

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Speaker 1: them as my personal enemy. So any time that I

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Speaker 1: got an opportunity to talk shit about that, I will

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Speaker 1: take it.

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Speaker 2: So indulge me.

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Speaker 1: So on the episode last week, we were talking about

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Speaker 1: this provision buried in Donald Trump's big, beautiful budget bill

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Speaker 1: that would ban states from enforcing laws on AI for

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Speaker 1: ten years.

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Speaker 2: Did you hear about this?

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Speaker 3: Oh yeah, yeah, States Rights? Oh yeah, miss exactly again,

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Speaker 3: Like okay, exactly.

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Speaker 5: So.

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Speaker 1: One of the kind of real world examples of how

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Speaker 1: this might impact people that I talked about when we

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Speaker 1: talked about that bill was how scumbag landlords are able

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Speaker 1: to use technology like real Page that allow landlords in

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Speaker 1: a whole area to work together, to coordinate to essentially

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Speaker 1: algorithmically determine how much they can raise your rents. Right,

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Speaker 1: So the government has called this price fixing, which I

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Speaker 1: happen to agree. So these are scumbags who make scumbag

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Speaker 1: technology for scumbag landlords, and as a lifelong renter, these

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Speaker 1: people are my biggest ops. Like, I cannot stand this.

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Speaker 1: I think this technology is ruining cities. I hate it.

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Speaker 1: Twenty two, a report from Republica linked real Page to

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Speaker 1: rising rent prices across the United States, alleging that its

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Speaker 1: algorithm allows landlords to coordinate pricing, and the Department of

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Speaker 1: Justice in eight states sued real Page last year, claiming

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Speaker 1: that it deprives renters like me of the benefits of

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Speaker 1: competition on apartment leasing terms. So, cities like Minneapolis, Jersey Cities,

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Speaker 1: and Francisco, Philadelphia and others have passed laws that are

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Speaker 1: meant to ban the use of this kind of algorithmic

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Speaker 1: AI based rent setting software, and other states have legislation

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Speaker 1: like this in the works. But if this budget bill passes,

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Speaker 1: that means that states can no longer enforce those kinds

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Speaker 1: of laws, which would be a reals win for scumbag

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Speaker 1: landlords and the company that makes this rent raising technology,

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Speaker 1: Real Page. So now senators are basically asking did Real

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Speaker 1: Page pay to get that provision buried in the budget bill?

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Speaker 2: This week?

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Speaker 1: Lawmakers say that they believe Real Page might have spent

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Speaker 1: millions of dollars pushing for this provision. In a letter

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Speaker 1: that they sent to Real Page a CEO, five Democratic

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Speaker 1: Senators Warren, Sanders, klobashar Booker, and Smith are asking for

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Speaker 1: more information about Real Page's potential involvement. The letter reads,

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Speaker 1: in light of this, we seek information on Real Page's

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Speaker 1: lobbying efforts and how the Republican's reconciliation provision would help

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Speaker 1: the bottom line of Real Page and other large corporations,

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Speaker 1: allowing them to take advantage of consumers. So I just

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Speaker 1: have to shout that out. First of all, just the

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Speaker 1: feeling of reading senators doing something that I feel like

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Speaker 1: does have a real impact on the lives of constituents,

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Speaker 1: Like it's just nice to be like, oh yeah, governing,

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Speaker 1: like somebody's out there doing it for sure.

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Speaker 3: No, literally, especially in these chaotic times. It sort of

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Speaker 3: like is anyone doing anything, I think is often how

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Speaker 3: people feel. Sometimes there's the feeling versus the reality. You know,

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Speaker 3: did somebody actually go and go to gov track and

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Speaker 3: see what bills were introduced? Right? There's always sort of

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Speaker 3: like that extra of things. But I think, for me,

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Speaker 3: when I think about this, to see that they're actually

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Speaker 3: looking at a tech piece of something, right, Like I

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Speaker 3: think that we understandably look at Congress at large and go,

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Speaker 3: oh my gosh, these old dudes that have no idea

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Speaker 3: how to even open Instagram unless like some assistant helps them.

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Speaker 3: You know, how could they ever understand AI and its

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Speaker 3: possible implications and everything else under that soun? So I

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Speaker 3: think to even see that they're reacting, I mean, I'm

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Speaker 3: not surprised that Elizabeth Warren was reacting given sort of

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Speaker 3: her expertise, but elsephies and I hope that it continues

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Speaker 3: to expand because AI it honestly, it freaks me the

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Speaker 3: fuck out, Like I really, really like, am not a fan.

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Speaker 3: I know it's integrated into so many things that we do,

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Speaker 3: even the things that we don't even realize, but I

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Speaker 3: just don't like it. I don't think it's a thing

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Speaker 3: for good. Oftentimes, I really think, especially given the hands

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Speaker 3: that it's coming out of, that it's a thing for

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Speaker 3: bad and that if we are one habit, that it

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Speaker 3: needs bar rails, and the fact that this bill would

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Speaker 3: get rid of those guardrails right and make it. I mean,

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Speaker 3: if thinking about like how much AI has come into

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Speaker 3: our lives, like literally the last year and a half,

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Speaker 3: how much it's taken over the conversation. I mean, a

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Speaker 3: year and a half ago, I wouldn't even imagine that

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Speaker 3: we were talking about AI as much as we are.

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Speaker 3: So think about what that looks like on an accelerated

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Speaker 3: page for ten years, right?

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Speaker 1: And I mean I think it comes down to, like,

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Speaker 1: do people want a world where the not just the

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Speaker 1: rent in your building, but the rent in your entire

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Speaker 1: city goes up so the ceo of real pages scumbag

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Speaker 1: ahi price fixing tool can make more money?

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Speaker 2: Is that a better world for you? Probably? You and me?

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Speaker 2: Probably not.

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Speaker 1: Definitely a better world for the CEO of this technology

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Speaker 1: company who is making money from it and profiting from it.

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Speaker 2: But yeah, I would just argue like I ken't.

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Speaker 1: I mean, I could talk all day the fact that

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Speaker 1: this provision would so clearly benefit fit the owners of

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Speaker 1: this tech company and harm just regular people trying to

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Speaker 1: make rent. You and me renters like people who you know,

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Speaker 1: just regular folks. I think is pretty clear, and so yeah,

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Speaker 1: I'm with you. I think when you think.

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Speaker 2: About the values of.

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Speaker 1: The people who make technology like this, I tend to

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Speaker 1: think it's not going to be technology that makes our

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Speaker 1: lives better. I think it's really poised to exploit.

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Speaker 3: And certainly this budget bill is not the way to

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Speaker 3: solve any of those potential problems. We look at this

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Speaker 3: budget bill too, and how it's going to increase the

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Speaker 3: deficit astronomically over the next ten years. So you pair

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Speaker 3: that with crashing essentially in a long way the economy

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Speaker 3: by knocking out the biggest you know, entry level segment

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Speaker 3: for white collar jobs. What like, what are we doing?

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Speaker 2: Yeah, it's it's not good.

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Speaker 1: And I have to say, like another story that I

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Speaker 1: was reading this week that kind of speaks to the

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Speaker 1: way that advancements of technology like AI really are hurting

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Speaker 1: us and particularly the most marginalized among us. This story

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Speaker 1: that this came out from Full for Media, Like shout

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Speaker 1: out to them for being on this. We use their

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Speaker 1: reporting on the show all the time because they are

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Speaker 1: experts over there. So the vast network of surveillance that

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Speaker 1: we have in this country, like license plate reader surveillance tools,

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Speaker 1: well all of that is being used to track people

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Speaker 1: who are suspected of getting abortions. This is not shocking,

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Speaker 1: like It's what abortion advocates and like tech privacy advocates

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Speaker 1: have been warning is going to happen, and it's happening.

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Speaker 1: So four h four Media found that authorities in Texas

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Speaker 1: performed a nationwide search of over eighty three thousand automatic

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Speaker 1: license plate reader cameras while looking for a woman that

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Speaker 1: they said had self administered an abortion, including looking at

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Speaker 1: cameras in states where abortion is legal, such as Illinois

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Speaker 1: and Washington. So this technology, this license plate reader camera

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Speaker 1: technology is made by a company called Flock, and it

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Speaker 1: is usually marketed as being able to track and stop

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Speaker 1: carjackings or like fine missing people, but it can also

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Speaker 1: be used to surveil people suspected of getting abortion care

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Speaker 1: across state lines. So the Texas sheriff in this case says, oh,

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Speaker 1: we were not trying to surveil her for having gotten

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Speaker 1: an abortion. On May night, an officer from the Johnson

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Speaker 1: County Sheriff's Office in Texas searched flop cameras and gave

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Speaker 1: the reason for why he was searching these cameras as.

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Speaker 1: Quote had an abortion search for female according to multiple

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Speaker 1: sets of data. So whenever officers are searching flop cameras,

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Speaker 1: they're required to provide a reason for doing so, and

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Speaker 1: so they don't need a court order or a warrant.

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Speaker 1: But like, that was the reason that he stated that

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Speaker 1: female had an abortion. So he claims that the only

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Speaker 1: reason he did this is because, oh, the woman's family

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Speaker 1: had contacted us and that they were worried. He says,

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Speaker 1: her family was worried that she was going to bleed

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Speaker 1: to death, and we were trying to find her to

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Speaker 1: get her to a hospital. We weren't trying to block

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Speaker 1: her from leaving the state or whatever to get an abortion.

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Speaker 1: It was absolutely about her safety. So I got my

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Speaker 1: start an abortion advocacy. So it's something I know a

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Speaker 1: bit about. I have a lot of questions about this

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Speaker 1: because when you're talking about like a self managed abortion,

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Speaker 1: typically you're talking about a pill based abortion, which is

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Speaker 1: incredibly safe. While some bleeding is common, the risk of

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Speaker 1: complications and death is very very low, And so I'm

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Speaker 1: curious why they would think that this woman was at

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Speaker 1: risk of bleeding to death from having what I assume

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Speaker 1: is a pill based abortion. And even if they were

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Speaker 1: worried about her safety, that doesn't mean you can't be

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Speaker 1: criminalized for it. For for Media spoke to Elizabeth Ling,

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Speaker 1: a senior counsel for IF When How, a reproductive rights

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Speaker 1: group that runs a reproductive legal rights hotline, who pointed

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Speaker 1: out that many of the criminal cases that they have

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Speaker 1: seen of people being criminalized for trying to get an

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Speaker 1: abortion originate after somebody close to that person reports it.

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Speaker 2: To the police.

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Speaker 1: They say that a research report published by the groups

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Speaker 1: found that about a quarter of adult cases were reported

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Speaker 1: to law enforcement by acquaintances entrusted with information like friends, parents,

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Speaker 1: or intimate partners. So even if they were like, oh,

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Speaker 1: we were just worried about her, that is not a

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Speaker 1: situation that would protect her from being criminalized, especially in

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Speaker 1: a state like Texas, where abortion is essentially most in

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Speaker 1: most cases criminalized already.

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Speaker 3: A thousand recent and I just I also just question

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Speaker 3: the way it's presented right that the family's first reaction

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Speaker 3: would be to call the cops, right help. I can't

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Speaker 3: think of a situation where if I thought somebody was

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Speaker 3: in need of medical care, that my first call would

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Speaker 3: be to the cops.

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Speaker 1: Right when I when I know that I am in Texas,

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Speaker 1: a place where that call could potentially wind with her

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Speaker 1: behind bars.

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Speaker 3: Totally also like to give like a slight benefit of

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Speaker 3: the doubt. I do think that there are so many

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Speaker 3: people in this country, in every state, that have no

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Speaker 3: idea what's going on with abortion laws. This has not

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Speaker 3: crossed their desk, Like I know, it seems like one

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Speaker 3: of those things like how could it not have? But

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Speaker 3: I as we've seen with also other cases too, people

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Speaker 3: are really really unaware until oftentimes they are in a

437
00:23:00,640 --> 00:23:04,320
Speaker 3: situation either miscarrying, trying to get their own care, whatever

438
00:23:04,359 --> 00:23:07,600
Speaker 3: it may be, that they are realizing, oh shoot, like

439
00:23:07,760 --> 00:23:12,960
Speaker 3: this is not what I realized our reality was. So like, yes,

440
00:23:13,119 --> 00:23:16,239
Speaker 3: I could understand a family not understanding that they are

441
00:23:16,280 --> 00:23:20,919
Speaker 3: actually putting their family member in legal jeopardy, But I

442
00:23:21,080 --> 00:23:27,240
Speaker 3: still just find that being their first call a little strange.

443
00:23:27,640 --> 00:23:32,359
Speaker 3: Not like the EMTs, you know, or not going after

444
00:23:33,240 --> 00:23:36,920
Speaker 3: trying to find your relative yourself. It just seems strange.

445
00:23:37,000 --> 00:23:39,560
Speaker 3: It's not the behavior that I would think most people

446
00:23:40,000 --> 00:23:43,679
Speaker 3: would have. Perhaps I'm in the minority on that, But

447
00:23:43,800 --> 00:23:48,520
Speaker 3: I just find it odd and I find it worrying too,

448
00:23:49,080 --> 00:23:51,280
Speaker 3: that this is what we're using this tech for, which

449
00:23:51,320 --> 00:23:55,360
Speaker 3: is again all the worries are back. It's like, if

450
00:23:55,400 --> 00:23:58,240
Speaker 3: we're going to have certain technology available, we need to

451
00:23:58,280 --> 00:24:00,239
Speaker 3: be very specific on how we're using that, and there

452
00:24:00,280 --> 00:24:04,840
Speaker 3: needs to be guidelines, laws, regulations on how that is

453
00:24:05,000 --> 00:24:06,359
Speaker 3: actually able to be used.

454
00:24:06,760 --> 00:24:09,639
Speaker 1: The organization Privacy International actually just came out with a

455
00:24:09,640 --> 00:24:12,919
Speaker 1: new report looking at how much very sensitive data that

456
00:24:13,000 --> 00:24:16,240
Speaker 1: apps like period trackers are collecting and sharing and how

457
00:24:16,480 --> 00:24:19,440
Speaker 1: that data can be potentially dangerous in a landscape where

458
00:24:19,440 --> 00:24:22,480
Speaker 1: abortion is increasingly criminalized. So the news is kind of

459
00:24:22,560 --> 00:24:25,879
Speaker 1: mixed and that their report showed that apps have gotten

460
00:24:25,920 --> 00:24:29,480
Speaker 1: better with their privacy policies, but the landscape has actually

461
00:24:29,480 --> 00:24:32,000
Speaker 1: gotten worse, and so the bar needs to be higher

462
00:24:32,000 --> 00:24:35,080
Speaker 1: when it comes to privacy. To your point about this

463
00:24:35,119 --> 00:24:37,480
Speaker 1: is what we're using this technology for, I think that

464
00:24:37,640 --> 00:24:42,960
Speaker 1: technology and AI makes things that were not uncommon in

465
00:24:43,160 --> 00:24:46,800
Speaker 1: the abortion fight just much easier to scale. Like back

466
00:24:46,840 --> 00:24:49,520
Speaker 1: in the day, you know, I was doing abortion advocacy

467
00:24:49,560 --> 00:24:51,560
Speaker 1: work for a really long time. Back in the day,

468
00:24:51,720 --> 00:24:53,800
Speaker 1: it used to be that anti abortion advocates would like

469
00:24:54,080 --> 00:24:57,520
Speaker 1: stand in parking lots and manually write down the license

470
00:24:57,560 --> 00:24:59,440
Speaker 1: place of people who drove into clinics.

471
00:24:59,440 --> 00:25:01,480
Speaker 2: And then they were incredibly well coordinated.

472
00:25:01,520 --> 00:25:03,520
Speaker 1: And I can tell you from like personal experience that

473
00:25:03,800 --> 00:25:07,240
Speaker 1: they essentially it felt like the police were on their side, right,

474
00:25:07,280 --> 00:25:09,760
Speaker 1: and so like that was just from my experience, So

475
00:25:09,880 --> 00:25:12,240
Speaker 1: like they they would. They were these people who were

476
00:25:12,240 --> 00:25:16,720
Speaker 1: like incredibly dedicated to busying themselves of the business of others,

477
00:25:16,760 --> 00:25:19,480
Speaker 1: and it took a lot of work and coordination and

478
00:25:19,560 --> 00:25:22,359
Speaker 1: like in real life, you know, coming out to stand

479
00:25:22,359 --> 00:25:25,320
Speaker 1: outside of clinics and things like that. But now companies

480
00:25:25,400 --> 00:25:28,280
Speaker 1: like blog are selling the ability to do that at scale,

481
00:25:28,760 --> 00:25:31,320
Speaker 1: maybe without even having to like leave your home with

482
00:25:31,400 --> 00:25:36,080
Speaker 1: this technology. And it's an incredibly terrifying harbingerk of how

483
00:25:36,160 --> 00:25:40,000
Speaker 1: technology will be used to like further surveil and criminalize

484
00:25:40,080 --> 00:25:42,320
Speaker 1: both the people who need abortions and the people who

485
00:25:42,400 --> 00:25:45,040
Speaker 1: help them, like people who you know in Texas, I

486
00:25:45,040 --> 00:25:48,399
Speaker 1: believe it's even criminalized to like support or help somebody

487
00:25:48,400 --> 00:25:50,440
Speaker 1: who was looking for an abortion, right And so it's

488
00:25:50,480 --> 00:25:54,080
Speaker 1: just a really scary vision of where we might be

489
00:25:54,200 --> 00:25:56,600
Speaker 1: heading when it comes to the use of this technology

490
00:25:56,600 --> 00:25:59,240
Speaker 1: to further criminalize people from getting abortions.

491
00:26:00,560 --> 00:26:02,600
Speaker 3: Yeah, I don't think we're headed in a great spot,

492
00:26:02,680 --> 00:26:05,439
Speaker 3: and I will be curious to see if any Blue

493
00:26:05,480 --> 00:26:10,920
Speaker 3: states react legislatively on anything like this. I haven't seen

494
00:26:10,960 --> 00:26:15,240
Speaker 3: anything cross my desk so far, but I would be

495
00:26:15,280 --> 00:26:19,560
Speaker 3: curious to see if anything in terms of data sharing,

496
00:26:20,119 --> 00:26:23,000
Speaker 3: uh sort of pops up in the next few months

497
00:26:23,080 --> 00:26:26,080
Speaker 3: for anyone that's continuing to be in session, or also.

498
00:26:25,880 --> 00:26:27,000
Speaker 2: Next year me too.

499
00:26:32,119 --> 00:26:44,600
Speaker 1: More after a quick break, let's get right back into it.

500
00:26:46,520 --> 00:26:48,600
Speaker 1: So I want to switch gears a little bit. So

501
00:26:49,160 --> 00:26:51,359
Speaker 1: I am I am not. I don't use dating apps.

502
00:26:51,359 --> 00:26:55,359
Speaker 1: I'm not on the market. However, I'm so fascinated by that.

503
00:26:55,400 --> 00:26:59,040
Speaker 1: But when they make changes to like entice users like,

504
00:26:59,080 --> 00:27:01,480
Speaker 1: I'm always, like very curious what those changes are. While

505
00:27:01,520 --> 00:27:05,760
Speaker 1: Tinder is testing letting users set a hype preference. So

506
00:27:06,080 --> 00:27:08,960
Speaker 1: Tinder is testing out this feature that let's paid subscribers

507
00:27:09,000 --> 00:27:11,160
Speaker 1: add hype preferences to their profile.

508
00:27:11,240 --> 00:27:12,639
Speaker 2: So it will not be a hard filter.

509
00:27:13,200 --> 00:27:15,320
Speaker 1: Rather, the setting will indicate a preference, and that means

510
00:27:15,359 --> 00:27:19,200
Speaker 1: it won't actually block or exclude profiles with that hype preference,

511
00:27:19,400 --> 00:27:22,120
Speaker 1: but that hype preference, if they add it will inform

512
00:27:22,400 --> 00:27:26,320
Speaker 1: what kind of recommendations you see when swiping. So this

513
00:27:26,359 --> 00:27:28,119
Speaker 1: is one of the ways that we already know that

514
00:27:28,240 --> 00:27:31,640
Speaker 1: dating apps inform people's preferences. As tech Crunch puts it,

515
00:27:31,640 --> 00:27:34,280
Speaker 1: it is not uncommon to come across profiles where women

516
00:27:34,320 --> 00:27:36,200
Speaker 1: state they're only looking for matches who are at least

517
00:27:36,200 --> 00:27:38,840
Speaker 1: six feet tall, for instance, even if in real life

518
00:27:38,880 --> 00:27:43,080
Speaker 1: they would be more flexible about this requirement. So I

519
00:27:43,119 --> 00:27:45,080
Speaker 1: will say we've talked a bit about this on the

520
00:27:45,080 --> 00:27:47,880
Speaker 1: podcast before. It's not just about height. Like we did

521
00:27:47,880 --> 00:27:50,440
Speaker 1: an episode with sociologist and author of the book Not

522
00:27:50,600 --> 00:27:54,400
Speaker 1: My Type, Automating Sexual Racism and Online Dating, doctor apral Williams,

523
00:27:54,480 --> 00:27:57,040
Speaker 1: and she has an entire body of research about this when.

524
00:27:56,920 --> 00:27:58,000
Speaker 2: It comes to race.

525
00:27:58,119 --> 00:28:01,400
Speaker 1: She found in her research that dating apps oftentimes are

526
00:28:01,400 --> 00:28:04,840
Speaker 1: not just like reflecting our biases, but they're actually reinforcing

527
00:28:04,840 --> 00:28:07,639
Speaker 1: our biases. And it makes me wonder if dating apps

528
00:28:07,640 --> 00:28:10,520
Speaker 1: are also doing this thing, the same thing with height.

529
00:28:11,359 --> 00:28:13,119
Speaker 2: And I actually did wanna.

530
00:28:13,600 --> 00:28:15,359
Speaker 1: I don't want to put anybody on the spot here,

531
00:28:15,960 --> 00:28:19,840
Speaker 1: but I think we have a short I mean you you,

532
00:28:20,080 --> 00:28:21,679
Speaker 1: I don't want to put words in your mouth. How

533
00:28:21,920 --> 00:28:24,879
Speaker 1: how do you, Mike producer Mike how do you identify

534
00:28:24,920 --> 00:28:25,800
Speaker 1: how do you identify?

535
00:28:26,200 --> 00:28:30,240
Speaker 6: No, No, I'm a shorter guy. I'm a shorter guy.

536
00:28:30,359 --> 00:28:33,520
Speaker 6: I am five six and change. I will own that.

537
00:28:34,080 --> 00:28:37,400
Speaker 6: It's it's who I am. And you know, a few

538
00:28:37,440 --> 00:28:40,120
Speaker 6: years ago, I was on the dating apps, and yeah,

539
00:28:40,160 --> 00:28:41,720
Speaker 6: there were a lot of women who had put in

540
00:28:41,760 --> 00:28:45,440
Speaker 6: their profiles, you know, only men six feet are above

541
00:28:45,800 --> 00:28:50,560
Speaker 6: or whatever their arbitrary threshold was. And that's fine, Like

542
00:28:50,760 --> 00:28:54,680
Speaker 6: I do, I have zero interest in dating a woman

543
00:28:55,040 --> 00:28:57,560
Speaker 6: who would put that in her dating profile. I wish

544
00:28:57,600 --> 00:28:59,800
Speaker 6: her luck. I think there's a lot of women out

545
00:28:59,800 --> 00:29:03,560
Speaker 6: there who probably would prefer a taller guy, but maybe

546
00:29:03,560 --> 00:29:08,040
Speaker 6: they're open to a shorter guy. Great, that's you know,

547
00:29:08,080 --> 00:29:11,520
Speaker 6: a more appropriate woman for me. Uh so that's fine.

548
00:29:11,600 --> 00:29:13,160
Speaker 2: I I don't know.

549
00:29:13,200 --> 00:29:16,200
Speaker 6: I feel it's like there's already a bit of an

550
00:29:16,240 --> 00:29:21,600
Speaker 6: auto filter. I would put my height in my profile

551
00:29:21,760 --> 00:29:25,160
Speaker 6: just to like skip that whole song and dance. So

552
00:29:25,960 --> 00:29:27,320
Speaker 6: I don't know that this is going to be like

553
00:29:27,360 --> 00:29:31,160
Speaker 6: the magic bullet that saves Tinder, but uh, you know,

554
00:29:31,320 --> 00:29:31,960
Speaker 6: let him try.

555
00:29:33,640 --> 00:29:38,000
Speaker 3: Yeah, I don't think so. Because also for hinge, you

556
00:29:38,080 --> 00:29:41,160
Speaker 3: automatically have your height on there, right, so you can

557
00:29:41,280 --> 00:29:43,320
Speaker 3: pay it to filter that I believe, I mean, you're

558
00:29:43,320 --> 00:29:44,840
Speaker 3: not going to find me paying for a dating app

559
00:29:44,920 --> 00:29:48,720
Speaker 3: like that is ridiculous in my opinion, Sorry, just like

560
00:29:48,720 --> 00:29:54,080
Speaker 3: like I can't but it is already a thing, So

561
00:29:54,480 --> 00:29:57,880
Speaker 3: I don't know how they're so late that's feature, Like

562
00:29:57,920 --> 00:30:01,280
Speaker 3: I just I feel like it's like we're going back

563
00:30:01,320 --> 00:30:03,440
Speaker 3: to I don't know, twenty fifteen, Like where have you

564
00:30:03,480 --> 00:30:06,080
Speaker 3: been in terms of tender.

565
00:30:06,120 --> 00:30:08,160
Speaker 1: So this is what I think too, And this is

566
00:30:08,200 --> 00:30:10,800
Speaker 1: what I found like almost a little offensive about the story.

567
00:30:11,160 --> 00:30:13,560
Speaker 1: In the tech Crunch piece on this, they say the

568
00:30:13,600 --> 00:30:15,800
Speaker 1: company may hope if the addition of a height setting

569
00:30:15,800 --> 00:30:18,400
Speaker 1: could encourage more women to use and pay for the app,

570
00:30:18,640 --> 00:30:21,320
Speaker 1: which tends to be heavily dominated by men in both

571
00:30:21,360 --> 00:30:24,640
Speaker 1: the US and internationally. So this, to me, I think,

572
00:30:24,680 --> 00:30:26,920
Speaker 1: really says it all. Like it seems to suggest that

573
00:30:26,960 --> 00:30:30,840
Speaker 1: women like, what women really want is the ability to

574
00:30:30,880 --> 00:30:33,959
Speaker 1: read people out by height, and that's what women are

575
00:30:34,000 --> 00:30:36,160
Speaker 1: willing to pay for, when in reality, I think the

576
00:30:36,160 --> 00:30:38,000
Speaker 1: reason why dating apps are in trouble and that like

577
00:30:38,080 --> 00:30:40,239
Speaker 1: women don't want to use them is because they're not

578
00:30:40,280 --> 00:30:43,240
Speaker 1: serving up women or really anybody good experiences.

579
00:30:43,280 --> 00:30:43,800
Speaker 2: And they're not.

580
00:30:43,760 --> 00:30:46,440
Speaker 1: Serving up good experiences for anybody men either, And so

581
00:30:46,640 --> 00:30:49,600
Speaker 1: the people designing these apps, like if that's what they

582
00:30:49,640 --> 00:30:53,960
Speaker 1: think will meaningfully woo women into paying for Tinder Premium.

583
00:30:54,280 --> 00:30:57,400
Speaker 1: I think that perhaps that gives us insight into like

584
00:30:57,440 --> 00:31:00,480
Speaker 1: why women are not paying for Tinder Premium or using

585
00:31:00,480 --> 00:31:02,080
Speaker 1: these apps at all, because I just don't think that

586
00:31:02,120 --> 00:31:05,440
Speaker 1: they really are functionally understanding what it is that women want,

587
00:31:05,520 --> 00:31:07,760
Speaker 1: or like how to give women dating experiences that.

588
00:31:07,800 --> 00:31:09,280
Speaker 2: Don't feel so bad.

589
00:31:09,800 --> 00:31:12,320
Speaker 1: Also, I have to say, Mike, when we were talking

590
00:31:12,320 --> 00:31:15,840
Speaker 1: about this before we got on the mic, you were like, oh, well,

591
00:31:16,480 --> 00:31:19,640
Speaker 1: we have data that suggests that men are just lying

592
00:31:19,640 --> 00:31:22,280
Speaker 1: about their heights on dating profiles, right, like, it wouldn't

593
00:31:22,280 --> 00:31:22,760
Speaker 1: even work.

594
00:31:22,960 --> 00:31:25,240
Speaker 6: It's true, you're both women. So I'm curious if you've

595
00:31:25,240 --> 00:31:27,680
Speaker 6: ever experienced this phenomenon of a man lying.

596
00:31:29,720 --> 00:31:35,080
Speaker 3: First of all, right, like just cutt and clips. But

597
00:31:35,240 --> 00:31:37,440
Speaker 3: second to that, I mean, you know, like when you

598
00:31:37,440 --> 00:31:40,360
Speaker 3: go on Pinge, for example, and the guy says five

599
00:31:40,360 --> 00:31:42,200
Speaker 3: to ten that he's five eight, you have to subtract

600
00:31:42,240 --> 00:31:44,440
Speaker 3: two inches from any any height.

601
00:31:44,840 --> 00:31:47,160
Speaker 1: Sammy, it is so funny that you say this because

602
00:31:47,160 --> 00:31:50,520
Speaker 1: according to Okcupan, who published this data, they say that

603
00:31:50,520 --> 00:31:53,120
Speaker 1: that is exactly the amount that men are adding, so

604
00:31:53,320 --> 00:31:57,440
Speaker 1: they're they're suspiciously adding two inches taller to their right.

605
00:31:57,480 --> 00:32:00,240
Speaker 1: So the man says five eight, you got to take

606
00:32:00,240 --> 00:32:02,240
Speaker 1: two off of that because that's the lie. And what

607
00:32:02,280 --> 00:32:06,360
Speaker 1: I feel like is like, okay, one inch, I'll give

608
00:32:06,360 --> 00:32:08,120
Speaker 1: you that two.

609
00:32:08,680 --> 00:32:09,840
Speaker 2: So that's a little much.

610
00:32:10,520 --> 00:32:12,400
Speaker 3: They think that they can get away with it if

611
00:32:12,440 --> 00:32:16,320
Speaker 3: they're wearing a hat. They're like, oh, let's see, it's

612
00:32:16,320 --> 00:32:20,520
Speaker 3: like that hat, like I sto's the extra extra.

613
00:32:20,880 --> 00:32:25,280
Speaker 1: The show's a platform boot at a stow top hat?

614
00:32:25,520 --> 00:32:29,040
Speaker 2: Is what is the Abraham Lincoln hats? Yeah, like like

615
00:32:29,080 --> 00:32:29,720
Speaker 2: a top hat.

616
00:32:29,880 --> 00:32:32,000
Speaker 6: Yeah, and then he just has to wear that for

617
00:32:32,040 --> 00:32:34,520
Speaker 6: the rest of the relationship, like he wears it to bed,

618
00:32:34,680 --> 00:32:35,640
Speaker 6: he wears it to work.

619
00:32:36,000 --> 00:32:39,000
Speaker 3: Honestly, at least he'd have a sense of humor. You'd

620
00:32:39,040 --> 00:32:42,640
Speaker 3: be like, wow, bringing something into the table Live Vestinel.

621
00:32:44,360 --> 00:32:44,640
Speaker 2: Yeah.

622
00:32:44,680 --> 00:32:47,880
Speaker 1: As a tall woman, so I'm five eleven and a

623
00:32:47,920 --> 00:32:50,680
Speaker 1: half in any kind of shoe, I'm six feet.

624
00:32:51,160 --> 00:32:54,200
Speaker 2: When I the short period that I was on.

625
00:32:54,200 --> 00:32:56,760
Speaker 1: Dating apps, I did put my height in my profile

626
00:32:56,840 --> 00:33:00,840
Speaker 1: because I mean, I I mean maybe I'm I feel

627
00:33:00,840 --> 00:33:04,680
Speaker 1: like that sort of you know, height wars stuff is

628
00:33:04,680 --> 00:33:07,200
Speaker 1: like a little overblown and I never found anybody who

629
00:33:07,280 --> 00:33:09,560
Speaker 1: had an issue with my height, but Mike, similar to you,

630
00:33:09,600 --> 00:33:11,800
Speaker 1: I didn't want to be on a date with somebody

631
00:33:11,800 --> 00:33:13,480
Speaker 1: who would like not want to be with someone who

632
00:33:13,520 --> 00:33:15,960
Speaker 1: was six feet tall. But again, I do think it

633
00:33:16,120 --> 00:33:19,320
Speaker 1: like it just suggests to me that dating the people

634
00:33:19,320 --> 00:33:23,200
Speaker 1: who make dating apps are just not really thinking too

635
00:33:23,240 --> 00:33:26,120
Speaker 1: hard or thoughtfully about the kinds of experience as they're

636
00:33:26,120 --> 00:33:28,800
Speaker 1: trying to curate for people. And I think that that

637
00:33:29,280 --> 00:33:31,280
Speaker 1: is the root of the problem as to why they're

638
00:33:31,280 --> 00:33:33,680
Speaker 1: in trouble, these little gimmicks about height or whatever.

639
00:33:33,800 --> 00:33:35,320
Speaker 2: Not only Samy, as.

640
00:33:35,240 --> 00:33:36,920
Speaker 1: You said, are they like out of Night out of

641
00:33:37,000 --> 00:33:38,320
Speaker 1: twenty fifteen or something.

642
00:33:38,320 --> 00:33:39,040
Speaker 2: They're very late.

643
00:33:39,240 --> 00:33:40,720
Speaker 1: But I just don't think there what is going to

644
00:33:40,800 --> 00:33:43,720
Speaker 1: save these platforms. I think they need to really be

645
00:33:43,760 --> 00:33:46,040
Speaker 1: thinking about, like how to improve experiences in a more

646
00:33:46,040 --> 00:33:47,520
Speaker 1: meaningful way totally.

647
00:33:47,560 --> 00:33:49,880
Speaker 3: And I think there's this idea out there. I don't

648
00:33:49,880 --> 00:33:53,760
Speaker 3: know if this is technologically accurate or not, but people

649
00:33:53,800 --> 00:33:56,840
Speaker 3: feel like they're being served matches that aren't accurate to

650
00:33:56,920 --> 00:34:01,960
Speaker 3: them or just absolutely trash. So and then if they

651
00:34:01,960 --> 00:34:05,560
Speaker 3: pay for the better, you know, the upgraded premium version

652
00:34:05,640 --> 00:34:08,440
Speaker 3: whatever it's called that, then they're going to be provided

653
00:34:08,480 --> 00:34:14,160
Speaker 3: better matches, and so that paywall to better matches is

654
00:34:14,160 --> 00:34:17,120
Speaker 3: also making people being like this isn't worth it, because

655
00:34:17,160 --> 00:34:20,319
Speaker 3: then if you do pay it isn't necessarily better and

656
00:34:20,400 --> 00:34:23,279
Speaker 3: it's a whole nother like level of gatekeeping, Like no

657
00:34:23,280 --> 00:34:26,800
Speaker 3: one's getting anything more necessarily from it. We're just creating

658
00:34:26,800 --> 00:34:29,680
Speaker 3: more barriers when I think people in the dating sphere

659
00:34:29,680 --> 00:34:33,360
Speaker 3: already feel like there's tons of barriers to meeting anyone.

660
00:34:33,719 --> 00:34:36,399
Speaker 3: So I think there's that element. And I also think

661
00:34:36,440 --> 00:34:39,319
Speaker 3: even too, I mean, like to your point about these

662
00:34:39,320 --> 00:34:41,360
Speaker 3: people that are making these ups, like maybe they should

663
00:34:41,360 --> 00:34:45,040
Speaker 3: look at the prompts available. Some of them are like

664
00:34:45,400 --> 00:34:49,160
Speaker 3: if you were doing orientation freshman year at college and

665
00:34:49,400 --> 00:34:52,279
Speaker 3: you're all looking around the room like this cannot be.

666
00:34:52,600 --> 00:34:57,520
Speaker 3: This is so cringe. That's what these things are. So

667
00:34:58,080 --> 00:35:01,920
Speaker 3: I just I think are used. The people in charge

668
00:35:01,960 --> 00:35:04,200
Speaker 3: of the tech are not asking the right questions, and

669
00:35:04,239 --> 00:35:06,640
Speaker 3: they don't know their audience. They think they know their audience,

670
00:35:06,680 --> 00:35:09,440
Speaker 3: they think that a subset of data has told them, oh,

671
00:35:09,520 --> 00:35:12,040
Speaker 3: this is who our audience is, but they clearly haven't

672
00:35:12,360 --> 00:35:16,200
Speaker 3: spoken with those people because I think and that seems

673
00:35:16,200 --> 00:35:19,319
Speaker 3: to be a missing link across the whole space, whether

674
00:35:19,360 --> 00:35:22,560
Speaker 3: it's tech, whether it's the consumer space, whether it's politics.

675
00:35:22,840 --> 00:35:25,160
Speaker 3: I feel as though people just aren't having conversations with

676
00:35:25,200 --> 00:35:28,160
Speaker 3: their actual target market. Yeah, because if they did, the

677
00:35:28,239 --> 00:35:30,879
Speaker 3: feedback would be so much different, because I'm having those

678
00:35:30,920 --> 00:35:34,279
Speaker 3: conversations and I could give you like fifteen different points

679
00:35:34,600 --> 00:35:37,799
Speaker 3: that are so different from what they are sharing in

680
00:35:37,840 --> 00:35:38,680
Speaker 3: their big reports.

681
00:35:39,000 --> 00:35:40,960
Speaker 2: Yeah, and I think you really nailed it.

682
00:35:41,040 --> 00:35:44,279
Speaker 1: Like, we are so much more complex than like a

683
00:35:44,320 --> 00:35:46,879
Speaker 1: set of data that a tech company has on us,

684
00:35:47,200 --> 00:35:49,560
Speaker 1: and I think that they are trying to make decisions

685
00:35:49,600 --> 00:35:54,600
Speaker 1: about platform experiences based on narrowing us down into a

686
00:35:54,760 --> 00:35:56,920
Speaker 1: very specific set of data that they have on us.

687
00:35:57,120 --> 00:35:58,960
Speaker 2: I think that's exactly right totally.

688
00:35:58,680 --> 00:36:02,400
Speaker 3: Because like, even think about someone's past, like dating experience,

689
00:36:02,480 --> 00:36:05,879
Speaker 3: that wouldn't be a part of that data necessarily. Say

690
00:36:05,920 --> 00:36:09,080
Speaker 3: they had their they were dating their high school sweetheart

691
00:36:09,200 --> 00:36:13,520
Speaker 3: for ten years and they were a short king and

692
00:36:13,560 --> 00:36:16,200
Speaker 3: then they had a terrible breakup, right, and then this

693
00:36:16,360 --> 00:36:19,160
Speaker 3: is their first entree onto the dating apps, and so

694
00:36:19,200 --> 00:36:21,360
Speaker 3: they don't want to do anything with any short kings.

695
00:36:21,400 --> 00:36:24,120
Speaker 3: They're over it. So therefore it's going to inform their

696
00:36:24,160 --> 00:36:27,520
Speaker 3: hype preferences, which obviously the dating app wouldn't know because

697
00:36:27,520 --> 00:36:29,480
Speaker 3: this is their first time on the app and swiping,

698
00:36:29,719 --> 00:36:31,200
Speaker 3: and they're only going to think of it as, oh,

699
00:36:31,280 --> 00:36:34,560
Speaker 3: this person's a snob about twelve versus short not understanding

700
00:36:34,719 --> 00:36:38,440
Speaker 3: their past personal interpersonal relationships. Right, So, like there's so

701
00:36:38,480 --> 00:36:42,839
Speaker 3: many of those things that aren't captured that again, we're

702
00:36:42,920 --> 00:36:45,520
Speaker 3: just they're just missing it. They're just missing it.

703
00:36:45,640 --> 00:36:47,759
Speaker 2: They're just missing it. That's a good way to put it.

704
00:36:48,200 --> 00:36:51,320
Speaker 1: Okay, So I have to ask you about this Nancy

705
00:36:51,400 --> 00:36:52,800
Speaker 1: Mace report from Wired.

706
00:36:53,440 --> 00:36:56,680
Speaker 2: It is a lot, I mean.

707
00:36:56,800 --> 00:37:00,799
Speaker 1: South Carolina Republican Representative Nancy Mace is a lot herself.

708
00:37:00,920 --> 00:37:03,640
Speaker 1: Like there's all I mean, she's just there's a lot

709
00:37:03,680 --> 00:37:06,600
Speaker 1: going on with her. So, according to Wired, who spoke

710
00:37:06,640 --> 00:37:09,640
Speaker 1: to a bunch of her former staffers, Nancy Mays would

711
00:37:09,719 --> 00:37:13,680
Speaker 1: frequently monitor her image on social media, even going so

712
00:37:13,840 --> 00:37:17,960
Speaker 1: far as to create bots to comment across social media

713
00:37:18,360 --> 00:37:21,680
Speaker 1: in support of her and in this attempt to boost

714
00:37:21,760 --> 00:37:25,120
Speaker 1: her image, and she allegedly also asked her staff to

715
00:37:25,200 --> 00:37:27,880
Speaker 1: create fake profiles on social media in order to keep

716
00:37:27,880 --> 00:37:31,160
Speaker 1: an eye on the discourse about her and generally boost

717
00:37:31,239 --> 00:37:34,360
Speaker 1: her image online. According to one of her former staffers.

718
00:37:34,360 --> 00:37:37,360
Speaker 1: They told Wired we had to make multiple accounts, burner accounts,

719
00:37:37,360 --> 00:37:39,680
Speaker 1: and go and reply to comments saying things that were

720
00:37:39,719 --> 00:37:42,720
Speaker 1: not true, even on Reddit forums. We were congressional staff

721
00:37:42,719 --> 00:37:44,400
Speaker 1: and there were actual things we could have been doing

722
00:37:44,480 --> 00:37:46,520
Speaker 1: to help our constituents. I would say that it was

723
00:37:46,560 --> 00:37:50,719
Speaker 1: at least a weekly comment, if not daily. People Magazine

724
00:37:50,760 --> 00:37:54,080
Speaker 1: called her staff or her aufice today for comment, and

725
00:37:54,120 --> 00:37:56,160
Speaker 1: they kind of like made a little joke. They were like, oh, well,

726
00:37:56,600 --> 00:38:00,800
Speaker 1: apparently we're too busy creating cop bots and making comments

727
00:38:00,800 --> 00:38:01,920
Speaker 1: to answer your query.

728
00:38:02,480 --> 00:38:04,040
Speaker 2: But this is just my opinion.

729
00:38:04,200 --> 00:38:07,200
Speaker 1: I absolutely can see her doing this, Like, I absolutely

730
00:38:07,200 --> 00:38:10,000
Speaker 1: see it. I have no trouble believing this about her, none.

731
00:38:09,960 --> 00:38:12,960
Speaker 3: Oh none. And also her past staff clearly hates her,

732
00:38:13,120 --> 00:38:16,400
Speaker 3: oh my god, yeah, in an inch of her life,

733
00:38:16,560 --> 00:38:21,640
Speaker 3: like absolutely hates her guts because obviously it's the hell

734
00:38:21,680 --> 00:38:26,080
Speaker 3: of the story. But it doesn't it just doesn't help anyone.

735
00:38:26,160 --> 00:38:28,600
Speaker 3: So you really have to be quite bitter to get

736
00:38:28,640 --> 00:38:30,960
Speaker 3: yourself in a mess like this talking about your old boss.

737
00:38:31,160 --> 00:38:33,279
Speaker 3: So the first thing that pops up. Second to that,

738
00:38:33,520 --> 00:38:37,640
Speaker 3: I really doubt that she is the only member of

739
00:38:37,680 --> 00:38:41,279
Speaker 3: Congress doing this, Like I don't know anyone personally doing this.

740
00:38:41,360 --> 00:38:44,880
Speaker 3: Let me just go on record saying okay, it's and

741
00:38:44,960 --> 00:38:47,840
Speaker 3: also second to that, I do wonder if it comes

742
00:38:47,880 --> 00:38:50,920
Speaker 3: into violation of campaign financeauce.

743
00:38:51,760 --> 00:38:54,440
Speaker 2: That's a really good question, very good question.

744
00:38:54,920 --> 00:38:57,600
Speaker 1: Yeah, I bet she's not the only person doing this.

745
00:38:57,880 --> 00:39:00,840
Speaker 1: But something tells me like, like, if you're gonna do

746
00:39:00,880 --> 00:39:02,800
Speaker 1: this and get your staff mixed up to them, you

747
00:39:02,880 --> 00:39:04,919
Speaker 1: better be treating that staff pretty good. And the fact

748
00:39:04,920 --> 00:39:07,440
Speaker 1: that your staff is this leaky and that they're like

749
00:39:07,680 --> 00:39:10,000
Speaker 1: running to wired to be like she makes us do this.

750
00:39:10,360 --> 00:39:12,239
Speaker 2: I think you're right. It really says something about the

751
00:39:12,320 --> 00:39:13,360
Speaker 2: dynamic on her staff.

752
00:39:13,680 --> 00:39:17,160
Speaker 1: And the report actually quoted a deposition from Wesley Donahue,

753
00:39:17,239 --> 00:39:20,720
Speaker 1: a South Carolina based campaign consultant who previously worked closely

754
00:39:20,719 --> 00:39:24,279
Speaker 1: with Mace's campaigns. Donna Hue told a court quote, she

755
00:39:24,400 --> 00:39:27,480
Speaker 1: programs her own bots, sets up Twitter burner accounts. This

756
00:39:27,520 --> 00:39:29,480
Speaker 1: is the kind of thing she does. She sits all

757
00:39:29,520 --> 00:39:31,759
Speaker 1: night on the couch and programs bots because she's very,

758
00:39:31,880 --> 00:39:34,840
Speaker 1: very computer savvy. She controls her own voter database, she

759
00:39:34,880 --> 00:39:37,520
Speaker 1: programs a lot of her own website. She programs Facebook

760
00:39:37,520 --> 00:39:39,759
Speaker 1: bots and Instagram bots and Twitter bots. It's what she

761
00:39:39,880 --> 00:39:42,200
Speaker 1: does for fun. And let me tell you what a

762
00:39:42,320 --> 00:39:47,240
Speaker 1: like grim projection of what an evening at Nancy Mace's

763
00:39:47,239 --> 00:39:49,600
Speaker 1: house must look like. A courage us on the couch

764
00:39:50,160 --> 00:39:54,680
Speaker 1: having a great time controlling her bot army like pretty grim.

765
00:39:54,840 --> 00:39:57,160
Speaker 3: No, for sure, I mean, holy narcissism.

766
00:39:57,360 --> 00:39:59,719
Speaker 1: And it's so wild to think that when Nancy Mays

767
00:39:59,760 --> 00:40:02,640
Speaker 1: first came on the scene many many many years ago,

768
00:40:02,760 --> 00:40:04,480
Speaker 1: was because she was the first woman trying to get

769
00:40:04,480 --> 00:40:08,520
Speaker 1: into the citadel, like she has had a very long

770
00:40:09,320 --> 00:40:13,320
Speaker 1: kind of life in public, and yeah, it just seems

771
00:40:13,320 --> 00:40:16,360
Speaker 1: like something's going on. And subsequently I have no trouble

772
00:40:16,360 --> 00:40:18,440
Speaker 1: believing this report from why I guess I'll just put it.

773
00:40:18,400 --> 00:40:21,720
Speaker 3: That way totally and again I would a thousand percent

774
00:40:21,719 --> 00:40:23,640
Speaker 3: guarantee she's not the only one doing it, maybe at

775
00:40:23,640 --> 00:40:27,239
Speaker 3: the obsessive capacity in which she is clearly doing it

776
00:40:27,440 --> 00:40:29,560
Speaker 3: aka the night at Nancy Macey's right.

777
00:40:30,080 --> 00:40:30,239
Speaker 5: But.

778
00:40:31,800 --> 00:40:34,759
Speaker 3: I mean, if you spend as we both do, you know,

779
00:40:34,880 --> 00:40:39,239
Speaker 3: being chronically online people, you start to understand what is

780
00:40:39,280 --> 00:40:43,399
Speaker 3: at what isn't a bot, what their sort of characteristics are,

781
00:40:43,520 --> 00:40:46,680
Speaker 3: and how they pop up occasionally. I bully want to

782
00:40:46,719 --> 00:40:50,600
Speaker 3: be honest, a nice little bott and bully just to

783
00:40:50,640 --> 00:40:52,719
Speaker 3: see them prove themselves as a bot can be a

784
00:40:52,760 --> 00:40:56,680
Speaker 3: little fun asking them insane questions and sort of seeing

785
00:40:56,680 --> 00:41:00,720
Speaker 3: what they spin out. It is satisfying. But I think

786
00:41:01,120 --> 00:41:04,200
Speaker 3: just seeing sort of you know, knowing that that is

787
00:41:04,320 --> 00:41:06,200
Speaker 3: the way of the world. That there's so many bots,

788
00:41:06,280 --> 00:41:08,279
Speaker 3: I mean, there are also so many people paying for

789
00:41:08,360 --> 00:41:10,720
Speaker 3: these bots and creating them on both sides of the aisle,

790
00:41:11,040 --> 00:41:15,719
Speaker 3: so and beyond that, beyond our borders. So yeah, we're

791
00:41:15,760 --> 00:41:17,359
Speaker 3: in a war of clips and we're in a war

792
00:41:17,400 --> 00:41:19,520
Speaker 3: of bots. But I think the more that people know

793
00:41:21,080 --> 00:41:24,920
Speaker 3: the media landscape like that is the reality that we

794
00:41:25,000 --> 00:41:27,080
Speaker 3: are in to be aware when you go to a

795
00:41:27,120 --> 00:41:31,440
Speaker 3: comment section that what you are seeing might not be reality,

796
00:41:31,960 --> 00:41:35,960
Speaker 3: Right that comment on TikTok that has fifty thousand likes

797
00:41:35,960 --> 00:41:39,560
Speaker 3: from user one, two, three, four, five, six, seven, like that,

798
00:41:39,760 --> 00:41:44,000
Speaker 3: that particular comment is trying to curate a reaction, and

799
00:41:44,040 --> 00:41:48,000
Speaker 3: supposed to it's also being utilized to curate more, you know,

800
00:41:48,040 --> 00:41:49,960
Speaker 3: not just reactions in terms of the likes to it,

801
00:41:50,000 --> 00:41:52,480
Speaker 3: but the comments and then the videos off of can

802
00:41:52,520 --> 00:41:55,319
Speaker 3: you believe this comment section? So I think at least

803
00:41:55,360 --> 00:41:58,240
Speaker 3: if we're in a position where people understand what they're

804
00:41:58,840 --> 00:42:02,520
Speaker 3: actually in taking and what risks there are to seeing

805
00:42:02,560 --> 00:42:07,200
Speaker 3: a comment section. I think we're at least a little

806
00:42:07,239 --> 00:42:11,320
Speaker 3: bit better at a spot. But hey, that's another area

807
00:42:11,600 --> 00:42:15,320
Speaker 3: where I say, hey, where's the regulation on this bot situation?

808
00:42:15,440 --> 00:42:18,960
Speaker 3: Because it's crazy and people really believe what they read

809
00:42:19,040 --> 00:42:21,759
Speaker 3: no matter what, so and even it doesn't matter how

810
00:42:21,760 --> 00:42:23,680
Speaker 3: smart the person is, Like, I think we're all guilty

811
00:42:23,680 --> 00:42:25,560
Speaker 3: of it too, being like, wait, oh my god, all

812
00:42:25,600 --> 00:42:28,840
Speaker 3: the vibe chuck on this comment section. People are really

813
00:42:28,880 --> 00:42:32,839
Speaker 3: feeling xyz way and right. It's just how you feel

814
00:42:32,840 --> 00:42:36,480
Speaker 3: about something. And we as people, I think, oftentimes want

815
00:42:36,520 --> 00:42:39,080
Speaker 3: to see the best in what we're taking in and

816
00:42:39,120 --> 00:42:41,600
Speaker 3: the best of intentions, but oftentimes that's not the case.

817
00:42:42,280 --> 00:42:44,480
Speaker 1: So Sammy, you are somebody who has a ton of

818
00:42:44,520 --> 00:42:46,719
Speaker 1: experience with this. This is very much in your wheelhouse.

819
00:42:46,880 --> 00:42:50,400
Speaker 1: You've had ten years in media, pr, comms, politics, and

820
00:42:51,239 --> 00:42:52,960
Speaker 1: you know, we were talking earlier about how people are

821
00:42:52,960 --> 00:42:54,759
Speaker 1: always just sort of wanting to pick your brain and

822
00:42:54,760 --> 00:42:57,000
Speaker 1: get advice about how these things shape so much of

823
00:42:57,000 --> 00:42:59,239
Speaker 1: our discourse. So you were telling me about this new

824
00:42:59,239 --> 00:43:01,200
Speaker 1: thing that you're doing called office hours.

825
00:43:01,880 --> 00:43:05,840
Speaker 3: Yeah, so office hours really born out of getting loads

826
00:43:05,840 --> 00:43:08,360
Speaker 3: and loads of questions as to how on earth to

827
00:43:08,520 --> 00:43:12,399
Speaker 3: deal with this new media sphere right from podcasting which

828
00:43:12,400 --> 00:43:15,720
Speaker 3: we're doing right now, to social to newsletters to everything

829
00:43:15,760 --> 00:43:19,600
Speaker 3: else in between, how to actually activate in this space

830
00:43:20,360 --> 00:43:23,520
Speaker 3: and basically office hours. You can book it for an hour,

831
00:43:23,560 --> 00:43:25,319
Speaker 3: you can book it for a half hour. You get

832
00:43:25,320 --> 00:43:28,160
Speaker 3: to pick my brain on strategy development across the board.

833
00:43:28,239 --> 00:43:31,719
Speaker 3: It can really be anything across that larger comm space again,

834
00:43:31,760 --> 00:43:37,480
Speaker 3: whether it's marketing, PR, strategy, social, understanding what you're seeing online,

835
00:43:37,560 --> 00:43:40,720
Speaker 3: how to actually develop a strategy, how to react, what works,

836
00:43:40,719 --> 00:43:45,400
Speaker 3: what doesn't, everything in that bucket and more. You're able

837
00:43:45,440 --> 00:43:47,880
Speaker 3: to book an office hour and chat with me about

838
00:43:47,920 --> 00:43:50,640
Speaker 3: it and we can figure out bust path forward for

839
00:43:50,920 --> 00:43:53,360
Speaker 3: what you're working on. And I do this on the

840
00:43:53,400 --> 00:43:55,080
Speaker 3: political end of things, but I also do it in

841
00:43:55,080 --> 00:44:00,160
Speaker 3: the consumer space too, So everybody's welcome at office hours,

842
00:44:00,239 --> 00:44:03,680
Speaker 3: and you can a book of time with your link

843
00:44:03,719 --> 00:44:04,719
Speaker 3: in bios situation.

844
00:44:04,840 --> 00:44:07,120
Speaker 1: It is always a classic, so well put the link

845
00:44:07,160 --> 00:44:08,840
Speaker 1: for folks to join in the bio. But it is

846
00:44:08,920 --> 00:44:12,320
Speaker 1: really important like I used to do, like disinfo trainings

847
00:44:12,360 --> 00:44:14,959
Speaker 1: and media trainings, and I think especially right now when

848
00:44:15,400 --> 00:44:17,720
Speaker 1: as you said, the media landscape is shipping, so quickly

849
00:44:17,760 --> 00:44:20,640
Speaker 1: that it can be really hard for folks to get

850
00:44:20,640 --> 00:44:22,680
Speaker 1: a handle on it, like it's hard for me sometimes.

851
00:44:22,719 --> 00:44:25,680
Speaker 1: And so I think that's a valuable service that you're offering.

852
00:44:25,680 --> 00:44:29,520
Speaker 1: And I actually know somebody who I think could probably be.

853
00:44:29,960 --> 00:44:31,319
Speaker 2: Like, take good use of this.

854
00:44:31,560 --> 00:44:32,960
Speaker 1: And that is the last story that I want to

855
00:44:32,960 --> 00:44:35,480
Speaker 1: want to talk about, which is that readers who read

856
00:44:35,800 --> 00:44:40,319
Speaker 1: fantasy author Lena McDonald's book called Dark Hollow Academy Year

857
00:44:40,400 --> 00:44:44,439
Speaker 1: two noticed an interesting editing note embedded into chapter three

858
00:44:44,719 --> 00:44:47,719
Speaker 1: that suggested that she might have used AI to write

859
00:44:47,760 --> 00:44:51,600
Speaker 1: this book. The note said, like Sandwiched in between the

860
00:44:51,640 --> 00:44:53,960
Speaker 1: regular dialogue of the book, there was a note that said,

861
00:44:54,520 --> 00:44:57,840
Speaker 1: I've rewritten the passage to align more with Jabree's style,

862
00:44:58,160 --> 00:45:02,280
Speaker 1: which features more tension, gritty undertones, and raw emotional subtext

863
00:45:02,480 --> 00:45:06,920
Speaker 1: beneath the supernatural elements. So not only was this author

864
00:45:07,040 --> 00:45:12,320
Speaker 1: using AI, allegedly she did not delete the AI note

865
00:45:12,440 --> 00:45:14,640
Speaker 1: that made an end of her published book, and she

866
00:45:14,760 --> 00:45:17,719
Speaker 1: was using AI to make her writing sound more like

867
00:45:17,760 --> 00:45:18,840
Speaker 1: another person's writing.

868
00:45:19,400 --> 00:45:21,040
Speaker 2: When I tell you, I would simply.

869
00:45:20,680 --> 00:45:23,040
Speaker 1: Have to die, like like I don't know if there

870
00:45:23,040 --> 00:45:25,280
Speaker 1: would be any coming back from this, Like I would

871
00:45:25,280 --> 00:45:26,320
Speaker 1: be so embarrassed.

872
00:45:26,320 --> 00:45:27,160
Speaker 2: I would pack it up.

873
00:45:27,360 --> 00:45:29,360
Speaker 3: No, literally, like you've never seen me again. I'd be

874
00:45:29,680 --> 00:45:33,440
Speaker 3: off on some island with no Wi Fi and just

875
00:45:33,600 --> 00:45:37,200
Speaker 3: maybe a nice little canoe like that would be truly all.

876
00:45:37,360 --> 00:45:39,719
Speaker 3: And it reminds me of I'm blinking on her name.

877
00:45:39,760 --> 00:45:42,479
Speaker 3: But she's a cookbook author and this wasn't her fault

878
00:45:42,520 --> 00:45:45,160
Speaker 3: at all. The editor unfortunately missed it. But there, I

879
00:45:45,160 --> 00:45:47,680
Speaker 3: guess for an image of her was a note left

880
00:45:47,680 --> 00:45:54,040
Speaker 3: in the book to like tone her arms more. Ah,

881
00:45:54,080 --> 00:45:59,319
Speaker 3: and all the copies went out like just horrendous. I

882
00:45:59,360 --> 00:46:01,120
Speaker 3: think it was I don't think it was her first

883
00:46:01,120 --> 00:46:03,759
Speaker 3: book at used. And again it wasn't her, It was,

884
00:46:03,880 --> 00:46:05,200
Speaker 3: you know, sort of the editor, which I still feel

885
00:46:05,239 --> 00:46:10,799
Speaker 3: badly for because obviously that was not their intent. But yikes, yikes,

886
00:46:11,239 --> 00:46:14,840
Speaker 3: And it makes me just think about how one of

887
00:46:15,000 --> 00:46:19,879
Speaker 3: the areas of expertise that we really have let lead

888
00:46:19,960 --> 00:46:24,880
Speaker 3: out our editors, right, people that catch these things, that

889
00:46:25,120 --> 00:46:27,759
Speaker 3: do the due diligence, that go back through things. We

890
00:46:28,239 --> 00:46:31,600
Speaker 3: look at technology and we go, oh, it's gonna it's

891
00:46:31,600 --> 00:46:33,879
Speaker 3: got us, It's fine. I can't delain how many times

892
00:46:33,880 --> 00:46:37,120
Speaker 3: I've controlled f on a spreadsheet and it's missed something, Okay.

893
00:46:37,520 --> 00:46:41,879
Speaker 3: So I just think that the the personal touch, the

894
00:46:42,719 --> 00:46:45,480
Speaker 3: human to human does get some of the things that

895
00:46:45,520 --> 00:46:49,040
Speaker 3: are missed, and that is just so wild and uncomfortable,

896
00:46:49,440 --> 00:46:53,680
Speaker 3: and I just, yeah, I don't know what one does.

897
00:46:53,800 --> 00:46:55,520
Speaker 3: I mean, I could probably think of a pr way

898
00:46:55,520 --> 00:47:00,480
Speaker 3: out of that one I have met, but yikes, that's

899
00:47:00,640 --> 00:47:02,839
Speaker 3: that's a long road. That's definitely not a quick fix,

900
00:47:02,840 --> 00:47:03,600
Speaker 3: I'll tell you that much.

901
00:47:03,760 --> 00:47:10,040
Speaker 2: Yeah, Lena reached out to Sammy for some crisis comes.

902
00:47:10,360 --> 00:47:12,120
Speaker 1: But it's also it's just a good reminder that, like,

903
00:47:12,520 --> 00:47:15,760
Speaker 1: there are so many reasons why people should be careful

904
00:47:15,840 --> 00:47:18,399
Speaker 1: about the way they use AI, particularly in their creative work.

905
00:47:18,520 --> 00:47:21,080
Speaker 1: You know, so many reasons why you should be If

906
00:47:21,120 --> 00:47:23,560
Speaker 1: you're gonna use AI, you need to really be careful.

907
00:47:23,960 --> 00:47:24,080
Speaker 2: Uh.

908
00:47:24,480 --> 00:47:28,120
Speaker 1: But the biggest one now is like you could be

909
00:47:28,400 --> 00:47:32,080
Speaker 1: horribly embarrassed all across the internet when your AI note

910
00:47:32,200 --> 00:47:34,200
Speaker 1: is published in your fucking book.

911
00:47:34,600 --> 00:47:37,479
Speaker 2: My god, so bad, so bad.

912
00:47:37,600 --> 00:47:41,040
Speaker 3: I just hope that if she does decide to write

913
00:47:41,040 --> 00:47:45,680
Speaker 3: another book, that AI as just in its existence right,

914
00:47:46,000 --> 00:47:50,640
Speaker 3: is integrated into the storyline. Oh yeah, that would be

915
00:47:50,719 --> 00:47:54,880
Speaker 3: like a way to partially solve it. I fel like,

916
00:47:54,960 --> 00:47:59,200
Speaker 3: actually it was any drag you guys caught it again.

917
00:47:59,239 --> 00:48:00,640
Speaker 3: I'd have to read the book. I really think if

918
00:48:00,640 --> 00:48:03,759
Speaker 3: that was a good strategy or not. But you know

919
00:48:03,920 --> 00:48:04,520
Speaker 3: there's something.

920
00:48:04,760 --> 00:48:07,400
Speaker 1: This is why you're the pr master, Sammy. People, that

921
00:48:07,840 --> 00:48:09,239
Speaker 1: was actually quite masterful.

922
00:48:10,040 --> 00:48:12,280
Speaker 3: Thank you, Thank you, Sammy.

923
00:48:12,440 --> 00:48:14,319
Speaker 1: Thank you so much for being here and helping us

924
00:48:14,320 --> 00:48:16,400
Speaker 1: a break down all of these stories across the internet.

925
00:48:16,680 --> 00:48:18,600
Speaker 1: Where can folks keep up with all the things that

926
00:48:18,640 --> 00:48:19,960
Speaker 1: you are doing across the internet.

927
00:48:20,120 --> 00:48:21,839
Speaker 3: Well, thank you so much for having me. This has

928
00:48:21,840 --> 00:48:25,960
Speaker 3: been so much fun. You can all tap in at

929
00:48:26,000 --> 00:48:29,799
Speaker 3: groolmigov dot com. You can find the newsletters there and

930
00:48:29,840 --> 00:48:31,920
Speaker 3: then for social Girl on the GUV and Girl on

931
00:48:31,960 --> 00:48:32,920
Speaker 3: the Gov the podcast.

932
00:48:33,200 --> 00:48:36,000
Speaker 1: Check it out. It is a very useful newsletter. The

933
00:48:36,040 --> 00:48:39,600
Speaker 1: podcast is great. You all talk to like legitimately important people,

934
00:48:39,640 --> 00:48:43,360
Speaker 1: like elected officials and like have genuinely important conversations that

935
00:48:43,360 --> 00:48:44,759
Speaker 1: are impactful for everyday lives.

936
00:48:44,840 --> 00:48:46,439
Speaker 2: I really really respect what y'all are doing.

937
00:48:46,640 --> 00:48:50,000
Speaker 3: Thank you. Yeah, it's been crazy. Sometimes I forget that

938
00:48:50,040 --> 00:48:54,880
Speaker 3: we've interviewed some of these movers and shakers myself in

939
00:48:54,960 --> 00:48:57,240
Speaker 3: this you know, just that way that your career sometimes

940
00:48:57,280 --> 00:48:59,440
Speaker 3: moves are like oh yeah, I can't believe I did

941
00:48:59,560 --> 00:49:02,719
Speaker 3: x y Z. Thanks, but we've done it and there

942
00:49:02,719 --> 00:49:05,880
Speaker 3: are some definitely some interesting conversations on the podcast feed

943
00:49:06,000 --> 00:49:08,440
Speaker 3: and then also on the social we've been mini micin

944
00:49:08,640 --> 00:49:11,840
Speaker 3: it up. Okay, those mini mics are living ten lives.

945
00:49:12,080 --> 00:49:12,960
Speaker 5: I'll tell you that much.

946
00:49:13,480 --> 00:49:14,600
Speaker 2: Given them a workout.

947
00:49:14,640 --> 00:49:14,839
Speaker 3: Well.

948
00:49:15,040 --> 00:49:16,640
Speaker 1: Thank you so much for being here, and if folks

949
00:49:16,640 --> 00:49:18,759
Speaker 1: want to follow me, you can follow me on Instagram

950
00:49:18,800 --> 00:49:21,640
Speaker 1: at bridget Marie DC, on TikTok at bridget Marie DC,

951
00:49:21,760 --> 00:49:24,600
Speaker 1: and on YouTube at there Are No Girls on the Internet.

952
00:49:24,719 --> 00:49:26,440
Speaker 1: Thank you so much for listening. We will see you

953
00:49:26,560 --> 00:49:35,640
Speaker 1: on the Internet. If you're looking for ways to support

954
00:49:35,680 --> 00:49:38,440
Speaker 1: the show, check out our mark store at tegoty dot com,

955
00:49:38,480 --> 00:49:42,160
Speaker 1: slash store. Got a story about an interesting thing in tech,

956
00:49:42,280 --> 00:49:44,200
Speaker 1: or just want to say hi, You can reach us

957
00:49:44,200 --> 00:49:46,560
Speaker 1: at Hello at tegody dot com. You can also find

958
00:49:46,560 --> 00:49:49,440
Speaker 1: transcripts for today's episode at tengody dot com. There Are

959
00:49:49,440 --> 00:49:51,600
Speaker 1: No Girls on the Internet was created by me Bridget Time.

960
00:49:52,000 --> 00:49:55,160
Speaker 1: It's a production of iHeartRadio and Unboss Creative, edited by

961
00:49:55,239 --> 00:49:59,280
Speaker 1: Joey pat Jonathan Strickland is our executive producer. Tari Harrison

962
00:49:59,360 --> 00:50:02,160
Speaker 1: is our producer and sound engineer. Michael Amado is our

963
00:50:02,160 --> 00:50:05,239
Speaker 1: contributing producer. I'm your host, bridget Todd. If you want

964
00:50:05,239 --> 00:50:07,640
Speaker 1: to help us grow, rate and review us on Apple Podcasts.

965
00:50:08,440 --> 00:50:11,239
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

966
00:50:11,280 --> 00:50:17,560
Speaker 1: Apple podcast or wherever you get your podcasts