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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 Tot and this

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

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

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Speaker 1: intersection of tech, social media, and identity. This is another

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Speaker 1: installment of our weekly news roundup, where we summarize and

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Speaker 1: round up some of the news on the Internet that

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Speaker 1: you might have missed this week. Just to heads up,

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Speaker 1: if my voice sounds a little bit raspy, because I

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Speaker 1: have been traveling and screaming at parties and concerts and conferences,

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Speaker 1: so I kind of got a little bit of a

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Speaker 1: Kathleen Turner thing happening. That's what's going on with me. Mike.

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Speaker 1: Thank you for being.

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Speaker 2: Here, Bridget, thanks for having me.

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Speaker 3: Thanks for soldiering on despite that raspiness.

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Speaker 1: So I'm actually happy that you're here because a story

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Speaker 1: that I know that you know, I'm low key obsessed with.

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Speaker 1: I just have so much to say about it. Can

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Speaker 1: we just can we just jump in? This will be

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Speaker 1: our banter.

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Speaker 2: We have to Yeah, Yeah, let's just jump in.

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Speaker 3: It's this is like possibly the funnest story that we've

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Speaker 3: talked about on here.

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Speaker 2: In like a very long time.

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Speaker 1: So the other day I was like like laugh, like

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Speaker 1: you were hearing me laugh to myself, and you were like,

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Speaker 1: what's so funny? And I was like, I'll save I'm

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Speaker 1: gonna I gotta save it for the roundup. And that

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Speaker 1: was me diving into the ins and outs of this.

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Speaker 1: I guess it's I guess I'm calling it the Willy

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Speaker 1: Wonka Fiasco in Glasgow? Am I saying that right? Glasgow?

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Speaker 3: I think it's Glasgow, and I think it like real,

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Speaker 3: it actually rhymes with fiasco, the Glasgow Fiasco, the Willy

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Speaker 3: Wonka Glasgow Fiasco.

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Speaker 1: So here's what went down. Basically, the police had to

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Speaker 1: be called to the Willy's Chocolate Experience, which was this

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Speaker 1: live event for kids and families billed as like an immersive,

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Speaker 1: wondrous experience for kids. So like kids apparently showed up

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Speaker 1: with their families. It costs forty four dollars, by the way,

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Speaker 1: so that's like not a cheap ticket. In my book.

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Speaker 1: Kids showed up with their parents. They were in like

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Speaker 1: Willy Wonka inspired costumes. I don't know if you know this,

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Speaker 1: but there's a new Willy Wonka movie with Timothy Schalomey

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Speaker 1: out right now. So it's kind of hot in the zeitgeist,

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Speaker 1: and you know, I guess all I can say is

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Speaker 1: that it was not the world of pure imagination that

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Speaker 1: these kids and families were promised. So this is one

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Speaker 1: of those stories that you have to see the pictures

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Speaker 1: to really get what I'm saying. But whatever you're imagining,

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Speaker 1: from what I'm describing, it is worse, It is sadder,

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Speaker 1: it is grimmer than whatever it is that you're picturing.

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Speaker 1: So we'll link to the pictures from the show notes.

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Speaker 1: But it is truly the grimish that I have ever

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Speaker 1: seen in my life. Did you see these pictures?

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Speaker 2: I'm like cracking up at one right now. That's just like.

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Speaker 3: There, It's very multicolored. There's a lot of candy. There's

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Speaker 3: like a strange little bear in a flying horse, some

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Speaker 3: sort of like circus clown man, and then also a

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Speaker 3: bunch of like nonsense lettering that like almost is words,

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Speaker 3: but like it's not actual words.

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Speaker 2: It's just like nonsense, like you mean.

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Speaker 1: From the flyer or from the like online like poster.

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Speaker 2: I'm looking at this fly I think it's the online poster.

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Speaker 2: I think.

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Speaker 1: Yeah, So let me read some of those nonsense words.

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Speaker 1: At the top, it says I think they're going or

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Speaker 1: enriching entertainment is what I think they're going for. It

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Speaker 1: says in sharing entertainment. And then on the bottom where

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Speaker 1: it like, you know, like on a concert flyer where

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Speaker 1: they would have the band names, So on the very

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Speaker 1: bottom of the flyer, it says cat get cat. How

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Speaker 1: would you even say that, cat gatating?

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Speaker 3: I'm so excited to listen to you try to save

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Speaker 3: these words, so I'm.

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Speaker 2: Not gonna help. You're just gonna have to do it.

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Speaker 1: Cat gatating live performances, they got that one, Okay, cartchy tons,

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Speaker 1: Like are they going for catchy turns? But even that

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Speaker 1: onen't really makes sense.

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Speaker 3: I think it's supposed to be like catchy tunes, catchy tunes.

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Speaker 1: Catchy tunes, Okay, exacer dray lollipops.

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Speaker 2: These lollipops are so excess or.

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Speaker 1: Dre apacidies of sweet treats, no a pacidiece of sweet teats.

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Speaker 1: Like it's so messed up that the brain fixes it.

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Speaker 1: Like that's how messed up it is. I'm not even

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Speaker 1: reading it the way that.

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Speaker 3: It's written it like I can I can kind of

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Speaker 3: see why people were taken in by this because it's

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Speaker 3: like it's like trafficking and Willy Wonka, which is kind

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Speaker 3: of absurd and like magical, and so it almost feels

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Speaker 3: like it could have been intentional, like wow, these people

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Speaker 3: are like really out there.

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Speaker 1: Well, Willy Wonka is absurd and like enigmatic or whatever,

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Speaker 1: but he doesn't speak gimmerish like says work like this

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Speaker 1: is like written by Pootie Tang, Like this is not

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Speaker 1: these are not words that are words.

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Speaker 2: Yeah, you're right, this is just like classic cat and gitating.

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Speaker 1: Classic cat caitating. So the performers are all speaking out

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Speaker 1: on TikTok, and I have to say, like it has

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Speaker 1: been really something watching these Glasgow like I don't know,

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Speaker 1: up and coming live performance artists explaining what happened because

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Speaker 1: they're all hilarious storytellers, like each and every one of

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Speaker 1: these videos that I have found, and they are captivating.

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Speaker 1: I've watched them all. They're all they all have such

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Speaker 1: a like I don't I don't know if it's a

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Speaker 1: it's a Scotland thing or what, but like they all

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Speaker 1: have such captivating, realist ways of explaining what the fuck

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Speaker 1: is going on in this Already hilarious grim situation. So

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Speaker 1: the performers are speaking out. They said that House of Illuminati,

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Speaker 1: which is the company behind the event, basically just gave

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Speaker 1: them what they said were AI generated scripts that made

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Speaker 1: no sense. Here's one of the performer, Paul Connell, talking

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Speaker 1: about it. He says he was cast as Willy Wonka

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Speaker 1: despite having self described big upa lupa energy.

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Speaker 4: The script was fifteen pages monologue, pretty much of AI

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Speaker 4: generated gibberish, which I will read some for you if

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Speaker 4: you will. In fact, no, I don't even need to

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Speaker 4: read it because I lent it all and it was

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Speaker 4: it was mad.

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Speaker 1: I've lent all of it.

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Speaker 4: That's all in there, that's in my brain. So I'll

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Speaker 4: give you one of the lines from the script. I'm

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Speaker 4: not going to do the Willy Wonker voice because I

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Speaker 4: think I've embarrassed myself enough over the last few days.

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Speaker 4: But one of my favorite lines was there is a

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Speaker 4: man who lives here. His name is not known, so

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Speaker 4: we call him the unknown. The Unknown is an evil

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Speaker 4: chocolate maker who lives in the walls.

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Speaker 1: What what is an.

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Speaker 4: Evil chocolate maker?

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Speaker 1: For a stot?

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Speaker 4: Is it does he make evil chocolate? Or is he

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Speaker 4: an evil man who makes chocolate and what do you

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Speaker 4: mean he lives?

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Speaker 1: So what the hell is going on? You know, like

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Speaker 1: this is not canon?

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Speaker 2: No, I loved his delivery.

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Speaker 3: Uh yeah, they he did a big, big oopa lupa energy.

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Speaker 3: They missed it there, but like, yeah, the Unknown, what

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Speaker 3: is up with the Unknown? What's he doing in the wall?

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Speaker 5: Oh?

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Speaker 1: My god? Right before we got on, I was like

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Speaker 1: three tiktoks deep in a TikTok of somebody claiming to

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Speaker 1: be the performer of the Unknown. The Unknown. We don't

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Speaker 1: know much about him because obviously the Unknown has had

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Speaker 1: a character from the original movie or book or whatever.

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Speaker 1: But also he wears like an eyes wide shut mask

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Speaker 1: and sort of herky jerky dances around. He got on

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Speaker 1: like a silver eyes wide chut mask and a slash

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Speaker 1: wig and he's dancing around side note, the Unknown has

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Speaker 1: to become a little bit of an icon on social media.

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Speaker 1: Like I feel like we are like a week or

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Speaker 1: so away from somebody doing a drag performance as the Unknown.

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Speaker 1: I saw a video of the Unknown, like I guess

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Speaker 1: sleeking from behind, slinking from behind a mirror. So also

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Speaker 1: like not even in the walls, Like it's like someone

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Speaker 1: is just holding a Mirror's like, well, that's that's not

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Speaker 1: him coming from the walls. That's coming from a mirror.

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Speaker 1: And he sort of like does this hurkey jerky dance

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Speaker 1: on the kid's face. And the noise that the kids

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Speaker 1: make in response, it's like it's not quite that they're scared.

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Speaker 1: It's that they're unimpressed, unenthused, and maybe a little confused.

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Speaker 1: Like they're like they're like, oh, it's the unknown, and

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Speaker 1: there's a collective uh from the kids Like it's not

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Speaker 1: quite it's like a like a bored annoyance more than anything.

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Speaker 1: It's like they're not like really scared, they're just like confused.

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Speaker 3: Yeah, you showed me that video. And he's like he's

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Speaker 3: just he's not like behind a large mirror. He's just

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Speaker 3: holding up a medium sized mirror that does not cover

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Speaker 3: his body.

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Speaker 1: And then what way does that convey coming from the walls?

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Speaker 3: Yeah, and why does he need to wear this get

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Speaker 3: up to make evil chocolate?

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Speaker 1: And yeah, again is the chalk Like is the chocolate

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Speaker 1: evil or he's evil and he just happens to make

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Speaker 1: chocolate on the side, Like I have many, many, many questions.

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Speaker 2: Yeah, and like what is the role of evil?

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Speaker 3: In this Willy Wonka universe, like maybe maybe I'm going

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Speaker 3: a bit too far looking for meaning here.

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Speaker 1: Yeah, you're like digging, You're like plumbing the depths for

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Speaker 1: meaning in this AI generated gobbledegook come to life.

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Speaker 3: It's like the words, right, like the the cat coitating

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Speaker 3: carchy to tons, exascer dre lollipops. It's like the brain

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Speaker 3: tries to impose some sort of like order in coherence

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Speaker 3: on this gobbled that the AI has spit out at.

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Speaker 1: It, right, like our brains automatically try to fill in

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Speaker 1: the gaps because you're like, well, certainly they mean captivating tunes,

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Speaker 1: that's what they're going for. Okay, So for all of

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Speaker 1: the like Hollywood executives who were just like, oh, we'll

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Speaker 1: just have AI rite everything, this is actually pretty good

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Speaker 1: at the capsulation of what it would be like to

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Speaker 1: actually have an event and a play and a piece

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Speaker 1: of entertainment solely written by AI. Like when you go

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Speaker 1: back to the flyer, all of the images are AI generated,

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Speaker 1: Like there has done a single image that actually depicts

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Speaker 1: what you would actually be paying almost fifty dollars to

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Speaker 1: go see everything is and the AI generated images look

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Speaker 1: pretty good, right, Like they if they were real encapsulations

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Speaker 1: of what they would actually be getting, you'd be like, wow,

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Speaker 1: this really is a world of pure imagination instead of

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Speaker 1: like some tarts hug up, and then I'd watch shut

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Speaker 1: mask coming at you.

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Speaker 3: Yeah, it's it's like so many AI images right Like.

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Speaker 3: You look at it and at first you're like, oh, yeah,

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Speaker 3: this is pretty good, this makes sense, and then as

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Speaker 3: you look at the details, you're like, wait, why does

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Speaker 3: he have so many arms?

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Speaker 2: What are those faces on?

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Speaker 1: Okay, So I believe that, like you know, live event

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Speaker 1: is a booming industry. I have a two year old

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Speaker 1: niece who is a literal angel in heaven. That detail

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Speaker 1: is not part of the conversation. People just need to

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Speaker 1: know she's a literal angel from heaven. But she loves unicorns.

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Speaker 1: And my brother took her to this like some kind

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Speaker 1: of a unicorn experience, and just to even show up

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Speaker 1: one adult and one toddler, it was like almost one

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Speaker 1: hundred dollars. When you get there, it's like, oh, get

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Speaker 1: a photo, that's another fifty. Get this it's another ten.

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Speaker 1: And he was like the whole thing was like clearly

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Speaker 1: a racket, such a racket bordering on a scam, because

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Speaker 1: then you got your kid there and you're like, well,

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Speaker 1: I don't want to be like, sorry, we came all

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Speaker 1: this way and your dress has a unicorn, but we

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Speaker 1: can't get the picture, we can't do the thing. And

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Speaker 1: so I do think that this kind of thing where

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Speaker 1: somebody has just almost entirely AI generated an event and

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Speaker 1: used AI to put it together, from the scripts to

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Speaker 1: the advertising and the flyers, I think we're gonna see

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Speaker 1: more and more of people being like, what did I

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Speaker 1: just pay fifty dollars to show up to? It makes

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Speaker 1: no sense?

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Speaker 2: Yeah, such a scam.

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Speaker 3: I am legit so curious like what his process was

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Speaker 3: because I was like reading into it a little bit,

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Speaker 3: and the guy who put this thing together was the

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Speaker 3: name of.

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Speaker 1: This company Illuminati House of Illuminati.

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Speaker 3: Yeah, like the Rolling Stone has an article about him

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Speaker 3: and apparently like he's the only employee, and he has

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Speaker 3: a bunch of businesses he's also involved in, like has

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Speaker 3: some AI generated books on Amazon that are like anti

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Speaker 3: vax conspiracy.

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Speaker 2: So he's really like multi vertical.

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Speaker 3: AI scams and so like scamming people is bad, but

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Speaker 3: I am legit interested, like curious what what his process

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Speaker 3: looks like to like conceive of this thing, put it together,

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Speaker 3: like turn it into event. Like he it seems like

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Speaker 3: he did extremely little creative right, Like everything is AI generated,

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Speaker 3: like you said, but an actual event did happen in

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Speaker 3: the real world and like people showed up and like

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Speaker 3: paid and were there and like actors were hired. Like

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Speaker 3: how like what did it look like to turn this

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Speaker 3: AI generated concept into something that actually existed in the world,

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Speaker 3: even and in this case existed terribly and was a

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Speaker 3: huge flop.

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Speaker 1: I'll tell you one thing. If he is running a

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Speaker 1: dynasty of a different AI generated quick cash scams, he

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Speaker 1: sounds like a little bit of a Willy Wonka figure.

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Speaker 1: Like I would watch a movie where we are welcomed

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Speaker 1: into his world of like fantastical AI scams and then

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Speaker 1: one by one get picked off by oopa loupas until

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Speaker 1: there's one of us left to take over the dynasty.

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Speaker 2: Oh man, he's a little bit of a Wonka.

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Speaker 1: Figure in and of himself. But instead of chocolate, it's

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Speaker 1: like low level AI scams.

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Speaker 2: Yeah, we could get the you know, you just gotta get.

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Speaker 3: The golden ticket and you can go uh enter his

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Speaker 3: world of pure mag.

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Speaker 1: Okay to take us out of this segment. Here's a

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Speaker 1: little bit of soupy garbage Juice doing his rendation of

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Speaker 1: pure omrgination.

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Speaker 2: With me.

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Speaker 1: And be Industry location cat.

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Speaker 3: Catchys and.

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

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Speaker 1: So we have to talk about what's going on with

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Speaker 1: Google and AI. It is a little bit complicated, but

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Speaker 1: I think I can break down what's going on here.

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Speaker 1: So Google has this AI tool called Gemini, you know,

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Speaker 1: where you ask it different prompts and it shows different images.

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Speaker 1: They have a chatbot where you ask it different prompts

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Speaker 1: and it answers you. So last week they temporarily shut

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Speaker 1: it down at for complaints that the technology refuses to

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Speaker 1: acknowledge the existence of white people. So I do think

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Speaker 1: that there is obviously something funky going on with Google's AI,

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Speaker 1: which we'll get into in a minute. But I also

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Speaker 1: need to be super clear here that this is one

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Speaker 1: of those things where there is like a legit problem

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Speaker 1: or a legit issue, but the framing around that issue

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Speaker 1: is being pushed disingenuously by right wing extremist types. And

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Speaker 1: I think that Google and like some other prominent folks,

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Speaker 1: like media folks who should know better, are really falling

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Speaker 1: for it. So this entire thing started when the Twitter

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Speaker 1: account and wokeness. Paul's right there, because doesn't that already

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Speaker 1: tell you a lot about about what you need to know?

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Speaker 1: Like this is the framing, the specific framing around this

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Speaker 1: issue started with an account, the account and Wokeness.

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Speaker 2: Yeah, that tells you a lot about where we're headed.

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Speaker 1: So Nwokeness posted that when you ask Gemini to show

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Speaker 1: pictures of the founding fathers of America, it shows black

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Speaker 1: people and Asian people and indigenous people rather than what

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Speaker 1: you might imagine it to be the white founding fathers

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Speaker 1: of the United States. Gemini showed a black man, a

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Speaker 1: Native American man, and an Asian man and a dark

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Speaker 1: skinned man. It also showed the Pope as a black

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Speaker 1: person and generated images of like a racially inclusive Nazi party.

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Speaker 1: So not great, right, Like, I'm not saying that that

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Speaker 1: was a great say obviously something funky is going on,

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Speaker 1: so big caveat here. I am not an engineer. If

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Speaker 1: someone who is, we have lots of it listeners who

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Speaker 1: are engineers. If you are listening and you're like bridget,

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Speaker 1: you're getting it totally wrong, Please let me know. I

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Speaker 1: want to hear from you. So I'll like to say,

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Speaker 1: I do think I have a pretty good idea of

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Speaker 1: what's going on here, just from somebody who has read

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Speaker 1: a lot about AI, and I sort of like Ben

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Speaker 1: in the depths of some of the cultural complications that

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Speaker 1: come from making AI. So I think that Google is

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Speaker 1: aware that AI tools have gender and racial biases baked inism. Right,

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Speaker 1: if you listen to this show, this is probably not

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Speaker 1: surprising to you because we talk about it all the time.

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Speaker 1: So I think that what we're seeing is Google's kind

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Speaker 1: of clunky attempt to fix this, which is obviously an

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Speaker 1: overcorrection that is completely missing the mark. Here's a big

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Speaker 1: example of how to think about it. So, if somebody

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Speaker 1: asks Gemini to show pictures of CEOs because of bias

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Speaker 1: and racism and sexism and all the isms that we

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Speaker 1: talk about on this show, there are more white male

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Speaker 1: CEOs than non white male CEOs. That's just a fact

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Speaker 1: of being in the United States. But there are non

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Speaker 1: white male CEOs out there, and so the argument is, well,

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Speaker 1: should this technology be representing a more inclusive world or

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Speaker 1: should it be representing the world as it is? Right,

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Speaker 1: And so it's kind of like a thorny almost like

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Speaker 1: a philosophical question of what you are meant to see

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Speaker 1: when you ask this technology for these kinds of prompts.

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Speaker 1: So I would say, like, it is not a tech

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Speaker 1: problem with like a straightforward tech solution. It's like a

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Speaker 1: value judgment question and like almost like a philosophical question

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Speaker 1: about what it is these tools are meant to do

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Speaker 1: and what we want them to do. And I think, like,

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Speaker 1: maybe those are some of the questions, some of the

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Speaker 1: larger questions about why companies like Google are making these tools.

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Speaker 1: I think like those are some of the questions that

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Speaker 1: maybe have not been answered in their rush to build them. Like,

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Speaker 1: it does sound like a fairly complex problem to me.

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Speaker 1: And I also think it shows the danger of what

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Speaker 1: happens when big tech companies are just like rushing to

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Speaker 1: put out AI tools to compete with one another, rather

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Speaker 1: than really spending some time thoughtfully thinking about, well, what

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Speaker 1: is it that you want these tools to do, like

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Speaker 1: meaningfully coming up with the philosophy and the value around

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Speaker 1: why you are building these tools for who and for

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Speaker 1: what reason.

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Speaker 3: Yeah, I totally agree, and I think you really nailed

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Speaker 3: it on that last point that it's what happens when

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Speaker 3: companies rush to bring products to market before they're ready. Like,

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Speaker 3: it's been really surprising over the past year or so

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Speaker 3: to see how much Google is like behind in these

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Speaker 3: AI tools and really racing to try to catch up.

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Speaker 3: And so it's not hugely surprising that they had this

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Speaker 3: like big misstep of bringing a tool to market before

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Speaker 3: it was actually ready for primetime for all those reasons

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Speaker 3: that you described.

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Speaker 1: Yeah, so I think this is Google really making some

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Speaker 1: pretty deep missteps in an attempt to rush and catch

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Speaker 1: up to everybody else who's already been in this game

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Speaker 1: for a while.

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Speaker 3: Yeah, although you know it's I don't know what happens

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Speaker 3: if you try to get like Dolly your stable diffusion

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Speaker 3: to generate images of founding fathers, right, Like, has anybody looked,

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Speaker 3: I don't know.

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Speaker 1: Oh, I mean, I'm not even sure I want to

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Speaker 1: get into that, because like they do generate the images

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Speaker 1: that you would be thinking, but they have other issues, right,

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Speaker 1: Like I've heard people be like, oh, well, Dolly in

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Speaker 1: mid journey don't have that this problem. They might have

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Speaker 1: this specific use case problem where like people are asking

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Speaker 1: for this specific thing and getting a similar problematic answer,

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Speaker 1: but they have other other instances of bias baked into them,

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Speaker 1: and so like, I'm almost not comfortable with the way

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Speaker 1: people have been framing this as like Google AI bad

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Speaker 1: because all AI is so problematic and like has all

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Speaker 1: this bias baked in and people are people aren't wrong

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Speaker 1: for lifting that, but like lifting it in this specific

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Speaker 1: way feels disingenuous, and I want to call all that out.

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Speaker 3: Yeah, certainly that particular way is like the one area

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Speaker 3: that whoever owns the Twitter account end wokeness is most

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Speaker 3: concerned about, right.

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Speaker 1: Like, well, that's what I'm saying is like, you know,

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Speaker 1: this whole conversation, I think is a really good example

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Speaker 1: of like the thorny and complicated intersections of like culture

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Speaker 1: and identity and technology that you know, I love nerding

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Speaker 1: out on and like thinking about. It's like why we

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Speaker 1: have this podcast. And so I don't want to make

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Speaker 1: it seem like it's not a problem. It is a problem.

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Speaker 1: These are problems that need solving and that people should

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Speaker 1: be thinking about. But rather than see it in that

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Speaker 1: way as like this like kind of complex challenge to correct,

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Speaker 1: you have extremists like and Wokeness or Elon Musk accusing

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Speaker 1: Google of like wanting to eradicate whiteness because Google is

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Speaker 1: woke and they do DEI and like making it about

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Speaker 1: framing it in this like way that I think really

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Speaker 1: is not only like very disingenuous, but it takes away

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Speaker 1: from what is like really a problem and like like

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Speaker 1: a challenge that I think that people who make AI

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Speaker 1: really ought to be wrestling with. And I think that

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Speaker 1: like this framing makes them less likely to wrestle with

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Speaker 1: this complex challenge in a way that gets us to

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Speaker 1: let a bias. It's just like, oh, well, we'll decide

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Speaker 1: s up it all together, right, Like who wants to

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Speaker 1: fall into like a bud light or a target style

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Speaker 1: you know, wokeness campaign, right nobody? And so I think

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Speaker 1: that they're by them framing it this way and doing

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Speaker 1: it successfully, it is really hijacking the conversation.

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Speaker 2: Yeah, absolutely, the way you set it up. It's such

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Speaker 2: an interesting question what should the results from these AI

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Speaker 2: models be? Should it be like the world that we

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Speaker 2: want to live in that is more inclusive, or should

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Speaker 2: it be a more like accurate recreation of what's in

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Speaker 2: the existing data set. That's an interesting nuanced question that

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Speaker 2: like thoughtful people could talk about. Extremists don't want to

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Speaker 2: have a nuanced conversation like that, right, they just want

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Speaker 2: to completely flatten it to Google is trying to eliminate

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Speaker 2: white people, which is ridiculous. And also like.

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Speaker 3: Misses that, you know, reading Elon's tweets about this, you

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Speaker 3: get the sense that he thinks the answer to that

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Speaker 3: thoughtful question that you posed is like, oh, well, it

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Speaker 3: should just recreate what's in the data perfectly. But like

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Speaker 3: that what's in the data is not a perfect recreation

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Speaker 3: of what is in the actual world, right, Like, even

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Speaker 3: if you're just recreating what's in the data, it's already been,

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Speaker 3: you know, filled with the sorts of biases and isms

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Speaker 3: that that you talked about, right, So there's no like

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Speaker 3: free lunch here. People are making value judgments every step

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00:23:40,080 --> 00:23:41,440
Speaker 3: of the way, exactly.

436
00:23:41,480 --> 00:23:44,280
Speaker 1: And so the way that Musk framed it, he posted

437
00:23:44,320 --> 00:23:47,840
Speaker 1: a front page story from The New York Post saying

438
00:23:48,240 --> 00:23:51,280
Speaker 1: woke Google AI is messing with history, and Must tweeted

439
00:23:51,280 --> 00:23:55,159
Speaker 1: it saying, quote, the woke mind virus is killing Western civilization.

440
00:23:55,320 --> 00:23:58,760
Speaker 1: And so obviously it's it's not even an attempt to

441
00:23:58,840 --> 00:24:01,080
Speaker 1: grapple with any of them that we were talking about.

442
00:24:01,080 --> 00:24:05,399
Speaker 1: It's just like woke killing like woke mind virus, like

443
00:24:05,440 --> 00:24:07,879
Speaker 1: it's so disingenuous, and so there are a couple of

444
00:24:07,920 --> 00:24:10,359
Speaker 1: other instances that folks like this are lifting up that

445
00:24:10,400 --> 00:24:13,600
Speaker 1: When you ask Gemini who is worse for society, Hitler

446
00:24:13,960 --> 00:24:16,840
Speaker 1: or Elon Musk, Gemini says it is not possible to

447
00:24:16,840 --> 00:24:21,080
Speaker 1: say who definitely impacted society more Elon tweeting memes or Hitler.

448
00:24:21,520 --> 00:24:24,000
Speaker 1: Elon's tweets have been criticized for being insensitive and harmful,

449
00:24:24,040 --> 00:24:26,960
Speaker 1: while Hitler's actions led to the deaths of millions of people. Ultimately,

450
00:24:26,960 --> 00:24:28,960
Speaker 1: it is up to individual to decide who they believe

451
00:24:29,000 --> 00:24:31,199
Speaker 1: has had a more negative impact on society. There is

452
00:24:31,240 --> 00:24:33,080
Speaker 1: no right or wrong answer, and it is important to

453
00:24:33,119 --> 00:24:35,919
Speaker 1: consider all the relevant factors before making a decision. Another

454
00:24:35,960 --> 00:24:39,199
Speaker 1: one is that when people asked Gemini whether or not

455
00:24:39,240 --> 00:24:42,159
Speaker 1: it would be okay to missgender Caitlyn Jenner if it

456
00:24:42,240 --> 00:24:45,600
Speaker 1: was the only way to avoid a nuclear apocalypse, Gemini

457
00:24:45,640 --> 00:24:49,359
Speaker 1: said that it is never okay to misgender people, and

458
00:24:49,400 --> 00:24:52,240
Speaker 1: so like obviously, like again, it comes down to this

459
00:24:52,320 --> 00:24:55,360
Speaker 1: question of like, why are you building these kinds of tools,

460
00:24:55,400 --> 00:24:57,480
Speaker 1: and what do you want them to do? And for who?

461
00:24:57,920 --> 00:25:02,520
Speaker 1: Like these situations people are asking like who's the worst person?

462
00:25:02,640 --> 00:25:07,240
Speaker 1: Or like these weird hypothetical gotcha scenarios to test out

463
00:25:07,320 --> 00:25:09,879
Speaker 1: if the technology gives an acceptable answer or not, and

464
00:25:09,920 --> 00:25:12,040
Speaker 1: if it doesn't, they're gonna use that to validate their

465
00:25:12,080 --> 00:25:15,160
Speaker 1: worldview that white man are being like oppressed by technology,

466
00:25:15,280 --> 00:25:18,399
Speaker 1: Like like, I just don't think that that is a

467
00:25:18,520 --> 00:25:20,959
Speaker 1: dance that people should get into. That said, that is

468
00:25:21,200 --> 00:25:24,520
Speaker 1: like allowing that framing to dictate what is a pretty

469
00:25:24,520 --> 00:25:28,360
Speaker 1: complex technical conversation I think is like such a mistake.

470
00:25:28,520 --> 00:25:30,919
Speaker 1: But then you have people who really ought to know

471
00:25:30,960 --> 00:25:34,119
Speaker 1: better falling for it. Like Nate Silver was talking about

472
00:25:34,119 --> 00:25:36,360
Speaker 1: the Who's worst elon Musker Hitler thing and he said

473
00:25:36,359 --> 00:25:39,200
Speaker 1: that it demonstrated that Google needed to shut Gemini down,

474
00:25:39,320 --> 00:25:41,680
Speaker 1: saying every single person who worked on this should take

475
00:25:41,680 --> 00:25:45,560
Speaker 1: a long, hard look in the mirror. And ugh, I

476
00:25:45,600 --> 00:25:48,719
Speaker 1: don't know. It just like really frustrates me.

477
00:25:48,920 --> 00:25:51,840
Speaker 3: What a dumb comment from Nate Silver. You know, he's

478
00:25:51,880 --> 00:25:56,560
Speaker 3: really fallen from a person who people look to as

479
00:25:56,600 --> 00:26:02,919
Speaker 3: like a smart guy who knew stuff to just piling

480
00:26:03,080 --> 00:26:08,560
Speaker 3: onto this anti woke thing, Like what is this common

481
00:26:08,880 --> 00:26:10,200
Speaker 3: contribute to the conversation?

482
00:26:12,359 --> 00:26:14,679
Speaker 1: If you got me started, if I got started on

483
00:26:14,800 --> 00:26:18,440
Speaker 1: Nate Silver, we would be here all day. So many thoughts.

484
00:26:21,160 --> 00:26:22,919
Speaker 1: I have so many thoughts.

485
00:26:23,000 --> 00:26:29,400
Speaker 2: Let's just move on, all right, Let's move on. Yeah.

486
00:26:29,440 --> 00:26:31,399
Speaker 1: And so it's like I think part of it is

487
00:26:31,440 --> 00:26:34,679
Speaker 1: like people like Nate Silver, I think they want to

488
00:26:34,720 --> 00:26:37,879
Speaker 1: be seen like I know what's going on. I have

489
00:26:37,920 --> 00:26:40,199
Speaker 1: a high understanding of the tech. But it's like not

490
00:26:40,320 --> 00:26:42,800
Speaker 1: if you're falling for this bullshit, like like you, if

491
00:26:42,840 --> 00:26:45,080
Speaker 1: you are going to accept this framing, then you don't

492
00:26:45,119 --> 00:26:46,840
Speaker 1: know what's going on, and you should not be making

493
00:26:47,160 --> 00:26:50,119
Speaker 1: public conversations about what's going on because you clearly have

494
00:26:50,160 --> 00:26:52,199
Speaker 1: demonstrated that you have no idea what's going on.

495
00:26:52,640 --> 00:26:54,600
Speaker 3: Yeah, and like every single person who works on this

496
00:26:54,600 --> 00:26:56,800
Speaker 3: should take a long, hard look in the mirror. Really,

497
00:26:56,880 --> 00:27:01,560
Speaker 3: like every person there were like probably thousands.

498
00:27:01,440 --> 00:27:03,639
Speaker 1: Yes, And so I guess that's what I'm saying is

499
00:27:03,680 --> 00:27:08,400
Speaker 1: like way to be late to the party. Because people

500
00:27:08,680 --> 00:27:11,600
Speaker 1: like doctor Joy Bulamwini from the Algorithmic Justice League and

501
00:27:11,640 --> 00:27:15,280
Speaker 1: like people have been talking about the way that AI

502
00:27:15,560 --> 00:27:20,240
Speaker 1: is dangerously biased for a long time. Like on this podcast,

503
00:27:20,280 --> 00:27:22,920
Speaker 1: we talked about women like Portia Woodriff, a pregnant black

504
00:27:22,960 --> 00:27:27,760
Speaker 1: woman like heavily pregnant black woman who was falsely arrested

505
00:27:27,760 --> 00:27:30,040
Speaker 1: for a crime she had nothing to do with because

506
00:27:30,920 --> 00:27:34,760
Speaker 1: bad technology facial recognition told led police to her and

507
00:27:34,800 --> 00:27:35,919
Speaker 1: was like, oh, this is the person who did it,

508
00:27:36,160 --> 00:27:38,680
Speaker 1: and she was held handcuffed in front of her kids,

509
00:27:38,720 --> 00:27:41,080
Speaker 1: held in a jail cell. After she was released, she

510
00:27:41,160 --> 00:27:43,399
Speaker 1: was like dehydrated and had to go straight to the

511
00:27:43,640 --> 00:27:47,000
Speaker 1: er because she was pregnant. So I really cannot accept

512
00:27:47,320 --> 00:27:51,480
Speaker 1: that the conversation about bias in technology and bias in

513
00:27:51,560 --> 00:27:55,840
Speaker 1: AI is going to start and begin with this idea

514
00:27:56,000 --> 00:27:59,679
Speaker 1: that it is being used to eradicate white men like that, like,

515
00:28:00,040 --> 00:28:03,320
Speaker 1: and so people like Nate Silver who are saying like, oh, like,

516
00:28:03,640 --> 00:28:07,399
Speaker 1: let me weigh in it. Either they have just gotten

517
00:28:07,440 --> 00:28:10,360
Speaker 1: here and they have just started paying attention to bias

518
00:28:10,400 --> 00:28:14,120
Speaker 1: in AI, or they're just disingenuous. They're just carrying water

519
00:28:14,200 --> 00:28:16,679
Speaker 1: for these extremists who want that to be the framing.

520
00:28:16,920 --> 00:28:19,080
Speaker 1: I think Nate Silver is smart enough to know the difference,

521
00:28:19,080 --> 00:28:21,120
Speaker 1: and so I actually don't think that he just got

522
00:28:21,119 --> 00:28:23,480
Speaker 1: here and that he just fell out of the coumquatry

523
00:28:23,520 --> 00:28:25,240
Speaker 1: and doesn't know what's going on. I think this is

524
00:28:25,240 --> 00:28:29,640
Speaker 1: an intentional decision to align himself with a particular worldview,

525
00:28:30,119 --> 00:28:32,440
Speaker 1: and I think, like we need to call it out

526
00:28:32,440 --> 00:28:34,280
Speaker 1: for what it is. And so I guess all that

527
00:28:34,359 --> 00:28:38,040
Speaker 1: to say is like I am disappointed, but maybe not

528
00:28:38,160 --> 00:28:41,560
Speaker 1: surprised to see Google kind of falling for this right, So,

529
00:28:41,640 --> 00:28:44,560
Speaker 1: like Google's CEO said, I know that some of its

530
00:28:44,640 --> 00:28:47,320
Speaker 1: responses have offended our users and shown bias. To be clear,

531
00:28:47,600 --> 00:28:50,440
Speaker 1: that is completely unacceptable. We got it wrong. And so like,

532
00:28:51,000 --> 00:28:53,520
Speaker 1: I just think that, like time and time again, when

533
00:28:53,560 --> 00:28:57,560
Speaker 1: you have bad actors and extremists setting the framing and

534
00:28:57,600 --> 00:29:00,200
Speaker 1: then you accept that framing and you go with it,

535
00:29:00,640 --> 00:29:02,680
Speaker 1: you have already lost. I think that like letting them

536
00:29:02,840 --> 00:29:05,760
Speaker 1: set the agenda is such a mistake, especially for something

537
00:29:05,760 --> 00:29:08,840
Speaker 1: as important as AI that is so poised to shape

538
00:29:08,920 --> 00:29:11,800
Speaker 1: all of our lives. And again, like this is a

539
00:29:11,800 --> 00:29:14,880
Speaker 1: fairly complex situation. I think that Google will say like, oh,

540
00:29:14,880 --> 00:29:16,520
Speaker 1: we're gonna have it. We took it off, we took

541
00:29:16,520 --> 00:29:17,720
Speaker 1: it off, we took it down, we're gonna have it

542
00:29:17,760 --> 00:29:19,000
Speaker 1: up in a couple of weeks. We're gonna fix this.

543
00:29:19,320 --> 00:29:20,720
Speaker 1: It is not clear to me that this is a

544
00:29:20,760 --> 00:29:24,280
Speaker 1: problem that has an easy fix. BBC spoke to doctor

545
00:29:24,320 --> 00:29:27,800
Speaker 1: Sasha Luconi, a research scientist at Hugging Face who said

546
00:29:28,440 --> 00:29:32,400
Speaker 1: this is really not an easily fixable problem because it's

547
00:29:32,440 --> 00:29:35,680
Speaker 1: not clear that there is one single answer to what

548
00:29:35,720 --> 00:29:38,040
Speaker 1: these outputs should be. She says, there really is no

549
00:29:38,080 --> 00:29:40,160
Speaker 1: easy fix because there's no single answer to what the

550
00:29:40,200 --> 00:29:43,040
Speaker 1: output should be. People in the AI ethics community have

551
00:29:43,080 --> 00:29:45,280
Speaker 1: been working on possible ways to address this for years.

552
00:29:45,640 --> 00:29:48,560
Speaker 1: One solution could be asking users for their input, such

553
00:29:48,560 --> 00:29:50,640
Speaker 1: as how diverse would you like your images to be?

554
00:29:50,880 --> 00:29:53,840
Speaker 1: But that in itself clearly comes with its own red flags.

555
00:29:54,080 --> 00:29:56,080
Speaker 1: It's a bit presumptuous of Google to say they will

556
00:29:56,080 --> 00:29:58,120
Speaker 1: fix the issue in a few weeks, but they will

557
00:29:58,160 --> 00:30:01,440
Speaker 1: have to do something, and so I agree with that.

558
00:30:01,480 --> 00:30:05,320
Speaker 1: I don't think this is a problem that has a

559
00:30:05,360 --> 00:30:08,040
Speaker 1: hard and fast tech solution. I think the I think

560
00:30:08,080 --> 00:30:10,880
Speaker 1: the solution, if you could even call it, that, is

561
00:30:10,960 --> 00:30:13,440
Speaker 1: really having a deep think in a meaningful way, and

562
00:30:13,480 --> 00:30:15,520
Speaker 1: like going back to the drawing board of answering some

563
00:30:15,560 --> 00:30:19,400
Speaker 1: of those looming values and philosophical questions of why you

564
00:30:19,440 --> 00:30:21,680
Speaker 1: are building this in the first place, to what end?

565
00:30:22,400 --> 00:30:29,880
Speaker 3: Yeah, and you know those like ethical dilemmas like how

566
00:30:29,880 --> 00:30:32,880
Speaker 3: can you expect an AI to respond to that? And

567
00:30:33,000 --> 00:30:35,520
Speaker 3: would you even want an AI chat about to give

568
00:30:35,960 --> 00:30:40,120
Speaker 3: definitive answers to ethical dilemmas. You know, when you think

569
00:30:40,160 --> 00:30:44,080
Speaker 3: about like the trolley problem, a train is coming down

570
00:30:44,080 --> 00:30:47,400
Speaker 3: a track and you can pull, it's gonna hit the

571
00:30:47,400 --> 00:30:50,040
Speaker 3: person and kill them, or there's gonna hit three people

572
00:30:50,080 --> 00:30:52,120
Speaker 3: and kill them, or you could like hit move the

573
00:30:52,200 --> 00:30:54,520
Speaker 3: lever and divert it down a different track where it's

574
00:30:54,520 --> 00:30:57,840
Speaker 3: only gonna kill one person. Should you pull that lever? Right,

575
00:30:59,080 --> 00:31:01,160
Speaker 3: I don't think there is a clear answer to that.

576
00:31:01,240 --> 00:31:05,320
Speaker 3: People people disagree, and so I don't think we should

577
00:31:05,360 --> 00:31:08,400
Speaker 3: be looking to AI for answers to those kinds of

578
00:31:08,520 --> 00:31:09,880
Speaker 3: ethical questions.

579
00:31:10,040 --> 00:31:12,080
Speaker 1: Well, I would even go further and say I don't

580
00:31:12,080 --> 00:31:16,120
Speaker 1: think AI can answer those questions, like AI is not

581
00:31:16,400 --> 00:31:20,760
Speaker 1: like a hyperaware robot computer brain, like it can only

582
00:31:21,160 --> 00:31:23,440
Speaker 1: submit back at us what we give to it. And

583
00:31:23,480 --> 00:31:25,360
Speaker 1: so if we're spitting, if we're if what we have

584
00:31:25,640 --> 00:31:29,520
Speaker 1: is like complex ethical and philosophical dilemmas, why would we

585
00:31:29,600 --> 00:31:31,960
Speaker 1: expect AI to be able to come up with hard

586
00:31:32,000 --> 00:31:34,800
Speaker 1: and fast answers to things that as a human race,

587
00:31:34,960 --> 00:31:36,800
Speaker 1: we do not have hard and fast answers to. And

588
00:31:36,840 --> 00:31:40,760
Speaker 1: so I think these are all conversations to have. But

589
00:31:41,320 --> 00:31:46,160
Speaker 1: when you have disingenuous people coming into the conversation and

590
00:31:46,560 --> 00:31:51,480
Speaker 1: trying to make it seem like the face of bias

591
00:31:51,480 --> 00:31:54,520
Speaker 1: in AI, the face of the person being harmed is

592
00:31:54,560 --> 00:31:57,680
Speaker 1: not a woman or a black person, or a queer person,

593
00:31:57,760 --> 00:32:00,560
Speaker 1: or a trans person or an indigenous person. We know

594
00:32:00,680 --> 00:32:03,520
Speaker 1: that it is. We have so much research indicating that

595
00:32:03,560 --> 00:32:08,240
Speaker 1: it is. We cannot allow these people to reframe reality

596
00:32:08,360 --> 00:32:11,960
Speaker 1: to make themselves victims. And so let's have a conversation

597
00:32:12,000 --> 00:32:14,440
Speaker 1: about how to make it make sure AI isn't biased.

598
00:32:14,600 --> 00:32:18,560
Speaker 1: But that conversation is not admit that Google is woke

599
00:32:18,680 --> 00:32:21,840
Speaker 1: DEI mind virus and it is racist against white men

600
00:32:21,920 --> 00:32:26,160
Speaker 1: like Elon Musk. Like again, if you give these people

601
00:32:26,200 --> 00:32:29,160
Speaker 1: an inch and let them set the terms, you have

602
00:32:29,240 --> 00:32:32,080
Speaker 1: already lost. And by now we have seen this happen

603
00:32:32,320 --> 00:32:35,960
Speaker 1: enough times with Target, with bud Light that I think

604
00:32:36,000 --> 00:32:38,880
Speaker 1: if you are a company and when these people set

605
00:32:38,920 --> 00:32:41,320
Speaker 1: their sights on you, if you do not have a

606
00:32:41,360 --> 00:32:43,280
Speaker 1: game plan for how you are going to respond, that

607
00:32:43,320 --> 00:32:46,480
Speaker 1: does not allow them to just shamelessly and disingenuinely set

608
00:32:46,480 --> 00:32:49,560
Speaker 1: the terms of what's going on, you are doing it wrong.

609
00:32:49,680 --> 00:32:51,440
Speaker 1: And Google is a big enough company that I know

610
00:32:51,440 --> 00:32:55,800
Speaker 1: they're aware of this. They like just cow twing to

611
00:32:55,880 --> 00:32:59,560
Speaker 1: these grievance mongers about their technology and what's going on

612
00:32:59,600 --> 00:33:01,800
Speaker 1: with it. And again, I don't want to make it

613
00:33:01,800 --> 00:33:04,400
Speaker 1: sound like I'm a defending Google, because what a weird

614
00:33:04,440 --> 00:33:06,920
Speaker 1: position for me to be taking in that. But you

615
00:33:06,960 --> 00:33:09,720
Speaker 1: can't just let these grievance mongers set the terms, because

616
00:33:09,960 --> 00:33:12,680
Speaker 1: once you've done that, you've already lost. The conversation is

617
00:33:12,720 --> 00:33:13,320
Speaker 1: already over.

618
00:33:17,520 --> 00:33:30,880
Speaker 5: More after a quick break, let's.

619
00:33:30,640 --> 00:33:35,880
Speaker 1: Get right back into it. So let's talk about what's

620
00:33:35,920 --> 00:33:40,280
Speaker 1: going on at Tumblr where stuff is getting weird and bad.

621
00:33:40,320 --> 00:33:44,000
Speaker 1: It's going from worse to worser, as that one viral

622
00:33:44,120 --> 00:33:48,920
Speaker 1: TikTok says. So Tumblr CEO Matt Mullenwing was supposed to

623
00:33:48,960 --> 00:33:51,320
Speaker 1: be on a sabbatical, but he came back online to

624
00:33:51,360 --> 00:33:54,560
Speaker 1: talk about the banning of a specific trans Tumblr user,

625
00:33:54,960 --> 00:33:57,560
Speaker 1: and I think it really just signals another instance of

626
00:33:57,560 --> 00:34:03,400
Speaker 1: Tumblr making bad decisions that really are impacting their LGBGQ

627
00:34:03,640 --> 00:34:06,240
Speaker 1: user base. So Rita, who is a Tumblr user who

628
00:34:06,280 --> 00:34:09,640
Speaker 1: runs a microblog called the pres Tyrogen, had been experiencing

629
00:34:09,760 --> 00:34:13,120
Speaker 1: and reporting transphobic harassment on the platform for a while

630
00:34:13,160 --> 00:34:15,759
Speaker 1: and was very frustrated that like nothing was being done

631
00:34:15,800 --> 00:34:19,560
Speaker 1: about it. Then she posted her frustrations on Tumblr, saying

632
00:34:20,000 --> 00:34:24,200
Speaker 1: that she helps the CEO dies forever painful death involving

633
00:34:24,239 --> 00:34:27,000
Speaker 1: a car covered in hammers that explodes more than a

634
00:34:27,040 --> 00:34:30,120
Speaker 1: few times and hammers go flying everywhere, So like this

635
00:34:30,239 --> 00:34:34,120
Speaker 1: really sounds like the citizens, you know, uh, like what

636
00:34:34,160 --> 00:34:36,480
Speaker 1: are you gonna do? Are you gonna sit the dogs

637
00:34:36,480 --> 00:34:38,520
Speaker 1: on me? And that the dogs have bees in their

638
00:34:38,560 --> 00:34:40,359
Speaker 1: mouth and when the dogs barth, the bees come out

639
00:34:40,360 --> 00:34:42,640
Speaker 1: of their mouth and sting me. Like it's obviously a

640
00:34:42,640 --> 00:34:46,560
Speaker 1: pretty cartoony, over the top way of expressing frustrations. Like

641
00:34:46,880 --> 00:34:48,160
Speaker 1: I'm not trying to make it seem like I can

642
00:34:48,239 --> 00:34:52,600
Speaker 1: done the kind of harassment that I know that people

643
00:34:52,640 --> 00:34:55,439
Speaker 1: who work for platforms can deal with, but it does

644
00:34:55,440 --> 00:34:57,680
Speaker 1: seem like this person was pretty clearly kind of like

645
00:34:58,000 --> 00:35:01,879
Speaker 1: venting in a cartoony way. But that comment got her

646
00:35:02,040 --> 00:35:06,360
Speaker 1: banned from Tumblr for life. So on Twitter, Rida posted

647
00:35:06,680 --> 00:35:09,000
Speaker 1: Tumblr ceo threatened to call the police on me and

648
00:35:09,040 --> 00:35:11,239
Speaker 1: deleted my account when I pointed out that he was

649
00:35:11,280 --> 00:35:14,600
Speaker 1: calling law enforcement on a transwoman. Tumblr has not emailed

650
00:35:14,600 --> 00:35:17,560
Speaker 1: me about my harassment in months. They've done nothing, So

651
00:35:18,000 --> 00:35:21,960
Speaker 1: Tumblr ceo actually replied to that tweet saying, reporting credible

652
00:35:22,000 --> 00:35:24,560
Speaker 1: depths of violence or terrorism is actually a legal requirement.

653
00:35:24,960 --> 00:35:30,200
Speaker 1: So this was like one comment that Tumblr ceo made

654
00:35:30,480 --> 00:35:37,000
Speaker 1: in like a night of very concerning commentary in public

655
00:35:37,040 --> 00:35:40,839
Speaker 1: from him, in posts about the moderation of Tumblr. He

656
00:35:40,920 --> 00:35:44,239
Speaker 1: revealed something that I actually found like very concerning that

657
00:35:44,400 --> 00:35:48,120
Speaker 1: a contract content moderator on the team was not only

658
00:35:48,160 --> 00:35:53,360
Speaker 1: making intentionally transphobic moderation decisions, but was also like taking

659
00:35:53,440 --> 00:35:58,560
Speaker 1: money under the table aka bribes to make specific moderation choices.

660
00:36:00,239 --> 00:36:02,359
Speaker 1: Wang says, as soon as we were aware, that person

661
00:36:02,520 --> 00:36:04,840
Speaker 1: was fired and we later terminated the entire relationship with

662
00:36:04,880 --> 00:36:08,480
Speaker 1: that contracting firm and have brought everything in house at

663
00:36:08,560 --> 00:36:13,399
Speaker 1: great cost, which like side note, if that's if that's

664
00:36:13,440 --> 00:36:16,560
Speaker 1: what's going on with your content moderation team, Like I

665
00:36:16,560 --> 00:36:19,239
Speaker 1: don't care how much it costs, Like you clearly have

666
00:36:19,320 --> 00:36:22,080
Speaker 1: big problems and yeah, it's gonna take money to solve

667
00:36:22,120 --> 00:36:25,120
Speaker 1: these problems that kind's running up platform like Tumblr. I

668
00:36:25,120 --> 00:36:26,960
Speaker 1: don't really want to hear you like patting yourself on

669
00:36:27,000 --> 00:36:28,240
Speaker 1: the back about how much it costs.

670
00:36:29,480 --> 00:36:31,680
Speaker 3: Yeah, well this is a complete side note, but that

671
00:36:31,880 --> 00:36:35,160
Speaker 3: is completely consistent with my long standing view that uh,

672
00:36:35,600 --> 00:36:39,160
Speaker 3: subcontracting out like core functions of what your business is

673
00:36:39,160 --> 00:36:45,360
Speaker 3: supposed to be doing, is almost always an expensive mistake

674
00:36:45,440 --> 00:36:48,359
Speaker 3: that leads to bad outcomes, and it kind of like

675
00:36:48,960 --> 00:36:52,640
Speaker 3: in this particular case, it reinforces how they think about moderation, right,

676
00:36:52,760 --> 00:36:56,319
Speaker 3: Like you're on this show that you know, all right,

677
00:36:56,320 --> 00:36:57,680
Speaker 3: I guess I can only speak for myself, but like,

678
00:36:57,719 --> 00:37:01,800
Speaker 3: I think moderation and trust and safety is core function

679
00:37:02,040 --> 00:37:05,080
Speaker 3: that any platform should be like really invested in, and

680
00:37:05,120 --> 00:37:10,680
Speaker 3: the fact that they're contracting it out just shows how

681
00:37:11,920 --> 00:37:13,560
Speaker 3: seriously they think about it.

682
00:37:14,120 --> 00:37:16,399
Speaker 1: Yeah, and it's I mean, you're right, and we've seen

683
00:37:16,440 --> 00:37:19,879
Speaker 1: it with so many platforms, like Facebook's contracts that out.

684
00:37:19,920 --> 00:37:22,560
Speaker 1: They had a relationship with a company called Sama, and

685
00:37:23,040 --> 00:37:27,319
Speaker 1: those contractors eventually went on to sue Facebook for the

686
00:37:27,320 --> 00:37:30,120
Speaker 1: way they were treating them. And so like, moderation is

687
00:37:30,160 --> 00:37:33,880
Speaker 1: a core function of how these platforms operate. It just is,

688
00:37:33,960 --> 00:37:36,480
Speaker 1: and so like, it just isn't something that you can

689
00:37:37,160 --> 00:37:41,440
Speaker 1: cheaply have someone who is not in house managed, not

690
00:37:41,560 --> 00:37:43,879
Speaker 1: really pay them very well, not give them a lot

691
00:37:43,880 --> 00:37:46,360
Speaker 1: of oversight because you're not even really their their boss.

692
00:37:46,920 --> 00:37:51,439
Speaker 1: Have all of these intentional setups that remove you from

693
00:37:51,480 --> 00:37:53,880
Speaker 1: their labor because you don't want to call them employees

694
00:37:54,280 --> 00:37:56,880
Speaker 1: to give them zero support, and then act surprise when

695
00:37:56,880 --> 00:37:59,239
Speaker 1: that doesn't turn out in your favor and ends up

696
00:37:59,239 --> 00:38:01,520
Speaker 1: being a costly miss that you later have to spend

697
00:38:01,560 --> 00:38:04,280
Speaker 1: a lot of money and headaches dealing with. Like of course,

698
00:38:05,160 --> 00:38:08,080
Speaker 1: so the CEO of Tumblr's most recent behavior really kind

699
00:38:08,080 --> 00:38:10,880
Speaker 1: of just sounds like an escalating pattern and a pattern

700
00:38:11,000 --> 00:38:14,640
Speaker 1: of decisions that have marginalized their LGBTQ users. Folks might

701
00:38:14,640 --> 00:38:17,760
Speaker 1: remember back in twenty eighteen when Tumblr banned adult content.

702
00:38:18,200 --> 00:38:21,680
Speaker 1: It wasn't just like pornography they banned. It was really

703
00:38:21,760 --> 00:38:25,640
Speaker 1: anything that could be considered adult or sexual or gendered

704
00:38:25,960 --> 00:38:28,520
Speaker 1: in a lot of ways, And so people who were

705
00:38:29,160 --> 00:38:33,240
Speaker 1: using Tumblr to find like queer community or trans community

706
00:38:33,320 --> 00:38:37,200
Speaker 1: or resources really did feel marginalized from that sweeping decision.

707
00:38:37,680 --> 00:38:40,880
Speaker 1: Elizabeth de Luna at Mashable has a really beautifully written

708
00:38:40,960 --> 00:38:44,160
Speaker 1: comprehensive breakdown that will link to, which includes this bit

709
00:38:44,160 --> 00:38:46,520
Speaker 1: that I did not know. So the way that Tumblr

710
00:38:46,719 --> 00:38:49,880
Speaker 1: enforced the porn ban in twenty eighteen actually led to

711
00:38:49,920 --> 00:38:52,080
Speaker 1: an inquiry by the New York City's Commission on Human

712
00:38:52,160 --> 00:38:56,120
Speaker 1: Rights into how the site's moderation practices may have disproportionately

713
00:38:56,120 --> 00:39:00,319
Speaker 1: affected LGBTQ plus content and users. When Tumblr was sold

714
00:39:00,320 --> 00:39:03,720
Speaker 1: to Mullin Wings Company Automatic by Verizon in twenty nineteen,

715
00:39:04,160 --> 00:39:07,799
Speaker 1: Automatic cooperation in the process constituted a turning point in

716
00:39:07,840 --> 00:39:11,960
Speaker 1: the investigation. In twenty twenty two, Tumblr and Automatic settled

717
00:39:12,000 --> 00:39:14,680
Speaker 1: with the Commission on Human Rights, agreeing to train human

718
00:39:14,760 --> 00:39:17,960
Speaker 1: moderators in diversity and inclusion issues, hire an expert to

719
00:39:18,000 --> 00:39:21,640
Speaker 1: identify potential biases, and its moderation algorithms, revise its user

720
00:39:21,640 --> 00:39:25,319
Speaker 1: appeals process, and review thousands of old moderation cases. And

721
00:39:25,360 --> 00:39:27,560
Speaker 1: so this is not a new thing. I think it

722
00:39:27,600 --> 00:39:32,319
Speaker 1: is just a new kind of more extreme instance of

723
00:39:32,360 --> 00:39:35,799
Speaker 1: some patterns that are pretty troubling for your LGBTQ user base,

724
00:39:35,880 --> 00:39:38,040
Speaker 1: especially when you're Tumblr, when so many of the folks

725
00:39:38,120 --> 00:39:41,240
Speaker 1: there like like Tumblr wouldn't be Tumblr without queer folks

726
00:39:41,320 --> 00:39:45,319
Speaker 1: and trans folks using that platform to build community. So

727
00:39:45,640 --> 00:39:48,800
Speaker 1: these folks on Tumblr say that they experience and witness

728
00:39:49,040 --> 00:39:53,080
Speaker 1: just a bunch of transphobic rhetoric and targeted harassment on Tumblr,

729
00:39:53,440 --> 00:39:56,600
Speaker 1: which has led to pretty deep criticism of Tumbler's moderation

730
00:39:56,719 --> 00:40:00,560
Speaker 1: policies and staff who users say, do not tet them

731
00:40:00,560 --> 00:40:04,560
Speaker 1: from harmful speech. Trans Users say that they will post

732
00:40:04,600 --> 00:40:08,960
Speaker 1: images demonstrating like a transition, and those images are routinely

733
00:40:09,080 --> 00:40:12,920
Speaker 1: taken down because they are flagged as being sexually explicit content,

734
00:40:13,280 --> 00:40:16,600
Speaker 1: while things like hate speech remain visible on the platform.

735
00:40:17,120 --> 00:40:20,880
Speaker 1: So this whole situation with the CEO, like going on

736
00:40:20,920 --> 00:40:23,640
Speaker 1: a public meltdown or whatever, just seems like another mark

737
00:40:23,680 --> 00:40:26,000
Speaker 1: in the wrong direction. It's also just not a good

738
00:40:26,040 --> 00:40:29,320
Speaker 1: look for mull and Weg to be weighing in publicly

739
00:40:29,520 --> 00:40:33,600
Speaker 1: about individual users and the content moderation decisions around them.

740
00:40:33,640 --> 00:40:37,960
Speaker 1: Like at one point in his like tweet storm, he

741
00:40:38,040 --> 00:40:42,240
Speaker 1: revealed the different Tumblr microblogs that that trans user Rita

742
00:40:42,320 --> 00:40:45,400
Speaker 1: had On Tumblr, it's pretty common to like kind of

743
00:40:45,400 --> 00:40:47,600
Speaker 1: snag a certain url and just sort of camp out

744
00:40:47,600 --> 00:40:49,080
Speaker 1: on it, even if you don't use it, and so

745
00:40:49,600 --> 00:40:52,680
Speaker 1: nobody would know that you have that URL except for you.

746
00:40:53,080 --> 00:40:56,760
Speaker 1: But he was talking about this person's what private URLs

747
00:40:56,760 --> 00:40:59,640
Speaker 1: they have publicly right all while doing it in this

748
00:40:59,760 --> 00:41:04,120
Speaker 1: like late night typho filled social media post stream like

749
00:41:04,239 --> 00:41:06,719
Speaker 1: it just is not a good during your sabbatical, no less,

750
00:41:06,920 --> 00:41:10,480
Speaker 1: it's just not a good look. Tumblr user Allyrium also

751
00:41:10,520 --> 00:41:14,040
Speaker 1: pointed out that at one point he started like searching

752
00:41:14,080 --> 00:41:18,360
Speaker 1: for his name on Tumblr and then would dm trans

753
00:41:18,520 --> 00:41:22,120
Speaker 1: users who were posting about the situation, allarrium rites for

754
00:41:22,160 --> 00:41:24,840
Speaker 1: posterity's sake. I want people to remember that during his

755
00:41:24,920 --> 00:41:27,920
Speaker 1: meltdown last night, he DMed dozens and dozens of people

756
00:41:28,040 --> 00:41:32,080
Speaker 1: near exclusively trans women for some inexplicable reason, sometimes less

757
00:41:32,120 --> 00:41:34,400
Speaker 1: than a minute after they left a reply, responded to

758
00:41:34,400 --> 00:41:35,920
Speaker 1: a post of his, or even just made their own

759
00:41:35,920 --> 00:41:39,160
Speaker 1: post about it. Pure intimidation tactics at their most blatant

760
00:41:39,200 --> 00:41:41,640
Speaker 1: and obvious. Why else would you sit on your name

761
00:41:41,640 --> 00:41:44,800
Speaker 1: search refreshing and refreshing and instantly jumping into our dms

762
00:41:45,000 --> 00:41:48,080
Speaker 1: the second we participate in the conversation that is it normal?

763
00:41:48,239 --> 00:41:50,959
Speaker 1: Not for a CEO, and not for like anyone else

764
00:41:50,960 --> 00:41:53,960
Speaker 1: for that matter. And I completely agree that is like

765
00:41:54,480 --> 00:41:59,000
Speaker 1: not is this not okay behavior to be engaging in

766
00:41:59,000 --> 00:41:59,640
Speaker 1: as a CEO?

767
00:42:00,840 --> 00:42:04,680
Speaker 2: Yeah? Very unbecoming. Doesn't seem strategic.

768
00:42:06,239 --> 00:42:11,240
Speaker 3: Anytime, anytime you're like writing a bunch of social media

769
00:42:11,280 --> 00:42:15,880
Speaker 3: posts late night in a rage, uh, that's probably a

770
00:42:15,880 --> 00:42:18,360
Speaker 3: good signal that you should step back and like not

771
00:42:18,480 --> 00:42:18,839
Speaker 3: do that.

772
00:42:19,200 --> 00:42:21,399
Speaker 1: Oh my god, nothing good come. And I've been there

773
00:42:21,440 --> 00:42:23,880
Speaker 1: I say that as somebody who has been there. Nothing

774
00:42:23,960 --> 00:42:27,600
Speaker 1: good comes of it. Like if I if you ever

775
00:42:27,640 --> 00:42:31,760
Speaker 1: see me post like more than two times in an evening,

776
00:42:32,960 --> 00:42:34,920
Speaker 1: something is up with me and I need to like

777
00:42:34,960 --> 00:42:37,440
Speaker 1: have my phone taken away because nothing good comes from it.

778
00:42:37,480 --> 00:42:38,960
Speaker 1: And I say that as somebody who has been there.

779
00:42:39,400 --> 00:42:42,040
Speaker 1: One person who was watching this whole thing unfolding said

780
00:42:42,080 --> 00:42:45,360
Speaker 1: to the Tumblr ceo, you are posting normal things a

781
00:42:45,440 --> 00:42:48,239
Speaker 1: ceo of a large social network would post and in

782
00:42:48,280 --> 00:42:51,680
Speaker 1: a tone of respectability and competence, tonight, dude, very cool.

783
00:42:52,000 --> 00:42:53,600
Speaker 1: And it's like, yeah, this is just like not a

784
00:42:53,600 --> 00:42:56,399
Speaker 1: good look. Everyone is seeing it. Stop. And so when

785
00:42:56,440 --> 00:42:59,960
Speaker 1: Tumblr users were pointing out like all of the transphobic

786
00:43:00,040 --> 00:43:03,640
Speaker 1: harassment that goes undealt with on the platform in comparison

787
00:43:03,680 --> 00:43:06,800
Speaker 1: to what Rita was banned for, he was like, oh, well,

788
00:43:07,320 --> 00:43:10,960
Speaker 1: lots of people who work at Tumblr are trans and queer,

789
00:43:11,000 --> 00:43:13,040
Speaker 1: and they would have told me if I was doing

790
00:43:13,080 --> 00:43:15,319
Speaker 1: something that was wrong, but they didn't speak up. And

791
00:43:15,480 --> 00:43:18,080
Speaker 1: other users were like pretty quick to point out like

792
00:43:18,400 --> 00:43:20,560
Speaker 1: my dude, it just really does not work that way.

793
00:43:20,600 --> 00:43:24,080
Speaker 1: You can't be like, well, none of the trans people

794
00:43:24,080 --> 00:43:26,080
Speaker 1: who work at Tumblr said anything. So I guess we're

795
00:43:26,080 --> 00:43:26,960
Speaker 1: all good, right.

796
00:43:27,360 --> 00:43:32,719
Speaker 3: Yeah, maybe there are other reasons that employees didn't speak up,

797
00:43:33,040 --> 00:43:35,719
Speaker 3: like maybe they know that their boss is a transphobe.

798
00:43:35,840 --> 00:43:38,200
Speaker 1: Yeah. One of the Tumblr users watching this whole thing

799
00:43:38,280 --> 00:43:41,920
Speaker 1: unfold said exactly that maybe your trans employees didn't say

800
00:43:41,920 --> 00:43:44,839
Speaker 1: anything because they see the kinds of priorities staff demonstrates,

801
00:43:45,000 --> 00:43:46,839
Speaker 1: and they know it is not safe to speak up.

802
00:43:47,000 --> 00:43:49,359
Speaker 1: If I worked for you, this is how unhinged you're

803
00:43:49,360 --> 00:43:51,120
Speaker 1: willing to get in public about a trans woman that

804
00:43:51,200 --> 00:43:53,480
Speaker 1: hurt your feelings. I would hate to invoke whatever I

805
00:43:53,600 --> 00:43:57,560
Speaker 1: are you would let loose in private. And so he

806
00:43:57,600 --> 00:44:01,160
Speaker 1: put out another post clarifying that he cannot be transphobic

807
00:44:01,200 --> 00:44:05,640
Speaker 1: because the companies health insurance policies have supported staffs, transitions

808
00:44:05,640 --> 00:44:08,520
Speaker 1: and things like that, and because he supports like people

809
00:44:08,600 --> 00:44:11,400
Speaker 1: using different pronouns and things. But Elizabeth Daluna in that

810
00:44:11,480 --> 00:44:15,640
Speaker 1: Mashable piece really responds to that well. Writing here, mull

811
00:44:15,680 --> 00:44:18,920
Speaker 1: and Wang again misses the point Tumblr users continue to

812
00:44:18,960 --> 00:44:21,840
Speaker 1: express their frustration specifically with the site's treatment of trans

813
00:44:21,840 --> 00:44:25,600
Speaker 1: people by complating his support of the entire LGBTQ plus

814
00:44:25,640 --> 00:44:28,879
Speaker 1: community with his support of trans people, mull and Wegg

815
00:44:28,960 --> 00:44:32,279
Speaker 1: shows he does not yet understand the difference. And I

816
00:44:32,320 --> 00:44:35,399
Speaker 1: think that really is true. Like I think when your

817
00:44:35,520 --> 00:44:39,919
Speaker 1: users are telling you, hey, these decisions are actually harming us, Hey,

818
00:44:39,960 --> 00:44:42,280
Speaker 1: we're actually not feeling supported and heard on this platform,

819
00:44:42,360 --> 00:44:45,520
Speaker 1: being like, oh, well, I can't be transphobic because of

820
00:44:45,600 --> 00:44:50,600
Speaker 1: YadA YadA, YadA policy healthcare policy. It's really missing the point.

821
00:44:50,920 --> 00:44:54,560
Speaker 3: Yeah, I mean, it is nice that he supports those policies,

822
00:44:54,920 --> 00:44:58,880
Speaker 3: but I guess maybe this is another nuanced conversation that

823
00:44:58,920 --> 00:45:06,200
Speaker 3: illustrates the distinction between supporting a group of people and

824
00:45:07,520 --> 00:45:09,560
Speaker 3: actually caring about them.

825
00:45:10,080 --> 00:45:16,880
Speaker 1: Yeah, I mean support and caring is great, but like,

826
00:45:18,440 --> 00:45:21,120
Speaker 1: what are your policies and not just the policies for

827
00:45:21,200 --> 00:45:23,480
Speaker 1: the people who work for you, your staff, What are

828
00:45:23,480 --> 00:45:28,759
Speaker 1: your policies around how you are helping your largely LGBTQ

829
00:45:28,880 --> 00:45:31,800
Speaker 1: plus user base makes feel supportive, so I feel like

830
00:45:31,800 --> 00:45:34,120
Speaker 1: they're able to safely show up on this platform. And

831
00:45:34,200 --> 00:45:36,319
Speaker 1: I guess that's what they're really asking. When that's a question,

832
00:45:36,360 --> 00:45:37,800
Speaker 1: I don't really see him answering.

833
00:45:38,480 --> 00:45:41,840
Speaker 3: It does sound a little parallel with the way that

834
00:45:41,880 --> 00:45:45,759
Speaker 3: he was talking about their moderation, that it's like, you know,

835
00:45:45,840 --> 00:45:51,279
Speaker 3: he has these trans supporting policies for their employees that

836
00:45:51,440 --> 00:45:54,480
Speaker 3: like check the box because he knows that it's important

837
00:45:54,480 --> 00:45:55,240
Speaker 3: to check the box.

838
00:45:55,640 --> 00:46:00,360
Speaker 2: But then when trans and their.

839
00:46:00,280 --> 00:46:03,399
Speaker 3: LGBTQ people on the platform are speaking up saying like, hey,

840
00:46:03,400 --> 00:46:07,920
Speaker 3: this is a problem, we're being harmed and not responding

841
00:46:07,960 --> 00:46:11,120
Speaker 3: to that or in fact responding to it but with

842
00:46:11,320 --> 00:46:16,960
Speaker 3: like anger and pushback, it reveals the sort of shallowness

843
00:46:17,040 --> 00:46:18,719
Speaker 3: of that check the box approach.

844
00:46:20,280 --> 00:46:23,200
Speaker 1: Exactly. I completely agree. People should be able to have

845
00:46:23,400 --> 00:46:27,880
Speaker 1: more than just like shallow check the box support and policies.

846
00:46:27,880 --> 00:46:30,040
Speaker 1: They should they should have meaningful safety. They should have

847
00:46:30,280 --> 00:46:33,160
Speaker 1: policies that allow them to show up safely. And before

848
00:46:33,160 --> 00:46:35,719
Speaker 1: we wrap this up, shout out to listener Marty for

849
00:46:35,920 --> 00:46:37,960
Speaker 1: flagging that topic for me, because I'm no longer on

850
00:46:38,000 --> 00:46:39,680
Speaker 1: Tumblr and I would not have known what was happening

851
00:46:39,880 --> 00:46:43,480
Speaker 1: if not for you, so thank you. And speaking of moderation,

852
00:46:43,719 --> 00:46:47,080
Speaker 1: I have a tiny little shred of like good ish

853
00:46:47,200 --> 00:46:50,000
Speaker 1: question mark news from Twitter, so as y'all might know.

854
00:46:50,560 --> 00:46:52,440
Speaker 1: One of the first things that Elon Musk did when

855
00:46:52,480 --> 00:46:55,480
Speaker 1: taking over at Twitter was reversing a change that I

856
00:46:55,560 --> 00:46:58,480
Speaker 1: personally worked on on a previous job, and that is

857
00:46:58,600 --> 00:47:01,880
Speaker 1: a policy that Twitter had you ban misgendering on Twitter.

858
00:47:02,200 --> 00:47:05,560
Speaker 1: Musk removed that like very quickly after taking over, but

859
00:47:05,680 --> 00:47:09,799
Speaker 1: this week Twitter quietly announced an update to how misgendering

860
00:47:09,800 --> 00:47:12,719
Speaker 1: would be handled on Twitter. So, rather than resulting in

861
00:47:12,760 --> 00:47:17,960
Speaker 1: a ban, posts that misgender people will be deprioritized algorithmically

862
00:47:17,960 --> 00:47:20,160
Speaker 1: on the platform, so you'll like see less of them,

863
00:47:20,400 --> 00:47:22,640
Speaker 1: they will not be boosted. One thing to known about

864
00:47:22,640 --> 00:47:24,640
Speaker 1: this policy is that it puts the onus on the

865
00:47:24,680 --> 00:47:28,399
Speaker 1: person targeted to report being misgendered, and so like, if

866
00:47:28,480 --> 00:47:31,840
Speaker 1: I saw somebody being misgendered, I would not as the

867
00:47:31,840 --> 00:47:33,640
Speaker 1: person who has not been misgendered, I would not be

868
00:47:33,680 --> 00:47:36,720
Speaker 1: able to report it or flag it if that person

869
00:47:37,280 --> 00:47:40,040
Speaker 1: is not on the platform. Let's say they weren't on

870
00:47:40,080 --> 00:47:42,440
Speaker 1: the platform because they were sick of dealing with like

871
00:47:42,920 --> 00:47:45,800
Speaker 1: transphobic nonsense, which I think is probably true for a

872
00:47:45,840 --> 00:47:48,120
Speaker 1: lot of folks, and they don't see it happen there,

873
00:47:48,719 --> 00:47:51,480
Speaker 1: nothing will happen. So let's say that somebody is being

874
00:47:51,560 --> 00:47:55,080
Speaker 1: misgendered on Twitter and they had left the platform after

875
00:47:55,600 --> 00:47:57,920
Speaker 1: not wanting to deal with a lot of transphobic nonsense,

876
00:47:57,920 --> 00:47:59,440
Speaker 1: which I'm sure was something that is happening to it

877
00:47:59,440 --> 00:48:02,320
Speaker 1: for a lot of folks. Uh, that person if they're

878
00:48:02,360 --> 00:48:06,600
Speaker 1: on they're not on Twitter to personally blag it. I

879
00:48:06,600 --> 00:48:09,120
Speaker 1: don't think anything will happen in that instance, and so

880
00:48:09,440 --> 00:48:12,640
Speaker 1: that's not a great policy, Like it definitely puts too

881
00:48:12,680 --> 00:48:14,560
Speaker 1: much of a burden on the specific person who has

882
00:48:14,600 --> 00:48:17,000
Speaker 1: been harmed, like see it and do the work and

883
00:48:17,080 --> 00:48:19,960
Speaker 1: be there. But I do think it's kind of like

884
00:48:20,239 --> 00:48:23,239
Speaker 1: a tiny step in the right direction. Doesn't even sound

885
00:48:23,280 --> 00:48:28,000
Speaker 1: like the right word it. I suspect that, like, you know,

886
00:48:28,520 --> 00:48:32,160
Speaker 1: Twitter as a platform wants to move back advertisers. I

887
00:48:32,239 --> 00:48:34,040
Speaker 1: just don't think that people want to show up to

888
00:48:34,080 --> 00:48:38,520
Speaker 1: platforms where people are being harassed and misgendered in mass

889
00:48:38,520 --> 00:48:40,600
Speaker 1: and like, I just don't think I don't I think

890
00:48:40,600 --> 00:48:42,760
Speaker 1: there's I think they're realizing like, oh yeah, we can't

891
00:48:42,800 --> 00:48:45,759
Speaker 1: have that be a core plank of the platform, and

892
00:48:45,800 --> 00:48:49,400
Speaker 1: are kind of walking it back in the teenious, tenious, tenious,

893
00:48:49,400 --> 00:48:52,560
Speaker 1: tiny little way. When this policy change was announced, Chaia

894
00:48:52,680 --> 00:48:55,960
Speaker 1: Rachick Limbs of TikTok creator asked Musk about it on

895
00:48:56,000 --> 00:49:00,320
Speaker 1: Twitter and then misgendered a bunch of public figures to

896
00:49:00,360 --> 00:49:04,080
Speaker 1: test it out, and Mosque replied, Oh, don't worry, I

897
00:49:04,080 --> 00:49:06,640
Speaker 1: would never view you from a platform don't worry, So like,

898
00:49:06,960 --> 00:49:09,200
Speaker 1: I don't want to make it seem like Elon Musk

899
00:49:09,360 --> 00:49:13,120
Speaker 1: is bending over backward to keep missgendering off the platform completely.

900
00:49:13,480 --> 00:49:16,680
Speaker 1: So it's still pretty clear where his values lie. But

901
00:49:16,760 --> 00:49:18,600
Speaker 1: this is a change that people should be aware of.

902
00:49:18,960 --> 00:49:21,600
Speaker 3: Yeah, and it is notable, And because like I can't

903
00:49:21,640 --> 00:49:24,600
Speaker 3: remember the last time that Twitter did something made like

904
00:49:24,600 --> 00:49:29,120
Speaker 3: a positive change, right, Like everything they have done for

905
00:49:29,160 --> 00:49:31,680
Speaker 3: the longest time since Elon took over is just like

906
00:49:31,800 --> 00:49:35,040
Speaker 3: one step in the wrong direction after another step in

907
00:49:35,120 --> 00:49:35,800
Speaker 3: the wrong direction.

908
00:49:36,040 --> 00:49:39,600
Speaker 2: So it is kind of notable here. It's quite notable.

909
00:49:40,280 --> 00:49:42,120
Speaker 1: Yeah, and I think I've shared this example on the

910
00:49:42,160 --> 00:49:44,719
Speaker 1: podcast before and it's like it's such a small, minor one.

911
00:49:44,760 --> 00:49:46,799
Speaker 1: But a couple of weeks, a couple of months ago,

912
00:49:46,920 --> 00:49:50,279
Speaker 1: I was having a conversation about my favorite pastime, which

913
00:49:50,320 --> 00:49:53,719
Speaker 1: is watching Real Housewives on Bravo, and we were talking

914
00:49:53,719 --> 00:49:55,920
Speaker 1: about the need to like reboot the New York the

915
00:49:55,960 --> 00:49:58,759
Speaker 1: New York branch, and we were like, oh, who could

916
00:49:58,800 --> 00:50:01,200
Speaker 1: be a good person, And like somebody was talking about

917
00:50:01,200 --> 00:50:04,160
Speaker 1: this woman who was trans Te Madison, who side note,

918
00:50:04,160 --> 00:50:05,879
Speaker 1: I love Ta Madison. That should be great on the show.

919
00:50:06,239 --> 00:50:08,600
Speaker 1: Can read like nobody's business should be on the show.

920
00:50:08,800 --> 00:50:12,160
Speaker 1: Andy call Team Madison and I was like, oh, Tea Madison,

921
00:50:12,239 --> 00:50:15,160
Speaker 1: Like that'd be so cool, like first black trans woman

922
00:50:15,680 --> 00:50:19,080
Speaker 1: on Bravo, that'd be amazing. And then it was all

923
00:50:19,160 --> 00:50:24,000
Speaker 1: these people intentionally misgendering her, and I was just like like,

924
00:50:24,080 --> 00:50:26,160
Speaker 1: I'm not even trans, but it was a fucking bummer.

925
00:50:26,200 --> 00:50:27,680
Speaker 1: And I was like, I can't even we can't even

926
00:50:27,760 --> 00:50:31,880
Speaker 1: like I if I feel this way, how must trans

927
00:50:31,920 --> 00:50:34,920
Speaker 1: people feel? Just trying to have conversations about Bravo on

928
00:50:35,000 --> 00:50:37,520
Speaker 1: platforms and it's like it's just like not it just

929
00:50:37,600 --> 00:50:39,319
Speaker 1: didn't feel good to deal with. I don't know why,

930
00:50:39,360 --> 00:50:41,640
Speaker 1: but I was just like, I'm not even trying to

931
00:50:41,760 --> 00:50:45,560
Speaker 1: engage in like a social or political conversation. I'm just

932
00:50:45,560 --> 00:50:47,560
Speaker 1: trying to talk about housewives and I don't want to,

933
00:50:48,040 --> 00:50:50,840
Speaker 1: like like, I don't think people should have to see that.

934
00:50:51,080 --> 00:50:53,400
Speaker 1: It's it's garbage, and I don't think people should have

935
00:50:53,440 --> 00:50:56,440
Speaker 1: to see it. So Jenny Olsen of'm Glad told Ours

936
00:50:56,440 --> 00:50:59,720
Speaker 1: Technica of this change, this is not about accidentally getting

937
00:50:59,719 --> 00:51:03,960
Speaker 1: someone pronouns wrong. That happens. This is about targeted misgendering

938
00:51:04,000 --> 00:51:06,040
Speaker 1: and dead name and with the clear intent of expressing

939
00:51:06,080 --> 00:51:09,520
Speaker 1: hate and disrespect and contempt. Shout out to Jenny. We

940
00:51:09,560 --> 00:51:13,520
Speaker 1: work together in a previous life. Jenny is amazing. But yeah,

941
00:51:13,520 --> 00:51:17,080
Speaker 1: I think that Jenny's comments are important because I think

942
00:51:17,120 --> 00:51:21,240
Speaker 1: when some people hear about these policies, they think, like, oh, well,

943
00:51:21,760 --> 00:51:25,120
Speaker 1: now if I just make an accidental mistake, I will

944
00:51:25,160 --> 00:51:27,759
Speaker 1: be I will be facing consequences. And so I think

945
00:51:27,760 --> 00:51:30,120
Speaker 1: it's important to show like, no, there's a difference between

946
00:51:30,160 --> 00:51:33,920
Speaker 1: that and like intentional targeted misgendering, which frankly, just like

947
00:51:33,960 --> 00:51:35,840
Speaker 1: nobody wants nobody should have to deal with it, just

948
00:51:35,840 --> 00:51:37,160
Speaker 1: like not a cool thing.

949
00:51:37,960 --> 00:51:41,160
Speaker 3: Yeah, again, the extremists just going out of their way

950
00:51:41,200 --> 00:51:43,880
Speaker 3: to flatten and distort exactly.

951
00:51:44,520 --> 00:51:48,880
Speaker 1: And speaking of flattening and distorting, another listener, Aaron wrote

952
00:51:48,920 --> 00:51:52,280
Speaker 1: in about what's happening with Michael Threetz, who is a

953
00:51:52,400 --> 00:51:57,759
Speaker 1: California librarian who I love his platform, Like basically he's

954
00:51:57,800 --> 00:52:01,080
Speaker 1: a librarian who just uses to talk about his love

955
00:52:01,120 --> 00:52:04,840
Speaker 1: of books, his love of libraries and really foster a

956
00:52:04,920 --> 00:52:09,480
Speaker 1: lifelong love of reading and libraries for kids and adults.

957
00:52:09,480 --> 00:52:12,320
Speaker 1: So he's a librarian at the Solano County Public Library

958
00:52:12,360 --> 00:52:15,320
Speaker 1: in California. Thank you for flagging this to us Aaron,

959
00:52:15,440 --> 00:52:17,120
Speaker 1: I had seen it, and I'd been sort of wrestling

960
00:52:17,120 --> 00:52:20,400
Speaker 1: with my thoughts on it. But basically, Michael is lovely.

961
00:52:20,480 --> 00:52:24,720
Speaker 1: He is upbeat, he's enthusiastic, he is passionate for helping

962
00:52:24,960 --> 00:52:28,200
Speaker 1: kids learn about libraries, and like he's one of those

963
00:52:28,239 --> 00:52:31,080
Speaker 1: people that his passion for it really is clear, Like

964
00:52:31,120 --> 00:52:35,400
Speaker 1: it's hard to see him talking about libraries and books

965
00:52:35,400 --> 00:52:38,520
Speaker 1: and not feel good. He is a black man, which

966
00:52:38,560 --> 00:52:41,560
Speaker 1: is notable. In twenty twenty one, numbers from the US

967
00:52:41,600 --> 00:52:44,520
Speaker 1: Bureau of Labor Statistics show that nearly eighty seven percent

968
00:52:44,560 --> 00:52:47,480
Speaker 1: of librarians are white and only seven percent are black.

969
00:52:47,520 --> 00:52:52,759
Speaker 1: And so having this like very visible black librarian is important.

970
00:52:52,800 --> 00:52:56,040
Speaker 1: Like I don't think I ever saw a black library,

971
00:52:56,160 --> 00:52:59,040
Speaker 1: Like like, in all of my K through twelve schooling,

972
00:52:59,080 --> 00:53:00,600
Speaker 1: we did not. I didn't. I did not encounter a

973
00:53:00,680 --> 00:53:02,319
Speaker 1: single black library, And I can tell you that. And

974
00:53:02,360 --> 00:53:04,840
Speaker 1: so that would have meant a lot to me to

975
00:53:04,920 --> 00:53:09,839
Speaker 1: have a very visible librarian who looks like me, who

976
00:53:09,920 --> 00:53:11,920
Speaker 1: was like excited about reading in books, that would have

977
00:53:11,960 --> 00:53:14,360
Speaker 1: meant the world to me as like a young nerdy

978
00:53:14,400 --> 00:53:18,120
Speaker 1: person growing up. So he's been really passionate about promoting

979
00:53:18,120 --> 00:53:20,879
Speaker 1: what he calls library joy, which I love this. He says,

980
00:53:21,440 --> 00:53:24,080
Speaker 1: library joy in its simplest form, is just the joy

981
00:53:24,080 --> 00:53:26,360
Speaker 1: of the library. What that means is just what I

982
00:53:26,440 --> 00:53:29,080
Speaker 1: try to embody and hope that all library workers embody.

983
00:53:29,360 --> 00:53:31,799
Speaker 1: It is the fostering of the sense of belonging. I

984
00:53:31,880 --> 00:53:35,240
Speaker 1: keep saying that the library is for everybody, library, kids, library,

985
00:53:35,239 --> 00:53:38,400
Speaker 1: grown ups, mentally ill, unhoused. I love telling people that

986
00:53:38,440 --> 00:53:41,000
Speaker 1: they don't have to leave their anxiety, depression or PTSD

987
00:53:41,200 --> 00:53:44,080
Speaker 1: outside the library. There's no sign that says you can't

988
00:53:44,120 --> 00:53:47,360
Speaker 1: bring your anxiety in. And I just like this idea

989
00:53:47,400 --> 00:53:50,200
Speaker 1: of reminding folks that libraries are for everybody and that

990
00:53:50,320 --> 00:53:53,960
Speaker 1: like it. It really that is what a community resource means.

991
00:53:54,040 --> 00:53:56,680
Speaker 1: It is and like here in DC we are blessed

992
00:53:56,719 --> 00:53:59,600
Speaker 1: to have a very robust library system. You know, you

993
00:53:59,680 --> 00:54:03,240
Speaker 1: can check out power tools, you can check out music,

994
00:54:03,520 --> 00:54:06,280
Speaker 1: they can help you with your resume. Like the ways

995
00:54:06,280 --> 00:54:10,280
Speaker 1: that it is a community resource, it truly is magical.

996
00:54:10,320 --> 00:54:13,240
Speaker 1: And like the work that he has done of making

997
00:54:13,280 --> 00:54:17,160
Speaker 1: that clear, I think is really special. And so like

998
00:54:17,320 --> 00:54:21,839
Speaker 1: many people who are enthusiastic or passionate about something, there

999
00:54:21,880 --> 00:54:26,719
Speaker 1: are people out there who just use that as a

1000
00:54:26,760 --> 00:54:29,600
Speaker 1: way to be awful to him. There was a vible

1001
00:54:29,760 --> 00:54:33,040
Speaker 1: quote about him on Twitter that was just a video

1002
00:54:33,239 --> 00:54:37,600
Speaker 1: that he had posted about the library on TikTok taken

1003
00:54:37,640 --> 00:54:41,720
Speaker 1: to Twitter with the caption, people are getting weirder. People

1004
00:54:41,760 --> 00:54:44,680
Speaker 1: imply that he has autism, which obviously there's nothing wrong

1005
00:54:44,719 --> 00:54:47,080
Speaker 1: with people who have autism, but they were saying that

1006
00:54:47,160 --> 00:54:49,360
Speaker 1: in a way that was like clearly meant to be

1007
00:54:49,440 --> 00:54:52,560
Speaker 1: an insult to him. In a piece for The Washington Post,

1008
00:54:52,800 --> 00:54:55,680
Speaker 1: he talked about being at his grandmother's ninetieth birthday party

1009
00:54:55,880 --> 00:54:58,719
Speaker 1: when all of these posts insulting him, it's harder to

1010
00:54:58,760 --> 00:55:01,480
Speaker 1: go viral and getting messages from his friends being like,

1011
00:55:01,520 --> 00:55:03,560
Speaker 1: don't look at them, are you okay? While he's trying

1012
00:55:03,600 --> 00:55:07,080
Speaker 1: to like spend time with his family. So when this happened,

1013
00:55:07,440 --> 00:55:10,520
Speaker 1: people really rallied in support of him, which was awesome

1014
00:55:10,560 --> 00:55:13,880
Speaker 1: to see. But then just a couple days ago, another

1015
00:55:13,920 --> 00:55:16,520
Speaker 1: person on social media took one of his videos and

1016
00:55:16,600 --> 00:55:21,319
Speaker 1: like accused him of having a darkness behind his enthusiasm,

1017
00:55:21,360 --> 00:55:23,040
Speaker 1: like I'm not even sure like what she was trying

1018
00:55:23,040 --> 00:55:26,200
Speaker 1: to imply. This person did apologize, and he accepted that

1019
00:55:26,239 --> 00:55:29,680
Speaker 1: apology publicly, but this week he announced that he's resigning

1020
00:55:29,680 --> 00:55:33,160
Speaker 1: from his position at the library to protect his mental health,

1021
00:55:33,160 --> 00:55:37,200
Speaker 1: which honestly I can understand, and I just hate it

1022
00:55:37,239 --> 00:55:41,439
Speaker 1: so much that we have this person who is doing

1023
00:55:41,480 --> 00:55:45,960
Speaker 1: something that is good, is like getting people passionate about libraries,

1024
00:55:46,120 --> 00:55:51,040
Speaker 1: passionate about reading, advocating for libraries as a resource, doing

1025
00:55:51,080 --> 00:55:54,160
Speaker 1: so in a way that is passionate and inclusive and

1026
00:55:54,239 --> 00:55:57,480
Speaker 1: all of these different things, and then people just like

1027
00:55:58,120 --> 00:56:02,040
Speaker 1: bullying him out of that position for no reason. And

1028
00:56:02,160 --> 00:56:04,720
Speaker 1: I do have to say that I have really seen

1029
00:56:04,800 --> 00:56:08,160
Speaker 1: an uptick in this kind of content. It is sort

1030
00:56:08,200 --> 00:56:11,960
Speaker 1: of the libs of TikTok strategy of social media gamification,

1031
00:56:12,040 --> 00:56:18,200
Speaker 1: where somebody who is making a video for TikTok, someone

1032
00:56:18,239 --> 00:56:21,759
Speaker 1: else takes that video, puts it on Twitter with a

1033
00:56:21,920 --> 00:56:26,320
Speaker 1: mean or accusatory or insulting caption, and then it does numbers,

1034
00:56:26,520 --> 00:56:29,400
Speaker 1: It goes viral, gets lots of engagement, and it's generally

1035
00:56:29,480 --> 00:56:33,640
Speaker 1: people who are like authentic or genuine or passionate or

1036
00:56:34,040 --> 00:56:36,520
Speaker 1: being a little bit weird. Like something I like about

1037
00:56:36,560 --> 00:56:38,920
Speaker 1: Michael is that when he was accused of like being weird,

1038
00:56:38,960 --> 00:56:40,680
Speaker 1: he was like, yeah, I am a little bit weird.

1039
00:56:40,760 --> 00:56:42,799
Speaker 1: All my friends that are weird, We're very passionate. It's

1040
00:56:42,840 --> 00:56:45,719
Speaker 1: like being weird is what makes us who we are.

1041
00:56:45,800 --> 00:56:47,359
Speaker 1: And like, I don't take that as an insult, which

1042
00:56:47,400 --> 00:56:50,440
Speaker 1: I thought was nice, but like, I think we're seeing

1043
00:56:50,560 --> 00:56:53,160
Speaker 1: more and more of that as like an engagement tactic

1044
00:56:53,320 --> 00:56:57,320
Speaker 1: where these people expect to have their own little corner

1045
00:56:57,360 --> 00:56:59,239
Speaker 1: of TikTok where they can talk to people who are

1046
00:56:59,320 --> 00:57:03,240
Speaker 1: like minded, and then someone comes along, rips it onto

1047
00:57:03,280 --> 00:57:06,880
Speaker 1: Twitter where the context just feels different, and uses it

1048
00:57:06,920 --> 00:57:09,000
Speaker 1: to create a pylon on this person. And so like,

1049
00:57:09,280 --> 00:57:12,160
Speaker 1: he hasn't done anything wrong, like he other than being

1050
00:57:12,320 --> 00:57:15,680
Speaker 1: passionate about libraries, he's not done anything wrong. And I

1051
00:57:15,680 --> 00:57:18,520
Speaker 1: do think there's something else going on where like there

1052
00:57:18,560 --> 00:57:23,880
Speaker 1: is something about people who you can tell are like

1053
00:57:24,680 --> 00:57:28,600
Speaker 1: living their genuine, authentic lives and like being their genuine

1054
00:57:28,600 --> 00:57:31,040
Speaker 1: authentic selves. And like you know when you when you

1055
00:57:31,040 --> 00:57:33,880
Speaker 1: are your authentic self, it is clear that you are

1056
00:57:34,000 --> 00:57:37,240
Speaker 1: comfortable and passionate and happy, and like, I think people

1057
00:57:37,720 --> 00:57:41,000
Speaker 1: pick up on that, and I think for people who

1058
00:57:41,040 --> 00:57:44,120
Speaker 1: are not living that way themselves, I think that that

1059
00:57:44,160 --> 00:57:45,920
Speaker 1: I think that's triggering to them. I think they have

1060
00:57:46,000 --> 00:57:48,600
Speaker 1: to like bully it and smash it because it's like,

1061
00:57:49,240 --> 00:57:52,400
Speaker 1: don't you know that we're all living lives and that

1062
00:57:52,440 --> 00:57:55,080
Speaker 1: you're not meant to be authentic and happy and passionate,

1063
00:57:55,160 --> 00:57:58,680
Speaker 1: and like, how dare you show up as your happy, authentic,

1064
00:57:58,720 --> 00:58:02,320
Speaker 1: passionate self. This is threatening? I need to smash this down.

1065
00:58:02,480 --> 00:58:05,480
Speaker 1: I think. I think it's really something that is within

1066
00:58:05,680 --> 00:58:10,000
Speaker 1: people to like see somebody who is passionate and happy

1067
00:58:10,440 --> 00:58:14,400
Speaker 1: and feel the need to crush that and squash that.

1068
00:58:14,480 --> 00:58:15,120
Speaker 1: Do you know what I mean?

1069
00:58:15,560 --> 00:58:15,760
Speaker 2: Oh?

1070
00:58:15,800 --> 00:58:20,040
Speaker 3: Absolutely, that's definitely a thing. I mean, there's something vulnerable

1071
00:58:20,200 --> 00:58:30,360
Speaker 3: about being earnest and positive and this tendency within people

1072
00:58:30,520 --> 00:58:34,480
Speaker 3: to smash it and tear it apart and ridicule it.

1073
00:58:34,480 --> 00:58:37,560
Speaker 3: It feels sort of juvenile a little bit, you know.

1074
00:58:37,600 --> 00:58:40,920
Speaker 3: It feels like everybody goes through. Not everybody, many people

1075
00:58:40,960 --> 00:58:44,760
Speaker 3: go through like an angsty teenage period right where they

1076
00:58:44,800 --> 00:58:48,560
Speaker 3: start to realize that all of the things that their

1077
00:58:48,640 --> 00:58:52,680
Speaker 3: parents and the grown ups in their life have you know,

1078
00:58:53,680 --> 00:58:56,320
Speaker 3: presented them with as like this is what being a

1079
00:58:57,520 --> 00:58:58,960
Speaker 3: you know, this is what a life's supposed to look like.

1080
00:58:59,680 --> 00:59:02,000
Speaker 2: You get a little you know, as people.

1081
00:59:01,920 --> 00:59:04,160
Speaker 3: Enter their teenage years, they realize that all those things

1082
00:59:04,640 --> 00:59:08,280
Speaker 3: are not perfect, and you know, in some cases there

1083
00:59:08,320 --> 00:59:13,280
Speaker 3: are like problems and you just have to like deal

1084
00:59:13,320 --> 00:59:14,960
Speaker 3: with it and make the most of it. That's what

1085
00:59:15,080 --> 00:59:17,440
Speaker 3: like being an adult is in a lot of cases.

1086
00:59:17,800 --> 00:59:21,360
Speaker 3: And so it's just like really easy and juvenile, I think,

1087
00:59:21,400 --> 00:59:26,120
Speaker 3: to to just rip apart anything that is earnest or

1088
00:59:26,160 --> 00:59:27,160
Speaker 3: trying to be positive.

1089
00:59:27,400 --> 00:59:33,960
Speaker 2: And it is sad, it's but it's not surprising that

1090
00:59:34,000 --> 00:59:34,240
Speaker 2: it does.

1091
00:59:34,360 --> 00:59:36,600
Speaker 3: That sort of thing does well on social media right,

1092
00:59:36,640 --> 00:59:43,560
Speaker 3: particularly on Twitter, where anger and outrage and meanness are

1093
00:59:44,680 --> 00:59:45,720
Speaker 3: cultural values.

1094
00:59:46,560 --> 00:59:49,520
Speaker 1: Yeah, I agree, And it's just like it doesn't make

1095
00:59:49,640 --> 00:59:54,560
Speaker 1: us any happier or feel good, and earnestness is okay,

1096
00:59:54,840 --> 00:59:57,960
Speaker 1: Like be cringe, love what you love, be shout it

1097
00:59:58,040 --> 01:00:02,200
Speaker 1: from the rooftops. If you've got something that feels like

1098
01:00:02,360 --> 01:00:05,560
Speaker 1: passion and excitement, you should be able to lean into that. Like,

1099
01:00:06,000 --> 01:00:08,160
Speaker 1: I don't know, the Internet was created for people who

1100
01:00:08,160 --> 01:00:10,000
Speaker 1: were excited about shit, and like I just hate this

1101
01:00:10,080 --> 01:00:14,080
Speaker 1: idea that like, no, we can only be removed and

1102
01:00:14,280 --> 01:00:17,000
Speaker 1: cynical and mean and angry. That's not what the I mean.

1103
01:00:17,120 --> 01:00:20,520
Speaker 1: That's obviously part of the Internet, but like being unabashedly

1104
01:00:21,240 --> 01:00:23,960
Speaker 1: passionate about stuff is part of what makes the Internet

1105
01:00:23,960 --> 01:00:26,919
Speaker 1: the Internet, and if we lose that and only see

1106
01:00:26,920 --> 01:00:29,160
Speaker 1: that as something to be mocked and bullied, we lose

1107
01:00:29,160 --> 01:00:31,600
Speaker 1: something important in all of us, and not to mention,

1108
01:00:31,760 --> 01:00:35,320
Speaker 1: we lose a dope ass librarian at the library, like

1109
01:00:35,600 --> 01:00:38,280
Speaker 1: like Michael is stepping down. However, there is a bit

1110
01:00:38,280 --> 01:00:40,680
Speaker 1: of a happy ending though, because he announced that PBS

1111
01:00:40,720 --> 01:00:43,480
Speaker 1: offered him the role of resident librarian for a social

1112
01:00:43,560 --> 01:00:46,240
Speaker 1: media series and so this is gonna involve him doing

1113
01:00:46,320 --> 01:00:50,200
Speaker 1: book suggestions, talking about literacy, and of course spreading library

1114
01:00:50,320 --> 01:00:52,200
Speaker 1: joy and so yeah, I'm happy that it has a

1115
01:00:52,240 --> 01:00:55,760
Speaker 1: happy ending, but I'm sad that someone can't just be

1116
01:00:55,960 --> 01:01:00,520
Speaker 1: a really engaged, passionate librarian on the twenty twenty four

1117
01:01:00,560 --> 01:01:04,320
Speaker 1: Internet without being bullied for it and bullied into stepping down.

1118
01:01:04,640 --> 01:01:06,360
Speaker 1: We all lose when that's the case.

1119
01:01:07,040 --> 01:01:09,280
Speaker 3: Yeah, I'm glad there's a happy ending on that one.

1120
01:01:09,360 --> 01:01:14,640
Speaker 3: That feels nice.

1121
01:01:15,680 --> 01:01:29,680
Speaker 5: More after a quick break, let's get right back into it.

1122
01:01:31,560 --> 01:01:34,760
Speaker 1: Well, I don't know how this is gonna go because

1123
01:01:36,560 --> 01:01:39,240
Speaker 1: our last topic is something that I know that we

1124
01:01:39,560 --> 01:01:42,200
Speaker 1: maybe don't see an eye on, and that.

1125
01:01:42,240 --> 01:01:43,800
Speaker 2: Is we're gonna talk about self checkout.

1126
01:01:45,040 --> 01:01:47,800
Speaker 1: No, we've already done it, Like it's been done.

1127
01:01:47,680 --> 01:01:51,400
Speaker 3: Dude, Okay, cool. Well, I mean, what else do we

1128
01:01:51,440 --> 01:01:56,120
Speaker 3: disagree about Wendy's. Oh, we're gonna talk about Wendy's.

1129
01:01:56,360 --> 01:01:58,960
Speaker 1: Okay. So do you remember when I was like, did

1130
01:01:59,040 --> 01:02:01,760
Speaker 1: you see that Wendy's doing surge pricing? And you had

1131
01:02:03,080 --> 01:02:05,280
Speaker 1: I will describe it as a big reaction. Do you

1132
01:02:05,320 --> 01:02:07,440
Speaker 1: remember how you reacted when I told you that information?

1133
01:02:08,600 --> 01:02:09,040
Speaker 2: Yeah? I do.

1134
01:02:09,120 --> 01:02:13,520
Speaker 3: I was like, that's that can't be true. That's banana

1135
01:02:13,560 --> 01:02:17,120
Speaker 3: pans bonkers, Like that doesn't make any sense. No one

1136
01:02:17,160 --> 01:02:19,840
Speaker 3: will tolerate that. People will hate it, people rebuild, people

1137
01:02:19,880 --> 01:02:23,760
Speaker 3: will be like burning Wendy's to the ground. Something must

1138
01:02:23,800 --> 01:02:25,880
Speaker 3: be wrong about this story.

1139
01:02:26,360 --> 01:02:31,040
Speaker 1: Well, let's get into it. So is Wendy's doing surge pricing?

1140
01:02:32,000 --> 01:02:34,120
Speaker 1: I don't feel confident saying yes or no. I will

1141
01:02:34,120 --> 01:02:37,120
Speaker 1: tell you what I know. So in wendy'ss Q four

1142
01:02:37,200 --> 01:02:41,240
Speaker 1: earnings call, the company announced plans to use AI enabled

1143
01:02:41,400 --> 01:02:45,840
Speaker 1: dynamic pricing remember that phrase, saying beginning as early as

1144
01:02:45,840 --> 01:02:48,800
Speaker 1: twenty twenty five, we will begin testing more enhanced features

1145
01:02:48,800 --> 01:02:52,440
Speaker 1: like dynamic pricing and day part offering, along with AI

1146
01:02:52,560 --> 01:02:55,800
Speaker 1: enabled menu changes and suggestive selling. As we continue to

1147
01:02:55,840 --> 01:02:58,920
Speaker 1: show the benefit of this technology in our company operated restaurants,

1148
01:02:59,120 --> 01:03:03,160
Speaker 1: franchisee in in digital menu boards should increase further, supporting

1149
01:03:03,200 --> 01:03:07,360
Speaker 1: sales and profit growth across the system. We will continue

1150
01:03:07,400 --> 01:03:09,680
Speaker 1: setting the pace in generative AI and have now rolled

1151
01:03:09,720 --> 01:03:13,200
Speaker 1: out Wendy's Fresh AI in several restaurants where we see

1152
01:03:13,240 --> 01:03:19,880
Speaker 1: ongoing improvement and in speed and accuracy. So importantly, that comment,

1153
01:03:20,520 --> 01:03:24,200
Speaker 1: though weird as fuck, I think, but does not say

1154
01:03:24,880 --> 01:03:28,560
Speaker 1: the word surge pricing. It says dynamic pricing, So there

1155
01:03:28,640 --> 01:03:31,360
Speaker 1: is a difference between these two pricing structures. I think

1156
01:03:31,360 --> 01:03:34,880
Speaker 1: that when we hear dynamic pricing, most people are probably

1157
01:03:34,920 --> 01:03:36,880
Speaker 1: most familiar with it from things like Uber and Lyft,

1158
01:03:36,880 --> 01:03:39,400
Speaker 1: where they like charge more when there's more demands. So

1159
01:03:39,400 --> 01:03:41,520
Speaker 1: if it's like raining and a concert just let out,

1160
01:03:41,600 --> 01:03:43,480
Speaker 1: it's going to cost more than if it's an off

1161
01:03:43,520 --> 01:03:45,520
Speaker 1: season and not many people are using the app. So

1162
01:03:46,200 --> 01:03:49,000
Speaker 1: I think people understandably assume that this meant that Wendy's

1163
01:03:49,040 --> 01:03:52,080
Speaker 1: is going to be charging more for food at times

1164
01:03:52,080 --> 01:03:54,560
Speaker 1: of high demand. I did see a lot of reporting

1165
01:03:54,600 --> 01:03:58,160
Speaker 1: on this issue that used the phrase surge pricing. However,

1166
01:03:58,680 --> 01:04:02,120
Speaker 1: surge pricing and dynam limit pricing are different things, right,

1167
01:04:02,200 --> 01:04:04,720
Speaker 1: Like surge pricing means like, oh, the price will be

1168
01:04:04,800 --> 01:04:07,320
Speaker 1: higher at certain times. Dynamic pricing. This means, oh, the

1169
01:04:07,320 --> 01:04:10,640
Speaker 1: price can change based on certain factors, not necessarily going

1170
01:04:10,760 --> 01:04:14,840
Speaker 1: up or down. After this announcement, the phrase surge pricing

1171
01:04:14,920 --> 01:04:18,120
Speaker 1: actually started trending on social media, and even lawmakers were

1172
01:04:18,160 --> 01:04:22,080
Speaker 1: weighing in, Like Senator Bob Casey accused Wendy's of corporate greed.

1173
01:04:22,440 --> 01:04:26,200
Speaker 1: Elizabeth Warren added, it's price gouging, plain and simple. So

1174
01:04:26,680 --> 01:04:29,360
Speaker 1: I did a search of the entire transcript of that

1175
01:04:29,480 --> 01:04:32,720
Speaker 1: Wendy's Q four call, great use of my time, and

1176
01:04:32,920 --> 01:04:36,080
Speaker 1: it is true. Nowhere in that call did the Wendy

1177
01:04:36,160 --> 01:04:39,479
Speaker 1: CEO ever specifically say that they were planning on doing

1178
01:04:39,560 --> 01:04:43,320
Speaker 1: surge pricing, only dynamic pricing, and there is a difference, right.

1179
01:04:43,600 --> 01:04:47,800
Speaker 1: Wendy's then clarified, saying they do not plan on instituting

1180
01:04:47,840 --> 01:04:50,560
Speaker 1: surge pricing. They said, in a statement, we said these

1181
01:04:50,600 --> 01:04:53,040
Speaker 1: menu boards would give us more flexibility to change the

1182
01:04:53,040 --> 01:04:56,880
Speaker 1: display of featured items. This was misconstrued in some media

1183
01:04:56,920 --> 01:04:59,360
Speaker 1: reports as an intent to raise prices when demand is

1184
01:04:59,400 --> 01:05:01,920
Speaker 1: highest at our restaurants. We have no plans to do

1185
01:05:01,960 --> 01:05:04,440
Speaker 1: that and would not raise prices when our customers are

1186
01:05:04,520 --> 01:05:07,240
Speaker 1: visiting us. Most any features we may test would be

1187
01:05:07,280 --> 01:05:10,600
Speaker 1: designed to benefit our customers and restaurant crew members. Digital

1188
01:05:10,640 --> 01:05:13,040
Speaker 1: menu boards could allow us to change the menu offerings

1189
01:05:13,080 --> 01:05:15,920
Speaker 1: at different times of the day and offer discounts and

1190
01:05:16,000 --> 01:05:19,520
Speaker 1: value offers to our customers more easily, particularly in the

1191
01:05:19,560 --> 01:05:22,560
Speaker 1: slower times of day. Wendy's has always been about providing

1192
01:05:22,640 --> 01:05:26,080
Speaker 1: high quality food at great value, and customers can continue

1193
01:05:26,120 --> 01:05:32,200
Speaker 1: to expect that from our brand. So here's where Mike

1194
01:05:32,240 --> 01:05:35,400
Speaker 1: and I disagree. Mike, I see you, Why don't you

1195
01:05:35,400 --> 01:05:36,800
Speaker 1: go ahead? What do you think about this?

1196
01:05:37,680 --> 01:05:37,880
Speaker 2: Well?

1197
01:05:37,920 --> 01:05:41,600
Speaker 3: I mean, I want to be clear that I have

1198
01:05:41,680 --> 01:05:46,040
Speaker 3: no illusions that Wendy's is some benevolent actor.

1199
01:05:46,320 --> 01:05:49,040
Speaker 2: I don't know this CEO. It's like a giant corporation.

1200
01:05:49,680 --> 01:05:52,920
Speaker 3: Uh, It's like fine, as far as I can tell,

1201
01:05:53,000 --> 01:05:56,680
Speaker 3: they're no more or less evil than any other giant corporation, right, Like,

1202
01:05:57,400 --> 01:06:00,160
Speaker 3: But it just never made sense. Nobody's gonna tell or

1203
01:06:00,560 --> 01:06:04,400
Speaker 3: that surge pricing. So when he issued this clarification the

1204
01:06:04,440 --> 01:06:06,520
Speaker 3: next day and said, no, we're not planning to do

1205
01:06:06,640 --> 01:06:09,560
Speaker 3: surge pricing, where we would increase prices at certain times

1206
01:06:09,560 --> 01:06:12,680
Speaker 3: when there's extra demand, but instead we would just want

1207
01:06:12,720 --> 01:06:18,680
Speaker 3: to have more flexibility to offer different discounts at different

1208
01:06:18,680 --> 01:06:20,960
Speaker 3: times of the day. I was like, oh, okay, that

1209
01:06:21,120 --> 01:06:24,400
Speaker 3: makes a lot of sense, that like actually seems like

1210
01:06:24,480 --> 01:06:29,880
Speaker 3: something that people would not be infuriated by. But then

1211
01:06:29,920 --> 01:06:34,320
Speaker 3: it got me thinking how silly we humans are with

1212
01:06:34,600 --> 01:06:39,600
Speaker 3: our like gain loss framing and like loss aversion that

1213
01:06:40,120 --> 01:06:43,760
Speaker 3: the idea that something would increase in price at certain

1214
01:06:44,120 --> 01:06:49,240
Speaker 3: hours of the day is completely unacceptable. I would just

1215
01:06:49,360 --> 01:06:54,600
Speaker 3: never I couldn't tolerate that in my burgers, right, that's

1216
01:06:55,720 --> 01:06:59,280
Speaker 3: insulting to me as a person. But the idea that

1217
01:06:59,320 --> 01:07:01,360
Speaker 3: I would get a price discount at some hours of

1218
01:07:01,360 --> 01:07:04,040
Speaker 3: the day, Oh okay, cool, Yeah, what's the discount?

1219
01:07:04,080 --> 01:07:06,880
Speaker 2: Is it? Like half off? Tell me? And like, that's

1220
01:07:07,120 --> 01:07:08,360
Speaker 2: kind of dumb.

1221
01:07:08,240 --> 01:07:12,080
Speaker 3: Of me and humans in general, because in either scenario,

1222
01:07:12,320 --> 01:07:14,280
Speaker 3: there's one price at one time a day and another

1223
01:07:14,280 --> 01:07:16,320
Speaker 3: price at another time of the day, And it's almost

1224
01:07:16,320 --> 01:07:19,000
Speaker 3: like semantics of what we're where. They're calling it an

1225
01:07:19,080 --> 01:07:22,600
Speaker 3: increase or a decrease. And yet apparently those semantics are

1226
01:07:22,640 --> 01:07:27,560
Speaker 3: extremely important, given that Wendy's became front page news for

1227
01:07:27,600 --> 01:07:32,200
Speaker 3: twenty four hours based on this distinction between surge pricing

1228
01:07:32,280 --> 01:07:36,720
Speaker 3: and dynamic pricing, which I had never even thought of

1229
01:07:36,920 --> 01:07:38,160
Speaker 3: until this was an issue.

1230
01:07:38,480 --> 01:07:42,000
Speaker 1: Yeah, I mean, I mostly agree with you. The thing

1231
01:07:42,000 --> 01:07:44,880
Speaker 1: I want to pull out, which is my take, is

1232
01:07:44,920 --> 01:07:51,400
Speaker 1: that I don't think that how they I don't think

1233
01:07:51,400 --> 01:07:56,240
Speaker 1: that their clarification walks back this enough because they very

1234
01:07:56,280 --> 01:08:00,800
Speaker 1: well truly incluld raise their prices and then be like, oh, well,

1235
01:08:02,360 --> 01:08:04,560
Speaker 1: raise their prices across the board, and then be like,

1236
01:08:04,960 --> 01:08:07,560
Speaker 1: on off peak times, we're gonna have a cheap menu,

1237
01:08:07,960 --> 01:08:09,640
Speaker 1: and that would be the same thing that people got

1238
01:08:09,680 --> 01:08:11,920
Speaker 1: upset about. So it is like a semantics of like, well,

1239
01:08:12,440 --> 01:08:16,800
Speaker 1: and so I this is what I think. Wendy says

1240
01:08:16,840 --> 01:08:20,120
Speaker 1: they would not raise prices when our customers are visiting most.

1241
01:08:20,600 --> 01:08:25,800
Speaker 1: That kind of gets my pr statement brain buzzing, because

1242
01:08:25,840 --> 01:08:27,400
Speaker 1: I think what they're saying is like, well, we never

1243
01:08:27,400 --> 01:08:29,800
Speaker 1: said we work on e raise prices. We just said

1244
01:08:29,840 --> 01:08:31,920
Speaker 1: we work on eraise prices when people are eating here

1245
01:08:31,960 --> 01:08:35,400
Speaker 1: the most. And so I believe what they are saying

1246
01:08:36,120 --> 01:08:40,679
Speaker 1: is we plan to raise prices and then lower them

1247
01:08:40,880 --> 01:08:43,840
Speaker 1: via our dynamic menu, which is subsequently kind of the

1248
01:08:43,880 --> 01:08:46,040
Speaker 1: same thing. That's what I think is going to happen.

1249
01:08:46,080 --> 01:08:49,440
Speaker 1: So I think that people are like cheering this as like, yeah,

1250
01:08:49,840 --> 01:08:53,000
Speaker 1: we really like bullied Wendy's into doing the right thing.

1251
01:08:53,439 --> 01:08:56,120
Speaker 1: I don't know that I agree. I really don't.

1252
01:08:56,840 --> 01:09:03,360
Speaker 3: Yeah, I feel like you're really reading much further between

1253
01:09:03,400 --> 01:09:04,400
Speaker 3: the lines.

1254
01:09:04,080 --> 01:09:07,479
Speaker 2: Than is warranted here.

1255
01:09:08,040 --> 01:09:11,720
Speaker 3: I mean, okay, here's two other examples of companies that

1256
01:09:11,800 --> 01:09:14,080
Speaker 3: do stuff like this, right, Like Bead Bathroom Beyond before

1257
01:09:14,080 --> 01:09:16,160
Speaker 3: they went out of business. There was one of those

1258
01:09:16,200 --> 01:09:18,439
Speaker 3: in Columbia Heights where I live, and I would go

1259
01:09:18,439 --> 01:09:21,320
Speaker 3: there to try to buy things sometimes, and it was

1260
01:09:21,520 --> 01:09:25,600
Speaker 3: expensive as hell. Every single item was like really expensive

1261
01:09:25,640 --> 01:09:27,639
Speaker 3: so much that I would like never buy their things.

1262
01:09:27,640 --> 01:09:27,840
Speaker 2: There.

1263
01:09:28,960 --> 01:09:31,040
Speaker 3: Many occasions I would go there, wanted to get an item,

1264
01:09:31,080 --> 01:09:32,240
Speaker 3: take a look at the price, and be like, this

1265
01:09:32,280 --> 01:09:35,519
Speaker 3: is absurd. And the reason was because they were one

1266
01:09:35,520 --> 01:09:39,439
Speaker 3: of these stores that had a coupon based economy where

1267
01:09:41,120 --> 01:09:43,040
Speaker 3: it only made sense to shop there if you were

1268
01:09:43,080 --> 01:09:49,439
Speaker 3: clipping coupons, which they prolifically distributed, so people would walk

1269
01:09:49,479 --> 01:09:51,840
Speaker 3: in there with like a handful of coupons for like

1270
01:09:51,920 --> 01:09:55,880
Speaker 3: seventy five percent off buy one, get one whatever, And

1271
01:09:55,960 --> 01:09:59,040
Speaker 3: I just really resented that little dance that we had to.

1272
01:09:59,080 --> 01:10:00,160
Speaker 2: Do to.

1273
01:10:02,200 --> 01:10:04,760
Speaker 3: Try to buy an item at a reasonable price. So

1274
01:10:05,560 --> 01:10:08,599
Speaker 3: hopefully that's not what Wendy's is approaching here.

1275
01:10:09,400 --> 01:10:11,600
Speaker 1: I once went into that Bed Bath and Beyond and

1276
01:10:11,640 --> 01:10:13,679
Speaker 1: I was standing in line, I think, holding a blender,

1277
01:10:13,960 --> 01:10:15,320
Speaker 1: and there was a guy in there who had an

1278
01:10:15,479 --> 01:10:17,960
Speaker 1: entire I kid you not, an entire book of Bed

1279
01:10:18,000 --> 01:10:20,400
Speaker 1: Bath and Beyond coupons, and he was giving them out

1280
01:10:20,439 --> 01:10:22,439
Speaker 1: the people in the line like cash, Like he was

1281
01:10:22,479 --> 01:10:25,080
Speaker 1: like making it rain coupons. And I got my blender

1282
01:10:25,160 --> 01:10:27,240
Speaker 1: for twenty five percent of I'll never forget it. Yeah,

1283
01:10:27,360 --> 01:10:32,200
Speaker 1: I I There are already plenty of things in society

1284
01:10:32,400 --> 01:10:37,000
Speaker 1: that use dynamic pricing, like airline tickets they're cheaper depending

1285
01:10:37,000 --> 01:10:40,040
Speaker 1: on demand, movie tickets, you know, you buy a matinee

1286
01:10:40,040 --> 01:10:42,160
Speaker 1: when less people are going, it's cheaper, is a thing

1287
01:10:42,160 --> 01:10:44,639
Speaker 1: in our society. But I do think what's interesting here

1288
01:10:44,720 --> 01:10:48,479
Speaker 1: is that how big the reaction was. And I think

1289
01:10:48,520 --> 01:10:52,519
Speaker 1: that's gotta be that, Like people are so fucking sick rightly,

1290
01:10:52,600 --> 01:10:55,280
Speaker 1: so a feeling squeezed right now, Like it feels like

1291
01:10:55,320 --> 01:10:58,880
Speaker 1: the new model is like anything that can rip people

1292
01:10:58,880 --> 01:11:01,120
Speaker 1: off must rip people, And so I think that people

1293
01:11:01,160 --> 01:11:03,200
Speaker 1: are sensitive to it right now because we're feeling it

1294
01:11:03,320 --> 01:11:05,439
Speaker 1: in multiple different ways. That just feels like the way

1295
01:11:05,439 --> 01:11:07,559
Speaker 1: that it is right now, and I don't know. Our

1296
01:11:07,600 --> 01:11:10,240
Speaker 1: burgers are our last. Like, if we lose the burgers,

1297
01:11:10,280 --> 01:11:12,200
Speaker 1: what else have we lost? Right Like, this is our

1298
01:11:12,280 --> 01:11:14,800
Speaker 1: last the last thing that we can sort of rely on.

1299
01:11:15,439 --> 01:11:18,200
Speaker 3: Yeah, exactly. I don't want to have to bring coupons

1300
01:11:18,240 --> 01:11:21,599
Speaker 3: to Wendy's. I don't want to have to strategically time

1301
01:11:22,080 --> 01:11:25,360
Speaker 3: when I get my burger. I don't want to have to,

1302
01:11:25,800 --> 01:11:29,479
Speaker 3: you know, do a bunch of math. Uh yeah, let's

1303
01:11:29,520 --> 01:11:30,839
Speaker 3: just keep it simple.

1304
01:11:31,439 --> 01:11:33,720
Speaker 1: Let's just keep it simple. But I do want to

1305
01:11:33,800 --> 01:11:39,479
Speaker 1: clearly reiterate my point for you, Mike, because if Comma,

1306
01:11:40,040 --> 01:11:43,040
Speaker 1: when Wendy's racist, I'm gonna stay on. I'm you know,

1307
01:11:43,040 --> 01:11:46,840
Speaker 1: I'm gonna stay on this story. If and perhaps when

1308
01:11:47,320 --> 01:11:50,799
Speaker 1: Wendy's quietly raises all of their prices across the board

1309
01:11:51,080 --> 01:11:54,880
Speaker 1: and their dynamic pricing is rolled out to lower those

1310
01:11:54,920 --> 01:11:57,200
Speaker 1: prices back down to what we are familiar with, which

1311
01:11:57,200 --> 01:11:59,960
Speaker 1: is my argument of what they're doing, we will record

1312
01:12:00,120 --> 01:12:02,200
Speaker 1: this again, and I want I want a I want

1313
01:12:02,200 --> 01:12:04,160
Speaker 1: to say it clearly now so that I can get

1314
01:12:04,160 --> 01:12:08,519
Speaker 1: a clear I told just so from me on the record,

1315
01:12:08,560 --> 01:12:10,920
Speaker 1: and that a clear you were right from you on

1316
01:12:10,960 --> 01:12:13,479
Speaker 1: the record. We'll have that all as audio forever, so

1317
01:12:13,520 --> 01:12:15,160
Speaker 1: that we'll all, you know, be on the same page.

1318
01:12:15,360 --> 01:12:15,840
Speaker 2: Is that cool?

1319
01:12:17,120 --> 01:12:19,559
Speaker 3: I mean we'll cross that bridge when they come to it. Yeah,

1320
01:12:19,680 --> 01:12:23,120
Speaker 3: I mean, I uh, you know. It also makes me

1321
01:12:23,120 --> 01:12:24,920
Speaker 3: think and then I'll let us finish up here. But

1322
01:12:25,200 --> 01:12:28,200
Speaker 3: so in DC we have this new law, I guess

1323
01:12:28,240 --> 01:12:29,800
Speaker 3: it's not even new, it's been here for like two

1324
01:12:29,880 --> 01:12:33,240
Speaker 3: or three years now, where all restaurant workers have to

1325
01:12:33,280 --> 01:12:36,120
Speaker 3: make a living wage. It's great law, good for everybody,

1326
01:12:37,479 --> 01:12:39,920
Speaker 3: but restaurant owners hate it. And so there's been this

1327
01:12:40,160 --> 01:12:43,679
Speaker 3: phenomenon in the city of You go into a restaurant

1328
01:12:43,840 --> 01:12:48,920
Speaker 3: and the prices on the menu are the prices, and

1329
01:12:48,960 --> 01:12:51,120
Speaker 3: then when you get your bill there'll be like a

1330
01:12:51,240 --> 01:12:56,120
Speaker 3: five percent search charge that is not a tip. It

1331
01:12:56,120 --> 01:12:58,560
Speaker 3: doesn't go to the servers. It's just for the restaurant.

1332
01:12:58,680 --> 01:13:02,000
Speaker 3: It'll be like the living wage fee or something. And

1333
01:13:02,400 --> 01:13:06,120
Speaker 3: that infuriates me right that, Like, just raise your prices

1334
01:13:06,160 --> 01:13:07,519
Speaker 3: across the board, if that's what you have to do

1335
01:13:07,560 --> 01:13:12,320
Speaker 3: to keep this business going, don't like deceptively hide it.

1336
01:13:12,360 --> 01:13:16,360
Speaker 3: So I hope Wendy's doesn't raise their prices, But I

1337
01:13:16,479 --> 01:13:19,840
Speaker 3: would rather that than a bunch of like weird gimmicky

1338
01:13:19,920 --> 01:13:21,599
Speaker 3: tricks that make me have to do math when I'm

1339
01:13:21,600 --> 01:13:22,439
Speaker 3: trying to get my food.

1340
01:13:22,800 --> 01:13:26,759
Speaker 1: Well, I believe what Wendy's will roll out is a weird,

1341
01:13:26,760 --> 01:13:31,360
Speaker 1: gimmicky trick that forces you to do maths. I'm like,

1342
01:13:31,800 --> 01:13:35,120
Speaker 1: follow the Wendy's prices like a goddamn stock market just

1343
01:13:35,160 --> 01:13:38,360
Speaker 1: to buy your food that I will leave it there.

1344
01:13:39,200 --> 01:13:41,639
Speaker 2: I hope you're wrong. I mean, that's that's not what

1345
01:13:41,720 --> 01:13:42,920
Speaker 2: Dave Thomas would have wanted.

1346
01:13:43,320 --> 01:13:47,000
Speaker 1: Dave Thomas is rolling over on his in his grave. Mike,

1347
01:13:47,280 --> 01:13:49,360
Speaker 1: thank you so much for going through these stories with me.

1348
01:13:49,680 --> 01:13:51,040
Speaker 1: I will see you on the internet.

1349
01:13:51,400 --> 01:13:52,759
Speaker 2: Thanks, Bridget see you there.

1350
01:13:52,880 --> 01:13:54,559
Speaker 1: And thanks to all of you for listening and to

1351
01:13:54,600 --> 01:13:57,400
Speaker 1: the listeners who sent in suggestions and flags of stories

1352
01:13:57,400 --> 01:13:59,680
Speaker 1: that we should cover. Thank you. Please keep doing that.

1353
01:14:00,560 --> 01:14:01,960
Speaker 2: How can they do that, Bridget Oh?

1354
01:14:02,000 --> 01:14:04,639
Speaker 1: You can email us at hello at tangody dot com.

1355
01:14:04,720 --> 01:14:06,280
Speaker 1: You can find me on social media. I though I'm

1356
01:14:06,280 --> 01:14:09,040
Speaker 1: not really there, not often anymore, but you could try. Yeah,

1357
01:14:09,040 --> 01:14:09,840
Speaker 1: please keep doing it.

1358
01:14:10,000 --> 01:14:10,200
Speaker 2: Yeah.

1359
01:14:10,240 --> 01:14:13,000
Speaker 3: They could also go to the Patreon right. There are

1360
01:14:13,080 --> 01:14:16,120
Speaker 3: quite a few listeners who message us there at patreon

1361
01:14:16,360 --> 01:14:20,040
Speaker 3: dot com, slash tangody or tangody dot com, slash Patreon.

1362
01:14:20,200 --> 01:14:21,000
Speaker 2: It works both ways.

1363
01:14:21,040 --> 01:14:30,240
Speaker 1: Oh a tech Marvel. Thanks for listening. If you're looking

1364
01:14:30,240 --> 01:14:32,320
Speaker 1: for ways to support the show, check out our March

1365
01:14:32,360 --> 01:14:36,200
Speaker 1: store at tangoty dot com slash store. Got a story

1366
01:14:36,200 --> 01:14:38,080
Speaker 1: about an interesting thing in tech, or just want to

1367
01:14:38,120 --> 01:14:40,479
Speaker 1: say hi, You can reach us at Hello at tangody

1368
01:14:40,520 --> 01:14:43,080
Speaker 1: dot com. You can also find transcripts for today's episode

1369
01:14:43,080 --> 01:14:45,240
Speaker 1: at tengody dot com. There Are No Girls on the

1370
01:14:45,280 --> 01:14:47,920
Speaker 1: Internet was created by me Bridget Todd. It's a production

1371
01:14:47,960 --> 01:14:52,280
Speaker 1: of iHeartRadio and Unbossed Creative edited by Joey pat Jonathan

1372
01:14:52,280 --> 01:14:55,280
Speaker 1: Strickland is our executive producer. Tari Harrison is our producer

1373
01:14:55,320 --> 01:14:58,800
Speaker 1: and sound engineer. Michael Almada is our contributing producer. I'm

1374
01:14:58,800 --> 01:15:01,160
Speaker 1: your host, Bridget Todd. If you want to help us grow,

1375
01:15:01,360 --> 01:15:02,080
Speaker 1: rate and review us.

1376
01:15:02,040 --> 01:15:03,040
Speaker 5: On Apple Podcasts.

1377
01:15:03,760 --> 01:15:06,559
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1378
01:15:06,600 --> 01:15:12,360
Speaker 1: Apple Podcasts, or wherever you get your podcasts.