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

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

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

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Speaker 1: another episode of There Are No Girls on the Internet,

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Speaker 1: where weeks for the intersection of identity, technology and social media.

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Speaker 1: And this is another iteration of our tangode weekly news

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Speaker 1: Roundup where we summarize and break down the news online

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

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Speaker 1: I am joined by my producer Mike. Mike, thank you

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Speaker 1: for being here. Welcome back to the show.

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Speaker 2: Bridget, Thanks for having me back. Always such a pleasure

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Speaker 2: to be here with you and the Tangoty News Roundup.

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Speaker 1: Okay, so listeners settle a debt for me or way

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Speaker 1: in on a conversation, So I was telling Mike off

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Speaker 1: Mike how much I wanted to watch this new movie

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Speaker 1: War of the Worlds with ice Cube. And the reason

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Speaker 1: I wanted to watch this movie is because it has

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Speaker 1: achieved something truly historic, which is a less than five

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Speaker 1: percent fresh rating on Rotten Tomatoes. And I honestly think

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Speaker 1: there's maybe there's like two movies in the whole world

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Speaker 1: that have ever had such.

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Speaker 3: A low rating on Rotten Tomatoes.

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Speaker 1: To me, when the rating is very high or very low,

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Speaker 1: they kind of achieved the same thing, and that I'm

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Speaker 1: just so curious what's going on. I was like, Mike,

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Speaker 1: we should watch this and talk about on the podcast.

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Speaker 1: You were like, absolutely not. I don't want to know

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Speaker 1: what's going on with a movie that is this badly reviewed.

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Speaker 2: So last time I checked, it's rating was not just

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Speaker 2: less than five percent, it was zero percent. It's had

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Speaker 2: a zero percent rating of Rotten Tomatoes, which I don't know.

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Speaker 2: Ordinarily I would be with you, like, let's watch this

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Speaker 2: train wreck. I'm there for it. I love bad movies.

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Speaker 2: I love Mystery Sides Theater three thousand. Bad movies are great.

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Speaker 2: But we watched the trailer. You got me interested to

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Speaker 2: watch the trailer.

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Speaker 3: As it didn't do it.

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Speaker 2: It was like, like, the whole thing is shot on

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Speaker 2: Microsoft teams.

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Speaker 1: Yes, spoiler alert, the entire movie takes place on Microsoft teams,

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Speaker 1: which I feel I guess the kind of idea that

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Speaker 1: somebody in a pitch meeting said and they were like, oh,

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Speaker 1: that sounds good, and then when you actually see it,

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Speaker 1: you're like, oh, no, this is not good.

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Speaker 2: It's like a sick joke, Like nobody wants to do

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Speaker 2: anything on Microsoft Teams, let alone like spend ninety minutes

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Speaker 2: recreationally watching a movie. It was just like jerky and

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Speaker 2: bouncing around. It couldn't make out what was going on.

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Speaker 2: I didn't want to make out what was going on

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Speaker 2: that the dialogue was stilted and bad. Ice Cube looked stressed.

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Speaker 1: He even ice Cube is like, how did I get

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Speaker 1: roped up into this?

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Speaker 4: Like?

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Speaker 1: Who?

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Speaker 3: Like where?

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Speaker 1: What ball was dropped on my team? That I am

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Speaker 1: here on teams in this remake of War of the World.

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Speaker 1: One of the reviews I read was in the mini

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Speaker 1: in the many remakes of this movie War of the World,

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Speaker 1: I remember the Tom Cruise remake.

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Speaker 3: I think in like the early adds, this is the

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Speaker 3: worst one.

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Speaker 1: And again that that peaked my interest. So I guess

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Speaker 1: what I'm hearing is you are not interested in watching

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Speaker 1: this and recap make it for the podcast if I

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Speaker 1: if I want to do that, I have to I

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Speaker 1: have to.

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Speaker 3: Get somebody else to be on the other end of

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Speaker 3: the microphone. That's what I'm hearing.

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Speaker 2: Yeah, I mean, I you know, I I love your

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Speaker 2: ambition that you're just gonna go for it to bring

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Speaker 2: this hard hitting news to the listeners. But I just, uh,

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Speaker 2: that trailer, I don't know it. It did not look good,

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Speaker 2: Like what did? Did any part of it leave you

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Speaker 2: wanting more?

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Speaker 1: Only reading the reviews because the reviews are so bad

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Speaker 1: that if you're if the reviewers were trying to make

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Speaker 1: me not curious to see this movie, they did the opposite,

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Speaker 1: because I was like, well, dang, this sounds so bad.

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Speaker 2: I have to see what what's going on with this?

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Speaker 2: You know what it kind of reminds me of is Megalopolis?

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Speaker 2: You remember that movie? Did you ever end up seeing that?

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Speaker 2: I didn't, but I wanted to and I still want to. Oh,

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Speaker 2: I have not made it a priority.

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Speaker 1: It is absolutely one of my favorite things in life

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Speaker 1: is to read the reviews of a movie that has

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Speaker 1: just been panned. I've not seen it either, but the

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Speaker 1: reviews of that movie are also similarly, they're just so

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Speaker 1: bad that I wasn't really that keen to see it.

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Speaker 1: The reviews made me keen to see it because they're like,

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Speaker 1: I almost want to let me see if I can

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Speaker 1: pull some up.

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Speaker 2: Yeah, you look for some reviews, because so that's a

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Speaker 2: movie that the reviews at least. The early reviews were

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Speaker 2: universally terrible, like this is the worst movie ever made?

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Speaker 3: What was Francis Bord Coppola smoking like?

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Speaker 2: And that made me want to watch it, like I

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Speaker 2: still want to watch it because it sounds interesting, like

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Speaker 2: there was there was ambition there. The synopsis that I

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Speaker 2: read sounded insane and like like disconnected plot points that

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Speaker 2: some movies that I want to watch watching ice Cube

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Speaker 2: like look around from one quarter of his screen to another. Well,

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Speaker 2: glitchy news reports come in about Aliens.

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Speaker 3: I guess uh that no, just no.

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Speaker 1: I will say that the reviews of Megalopolis are now

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Speaker 1: I would call them mixed. They were not as like

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Speaker 1: universally bad as when I first checked in. My favorite though,

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Speaker 1: is from Johnny Olenski from The.

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Speaker 3: New York Post.

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Speaker 1: A zero star wacko disaster.

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Speaker 2: Right, a wacko disaster. I'm here for a wacko disaster.

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Speaker 2: I do not want to see a ninety minute Microsoft

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Speaker 2: team's disaster.

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Speaker 1: Okay, Well, if you watch War of the Worlds with

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Speaker 1: me to recap it for the podcast, I will I

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Speaker 1: will watch Megalopolis with you.

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Speaker 3: We'll do a like.

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Speaker 2: We'll do a little agreement, okay, and then we'll like

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Speaker 2: force our listeners to listen to recaps of well these

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Speaker 2: two terrible movies.

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Speaker 1: We will lose every listener that we have. Okay, wait, well,

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Speaker 1: speaking of wacko disasters, let's talk about Elon Musk.

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Speaker 3: Yeah, the most wacko, the most disaster. Yeah.

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Speaker 1: Do you remember when Elon Musk had these big grand

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Speaker 1: plans to make updates to his chatbot Groc, and then

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Speaker 1: like a day later, Groc started praising the Nazis and

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Speaker 1: saying that my name is no longer Groc, my name

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Speaker 1: is Mecha Hitler.

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Speaker 3: Oh yeah, I remember.

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Speaker 2: I mean he made those updates, he trained it on

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Speaker 2: four Chan and x and probably some other cesspools of hate,

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Speaker 2: and unleashed it on the world.

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Speaker 3: He achieved his goal.

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Speaker 1: Okay, well don't worry, because now it's also creating deep

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Speaker 1: fake images of Taylor Swift completely unprompted.

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Speaker 3: Oh good, Oh good.

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Speaker 1: So we're right, We're right on schedule because Groc Imagined,

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Speaker 1: which is xai's new generative AI tool, created explicit deep

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Speaker 1: fakes of Taylor Swift without even being specifically prompted or

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Speaker 1: asked to do so. This is according to new reporting

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Speaker 1: from The Verge. Now folks will remember this is not

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Speaker 1: the first time that X has been used in kind

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Speaker 1: of a similar way. Back in January twenty twenty four,

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Speaker 1: AI generated Taylor Swift deep fakes went viral on X

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Speaker 1: will drop the episode that we did from twenty twenty

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Speaker 1: four about that in the show notes just in case

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Speaker 1: you missed it. So if you're curious what happened this time, well,

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Speaker 1: Jess Weatherbed of The Verge discovered that grock Imagine spit

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Speaker 1: out uncensored, topless videos of Taylor Swift the very first

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Speaker 1: time she used this tool.

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Speaker 3: She did not direct or ask the bot.

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Speaker 1: To depict these images, but once she turned on grock

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Speaker 1: Imagine's Spicy mode, which is the setting that Elon Musk

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Speaker 1: promoted in the days right after its launch, it turned

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Speaker 1: out a video in which Taylor Swift tore off her

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Speaker 1: clothing and began dancing around in a thong. And again,

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Speaker 1: this was not something that she asked for. It's like, oh,

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Speaker 1: let me see what this AI tool is doing. Oh,

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Speaker 1: it's spitting out videos of Taylor Swift undressed. Now this

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Speaker 1: is not terribly surprising. There was a really good report

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Speaker 1: and Mashable that pointed out all the ways that groc

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Speaker 1: Imagine lacks even the most basic guardrails around sexual deep fakes.

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Speaker 1: Right now, the xai Acceptable Use Policy prohibits users from

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Speaker 1: depicting the likenesses of persons in pornographic manners. Unfortunately, there

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Speaker 1: is some distance between sexual and pornographic, and groc Imagine

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Speaker 1: seems to be carefully calibrated to take advantage of that

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Speaker 1: specific gray area. Groc Imagine will readily create sexually suggestive

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Speaker 1: images and videos, but it does stop short of depicting

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Speaker 1: actual nudity or sex acts.

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Speaker 2: So it's like the whole thing about the definition of

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Speaker 2: pornography being I'll know what when I see it. We've

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Speaker 2: now left that to Grock and Elon Musk to define.

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Speaker 1: Pretty much pretty much, you know, and most mainstream AI

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Speaker 1: companies will usually have a rule that is spelled out

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Speaker 1: explicitly prohibiting folks from creating harmful content, which typically calls

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Speaker 1: out by name sexual material or celebrity deep stakes, right

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Speaker 1: you know. Rival AI generators like Google vo three or

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Speaker 1: Sora from Open Ai usually have these built in protections

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Speaker 1: that are an attempt to stop users from creating this

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Speaker 1: kind of content, So you could not just type in

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Speaker 1: for instance, tailor Swift Deep stakes and actually generate them.

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Speaker 1: When those Tailor Swift Deep stakes went viral on x

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Speaker 1: the last time around, users had these workarounds to create it.

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Speaker 1: So obviously explicitly prohibiting users from making this kind of

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Speaker 1: sexually charged imagery doesn't stop the problem, but it arguably

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Speaker 1: does create a bit of a barrier.

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Speaker 2: Yeah, and it just kind of, I don't know, like

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Speaker 2: appeals to common decency that you shouldn't be promoting non

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Speaker 2: consensual nude imagery of people, even if they are celebrities. Like,

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Speaker 2: it's just unsavory, and it seems like polite society shouldn't

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Speaker 2: be pushing those tools onto everyone else.

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Speaker 1: Well, not at x because, unlike its rivals, XAI does

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Speaker 1: not shy away from not safe for work content in groc,

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Speaker 1: and folks might remember that it recently introduced Annie, which

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Speaker 1: is kind of a thirty sexpot anime avatar that will

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Speaker 1: engage in not safe for work chats. Mashable reports that

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Speaker 1: Groc's image generation tool does let users create images of

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Speaker 1: celebrities and politicians. So it's telling to me that the

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Speaker 1: competitors of Groc and Imagine spell out like, hey, don't

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Speaker 1: use our platform in this way, not at X, they

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Speaker 1: have no such qualms.

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Speaker 2: It's like tempting to go down the free speech rabbit

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Speaker 2: hole and be like, well, you know, the rules about

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Speaker 2: free speech and First Amendment rates are different for celebrities politicians,

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Speaker 2: like you don't want to limit speech about them.

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Speaker 3: But this just sounds like really gross.

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Speaker 2: Sending aside the fact that that Verge reporter didn't even

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Speaker 2: ask for, you know, nude images of Taylor Swift.

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Speaker 3: It just it feels super gross.

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Speaker 2: You know, we know that Elon Musk has written a

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Speaker 2: bunch of like highly specific rules into Grok. He's had

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Speaker 2: or had his engineering teams do it because he I

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Speaker 2: don't think can write code at all, but he's written

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Speaker 2: a bunch of very specific stuff into Grock where before

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Speaker 2: Grok answers a question, it will search Elon Musk's tweets

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Speaker 2: to see how he what he thinks about an issue

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Speaker 2: before providing a response. And so I was curious whether

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Speaker 2: something like that might exist to prevent images of Elon Musk.

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Speaker 2: So I don't pay for Grock Premium or whatever it

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Speaker 2: is because I'm not a Nazi, so I couldn't test out

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Speaker 2: groc imagine. I think that's only available to those folks Nazis,

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Speaker 2: Nazis and X users almost event diagram that is almost

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Speaker 2: a perfect circle at this point. But so I just asked,

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Speaker 2: like I asked it to draw me an image of

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Speaker 2: Elon Musk in a bikini, and it did, and it

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Speaker 2: was terrible. And so I do have to give him

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Speaker 2: that that he didn't add special rules to exempt himself

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Speaker 2: from being depicted by Grock, at least in Grock three

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Speaker 2: point zero, which I guess is a couple models below

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Speaker 2: this new imagined one. But it was still terrible, and

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Speaker 2: I didn't feel any better after having done it. But

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Speaker 2: I was curious and that curiosity has been satisfied, and.

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Speaker 3: It was terrible.

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Speaker 1: Yeah, I mean, you can really see what they mean

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Speaker 1: when tech leaders tell us that this kind of technology,

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Speaker 1: this is going to be the linchpin of our entire economy.

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Speaker 1: Communities can't have clean water or clean air, but we

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Speaker 1: can be served up AI videos of Taylor Swift undressing

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Speaker 1: without even asking for it or going looking for it.

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Speaker 3: That is the future. People for freedom, for freedom.

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

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Speaker 1: Speaking of freedom, did you know that we recently might

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Speaker 1: have solved gender equality? Can I tell you about it.

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Speaker 2: Oh, thank goodness because I heard that. Actually, it's been

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Speaker 2: a problem lately.

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Speaker 3: Don't worry.

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Speaker 1: We have solved it because we have another update about

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Speaker 1: the continuing fallout of the Tea app That's right, the

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Speaker 1: app designed for women to share information about men that

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Speaker 1: they might be dating. We did a whole episode about

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Speaker 1: this where we talked about how men were saying we

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Speaker 1: should have our own t app where men can talk

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Speaker 1: about the women, notwithstanding the fact that do you think

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Speaker 1: that men need a dedicated app to do this? Men

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Speaker 1: have been like sharing like pictures of women and talking

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Speaker 1: about women on group chats since forever. They don't need it.

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Speaker 1: You don't need a dedicated platform to do it, but whatever.

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Speaker 1: So the Tea on Her app was meant to be

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Speaker 1: exactly that, and TechCrunch reports that the Tea on Her app,

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Speaker 1: the app for men to Talk about women, has also

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Speaker 1: exposed users personal information, including government IDs and selfies. A

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Speaker 1: step in the right direction when it comes to gender

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Speaker 1: equality and progress.

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Speaker 2: No just classic race to the bottom stuff like no

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Speaker 2: one can have anything nice. If everyone has nothing, then

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Speaker 2: then we're equal.

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Speaker 3: You know.

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Speaker 1: I envision a world where all genders can have our

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Speaker 1: data privacy compromised by sketchy apps in the name of

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Speaker 1: gender equality. The ta on Her app, which launched on

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Speaker 1: the Apple App Store earlier this week and shopped to

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Speaker 1: number two in the Lifestyle category on the App Store.

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Speaker 1: It borrowed language from the original t app for women

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Speaker 1: in its description. Here's what tech crunch found. TechCrunch found

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Speaker 1: at least one security flaw that allows anyone to access

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Speaker 1: data belonging to t on Her app users, including their

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Speaker 1: usernames and associated email addresses, as well as driver's licenses

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Speaker 1: and selfies that users uploaded to the app. Images of

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Speaker 1: these driver's licenses are publicly accessible web addresses, allowing anybody

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Speaker 1: with the links to access them using their web browser.

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Speaker 1: In one case, tech Crunch saw a list of posts

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Speaker 1: shared on the Tea on Her app appended with each

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Speaker 1: user's email address, display name, and self reported location. It

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Speaker 1: gets worse because TechCrunch also identified a potential second security

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Speaker 1: issue in which an email address and plaintext password belonging

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Speaker 1: to the apps creator was left exposed on the server.

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Speaker 1: The credentials appeared to grant access to the app's admin panel.

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Speaker 1: Tech Crunch did not use the credentials because doing so

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Speaker 1: would be illegal. But it does highlight the risks of

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Speaker 1: inadvertently leaving admin credentials exposed to the web. What are

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Speaker 1: we doing?

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Speaker 3: Oh? Really, that's risky. It's risky.

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Speaker 2: You shouldn't do it to expose your admin credentials to

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Speaker 2: the entire internet.

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Speaker 1: I mean I wouldn't.

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Speaker 3: No, I generally keep my ADMIN credentials to myself.

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Speaker 1: What's interesting to me is that the conversation when the

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Speaker 1: t app breach first happened, where all these women who

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Speaker 1: had uploaded their driver's licenses and their selfies to this

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Speaker 1: app had their personal information leaked online. A lot of

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Speaker 1: people were saying, well, the women wanted to gossip about men,

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Speaker 1: and you know, turn about is fair play that their

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Speaker 1: stuff would be leaked online. I feel like I have

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Speaker 1: one heard a lot less about the tea on her

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Speaker 1: app potentially being susceptible to these kind of And two

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Speaker 1: I don't feel like there's the same kind of like

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Speaker 1: moral hand ringing about well do these men like, is

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Speaker 1: this turnabout is fair play for these men who wanted

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Speaker 1: tea on women? Now they're having their information exposed. It's

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Speaker 1: very interesting to be how different the conversations were around

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Speaker 1: these two different apps in their user bases.

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Speaker 2: Yeah, did you ever notice how like their stuff is shit,

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Speaker 2: but like your shit is stuff?

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Speaker 1: I mean you know that that that I mean rip

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Speaker 1: George Carltt. I feel like so much of how I

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Speaker 1: understand the world I feel like I have gotten from him,

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Speaker 1: but specifically that one bit of how well when I

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Speaker 1: do it, it's justified. When I do it, It's like

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Speaker 1: it's like I don't deserve anything bad happening to me

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Speaker 1: when I do it, because I'm me, you see.

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Speaker 2: Yeah, right, And like not surprising that there aren't hordes

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Speaker 2: of women taking to the Internet to be like I'm

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Speaker 2: glad these men had their personal information exposed, Like how

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Speaker 2: dare they try to run a whisper network and like

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Speaker 2: talk to each other. That's just not something that women

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Speaker 2: would say. That's not something that like, well, people of

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Speaker 2: any gender would say.

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Speaker 1: And I read this really interesting piece in Encyclopedia Britannica

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Speaker 1: by Brick bisk Off called the Manosphere is the motherboard

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Speaker 1: the t app hack media landscape, And one of the

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Speaker 1: points that I found very interesting and kind of clarifying

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Speaker 1: in that piece was how easily the initial t app

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Speaker 1: breach was hijacked and how it instantly became this flashpoint

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Speaker 1: of manosphere talking points, And I do think the t

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Speaker 1: app was this was a situation that was ripe for

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Speaker 1: a discussion about you know, gender and dating and sexuality

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Speaker 1: and privacy and how all of these things intersect with

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Speaker 1: this one story, but we didn't really have that conversation

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Speaker 1: because it was so easily weaponized by man sphere communities

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Speaker 1: talking about how women who were interested in sort of

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Speaker 1: getting information on men deserve what they get and how

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Speaker 1: you know, it's it's women who should really be being

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Speaker 1: gossiped about because of you know, like what however you

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Speaker 1: want to say it. But how how easily that conversation,

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Speaker 1: which was genuinely fertile soil for like genuinely interesting conversations

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Speaker 1: about how we interact with each other, how quickly it

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Speaker 1: just became about gender wars nonsense.

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Speaker 2: It feels like there's really like two groups that are

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Speaker 2: keeping this thing going. There's like shitheads that are intentionally

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Speaker 2: trying to like weaponize gender wars as a way to

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Speaker 2: either farm engagement or just actively like suppress women. They're

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Speaker 2: like largely the minority.

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Speaker 3: But then it also.

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Speaker 2: Seems like there's this huge like not huge, but like

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Speaker 2: it seems like, there's also this group of men who

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Speaker 2: are like caught up in the nonsense of that first group,

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Speaker 2: and uh and are just like afraid of women and

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Speaker 2: like the only thing they know is gender wars, and

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Speaker 2: so like every single event that is in the news

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Speaker 2: or is being talked about online gets like filtered and

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Speaker 2: transformed to reinforce like a gender war framework.

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Speaker 1: Absolutely, and I don't remember if it was you and

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Speaker 1: me that we're talking about this, But as much as

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Speaker 1: I love the internet and I am of the internet

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Speaker 1: and was raised on the internet and am SuperM online,

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Speaker 1: I do think that gender is one of those issues

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Speaker 1: where the worst people, the most extreme people, the people

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Speaker 1: with an axe to grind, the people who are grifting

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Speaker 1: and trying to make money off of the discontent of others,

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Speaker 1: They have really been able to take an outsized footprint

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Speaker 1: in the conversation about gender dynamics in my experience when

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Speaker 1: I'm like out in the world. As hard as it

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Speaker 1: is being a woman in the world, a black woman especially,

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Speaker 1: I genuinely think that the in real life way that

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Speaker 1: we interact with each other, which is not perfect by

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Speaker 1: any means, I don't think it is, it is so

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Speaker 1: deeply colored the way that online conversations would have you believe.

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Speaker 1: Sometimes I genuinely believe that we have let a small

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Speaker 1: sub section of very loud, very vocal extremists who are

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Speaker 1: in the minority dictate what the conversation is and like

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Speaker 1: really paint an impression that perhaps it's not always the

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Speaker 1: impression that is happening. And then when you have big

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Speaker 1: flash points like the t APP, that faction explodes, right,

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Speaker 1: and then they get to be like we told you so,

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Speaker 1: they get to really so easily dominate the conversation. And

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Speaker 1: I feel very bad for people who are just looking

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Speaker 1: for information about others, right, Like you're just interested in

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Speaker 1: a woman's perspective, or you're just interested, like if you're

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Speaker 1: a guy in your like, oh, I want to know

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Speaker 1: how to approach women or talk to women. The Internet

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Speaker 1: is the worst place to go for good faith information

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Speaker 1: in that vein, because it is the worst people who

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Speaker 1: are owning the conversation and owning the landscape. It is

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Speaker 1: a real problem.

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Speaker 4: You know.

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Speaker 2: When I was a graduate student at the University of Wisconsin,

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Speaker 2: I had the great fortune to study with this amazing professor,

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Speaker 2: doctor Janet Hyde, brilliant, brilliant professor, scientist, woman, and she

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Speaker 2: a big part of her career was advocating for what

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Speaker 2: she called the gender similarities hypothesis, which is both like

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Speaker 2: so common sense but also like somewhat radical, like the

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Speaker 2: idea that the men and women have so much more

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Speaker 2: in common than they have than are different between them,

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00:22:52,520 --> 00:22:56,800
Speaker 2: and yet for various reasons, partly because of that cadre

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Speaker 2: of extremists you mentioned, but also other reasons, starts to say,

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Speaker 2: we just love to focus on the differences, and and

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00:23:05,440 --> 00:23:07,320
Speaker 2: so we do. We talk about the differences, and we

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00:23:07,320 --> 00:23:09,679
Speaker 2: focus on the differences and reinforce the differences to the

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Speaker 2: exclusion of the fact that like actually, come on, like

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Speaker 2: it's it's not really that different, like men and women

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Speaker 2: for the most part, Like we just want to watch

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Speaker 2: a comedy and like eat a burger or like a

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Speaker 2: salad if you're a vegetarian, like whatever, Like we're just

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Speaker 2: and yet there's this big investment in like focusing on

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Speaker 2: the differences that gives them this outsize weight.

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Speaker 3: Yeah, I agree.

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Speaker 1: I mean who among us doesn't just want to put

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Speaker 1: on War of the World and see what happens.

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Speaker 3: Okay, well I don't want to watch that.

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Speaker 1: We're so different, Mike, I guess.

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Speaker 3: This is just a woman thing. You know how women are, Oh,

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Speaker 3: watch war the world.

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Speaker 1: Women be, women be nicid, women be shopping, women be

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Speaker 1: watching War of the World. Wait, so you you actually

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Speaker 1: did a little bit of research into the Tea on

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Speaker 1: her app, right, more than I did.

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Speaker 3: I did just a little bit.

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Speaker 2: I'm just like curious about it because something about it

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00:24:04,800 --> 00:24:06,480
Speaker 2: felt strange to me.

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Speaker 1: That's when I was reporting on the tea app. The

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Speaker 1: thing that I said was that it felt hinky and

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Speaker 1: that it just didn't something didn't sound right. And this

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Speaker 1: it sounds like you found the tea on her app

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00:24:17,920 --> 00:24:18,840
Speaker 1: similarly hinky.

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Speaker 2: It was just like the story arc was too quick

436
00:24:23,440 --> 00:24:27,080
Speaker 2: that it went from like not existing to being released

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Speaker 2: to suffering the same types of security breaches as the

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Speaker 2: tee app within like like a week. Yeah, I think, uh,

439
00:24:36,640 --> 00:24:42,880
Speaker 2: and like specifically, uh, just putting sensitive information in public

440
00:24:42,960 --> 00:24:46,240
Speaker 2: buckets with public or else that were not secure, which

441
00:24:46,280 --> 00:24:52,919
Speaker 2: is insane, Like that's that's just completely nuts, Like I

442
00:24:52,920 --> 00:24:55,680
Speaker 2: can't It's it's hard to imagine that somebody would do

443
00:24:55,760 --> 00:24:59,280
Speaker 2: that in the first place, and it's even harder to

444
00:24:59,320 --> 00:25:05,280
Speaker 2: imagine that some nobody would commit that same error of

445
00:25:05,320 --> 00:25:07,720
Speaker 2: an app that they had replicated that had suffered that

446
00:25:07,800 --> 00:25:09,520
Speaker 2: exact breach like a week before.

447
00:25:09,720 --> 00:25:10,960
Speaker 3: But you might think that, like.

448
00:25:11,119 --> 00:25:14,080
Speaker 2: If you were creating a copycat app that was adapted

449
00:25:14,080 --> 00:25:16,480
Speaker 2: for men instead of women, maybe a little light bulb

450
00:25:16,560 --> 00:25:18,800
Speaker 2: would go off and be like, hey, what if we

451
00:25:18,880 --> 00:25:24,040
Speaker 2: make these private URLs? And also like requiring the users

452
00:25:24,080 --> 00:25:27,600
Speaker 2: to upload their driver's licenses. That's another big red flag

453
00:25:27,640 --> 00:25:29,879
Speaker 2: for me, because you know, as you talked about I

454
00:25:29,920 --> 00:25:32,600
Speaker 2: think it was last week on the news roundup, uh

455
00:25:33,520 --> 00:25:40,000
Speaker 2: age verification is actually a dangerous morass. And it sounds

456
00:25:40,000 --> 00:25:43,000
Speaker 2: like the t app realized this like two years ago

457
00:25:43,040 --> 00:25:46,960
Speaker 2: and stopped requiring users to do that. That's right, yeah,

458
00:25:47,000 --> 00:25:49,520
Speaker 2: because they you know, I guess I don't know what

459
00:25:49,560 --> 00:25:51,680
Speaker 2: the reasons were, but I have to assume that part

460
00:25:51,680 --> 00:25:53,840
Speaker 2: of their rationale is that this is a dangerous thing

461
00:25:53,920 --> 00:25:57,760
Speaker 2: to do, to just be even like handling that level

462
00:25:57,800 --> 00:26:00,919
Speaker 2: of sensitive information. And yet the Tea on her app

463
00:26:01,480 --> 00:26:04,919
Speaker 2: did it, and like I have to ask, I have

464
00:26:04,960 --> 00:26:07,919
Speaker 2: to wonder why, and part of me, you know, like

465
00:26:08,680 --> 00:26:11,600
Speaker 2: you know, I've got my tinfoil hat on pretty pretty firmly,

466
00:26:11,720 --> 00:26:16,000
Speaker 2: but like perhaps this was all just a fishing expedition

467
00:26:16,080 --> 00:26:18,240
Speaker 2: to get a bunch of sensitive information about a bunch

468
00:26:18,240 --> 00:26:20,680
Speaker 2: of men, you know, seemed to be created by this guy,

469
00:26:21,000 --> 00:26:25,399
Speaker 2: Xavier Lampkin from the Newville Media Corporation. Never heard of

470
00:26:25,400 --> 00:26:29,280
Speaker 2: any of these companies. There's a billion app developing companies

471
00:26:29,320 --> 00:26:31,640
Speaker 2: that I've never heard of, but like I couldn't find

472
00:26:31,680 --> 00:26:33,240
Speaker 2: a whole lot about either.

473
00:26:33,040 --> 00:26:34,280
Speaker 3: Of them online.

474
00:26:34,960 --> 00:26:37,560
Speaker 2: Uh, but it just it just seemed really I guess

475
00:26:37,560 --> 00:26:39,119
Speaker 2: hanky is a good word.

476
00:26:39,240 --> 00:26:41,439
Speaker 1: I mean, that was exactly my experience when I was

477
00:26:41,520 --> 00:26:45,320
Speaker 1: first being researched on the tee app of I almost

478
00:26:45,359 --> 00:26:47,199
Speaker 1: I mean, I am not saying this is what was

479
00:26:47,240 --> 00:26:49,680
Speaker 1: going on. And I looked into the founder and I

480
00:26:49,840 --> 00:26:53,320
Speaker 1: was like, Okay, he has a paper trail, like a history,

481
00:26:53,600 --> 00:26:57,600
Speaker 1: an employment history that checks out. But it seemed so

482
00:26:57,880 --> 00:26:59,720
Speaker 1: absurd that I was like, is this some sort of

483
00:26:59,720 --> 00:27:00,320
Speaker 1: a seme?

484
00:27:00,800 --> 00:27:02,959
Speaker 2: And I remember we talked about that, and I actually

485
00:27:03,160 --> 00:27:04,840
Speaker 2: talked to you down from that. I was like, no, no,

486
00:27:04,880 --> 00:27:08,639
Speaker 2: I don't think it's a scheme. Like you're really underestimating

487
00:27:08,960 --> 00:27:14,359
Speaker 2: the uh incompetence and laziness of humans who work in

488
00:27:14,520 --> 00:27:17,879
Speaker 2: like software development and data where you're just trying to

489
00:27:17,920 --> 00:27:21,080
Speaker 2: do things really fast, and like I was like, YadA, YadA.

490
00:27:21,119 --> 00:27:23,920
Speaker 3: I could see how somebody would, you.

491
00:27:23,880 --> 00:27:29,120
Speaker 2: Know, back up that database to a public bucket and

492
00:27:29,920 --> 00:27:33,200
Speaker 2: plan to go back and lock it down later. They

493
00:27:33,200 --> 00:27:37,679
Speaker 2: never did, or like, so I talked you down. I

494
00:27:37,680 --> 00:27:40,159
Speaker 2: was like, no, it's not I think then competence can

495
00:27:40,200 --> 00:27:42,600
Speaker 2: fully explain what happened with the tea app. But then

496
00:27:42,640 --> 00:27:45,199
Speaker 2: for somebody else to replicate it a week later and

497
00:27:45,280 --> 00:27:47,879
Speaker 2: the same errors, I don't know.

498
00:27:48,040 --> 00:27:51,960
Speaker 1: And after there was so much reporting about the driver's

499
00:27:52,000 --> 00:27:55,520
Speaker 1: license and selfy aspect of it that oh, these women

500
00:27:55,920 --> 00:27:59,240
Speaker 1: when this app was breached, their driver's license is they're

501
00:27:59,440 --> 00:28:02,800
Speaker 1: but they're addresses on them. We're floating around the internet.

502
00:28:03,119 --> 00:28:05,919
Speaker 1: I don't know how you could have been even nominally

503
00:28:06,000 --> 00:28:09,040
Speaker 1: paying attention to that story. And then a handful of

504
00:28:09,119 --> 00:28:12,600
Speaker 1: days later be like, new app wants my driver's license

505
00:28:12,600 --> 00:28:15,160
Speaker 1: that has tea in the name of the app. I'm

506
00:28:15,200 --> 00:28:20,000
Speaker 1: gonna do it. I'm with you, So I as you know,

507
00:28:20,160 --> 00:28:22,720
Speaker 1: I went on an evolution of thinking this is some

508
00:28:22,760 --> 00:28:26,240
Speaker 1: sort of a scheme to this is just incompetence, and

509
00:28:26,280 --> 00:28:28,560
Speaker 1: I do suspect like if I don't know, but this

510
00:28:28,680 --> 00:28:31,840
Speaker 1: is my opinion, my take. I think that these apps

511
00:28:31,880 --> 00:28:36,520
Speaker 1: are just being quickly pushed out to capitalize on exactly

512
00:28:36,560 --> 00:28:39,680
Speaker 1: the kind of gender wars stuff we were just talking about.

513
00:28:39,720 --> 00:28:43,160
Speaker 1: And so I think when you are rushing, when you

514
00:28:43,320 --> 00:28:46,480
Speaker 1: want to be part of a current conversation, you want

515
00:28:46,480 --> 00:28:49,000
Speaker 1: to get there quickly. It doesn't really surprise me then

516
00:28:49,040 --> 00:28:51,920
Speaker 1: that maybe things like, oh, I don't know, security for

517
00:28:52,000 --> 00:28:54,400
Speaker 1: your user base would take a back seat when the

518
00:28:54,480 --> 00:28:57,640
Speaker 1: only thing that you're worried about is capitalizing on this

519
00:28:57,720 --> 00:29:01,640
Speaker 1: big engagement splashing moment about gender wars. Do you know

520
00:29:01,680 --> 00:29:02,840
Speaker 1: what I'm saying? I do.

521
00:29:02,920 --> 00:29:03,520
Speaker 3: I totally do.

522
00:29:03,640 --> 00:29:06,760
Speaker 2: I think I completely agree, And I also think that

523
00:29:06,800 --> 00:29:10,160
Speaker 2: there's a little bit of I don't know if irony

524
00:29:10,240 --> 00:29:12,720
Speaker 2: is the word here, and poetic justice makes it sound

525
00:29:12,720 --> 00:29:17,880
Speaker 2: more satisfying than it is. But like that same asymmetrical

526
00:29:17,920 --> 00:29:20,360
Speaker 2: criticism that you described of how like when the tea

527
00:29:20,440 --> 00:29:24,120
Speaker 2: app came out, so many men were writing about how

528
00:29:24,160 --> 00:29:27,000
Speaker 2: it was like messed up and like how dare these

529
00:29:27,040 --> 00:29:31,520
Speaker 2: women like a smirch men and it's unfair? Uh, and

530
00:29:31,560 --> 00:29:34,360
Speaker 2: that there was you know, with this new tea on

531
00:29:34,440 --> 00:29:36,600
Speaker 2: her app, there's just like none of that. It's just

532
00:29:36,680 --> 00:29:41,280
Speaker 2: a vacuum of conversation. And I think that that lack

533
00:29:41,280 --> 00:29:44,320
Speaker 2: of scrutiny, and that lack of conversation about it, I

534
00:29:44,400 --> 00:29:49,280
Speaker 2: think perhaps is giving companies like the Newville Media Corporation

535
00:29:50,320 --> 00:29:55,440
Speaker 2: a pass to just exploit and steal data from their

536
00:29:55,480 --> 00:30:00,560
Speaker 2: male users without commentary. Like I couldn't find anything about

537
00:30:00,600 --> 00:30:02,040
Speaker 2: it when I was searching earlier today.

538
00:30:02,280 --> 00:30:05,320
Speaker 1: Neither could I, and I guess that's my thing. If

539
00:30:05,360 --> 00:30:08,160
Speaker 1: you are. I don't want to use the word grifter,

540
00:30:08,320 --> 00:30:11,560
Speaker 1: because I don't think this person is grifting in that sense,

541
00:30:11,720 --> 00:30:14,520
Speaker 1: but if you were interested in like the men who

542
00:30:14,640 --> 00:30:17,040
Speaker 1: sign up for this app are the product, right, Like

543
00:30:17,560 --> 00:30:20,600
Speaker 1: I saw through this as marketing bullshit, But at least

544
00:30:20,680 --> 00:30:23,520
Speaker 1: for the tea app for women, the creator, who was

545
00:30:23,560 --> 00:30:26,400
Speaker 1: a man, was like, oh, well, my mother dated, and

546
00:30:26,480 --> 00:30:28,760
Speaker 1: I wanted to make an app where women wouldn't have

547
00:30:28,840 --> 00:30:32,200
Speaker 1: to face dangerous experiences when they dated. YadA, YadA, YadA.

548
00:30:32,400 --> 00:30:34,040
Speaker 1: Whether you believe that or not, I happen to not

549
00:30:34,080 --> 00:30:36,400
Speaker 1: really believe that. I think it's just marketing. At least

550
00:30:36,400 --> 00:30:39,040
Speaker 1: there was some nod to the fact that this is

551
00:30:39,200 --> 00:30:42,840
Speaker 1: an app created for women to keep women safe. Sure, fine, whatever.

552
00:30:43,760 --> 00:30:46,560
Speaker 1: I think that For the t TA on her app

553
00:30:46,600 --> 00:30:49,959
Speaker 1: for men to talk about women, I think it is

554
00:30:50,160 --> 00:30:52,920
Speaker 1: like this is someone who was interested in capitalizing on

555
00:30:52,960 --> 00:30:56,080
Speaker 1: the current conversation around gender wars, and the men who

556
00:30:56,200 --> 00:30:59,120
Speaker 1: sign up for the app, even if they're a promised

557
00:30:59,160 --> 00:31:00,600
Speaker 1: like oh you can find out all the tea on

558
00:31:00,640 --> 00:31:03,120
Speaker 1: these women, Da da da da, they are the product

559
00:31:03,120 --> 00:31:05,520
Speaker 1: who is being exploited, as evidence by the fact that

560
00:31:05,560 --> 00:31:07,200
Speaker 1: they didn't even do the bare minimum to keep their

561
00:31:07,280 --> 00:31:11,560
Speaker 1: data safe. So yeah, it's just the worst people dominating

562
00:31:11,600 --> 00:31:15,280
Speaker 1: the conversation, and people who of all genders, who might

563
00:31:15,360 --> 00:31:19,280
Speaker 1: actually be trying to use these apps to keep themselves safe,

564
00:31:19,480 --> 00:31:22,880
Speaker 1: are being exploited by people who do not actually care

565
00:31:22,920 --> 00:31:25,160
Speaker 1: about that that are selling them a FOSSi bill of sale.

566
00:31:25,320 --> 00:31:26,800
Speaker 1: I guess that's my ultimate point.

567
00:31:27,040 --> 00:31:31,120
Speaker 2: Yeah, absolutely, everybody deserves better. Women deserve better, men deserve better.

568
00:31:31,200 --> 00:31:36,200
Speaker 3: We all deserve better. Exactly more.

569
00:31:36,200 --> 00:31:50,280
Speaker 1: After a quick break, let's get right back into it,

570
00:31:53,720 --> 00:31:57,719
Speaker 1: speaking of wits, speaking of deserving better and justice. I

571
00:31:57,720 --> 00:31:59,960
Speaker 1: guess one of the stories I've been keeping a very

572
00:32:00,080 --> 00:32:02,240
Speaker 1: close eye on in the wake of the Supreme Court

573
00:32:02,320 --> 00:32:07,520
Speaker 1: ruling gutting Row is these period tracking apps. When Row

574
00:32:07,680 --> 00:32:11,320
Speaker 1: first fell, probably the number one question that listeners and

575
00:32:11,360 --> 00:32:13,320
Speaker 1: people in my own community would ask me was, oh,

576
00:32:13,360 --> 00:32:16,080
Speaker 1: bridget should I be using period tracking apps? And I

577
00:32:16,080 --> 00:32:18,280
Speaker 1: always say I mean, I err on the side of caution.

578
00:32:18,840 --> 00:32:22,200
Speaker 1: We had a privacy expert on the show who said, honestly,

579
00:32:22,280 --> 00:32:24,320
Speaker 1: for that kind of thing, good old fashioned pen and

580
00:32:24,320 --> 00:32:26,160
Speaker 1: paper is going to be your best bet. But there

581
00:32:26,280 --> 00:32:29,920
Speaker 1: was one specific, very popular app called Flow bet and

582
00:32:29,920 --> 00:32:32,400
Speaker 1: bet in court because of a lawsuit filed over the

583
00:32:32,480 --> 00:32:37,000
Speaker 1: apps practice of sharing people's sensitive information with third parties,

584
00:32:37,200 --> 00:32:41,320
Speaker 1: including Meta, without permission. So this being a period tracking app,

585
00:32:41,360 --> 00:32:46,480
Speaker 1: when I say sensitive information, I'm talking about your menstrual cycles,

586
00:32:46,560 --> 00:32:50,320
Speaker 1: like the most intimate stuff happening in your body. That

587
00:32:50,520 --> 00:32:53,880
Speaker 1: is the information they are sharing with third parties, including Meta.

588
00:32:53,920 --> 00:32:56,440
Speaker 1: I don't really want to talk to Mark Zuckerberg and

589
00:32:56,520 --> 00:33:01,400
Speaker 1: Adamoseri from Instagram about my cycles. Actually, So, this lawsuit

590
00:33:01,440 --> 00:33:04,160
Speaker 1: was filed in the wake of a twenty nineteen bombshell

591
00:33:04,360 --> 00:33:08,840
Speaker 1: Wall Street Journal story reporting that despite promises of confidentiality,

592
00:33:08,880 --> 00:33:11,920
Speaker 1: because remember these companies can and do, just say whatever

593
00:33:11,960 --> 00:33:14,680
Speaker 1: and then they'll do a completely different thing, Flow shared

594
00:33:14,840 --> 00:33:18,360
Speaker 1: users period data with Meta, Google and other third parties,

595
00:33:18,400 --> 00:33:23,320
Speaker 1: who then used it for targeted advertising. So the other companies,

596
00:33:23,480 --> 00:33:25,600
Speaker 1: Google and some of the other third parties they just

597
00:33:25,640 --> 00:33:28,280
Speaker 1: settled in a suit, but not Flow and not Meta,

598
00:33:28,360 --> 00:33:31,840
Speaker 1: so went to court. So no one disputes that the

599
00:33:31,920 --> 00:33:34,360
Speaker 1: data was shared it happened. The thing that was really

600
00:33:34,400 --> 00:33:37,840
Speaker 1: in dispute was whether or not menstrual data counts as

601
00:33:38,200 --> 00:33:41,240
Speaker 1: health data, which is a special protected class of data

602
00:33:41,280 --> 00:33:44,320
Speaker 1: in the US, and some states, most notably California, where

603
00:33:44,320 --> 00:33:47,960
Speaker 1: this lawsuit took place, have even stronger state specific protections

604
00:33:48,000 --> 00:33:51,080
Speaker 1: for health data specifically, so there was a jury trial

605
00:33:51,160 --> 00:33:53,600
Speaker 1: about two weeks ago to determine whether or not Flow

606
00:33:53,600 --> 00:33:56,960
Speaker 1: and Meta used this class as personal health data for

607
00:33:57,040 --> 00:34:00,960
Speaker 1: advertising and other purposes. Carol cee Vealgas, the attorney for

608
00:34:01,000 --> 00:34:03,640
Speaker 1: the plaintiffs, asked the jury and her opening statement to

609
00:34:03,640 --> 00:34:08,480
Speaker 1: decide how seriously big tech takes women's privacy. She said,

610
00:34:08,800 --> 00:34:11,319
Speaker 1: this wasn't an accident, this wasn't a mistake. This is

611
00:34:11,360 --> 00:34:14,560
Speaker 1: how Meta makes money. This is their business, and honestly,

612
00:34:14,719 --> 00:34:17,440
Speaker 1: knowing what I know about Metta, she is not wrong.

613
00:34:17,640 --> 00:34:20,320
Speaker 1: An you know who else agrees with me the courts,

614
00:34:20,360 --> 00:34:23,959
Speaker 1: because this week a California jury found that Meta did,

615
00:34:24,000 --> 00:34:27,200
Speaker 1: in fact a legally collect user health data from the

616
00:34:27,280 --> 00:34:32,400
Speaker 1: Flow period tracking app, violating California's wiretap law. In a

617
00:34:32,440 --> 00:34:37,000
Speaker 1: statement about the verdict, the attorneys said, this verdict sends

618
00:34:37,000 --> 00:34:39,479
Speaker 1: a clear message about the protection of digital health data

619
00:34:39,520 --> 00:34:42,680
Speaker 1: and the responsibility of big tech companies like Meta that

620
00:34:42,760 --> 00:34:47,600
Speaker 1: covertly profit from users. Most intimate information must be held accountable,

621
00:34:47,760 --> 00:34:49,320
Speaker 1: and I could not agree more.

622
00:34:49,600 --> 00:34:53,000
Speaker 2: I thought this was a particularly interesting case because the

623
00:34:53,040 --> 00:34:56,480
Speaker 2: way that apps like to get around the legality of

624
00:34:56,600 --> 00:34:59,160
Speaker 2: sharing information like this with third parties is that they'll

625
00:34:59,160 --> 00:35:00,520
Speaker 2: say we aren't help the apps.

626
00:35:01,280 --> 00:35:01,480
Speaker 3: You know.

627
00:35:01,520 --> 00:35:04,479
Speaker 2: We've seen that in the realm of therapy. We've seen

628
00:35:04,520 --> 00:35:07,080
Speaker 2: it in terms of period.

629
00:35:06,719 --> 00:35:09,319
Speaker 3: Tracking, you know.

630
00:35:09,400 --> 00:35:13,200
Speaker 2: And it's it is kind of like a gray area

631
00:35:13,440 --> 00:35:17,360
Speaker 2: in some regards what is and is not health information,

632
00:35:17,400 --> 00:35:20,440
Speaker 2: because a lot of stuff is like very personal but

633
00:35:20,480 --> 00:35:25,040
Speaker 2: not necessarily health information, like the food you eat. It's

634
00:35:25,320 --> 00:35:28,200
Speaker 2: related to your health, but we probably wouldn't call that

635
00:35:28,239 --> 00:35:31,480
Speaker 2: health information. The World Health Organization, I think has a

636
00:35:31,520 --> 00:35:34,720
Speaker 2: really good definition of health. They define it as quote

637
00:35:34,840 --> 00:35:38,200
Speaker 2: a state of complete physical, mental, and social well being

638
00:35:38,320 --> 00:35:43,080
Speaker 2: and not merely the absence of disease or infirmity. And I,

639
00:35:43,600 --> 00:35:49,080
Speaker 2: you know, as a public health preventive medicine guy, I

640
00:35:49,120 --> 00:35:51,120
Speaker 2: really resonate with that. I think it's really important to

641
00:35:51,160 --> 00:35:54,440
Speaker 2: take a holistic approach to what is health. But that

642
00:35:54,640 --> 00:35:59,839
Speaker 2: very holistic approach does run into some problems when it's

643
00:36:00,320 --> 00:36:04,719
Speaker 2: up against the law where the law covers specifically health data. Right,

644
00:36:04,760 --> 00:36:08,560
Speaker 2: So we have HIPPA at the federal level, which protects

645
00:36:08,840 --> 00:36:11,799
Speaker 2: health information, and then we have states like California that

646
00:36:11,880 --> 00:36:16,320
Speaker 2: also have health information laws. And so in this case,

647
00:36:17,480 --> 00:36:22,400
Speaker 2: my understanding is that Meta was suggesting that like period

648
00:36:22,440 --> 00:36:25,480
Speaker 2: tracking information was not health data, so there's no reason

649
00:36:25,520 --> 00:36:29,360
Speaker 2: that it should be held to this higher standard of privacy.

650
00:36:31,320 --> 00:36:34,640
Speaker 2: And they were just really trying to exploit that gray

651
00:36:34,680 --> 00:36:37,400
Speaker 2: area in the way that we see them try to

652
00:36:37,480 --> 00:36:42,040
Speaker 2: exploit every opportunity they can to harvest as much data

653
00:36:42,160 --> 00:36:46,920
Speaker 2: from their users as possible and then use it to

654
00:36:46,960 --> 00:36:50,120
Speaker 2: sell ads or whatever they can think of to make

655
00:36:50,200 --> 00:36:53,080
Speaker 2: money without any concern for the health and well being

656
00:36:53,080 --> 00:36:55,080
Speaker 2: of their actual users exactly.

657
00:36:55,120 --> 00:36:57,200
Speaker 1: And we see this time and time again with other

658
00:36:57,400 --> 00:37:01,480
Speaker 1: kind of apps that I guess you do think of

659
00:37:01,520 --> 00:37:03,759
Speaker 1: them as health apps, but they get to say like, oh,

660
00:37:03,800 --> 00:37:05,440
Speaker 1: we're not a health app, Like I came up in

661
00:37:05,440 --> 00:37:08,120
Speaker 1: our conversation that we had a while ago about better Help,

662
00:37:08,120 --> 00:37:10,600
Speaker 1: where they say, oh, well, we aren't a mental health

663
00:37:10,640 --> 00:37:13,040
Speaker 1: app or we're not. We don't have health information, we

664
00:37:13,040 --> 00:37:16,680
Speaker 1: shouldn't be held to that scrutiny. We're just connecting people

665
00:37:17,120 --> 00:37:19,759
Speaker 1: with people who are mental health professionals.

666
00:37:20,000 --> 00:37:25,200
Speaker 2: And you know, I used to work for a company

667
00:37:25,239 --> 00:37:29,800
Speaker 2: where we built apps that operated in a HIPPA environment,

668
00:37:29,840 --> 00:37:32,440
Speaker 2: where all of the data that we were collecting from users,

669
00:37:33,160 --> 00:37:36,839
Speaker 2: and you know, we were processing it and using it

670
00:37:36,880 --> 00:37:39,360
Speaker 2: to try to help people, but all of it was

671
00:37:39,440 --> 00:37:43,080
Speaker 2: governed by HIPPA. So I do have some I'm not

672
00:37:43,120 --> 00:37:46,440
Speaker 2: a lawyer, but I do have some experience leading the

673
00:37:46,520 --> 00:37:50,480
Speaker 2: data team that had to operate within HIPPA rules. And

674
00:37:51,000 --> 00:37:54,160
Speaker 2: I'll share that in my personal experience just talking with

675
00:37:54,200 --> 00:37:56,040
Speaker 2: people at like parties or wherever.

676
00:37:56,719 --> 00:37:59,120
Speaker 3: People really misunderstand HIPPA.

677
00:37:59,200 --> 00:38:05,160
Speaker 2: And I think often the average person has this idea that, uh,

678
00:38:05,400 --> 00:38:08,040
Speaker 2: it just like protects all health data and anything that

679
00:38:08,120 --> 00:38:11,360
Speaker 2: might that might be related to your health in a

680
00:38:11,400 --> 00:38:15,040
Speaker 2: common sense way is protected by HIPPA, And that's just

681
00:38:15,120 --> 00:38:15,640
Speaker 2: not the case.

682
00:38:15,680 --> 00:38:16,719
Speaker 3: It is a very.

683
00:38:18,040 --> 00:38:22,520
Speaker 2: Narrow set of data and information about your body and

684
00:38:22,560 --> 00:38:24,759
Speaker 2: your health that is protected by HIPPA. And so if

685
00:38:24,800 --> 00:38:29,640
Speaker 2: you're using an app, unless you sign off on like

686
00:38:29,760 --> 00:38:33,120
Speaker 2: what's called a HIPPA authorization right when you're like onboarding

687
00:38:33,120 --> 00:38:35,399
Speaker 2: onto the app, when you're setting up your account, they'll

688
00:38:35,400 --> 00:38:37,400
Speaker 2: have you read like the terms of service and like

689
00:38:37,440 --> 00:38:40,040
Speaker 2: you'll sign off on the terms of service, but there

690
00:38:40,080 --> 00:38:42,360
Speaker 2: will also be a separate thing that you have to

691
00:38:42,400 --> 00:38:46,560
Speaker 2: sign off on that is the Hippa authorization that essentially,

692
00:38:47,600 --> 00:38:49,839
Speaker 2: you know, says that you give up some of your

693
00:38:49,880 --> 00:38:55,080
Speaker 2: Hippa writes so that they can process the data. Unless

694
00:38:55,200 --> 00:38:57,399
Speaker 2: unless that's part of what you sign off on during

695
00:38:57,400 --> 00:39:01,120
Speaker 2: the onboarding service, that's not being true as health data

696
00:39:01,160 --> 00:39:04,040
Speaker 2: by that app, and so they're just considering it any

697
00:39:04,080 --> 00:39:08,120
Speaker 2: other data like whether you know what size shirt you wear,

698
00:39:08,360 --> 00:39:13,080
Speaker 2: or whether you prefer blue to green, or some sort

699
00:39:13,080 --> 00:39:13,839
Speaker 2: of data like that.

700
00:39:14,239 --> 00:39:17,040
Speaker 1: And it really is troubling when we're talking about something

701
00:39:17,120 --> 00:39:20,640
Speaker 1: that is so intimate about your body. It's like some

702
00:39:21,040 --> 00:39:24,560
Speaker 1: of the most intimate information about us. And I will say,

703
00:39:24,800 --> 00:39:27,279
Speaker 1: the damages have yet to be set in this case,

704
00:39:27,280 --> 00:39:31,560
Speaker 1: and I suspect that Meta will appeal, but basically the

705
00:39:32,320 --> 00:39:35,560
Speaker 1: flow app Google settled pretty quickly.

706
00:39:36,000 --> 00:39:36,680
Speaker 3: Meta lost.

707
00:39:37,360 --> 00:39:40,680
Speaker 1: They will appeal, but we'll see everybody involved in this

708
00:39:40,920 --> 00:39:43,719
Speaker 1: basically has now been found to.

709
00:39:43,719 --> 00:39:45,320
Speaker 3: Have been doing something they shouldn't have been doing with

710
00:39:45,360 --> 00:39:46,200
Speaker 3: our data.

711
00:39:46,320 --> 00:39:48,520
Speaker 1: And I do think that that is still a win

712
00:39:48,640 --> 00:39:51,120
Speaker 1: for privacy, Like, I think it sends a message that

713
00:39:51,680 --> 00:39:54,080
Speaker 1: you can't just there are limits to how much you

714
00:39:54,120 --> 00:39:57,279
Speaker 1: can exploit people, like not everything about us is for

715
00:39:57,400 --> 00:39:59,560
Speaker 1: sale or simply hours for the taking, and that there

716
00:39:59,600 --> 00:40:03,799
Speaker 1: will be consequences. Again, Meta will appeal. Even if they

717
00:40:03,960 --> 00:40:06,080
Speaker 1: they lose that appeal, what they will end up paying

718
00:40:06,080 --> 00:40:08,600
Speaker 1: on I'm sure will be nothing. And I also think

719
00:40:08,640 --> 00:40:11,960
Speaker 1: the fact that flow Meta, you know, we're all found

720
00:40:12,160 --> 00:40:14,520
Speaker 1: to be doing something they shouldn't be doing. When the

721
00:40:14,560 --> 00:40:16,960
Speaker 1: conversation comes up of like, well can I trust this

722
00:40:17,040 --> 00:40:20,760
Speaker 1: period tracking app? I even if the damages aren't much,

723
00:40:21,920 --> 00:40:24,480
Speaker 1: that being part of the public record, that know, you

724
00:40:24,560 --> 00:40:27,719
Speaker 1: cannot trust this this specific app like nope, Mick, I

725
00:40:27,719 --> 00:40:30,160
Speaker 1: think that that will be that will go a long

726
00:40:30,200 --> 00:40:34,320
Speaker 1: way in helping people understand the dynamic that we actually

727
00:40:34,320 --> 00:40:36,759
Speaker 1: live in. That like, these apps will say anything, right,

728
00:40:36,880 --> 00:40:39,000
Speaker 1: the tea app said they were deleting selfies where they

729
00:40:39,040 --> 00:40:42,440
Speaker 1: know these you really can't, like how much do you

730
00:40:42,480 --> 00:40:45,160
Speaker 1: trust Mark Zuckerberg with this kind of information about yourself?

731
00:40:45,200 --> 00:40:47,879
Speaker 1: I don't trust them at all? Right, And so even

732
00:40:47,920 --> 00:40:51,360
Speaker 1: if it's not going to be something that actually financially

733
00:40:51,440 --> 00:40:53,600
Speaker 1: hurts any of these companies that have been, you know,

734
00:40:53,680 --> 00:40:55,719
Speaker 1: shown to be doing the wrong thing. I think it

735
00:40:55,760 --> 00:40:58,759
Speaker 1: helps us see, hey, maybe I really can't just take

736
00:40:58,760 --> 00:40:59,520
Speaker 1: them at their words.

737
00:41:00,000 --> 00:41:03,720
Speaker 2: Absolutely, you can't trust them, and uh and we also

738
00:41:04,160 --> 00:41:05,680
Speaker 2: have to stand up for privacy.

739
00:41:05,719 --> 00:41:08,200
Speaker 3: It's nice to see people doing that and winning.

740
00:41:08,640 --> 00:41:10,759
Speaker 1: Speaking of Meta, I did want to give a quick

741
00:41:10,760 --> 00:41:15,400
Speaker 1: psa that Meta just rolled out this new Instagram map

742
00:41:15,440 --> 00:41:17,880
Speaker 1: feature which is sort of like Sign my Friends or

743
00:41:17,880 --> 00:41:21,000
Speaker 1: the snap map, which will allow friends to monitor each

744
00:41:21,040 --> 00:41:25,080
Speaker 1: other's real time location. So essentially, this new feature on

745
00:41:25,120 --> 00:41:29,040
Speaker 1: Instagram allows users to opt in to sharing their location

746
00:41:29,360 --> 00:41:34,239
Speaker 1: with people with a bevy of options including friends, followers,

747
00:41:34,280 --> 00:41:38,000
Speaker 1: you follow back, close friends, selected friends, or no one.

748
00:41:38,320 --> 00:41:41,239
Speaker 1: So obviously whether you want to use this feature is

749
00:41:41,320 --> 00:41:44,319
Speaker 1: up to you. I just do not trust Meta. And

750
00:41:44,360 --> 00:41:48,160
Speaker 1: the thing with Meta specifically is that they are known

751
00:41:48,200 --> 00:41:54,200
Speaker 1: to have this like just a dizzying amount of third

752
00:41:54,280 --> 00:41:57,640
Speaker 1: parties and contractors that they share information with. So when

753
00:41:57,680 --> 00:42:00,879
Speaker 1: you share information on Meta, a lot of companies are

754
00:42:00,920 --> 00:42:03,520
Speaker 1: like this, but like Meta is like like this at

755
00:42:03,560 --> 00:42:06,960
Speaker 1: a different on a different level. You just have no

756
00:42:07,120 --> 00:42:11,080
Speaker 1: idea who all, like what landscape that is being shared with,

757
00:42:11,160 --> 00:42:13,160
Speaker 1: which is why I'm like very particular about how my

758
00:42:13,200 --> 00:42:14,520
Speaker 1: information is shared via Meta.

759
00:42:14,960 --> 00:42:18,000
Speaker 2: Yeah, I think that's like such a good way to

760
00:42:18,040 --> 00:42:20,000
Speaker 2: say it, and I think that applies to so many situations.

761
00:42:20,080 --> 00:42:22,960
Speaker 2: It's like a lot of companies are like this, but

762
00:42:23,080 --> 00:42:25,080
Speaker 2: Meta is like at another level.

763
00:42:25,200 --> 00:42:27,920
Speaker 1: And again, I mean, as we just said, Meta has

764
00:42:27,960 --> 00:42:30,120
Speaker 1: like I don't trust any of these companies. I'm not

765
00:42:30,160 --> 00:42:34,360
Speaker 1: saying that any one is like doing greater or whatever whatever. However,

766
00:42:35,360 --> 00:42:38,680
Speaker 1: Facebook and Meta are so bad and so in a

767
00:42:38,760 --> 00:42:41,840
Speaker 1: landscape where it's full of people, full of companies and

768
00:42:41,880 --> 00:42:43,600
Speaker 1: people that run them that are doing the wrong thing,

769
00:42:43,920 --> 00:42:46,960
Speaker 1: Meta is doing the wrong thing double right. Luckily, this

770
00:42:47,040 --> 00:42:50,640
Speaker 1: is very easy to disable. Even though Meta says that

771
00:42:50,680 --> 00:42:52,760
Speaker 1: it is an opt in system, I would still recommend

772
00:42:52,840 --> 00:42:54,960
Speaker 1: checking your settings to see if you are currently sharing

773
00:42:54,960 --> 00:42:58,440
Speaker 1: your location, So please go and check your settings. However,

774
00:42:58,680 --> 00:43:01,480
Speaker 1: even if Instagram does not know your location, people who

775
00:43:01,600 --> 00:43:04,520
Speaker 1: tech their posts with location can still be seen if

776
00:43:04,560 --> 00:43:08,800
Speaker 1: they opt in. We'll throw those instructions into the show notes.

777
00:43:08,920 --> 00:43:11,279
Speaker 1: But again, I just think people should be aware that

778
00:43:11,360 --> 00:43:14,239
Speaker 1: this is a new feature, spend some time thinking about

779
00:43:14,239 --> 00:43:16,360
Speaker 1: whether or not you want to share this with Instagram

780
00:43:16,400 --> 00:43:20,680
Speaker 1: and Meta and also in general. I mean, like, who

781
00:43:20,760 --> 00:43:23,279
Speaker 1: really was clamoring for this? But it just really makes me think,

782
00:43:23,520 --> 00:43:27,160
Speaker 1: I don't know if the geniuses over there at Meta

783
00:43:27,200 --> 00:43:30,920
Speaker 1: and the geniuses like Adam MOSERI who you know, is

784
00:43:31,760 --> 00:43:33,960
Speaker 1: the tech guy I probably hate the most out of

785
00:43:34,000 --> 00:43:35,840
Speaker 1: all the tech guys we talk about on this podcast.

786
00:43:35,880 --> 00:43:38,080
Speaker 1: For some reason, I don't know that they really know

787
00:43:38,239 --> 00:43:41,360
Speaker 1: what people want. If you've ever talked to a woman,

788
00:43:41,640 --> 00:43:46,919
Speaker 1: I bet that you might know that opting people in

789
00:43:46,960 --> 00:43:49,640
Speaker 1: to sharing their location when they post on social media

790
00:43:49,800 --> 00:43:52,600
Speaker 1: is not something that I think most women would think

791
00:43:52,680 --> 00:43:56,040
Speaker 1: was super cool. But you know, on at a company

792
00:43:56,120 --> 00:43:59,480
Speaker 1: run by Mark Zuckerberg, who initially started his tech Empire

793
00:43:59,680 --> 00:44:02,319
Speaker 1: to the looks of women, guess I can't say I

794
00:44:02,360 --> 00:44:03,200
Speaker 1: expect much more.

795
00:44:03,480 --> 00:44:05,719
Speaker 2: Yeah, it has nothing to do with what people want

796
00:44:05,760 --> 00:44:08,600
Speaker 2: and everything to do with them getting more information about

797
00:44:08,600 --> 00:44:11,680
Speaker 2: you so they can sell more stuff, some of it

798
00:44:11,719 --> 00:44:13,879
Speaker 2: to you, some of it to other people like you.

799
00:44:14,280 --> 00:44:15,800
Speaker 2: Doesn't even matter. They don't even have to have a

800
00:44:15,840 --> 00:44:19,319
Speaker 2: specific reason why they want this information. They just want

801
00:44:19,320 --> 00:44:22,080
Speaker 2: as much information about you as they can have, and

802
00:44:22,200 --> 00:44:24,920
Speaker 2: they're not real juicy with who they share that with.

803
00:44:25,360 --> 00:44:29,879
Speaker 1: The conversation around this location sharing change on Instagram has

804
00:44:29,960 --> 00:44:33,800
Speaker 1: been really interesting. It's mostly been happening on threads where

805
00:44:34,239 --> 00:44:40,000
Speaker 1: people are essentially complaining about this change, in some cases

806
00:44:40,320 --> 00:44:42,680
Speaker 1: maybe sort of fear mongering about it a little bit.

807
00:44:42,680 --> 00:44:45,680
Speaker 1: They're kind of leaving out the fact that according to Meta,

808
00:44:45,760 --> 00:44:49,360
Speaker 1: this change is opt in, so it's not automatically sharing

809
00:44:49,400 --> 00:44:52,520
Speaker 1: your location. However, as the story that we just talked

810
00:44:52,520 --> 00:44:55,440
Speaker 1: about indicates, you really can't trust what Meta says, so

811
00:44:55,480 --> 00:44:58,800
Speaker 1: they are a company that, per a court, will say one.

812
00:44:58,640 --> 00:44:59,520
Speaker 3: Thing and do another.

813
00:45:00,000 --> 00:45:04,560
Speaker 1: And Adamosi himself is in people's posts on thread saying no,

814
00:45:04,640 --> 00:45:08,279
Speaker 1: that's not true. We're not automatically sharing anybody's location. It's

815
00:45:08,320 --> 00:45:11,600
Speaker 1: totally opt in. And I think what they are missing

816
00:45:11,800 --> 00:45:15,080
Speaker 1: is that this is their fault. This is the level

817
00:45:15,160 --> 00:45:19,560
Speaker 1: to which consumers simply do not trust Meta at Instagram,

818
00:45:19,600 --> 00:45:24,480
Speaker 1: and clearly those consumers are not wrong for, you know,

819
00:45:24,600 --> 00:45:27,960
Speaker 1: not giving Facebook or Meta or Instagram the benefit of

820
00:45:28,000 --> 00:45:34,040
Speaker 1: the doubt. I've really been kind of surprised seeing people say, wow,

821
00:45:34,840 --> 00:45:39,560
Speaker 1: average consumers are misunderstanding this change and mid fear mongering

822
00:45:39,600 --> 00:45:41,640
Speaker 1: and misreporting it. I don't think it's any of that.

823
00:45:41,719 --> 00:45:45,560
Speaker 1: I think that Facebook really has not seen the level

824
00:45:45,600 --> 00:45:48,080
Speaker 1: to which people simply do not trust this company, and

825
00:45:48,080 --> 00:45:49,719
Speaker 1: they're not wrong for doing.

826
00:45:49,480 --> 00:45:51,400
Speaker 3: So, completely agree.

827
00:45:51,960 --> 00:45:57,440
Speaker 2: This really exposes how little trust people have for Meta,

828
00:45:57,480 --> 00:46:01,239
Speaker 2: And like you just said, it's not that people are misunderstanding.

829
00:46:01,239 --> 00:46:06,759
Speaker 2: People understand perfectly well that Meta over and over again

830
00:46:06,840 --> 00:46:10,200
Speaker 2: has demonstrated that they can't be trusted, that they will lie,

831
00:46:10,360 --> 00:46:13,799
Speaker 2: that they will try to sneak your data however they can.

832
00:46:15,000 --> 00:46:20,080
Speaker 2: And also, I think the claim of it's totally opt

833
00:46:20,120 --> 00:46:23,799
Speaker 2: in it miss is an important piece That a lot

834
00:46:23,840 --> 00:46:29,560
Speaker 2: of people probably allowed Meta to access their locations when

835
00:46:29,560 --> 00:46:32,600
Speaker 2: they first downloaded these apps, you know, some cases years

836
00:46:32,640 --> 00:46:36,920
Speaker 2: ago before this feature existed, and if that's the case,

837
00:46:37,640 --> 00:46:40,080
Speaker 2: I'm pretty sure it would still be on.

838
00:46:40,680 --> 00:46:43,080
Speaker 3: So it's not surprising to.

839
00:46:43,040 --> 00:46:47,640
Speaker 2: Me that we've seen some people be surprised that their

840
00:46:47,760 --> 00:46:53,640
Speaker 2: location sharing is turned on. You know, maybe they technically

841
00:46:53,800 --> 00:46:56,680
Speaker 2: did opt into it at some point in the past

842
00:46:56,760 --> 00:47:02,080
Speaker 2: before this feature existed, but that doesn't change the fact

843
00:47:02,080 --> 00:47:04,799
Speaker 2: that they now feel surprised that their location is being

844
00:47:04,960 --> 00:47:07,120
Speaker 2: shared in this way exactly.

845
00:47:07,239 --> 00:47:10,560
Speaker 1: And it's really been interesting to see folks like Adamosi

846
00:47:10,760 --> 00:47:14,719
Speaker 1: clearly be in damage control mode, and it just shows me,

847
00:47:14,880 --> 00:47:18,759
Speaker 1: they don't understand the role that they play in the

848
00:47:18,800 --> 00:47:23,400
Speaker 1: media diet of their own consumers. Diego Jimenez, who is

849
00:47:23,480 --> 00:47:28,320
Speaker 1: a product designer at Instagram, posted on threads Misinformation Aside

850
00:47:28,360 --> 00:47:31,520
Speaker 1: the reactions to the new ig friends map are pretty funny.

851
00:47:31,800 --> 00:47:34,560
Speaker 1: Younger generations get it and love it. They already use

852
00:47:34,600 --> 00:47:37,920
Speaker 1: social maps. Boomers don't understand it and freak out they

853
00:47:37,960 --> 00:47:41,080
Speaker 1: want photos back. The real takeaway is, no matter how

854
00:47:41,160 --> 00:47:44,040
Speaker 1: clearly a new feature is explained, people won't read the

855
00:47:44,040 --> 00:47:47,000
Speaker 1: explanation or give it a try before rushing to alarm

856
00:47:47,080 --> 00:47:51,200
Speaker 1: the world about it. Shruggy emoji and that that coming

857
00:47:51,239 --> 00:47:55,239
Speaker 1: from somebody who is an internal product designer at Instagram

858
00:47:55,360 --> 00:47:58,920
Speaker 1: tells me so much. These people simply do not get it.

859
00:47:59,000 --> 00:48:01,480
Speaker 1: They do not get the way that people are so

860
00:48:01,680 --> 00:48:05,840
Speaker 1: distrustful of them, their company, their platform, their products, these changes.

861
00:48:06,160 --> 00:48:08,920
Speaker 1: The fact that there's been so much loud and vocal

862
00:48:09,040 --> 00:48:12,040
Speaker 1: backlash that they basically have to resort to you're just

863
00:48:12,080 --> 00:48:15,240
Speaker 1: an uncool boom or if you don't like this, really

864
00:48:15,320 --> 00:48:18,120
Speaker 1: tells me that they have no idea how the public

865
00:48:18,320 --> 00:48:22,640
Speaker 1: actually perceives them, and you know, until they're ready to

866
00:48:23,560 --> 00:48:26,799
Speaker 1: hear that and like deeply in a meaningful way, make

867
00:48:26,840 --> 00:48:31,680
Speaker 1: amends for the rightful reasons why people are not willing

868
00:48:31,719 --> 00:48:33,600
Speaker 1: to trust them and are not willing to give them

869
00:48:33,640 --> 00:48:36,320
Speaker 1: the benefit of the doubt and are rushing to sound

870
00:48:36,400 --> 00:48:39,320
Speaker 1: the alarm they're not gonna learn. Like it just is

871
00:48:39,320 --> 00:48:42,680
Speaker 1: is sad to see people essentially say, no, it's the

872
00:48:42,800 --> 00:48:43,760
Speaker 1: users who are wrong.

873
00:48:44,600 --> 00:48:45,640
Speaker 3: That quote you just read.

874
00:48:45,800 --> 00:48:49,280
Speaker 2: I thought that was some sort of like pick journalists

875
00:48:49,640 --> 00:48:53,120
Speaker 2: or podcast or something. It's nuts that that's an actual,

876
00:48:53,520 --> 00:48:57,479
Speaker 2: like internal product person at Meta talking about their own

877
00:48:57,640 --> 00:48:58,879
Speaker 2: users in that way.

878
00:48:59,120 --> 00:49:00,600
Speaker 3: It sounds ainful.

879
00:49:00,880 --> 00:49:02,960
Speaker 1: And if you had, if you worked on a team

880
00:49:03,280 --> 00:49:07,800
Speaker 1: that rolled something out that had this level of loud backlash, again,

881
00:49:07,880 --> 00:49:12,120
Speaker 1: even if some of it is perhaps not entirely accurate,

882
00:49:12,400 --> 00:49:15,600
Speaker 1: in that Meta says, oh, this is all opt in.

883
00:49:15,640 --> 00:49:18,839
Speaker 1: We're not automatically sharing anybody's location with anybody, But they

884
00:49:18,840 --> 00:49:20,719
Speaker 1: are a company, per the courts that you can that

885
00:49:20,760 --> 00:49:24,319
Speaker 1: you cannot trust if you've like if the only way

886
00:49:24,360 --> 00:49:26,759
Speaker 1: that you can respond to that is essentially telling the

887
00:49:26,760 --> 00:49:29,440
Speaker 1: people who are speaking out about how they're responding to

888
00:49:29,480 --> 00:49:33,359
Speaker 1: this rollout by saying you're uncool, you don't understand tech,

889
00:49:33,400 --> 00:49:36,000
Speaker 1: and you don't get it. That really says a lot.

890
00:49:36,160 --> 00:49:38,680
Speaker 1: I mean, is it any wonder why people do not

891
00:49:38,760 --> 00:49:42,319
Speaker 1: trust this company more?

892
00:49:42,320 --> 00:49:54,080
Speaker 4: After a quick break, let's.

893
00:49:53,840 --> 00:49:59,960
Speaker 1: Get right back into it, all right, So we got

894
00:50:00,200 --> 00:50:04,359
Speaker 1: to talk about Chris Cuomo, the brother of local sex

895
00:50:04,480 --> 00:50:08,120
Speaker 1: pest Andrew Cuomo. Chris Cuomo was fired from CNN a

896
00:50:08,160 --> 00:50:10,880
Speaker 1: few years ago after it came to light the extent

897
00:50:10,960 --> 00:50:14,440
Speaker 1: to which he was helping his brother Andrew Cuomo in

898
00:50:14,560 --> 00:50:18,360
Speaker 1: his defense against sexual harassment allegations that led to Andrew

899
00:50:18,400 --> 00:50:20,600
Speaker 1: Cuomo resigning as governor of New York.

900
00:50:21,200 --> 00:50:22,319
Speaker 3: So real, just.

901
00:50:23,880 --> 00:50:25,359
Speaker 1: A list family over here.

902
00:50:25,719 --> 00:50:27,600
Speaker 2: Yeah, And just to put a point on it, the

903
00:50:27,640 --> 00:50:33,080
Speaker 2: reason he was fired was because the network that employed him, CNN,

904
00:50:33,120 --> 00:50:35,400
Speaker 2: which like you know, questionable, but we can move on

905
00:50:35,480 --> 00:50:41,440
Speaker 2: from that. But like his journalistic employer felt that he

906
00:50:41,600 --> 00:50:45,680
Speaker 2: was not upholding journalistic standards, right, Like that's why he

907
00:50:45,719 --> 00:50:51,960
Speaker 2: was fired. Correct, Yes, this will come become important again later.

908
00:50:52,280 --> 00:50:57,600
Speaker 1: Well. Chris Cuomo, phenomenal journalist that he is currently now

909
00:50:57,680 --> 00:51:02,240
Speaker 1: at News Nation, shared this video of AOC talking about

910
00:51:02,280 --> 00:51:06,400
Speaker 1: the Sydney Sweeney American Eagle jeans ad on X.

911
00:51:06,960 --> 00:51:10,400
Speaker 5: Sydney Sweeney looks like an Aryan goddess and the American

912
00:51:10,440 --> 00:51:16,200
Speaker 5: Eagle Jens campaign is blatant Nazi propaganda. I mean, watching

913
00:51:16,239 --> 00:51:20,680
Speaker 5: that sultry little temptress squeeze into a Canadian tuxedo three

914
00:51:20,760 --> 00:51:24,480
Speaker 5: sizes too small, with her bouncy little fun bags on

915
00:51:24,600 --> 00:51:27,799
Speaker 5: the screen, staring at you, piercing through the core of

916
00:51:27,840 --> 00:51:31,640
Speaker 5: your soul, with those ocean blue eyes that could resurrect

917
00:51:31,640 --> 00:51:35,080
Speaker 5: the Furor from his grave in Argentina is something that

918
00:51:35,120 --> 00:51:40,040
Speaker 5: should alarm every American citizen, because in America, beauty is

919
00:51:40,080 --> 00:51:44,120
Speaker 5: not defined by whiteness. Oh no, it is defined by

920
00:51:44,120 --> 00:51:47,960
Speaker 5: the number of victim groups of which you are a member. Skinny, attractive,

921
00:51:48,000 --> 00:51:52,160
Speaker 5: blonde haired, blue eyed cisgender women descend from the slave

922
00:51:52,280 --> 00:51:56,120
Speaker 5: daddy oppressors of this nation. And any man who cranks

923
00:51:56,160 --> 00:51:59,520
Speaker 5: one out while thinking about a woman like this probably

924
00:51:59,560 --> 00:52:04,000
Speaker 5: hates black people, probably hates gay people, and they certainly

925
00:52:04,080 --> 00:52:07,520
Speaker 5: hate the diversity of our great nation. So I say,

926
00:52:07,640 --> 00:52:10,719
Speaker 5: instead of simping for the Sydneys, we should be celebrating

927
00:52:10,760 --> 00:52:11,600
Speaker 5: the Shaniquas.

928
00:52:12,360 --> 00:52:14,000
Speaker 1: Instead of worshiping the hot.

929
00:52:13,800 --> 00:52:17,759
Speaker 5: Straight blonde what about the obese alphabet people with blue hair?

930
00:52:18,400 --> 00:52:21,160
Speaker 5: They need love too. And to all the haters who

931
00:52:21,200 --> 00:52:24,440
Speaker 5: say companies that go woke, go broke. I'd rather be

932
00:52:24,560 --> 00:52:26,200
Speaker 5: poor than a Nazi.

933
00:52:26,600 --> 00:52:30,560
Speaker 1: Only one problem. It's a deep bake, and honestly, I

934
00:52:30,600 --> 00:52:33,440
Speaker 1: think a pretty obvious deep fake at that.

935
00:52:34,120 --> 00:52:35,920
Speaker 3: It is so clear that he thought this.

936
00:52:35,960 --> 00:52:39,920
Speaker 1: Video was real, even though the clip has a watermark

937
00:52:39,960 --> 00:52:43,280
Speaker 1: on it that says parody one hundred percent made from AI.

938
00:52:43,360 --> 00:52:46,359
Speaker 2: I guess he didn't see it. It's got a watermark

939
00:52:46,520 --> 00:52:48,520
Speaker 2: right on it, Like, come on, man.

940
00:52:48,920 --> 00:52:51,960
Speaker 1: So he shared this on s with the message nothing

941
00:52:52,000 --> 00:52:55,760
Speaker 1: about hamas or people burning Jews in cars, but Sweeney

942
00:52:55,920 --> 00:52:59,960
Speaker 1: jeans ad deserved time on floor of Congress. What happened

943
00:53:00,040 --> 00:53:04,000
Speaker 1: to this party? Fight for small business, not small culture wars?

944
00:53:04,520 --> 00:53:06,839
Speaker 1: Never want to back down. AOC called him out and said,

945
00:53:06,920 --> 00:53:09,320
Speaker 1: this is a deep thake, dude, Please use your critical

946
00:53:09,400 --> 00:53:12,799
Speaker 1: thinking skills. At this point, you're disreposting Facebook memes and

947
00:53:12,840 --> 00:53:16,000
Speaker 1: calling it journalism, which that's absolutely what he's doing. It's

948
00:53:16,040 --> 00:53:19,440
Speaker 1: like if my uncle was a journalist and was like

949
00:53:20,200 --> 00:53:23,359
Speaker 1: reporting on Facebook's AI slop as if it was real.

950
00:53:23,560 --> 00:53:25,000
Speaker 3: It's also such a double standard.

951
00:53:25,040 --> 00:53:31,560
Speaker 2: It's like, what about like hamas or people burning other

952
00:53:31,600 --> 00:53:34,160
Speaker 2: people in cars has to do with fighting for small business.

953
00:53:34,280 --> 00:53:38,200
Speaker 2: It's like you're fighting the culture war in this tweet

954
00:53:38,360 --> 00:53:43,760
Speaker 2: calling to not fight culture wars like have some self awareness.

955
00:53:43,280 --> 00:53:47,640
Speaker 1: And Chris Cuomo has a platform on News Nation. If

956
00:53:47,640 --> 00:53:51,600
Speaker 1: he wants to highlight any of those issues, he absolutely

957
00:53:51,719 --> 00:53:53,640
Speaker 1: has a platform to do it. He doesn't have to

958
00:53:53,680 --> 00:53:57,640
Speaker 1: scream at AOC for not doing it, even though it's

959
00:53:57,680 --> 00:54:00,280
Speaker 1: completely made up, like he's just like making up stuff

960
00:54:00,320 --> 00:54:02,640
Speaker 1: to be mad at. So he took the video down

961
00:54:02,680 --> 00:54:06,040
Speaker 1: and said, you are correct. That was a deep fake,

962
00:54:06,160 --> 00:54:09,440
Speaker 1: but it really does sound like you. Thank you for correcting.

963
00:54:09,680 --> 00:54:12,720
Speaker 1: But now to the central claim. Show me you calling

964
00:54:12,760 --> 00:54:15,319
Speaker 1: on Hamas to surrender or addressing the bombing of a

965
00:54:15,360 --> 00:54:20,440
Speaker 1: car in Saint Louis belonging to the IDF American soldier dude.

966
00:54:20,680 --> 00:54:23,960
Speaker 1: And then he doubled down on this on his show.

967
00:54:24,000 --> 00:54:27,719
Speaker 1: Here's what he said. She was right, they got ai.

968
00:54:28,080 --> 00:54:29,920
Speaker 1: It was really good and it did seem like something

969
00:54:29,920 --> 00:54:33,520
Speaker 1: she would say. So I thanked AOC for correct but I.

970
00:54:33,480 --> 00:54:34,359
Speaker 3: Didn't reminded her.

971
00:54:34,960 --> 00:54:36,440
Speaker 1: She ignored the part of.

972
00:54:36,360 --> 00:54:40,320
Speaker 6: The tweet that met Okay not Sweeney, which really should

973
00:54:40,400 --> 00:54:41,000
Speaker 6: never been a fake.

974
00:54:41,040 --> 00:54:41,560
Speaker 4: Let's be honest.

975
00:54:42,239 --> 00:54:45,680
Speaker 6: Why did AOC, the most popular Democrat in the country,

976
00:54:45,719 --> 00:54:51,680
Speaker 6: powerful reportedly ignore what I asked about calling on Hamas

977
00:54:51,719 --> 00:54:54,200
Speaker 6: to surrender to end the more they saw her, she

978
00:54:54,320 --> 00:54:58,719
Speaker 6: has never said that that I could find think about that.

979
00:55:00,239 --> 00:55:02,279
Speaker 1: So it is so clear to me that when it

980
00:55:02,320 --> 00:55:04,759
Speaker 1: comes to AI, one of the things that makes it

981
00:55:04,800 --> 00:55:08,799
Speaker 1: tricky is that, especially when it adheres to a worldview

982
00:55:08,800 --> 00:55:12,960
Speaker 1: that we already hold, it can be difficult to see like, oh,

983
00:55:13,040 --> 00:55:15,160
Speaker 1: this is not real. I am getting taken by a

984
00:55:15,200 --> 00:55:18,680
Speaker 1: fake video. So even though in my opinion, this was

985
00:55:19,000 --> 00:55:23,400
Speaker 1: very obviously AI down through the watermark spelling out that

986
00:55:23,440 --> 00:55:26,279
Speaker 1: it is AI and not real, the fact that it

987
00:55:26,360 --> 00:55:30,359
Speaker 1: aligns with Cuomo's worldview, an attitude and an opinion that

988
00:55:30,360 --> 00:55:33,880
Speaker 1: he's already hold, and it's like very ready to uphold

989
00:55:33,960 --> 00:55:36,920
Speaker 1: and very ready to believe and share any sliver of

990
00:55:36,960 --> 00:55:40,080
Speaker 1: information that it hears and upholds that attitude. I think

991
00:55:40,200 --> 00:55:43,200
Speaker 1: that is why we see Quomo now basically saying, well,

992
00:55:43,760 --> 00:55:46,120
Speaker 1: it doesn't matter or not if she actually said this,

993
00:55:46,200 --> 00:55:48,160
Speaker 1: It doesn't matter or not if this was actually fake,

994
00:55:48,320 --> 00:55:51,280
Speaker 1: because I think she would say this, and so AOC,

995
00:55:51,760 --> 00:55:54,480
Speaker 1: you have to defend yourself against this allegation that I

996
00:55:54,520 --> 00:55:58,399
Speaker 1: basically just made up that was buttressed by something that,

997
00:55:58,440 --> 00:56:01,520
Speaker 1: like I understand now was fake because it is a

998
00:56:01,520 --> 00:56:04,600
Speaker 1: worldview that I hold. It's basically just using AI to

999
00:56:04,840 --> 00:56:08,520
Speaker 1: hold people accountable for a worldview or opinion that was

1000
00:56:08,600 --> 00:56:10,760
Speaker 1: bullshit that you just made up, that he has made

1001
00:56:10,800 --> 00:56:12,120
Speaker 1: something up to be mad at.

1002
00:56:12,560 --> 00:56:14,399
Speaker 2: I think this is the darkest thing we've talked about

1003
00:56:14,400 --> 00:56:20,560
Speaker 2: in this entire episode, because it just really illustrates exactly

1004
00:56:20,760 --> 00:56:28,239
Speaker 2: the way that AI is like destroying society, destroying democracy,

1005
00:56:28,480 --> 00:56:33,960
Speaker 2: that like it's it's not real, it's fake. Like this

1006
00:56:34,000 --> 00:56:36,359
Speaker 2: is the dangerous thing about AI is it is blurring

1007
00:56:36,880 --> 00:56:41,879
Speaker 2: the line between what's real and what's not. And that's scary.

1008
00:56:41,920 --> 00:56:45,840
Speaker 2: But what's even worse is that someone who purports to

1009
00:56:45,880 --> 00:56:50,680
Speaker 2: be a journalist would just act like that distinction doesn't

1010
00:56:50,680 --> 00:56:54,560
Speaker 2: even matter. Like even after he's called out on the

1011
00:56:54,600 --> 00:56:58,080
Speaker 2: fact that this is a fake video, he just doubles

1012
00:56:58,239 --> 00:57:00,799
Speaker 2: down on it and like you said, like demands that

1013
00:57:00,840 --> 00:57:07,160
Speaker 2: she respond to this fake thing that she didn't say.

1014
00:57:07,400 --> 00:57:12,799
Speaker 2: It's it's dark and frightening, and that's like he is

1015
00:57:13,000 --> 00:57:22,880
Speaker 2: just working so hard to bolster the authoritarian takeover of

1016
00:57:22,920 --> 00:57:27,240
Speaker 2: our entire information ecosystem with this kind of behavior.

1017
00:57:27,440 --> 00:57:30,960
Speaker 1: And I just I mean not to make it about me,

1018
00:57:31,720 --> 00:57:36,800
Speaker 1: but I simply cannot imagine being called out for getting

1019
00:57:36,800 --> 00:57:40,640
Speaker 1: something so for being a journalist and getting something so wrong,

1020
00:57:42,280 --> 00:57:44,720
Speaker 1: and then doubling down on it and being like, but

1021
00:57:44,760 --> 00:57:47,600
Speaker 1: I'm still owed. Some answers like where is the shame?

1022
00:57:47,840 --> 00:57:51,480
Speaker 1: Bring back shame? And not for nothing. Congress is not

1023
00:57:51,520 --> 00:57:54,480
Speaker 1: even in session right now, you moron, You like, like,

1024
00:57:54,560 --> 00:57:58,000
Speaker 1: when do you think this happened? I just honestly, these

1025
00:57:58,040 --> 00:58:01,080
Speaker 1: are meant to be our journalists, and I listen. I

1026
00:58:01,120 --> 00:58:03,200
Speaker 1: told a story on the podcast last week. I got

1027
00:58:03,200 --> 00:58:06,560
Speaker 1: taken by an AI video some bunnies jumping on a trampoline.

1028
00:58:06,640 --> 00:58:08,720
Speaker 1: Although I when I told you about that, you were like,

1029
00:58:08,760 --> 00:58:10,920
Speaker 1: I thought it was AI, But I didn't want to

1030
00:58:10,920 --> 00:58:11,640
Speaker 1: burst your bubble.

1031
00:58:13,320 --> 00:58:15,240
Speaker 2: I mean I said that after the fact, so you

1032
00:58:15,360 --> 00:58:20,120
Speaker 2: thought it was real. No, But like, those buddies did

1033
00:58:20,160 --> 00:58:21,120
Speaker 2: not jump, Bridget.

1034
00:58:21,240 --> 00:58:23,600
Speaker 1: But it's one thing. I mean, it all comes from

1035
00:58:23,600 --> 00:58:25,000
Speaker 1: the same place. I don't want to say that me

1036
00:58:25,080 --> 00:58:27,800
Speaker 1: getting taken is the same thing as Cuomo getting taken,

1037
00:58:28,080 --> 00:58:33,240
Speaker 1: but I think it really shows exactly what you were saying,

1038
00:58:33,320 --> 00:58:35,080
Speaker 1: that how.

1039
00:58:36,200 --> 00:58:37,920
Speaker 3: Bad and eroded.

1040
00:58:37,400 --> 00:58:41,120
Speaker 1: Our information and digital media ecosystem has gotten that he

1041
00:58:41,240 --> 00:58:44,040
Speaker 1: could be get something so wrong.

1042
00:58:43,920 --> 00:58:44,800
Speaker 3: As a journalist.

1043
00:58:45,440 --> 00:58:50,360
Speaker 1: Basic stuff still sort of be like, well it sounds

1044
00:58:50,360 --> 00:58:52,320
Speaker 1: like you, but something that even if you didn't say

1045
00:58:52,400 --> 00:58:53,960
Speaker 1: as something that you would say.

1046
00:58:54,240 --> 00:58:57,320
Speaker 2: Like what like, yeah, it is similar because you were

1047
00:58:57,320 --> 00:59:00,160
Speaker 2: like those bunnies would jump on a trampoline, Like, how

1048
00:59:00,160 --> 00:59:01,880
Speaker 2: do you know they wouldn't get up there they could jump.

1049
00:59:01,960 --> 00:59:04,800
Speaker 1: I still stand by that. I still stand by that

1050
00:59:04,920 --> 00:59:07,800
Speaker 1: when the when night falls, bunnies are doing all kinds

1051
00:59:07,840 --> 00:59:09,400
Speaker 1: of things we don't know about. I stand by that.

1052
00:59:09,680 --> 00:59:11,959
Speaker 2: It was a fake. It was a fake. They didn't

1053
00:59:12,040 --> 00:59:16,360
Speaker 2: jump on that trampoline. They're just down there eating grass, dandelions,

1054
00:59:16,680 --> 00:59:18,800
Speaker 2: big broad leaf vegetation.

1055
00:59:19,000 --> 00:59:19,600
Speaker 3: That's what they like.

1056
00:59:19,720 --> 00:59:23,040
Speaker 1: You know a lot about bunny diet.

1057
00:59:23,720 --> 00:59:25,600
Speaker 2: Yeah, that's how I knew it was a fake. It

1058
00:59:25,640 --> 00:59:26,800
Speaker 2: wasn't a real bunny thing.

1059
00:59:27,200 --> 00:59:29,040
Speaker 1: They don't jump on You don't think they would jump

1060
00:59:29,040 --> 00:59:29,640
Speaker 1: on trampolines.

1061
00:59:29,920 --> 00:59:32,120
Speaker 3: No, they would hate that. First of all, how are

1062
00:59:32,080 --> 00:59:34,480
Speaker 3: they gonna get up there? Why would they get up there?

1063
00:59:34,840 --> 00:59:37,280
Speaker 1: Wileye haven't you ever watched a cartoon?

1064
00:59:37,600 --> 00:59:41,200
Speaker 2: They're afraid, they're skiddish. They may as well be hold

1065
00:59:41,320 --> 00:59:44,000
Speaker 2: up a sign while they're jumping. It's like, hey, come

1066
00:59:44,040 --> 00:59:46,840
Speaker 2: and eat me, foxes whatever.

1067
00:59:48,000 --> 00:59:50,040
Speaker 1: Every now and then on the show, I will talk

1068
00:59:50,040 --> 00:59:52,480
Speaker 1: about a story that sounds like it was created in

1069
00:59:52,520 --> 00:59:56,800
Speaker 1: a lab to infuriate me specifically, and this is one

1070
00:59:56,800 --> 00:59:59,720
Speaker 1: of those stories. Because over the past two weeks, they're

1071
01:00:00,320 --> 01:00:05,160
Speaker 1: six known incidents of green sex toys being thrown onto

1072
01:00:05,160 --> 01:00:09,160
Speaker 1: the court during WNBA games. The latest happened this week

1073
01:00:09,240 --> 01:00:11,560
Speaker 1: during a game between the Indiana Fever and the La

1074
01:00:11,600 --> 01:00:14,720
Speaker 1: Sparks at Crypto dot Com Arena in LA. Sex toys

1075
01:00:14,720 --> 01:00:16,760
Speaker 1: were also thrown in the stands at the New York

1076
01:00:16,800 --> 01:00:20,440
Speaker 1: Liberty and Phoenix Mercury Games this week, and another two

1077
01:00:20,520 --> 01:00:23,080
Speaker 1: were thrown at the Atlanta Dream Games last week. In

1078
01:00:23,120 --> 01:00:26,600
Speaker 1: a Chicago Sky game on Friday, it has gotten pretty serious.

1079
01:00:26,640 --> 01:00:29,800
Speaker 1: Two different people were arrested on multiple charges for allegedly

1080
01:00:29,920 --> 01:00:33,200
Speaker 1: throwing these sex toys. We were going to play audio

1081
01:00:33,240 --> 01:00:35,520
Speaker 1: of her speaking, but it is so drowned out with

1082
01:00:35,640 --> 01:00:38,600
Speaker 1: sounds of dribbling and basketball squeaks that I'll just read

1083
01:00:38,640 --> 01:00:41,080
Speaker 1: what she said, but we put the link to her

1084
01:00:41,200 --> 01:00:44,920
Speaker 1: speaking in the show notes. Her words are very powerful,

1085
01:00:44,960 --> 01:00:46,800
Speaker 1: and I encourage folks to hear what she had to

1086
01:00:46,840 --> 01:00:49,200
Speaker 1: say in her own words. But she said this has

1087
01:00:49,240 --> 01:00:52,680
Speaker 1: been going on for centuries, the sexualization of women. This

1088
01:00:52,800 --> 01:00:55,240
Speaker 1: is the latest version of that, and it's not fun

1089
01:00:55,280 --> 01:00:57,080
Speaker 1: and it should not be the butt of jokes on

1090
01:00:57,120 --> 01:00:59,960
Speaker 1: any radio shows, or on print or on any comment.

1091
01:01:00,400 --> 01:01:03,520
Speaker 1: The sexualization of women is what's used to hold women down,

1092
01:01:03,800 --> 01:01:05,880
Speaker 1: and this is no different. This is just a great

1093
01:01:05,880 --> 01:01:08,520
Speaker 1: example and we should write about it in that way.

1094
01:01:08,760 --> 01:01:11,680
Speaker 1: And these people that are doing this should be held accountable.

1095
01:01:11,880 --> 01:01:14,480
Speaker 1: We're not the butt of the joke. They are the problem,

1096
01:01:14,520 --> 01:01:17,160
Speaker 1: and we need to take action. So I have been

1097
01:01:17,160 --> 01:01:19,080
Speaker 1: following this story and trying to figure out what the

1098
01:01:19,080 --> 01:01:21,640
Speaker 1: heck is going on here, But right as we were

1099
01:01:21,680 --> 01:01:24,960
Speaker 1: sitting down to record, USA Today had a break in

1100
01:01:25,000 --> 01:01:29,200
Speaker 1: this story about what is going on. USA Today spoke

1101
01:01:29,240 --> 01:01:33,640
Speaker 1: to the spokesmen for a crypto group called Green dildo Coin,

1102
01:01:34,760 --> 01:01:38,520
Speaker 1: and that spokesperson said that they were not trying to

1103
01:01:38,560 --> 01:01:43,120
Speaker 1: be disrespectful or disrespect women. He described their group as

1104
01:01:43,160 --> 01:01:46,840
Speaker 1: a group of crypto enthusiasts and traders who launched Green

1105
01:01:46,960 --> 01:01:49,520
Speaker 1: dildo Coin, and that the whole thing was meant to

1106
01:01:49,560 --> 01:01:52,800
Speaker 1: be lighthearted and perceived as a joke or a prank

1107
01:01:53,360 --> 01:01:57,040
Speaker 1: in order to protest what they describe as a toxic

1108
01:01:57,240 --> 01:02:00,560
Speaker 1: environment taking over the crypto world. So if you don't

1109
01:02:00,560 --> 01:02:03,480
Speaker 1: know what a meme coin is. It's a type of

1110
01:02:03,480 --> 01:02:07,120
Speaker 1: crypto asset that is sort of inspired by Internet memes

1111
01:02:07,200 --> 01:02:10,360
Speaker 1: or characters or trends, for which the promoter seeks to

1112
01:02:10,400 --> 01:02:14,480
Speaker 1: attract an enthusiastic online community to purchase the meme coin

1113
01:02:14,560 --> 01:02:17,440
Speaker 1: and engage in its training. This is according to the SEC,

1114
01:02:18,120 --> 01:02:21,400
Speaker 1: so meme coins are kind of like collectibles with limited

1115
01:02:21,520 --> 01:02:25,760
Speaker 1: or no functionality per the SEC, and their entire value

1116
01:02:26,080 --> 01:02:29,400
Speaker 1: is dictated by social and cultural influences.

1117
01:02:29,560 --> 01:02:30,560
Speaker 3: They're beanie babies.

1118
01:02:30,760 --> 01:02:34,400
Speaker 1: Yeah, that's a good comparison. Meme coin generally carries a

1119
01:02:34,440 --> 01:02:37,200
Speaker 1: lot more risk compared to other cryptocurrencies, which are already

1120
01:02:37,200 --> 01:02:40,360
Speaker 1: pretty risky. So according to the people in this Dildo

1121
01:02:41,040 --> 01:02:43,840
Speaker 1: meme coin group, talk about a sentence if you had

1122
01:02:43,840 --> 01:02:47,360
Speaker 1: to explain that sentence to yourself, if you went back

1123
01:02:47,400 --> 01:02:49,560
Speaker 1: in time and you had to talk to your ten

1124
01:02:49,640 --> 01:02:53,680
Speaker 1: year old self back in like nineteen ninety whatever. Imagine

1125
01:02:53,840 --> 01:02:55,680
Speaker 1: having to say I'm going back for.

1126
01:02:55,920 --> 01:02:57,040
Speaker 3: I'm like so much.

1127
01:02:57,160 --> 01:02:58,800
Speaker 2: I like to do a little thought experiment of like

1128
01:02:58,920 --> 01:03:02,280
Speaker 2: what of George washing Ishington and Ben Franklin like traveled

1129
01:03:02,320 --> 01:03:05,920
Speaker 2: forward through time and I was like, well, the dil

1130
01:03:05,960 --> 01:03:07,440
Speaker 2: do meme coin.

1131
01:03:08,640 --> 01:03:10,480
Speaker 1: Will you and I have that joke where it's like

1132
01:03:11,040 --> 01:03:13,360
Speaker 1: you go back in time and you wake up your

1133
01:03:13,400 --> 01:03:15,880
Speaker 1: young self and you're like, wake up the guy from

1134
01:03:15,920 --> 01:03:18,200
Speaker 1: home alone to his president and he's rounding people up,

1135
01:03:18,200 --> 01:03:20,280
Speaker 1: and you're like Tim Curry's president.

1136
01:03:24,720 --> 01:03:26,680
Speaker 2: I mean pretty much anything from the future. If you

1137
01:03:26,720 --> 01:03:28,520
Speaker 2: told if someone of the past, they would be horrified.

1138
01:03:28,520 --> 01:03:30,720
Speaker 2: And by the future, I mean the president correct anyone

1139
01:03:30,760 --> 01:03:33,080
Speaker 2: from the past about what's happening right now, they'll be horrified.

1140
01:03:33,360 --> 01:03:36,680
Speaker 1: And I mean, I'm horrified by this because basically this

1141
01:03:36,760 --> 01:03:40,360
Speaker 1: group says that, oh, smaller players in the crypto and

1142
01:03:40,480 --> 01:03:43,480
Speaker 1: meme coin space are struggling to keep up with all

1143
01:03:43,480 --> 01:03:47,880
Speaker 1: the influencers and scammers, and we need to do something.

1144
01:03:48,520 --> 01:03:50,760
Speaker 1: If you ask me, that space has pretty much always

1145
01:03:50,800 --> 01:03:54,360
Speaker 1: been scammers and influencers, but sure do your thing, Green

1146
01:03:54,440 --> 01:03:58,439
Speaker 1: dildo coin. So as a form of protest, the meme

1147
01:03:58,520 --> 01:04:03,240
Speaker 1: coin was created. The faction began infiltrating WNBA arenas with

1148
01:04:03,400 --> 01:04:08,560
Speaker 1: color coordinated sex toys to coincide with its launch. USA

1149
01:04:08,640 --> 01:04:12,400
Speaker 1: Today Sports obtained text messages showing the group's coordination and

1150
01:04:12,440 --> 01:04:15,520
Speaker 1: planning before the coins launched on July twenty eighth, and

1151
01:04:15,600 --> 01:04:18,320
Speaker 1: the first sex toy being thrown out at a WNBA

1152
01:04:18,360 --> 01:04:23,160
Speaker 1: game on July twenty ninth, So you're probably thinking, wow,

1153
01:04:23,360 --> 01:04:27,760
Speaker 1: that is so disrespectful to the women athletes who are playing,

1154
01:04:27,840 --> 01:04:29,840
Speaker 1: to the people who are just trying to enjoy a

1155
01:04:29,920 --> 01:04:32,160
Speaker 1: WNBA game with their families. But don't worry, don't worry,

1156
01:04:32,160 --> 01:04:34,720
Speaker 1: don't worry. It's still it's chill us chill, because they said,

1157
01:04:35,200 --> 01:04:38,760
Speaker 1: we're not trying to harm anybody, or embarrass anybody, or

1158
01:04:38,800 --> 01:04:41,960
Speaker 1: disrespect anybody. They say that the community has only been

1159
01:04:42,000 --> 01:04:44,840
Speaker 1: advised to throw their sex toys if there is a

1160
01:04:44,920 --> 01:04:48,760
Speaker 1: level of personal comfort and if the objects can land

1161
01:04:48,800 --> 01:04:52,600
Speaker 1: without hitting somebody. They also were like, don't worry, this

1162
01:04:52,640 --> 01:04:56,720
Speaker 1: is not about being disrespectful to the women athletes or anything.

1163
01:04:57,000 --> 01:05:00,160
Speaker 1: He said, we didn't do this because we dislike women's sports,

1164
01:05:00,240 --> 01:05:02,440
Speaker 1: or like some of the narratives trending right now that

1165
01:05:02,480 --> 01:05:06,040
Speaker 1: are ridiculous creating disruptions at games. It happens at every

1166
01:05:06,080 --> 01:05:08,360
Speaker 1: single sport. We've seen it at the NFL, We've seen

1167
01:05:08,360 --> 01:05:11,600
Speaker 1: it at hockey. Fans doing random things more or less

1168
01:05:11,600 --> 01:05:14,840
Speaker 1: to create attention. So they go on to explain how

1169
01:05:14,840 --> 01:05:17,640
Speaker 1: they are just trying to spread awareness about the culture

1170
01:05:17,680 --> 01:05:20,920
Speaker 1: that they want to perpetuate in the meme coin and

1171
01:05:21,040 --> 01:05:26,320
Speaker 1: crypto community, cultivated around lightheartedness, jokes, pranks, and various stunts.

1172
01:05:26,960 --> 01:05:30,360
Speaker 1: The coordinated effort, they said, is a very strategic protest

1173
01:05:30,760 --> 01:05:34,480
Speaker 1: against what meme coin creators view as a small group

1174
01:05:34,600 --> 01:05:38,960
Speaker 1: of individuals controlling the crypto space. It's not about women,

1175
01:05:39,200 --> 01:05:43,400
Speaker 1: it's not about being disrespectful. It's just about meme coins

1176
01:05:43,440 --> 01:05:47,120
Speaker 1: and crypto, to which I say, each shit, do you

1177
01:05:47,280 --> 01:05:50,040
Speaker 1: think the women whose games you are disrupting give a

1178
01:05:50,240 --> 01:05:54,040
Speaker 1: crap about your crypto memestock? Do you think that anybody

1179
01:05:54,080 --> 01:05:56,640
Speaker 1: cares about the vibes that you were trying to curate

1180
01:05:56,720 --> 01:05:57,800
Speaker 1: in the crypto space.

1181
01:05:58,480 --> 01:05:58,520
Speaker 4: No.

1182
01:05:59,080 --> 01:06:02,640
Speaker 1: Women athletes already have to struggle for respect, both on

1183
01:06:02,760 --> 01:06:04,880
Speaker 1: and off the court. We have talked about the ways

1184
01:06:04,880 --> 01:06:08,720
Speaker 1: that technology intersects with the very real threats of harassment

1185
01:06:08,760 --> 01:06:11,280
Speaker 1: that these athletes face. You've expect us to think that

1186
01:06:11,320 --> 01:06:14,120
Speaker 1: it's a coincidence that they're doing this at WNBA games

1187
01:06:14,160 --> 01:06:15,360
Speaker 1: instead of NBA games.

1188
01:06:15,840 --> 01:06:18,280
Speaker 3: Literally, go choke on your green dildos.

1189
01:06:18,720 --> 01:06:21,400
Speaker 1: With the rise of things like social media and online

1190
01:06:21,400 --> 01:06:25,320
Speaker 1: sports betters, ESPN reports that harassment of athletes is on

1191
01:06:25,400 --> 01:06:27,400
Speaker 1: the rise. So I don't give a fuck if you

1192
01:06:27,440 --> 01:06:31,360
Speaker 1: are trying to raise awareness for your lighthearted meme coins,

1193
01:06:31,520 --> 01:06:33,600
Speaker 1: if you are dooming it off the backs of women

1194
01:06:33,640 --> 01:06:35,880
Speaker 1: who are just trying to show up and do their

1195
01:06:36,000 --> 01:06:39,400
Speaker 1: jobs on the court, you fus. If anybody from this

1196
01:06:39,480 --> 01:06:43,320
Speaker 1: group is listening, I cannot imagine a more pathetic way

1197
01:06:43,400 --> 01:06:47,960
Speaker 1: to spend your time. Not to mention, dildos are not cheap.

1198
01:06:48,120 --> 01:06:51,000
Speaker 1: You're buying a WNBA ticket and a dildo to throw

1199
01:06:51,040 --> 01:06:51,720
Speaker 1: onto the court.

1200
01:06:52,040 --> 01:06:52,920
Speaker 3: What are you doing.

1201
01:06:53,160 --> 01:06:57,080
Speaker 1: I have never heard of something more pathetic and embarrassing

1202
01:06:57,120 --> 01:06:59,120
Speaker 1: in my life. I have nothing to do with this,

1203
01:06:59,200 --> 01:07:00,520
Speaker 1: and I am embarrass about it.

1204
01:07:01,120 --> 01:07:03,040
Speaker 2: Yeah, and if they were really serious about it, I

1205
01:07:03,080 --> 01:07:05,919
Speaker 2: mean they should be bringing their green dildos into other

1206
01:07:05,960 --> 01:07:08,880
Speaker 2: sporting events, like bring it to a UFC fight, bring

1207
01:07:08,920 --> 01:07:11,840
Speaker 2: it to an NFL game, bring it to a hockey game.

1208
01:07:11,960 --> 01:07:15,840
Speaker 2: You know, just bring your dildos all over the place

1209
01:07:15,880 --> 01:07:18,720
Speaker 2: with you if that's like your thing, if your whole

1210
01:07:18,760 --> 01:07:22,000
Speaker 2: thing is dildos, don't just throw them at women.

1211
01:07:22,320 --> 01:07:26,000
Speaker 1: No, they never would, because it's about humiliating women, like

1212
01:07:26,040 --> 01:07:28,040
Speaker 1: they can say whatever they want. There's a reason they

1213
01:07:28,040 --> 01:07:30,480
Speaker 1: didn't do this at a UFC fight or an NBA

1214
01:07:30,680 --> 01:07:33,720
Speaker 1: game or something like that there is or a NASCAR event.

1215
01:07:33,880 --> 01:07:36,080
Speaker 1: There is a reason, and that reason is because they

1216
01:07:36,120 --> 01:07:39,680
Speaker 1: are about humiliating women. They are about getting engagement and

1217
01:07:39,760 --> 01:07:43,959
Speaker 1: traffic and eyeballs off of humiliating women. They would never

1218
01:07:44,160 --> 01:07:46,560
Speaker 1: do this at a sporting event where there's going to

1219
01:07:46,600 --> 01:07:48,840
Speaker 1: be lots of men, because they probably get their clocks

1220
01:07:48,880 --> 01:07:52,000
Speaker 1: clean deservedly, and they don't want to show up to

1221
01:07:52,080 --> 01:07:54,120
Speaker 1: spaces where there's going to be men to do that.

1222
01:07:54,240 --> 01:07:56,480
Speaker 1: They want to do stuff like this in spaces where

1223
01:07:56,520 --> 01:07:58,920
Speaker 1: they assume they are not going to be physically challenged.

1224
01:07:59,040 --> 01:08:01,360
Speaker 1: So it has ever everything to do with humiliating women.

1225
01:08:01,480 --> 01:08:03,400
Speaker 1: I don't give a fuck if they say otherwise, like

1226
01:08:04,000 --> 01:08:08,400
Speaker 1: it's so disrespectful, as if women athletes don't have enough

1227
01:08:08,720 --> 01:08:11,960
Speaker 1: think about the kind of homophobic harassment that people like

1228
01:08:12,080 --> 01:08:14,240
Speaker 1: Britney Griner have had to face. I don't know if

1229
01:08:14,240 --> 01:08:18,000
Speaker 1: people have read Hurt memoir, but like to go to

1230
01:08:18,360 --> 01:08:21,639
Speaker 1: a WNBA game and throw a sex toy at someone

1231
01:08:21,640 --> 01:08:24,240
Speaker 1: that's trying to do their job is so humiliating and

1232
01:08:24,240 --> 01:08:27,479
Speaker 1: degrading and disgusting, and then to play in our faces

1233
01:08:27,520 --> 01:08:29,479
Speaker 1: and say oh no, no, no, no no, it has

1234
01:08:29,520 --> 01:08:33,200
Speaker 1: nothing to do with women or humiliating women. That's nonsense.

1235
01:08:33,600 --> 01:08:35,280
Speaker 1: It's about my memes stock.

1236
01:08:35,479 --> 01:08:36,000
Speaker 3: Fuck you.

1237
01:08:36,640 --> 01:08:39,000
Speaker 1: It's like gaslining and I will not have it.

1238
01:08:39,560 --> 01:08:42,800
Speaker 2: Yeah, absolutely, and like not even an effective gas lading,

1239
01:08:42,880 --> 01:08:46,800
Speaker 2: Like come on this fucking meme stock, Like get out

1240
01:08:46,800 --> 01:08:47,280
Speaker 2: of here.

1241
01:08:47,160 --> 01:08:50,920
Speaker 1: Get out of here, get out of here before we end.

1242
01:08:51,000 --> 01:08:54,320
Speaker 1: I have one quick note, which is just really funny.

1243
01:08:54,760 --> 01:08:57,439
Speaker 1: Lynna McMahon, the Secretary of Education and former chairwoman of

1244
01:08:57,479 --> 01:09:01,519
Speaker 1: the WWE. Speaking of wrestling, folks might know her as

1245
01:09:01,520 --> 01:09:03,400
Speaker 1: the person who was trying to talk about AI at

1246
01:09:03,520 --> 01:09:06,120
Speaker 1: Education and kept calling it a one like the stik sauce,

1247
01:09:06,200 --> 01:09:08,840
Speaker 1: which I actually love.

1248
01:09:10,200 --> 01:09:13,400
Speaker 2: Our children need to know about this tangy sauce to

1249
01:09:13,439 --> 01:09:15,679
Speaker 2: be able to compete against China in the future.

1250
01:09:15,800 --> 01:09:18,080
Speaker 1: My children will how my children will never know about

1251
01:09:18,120 --> 01:09:20,679
Speaker 1: steak sauce. How dare you? My children will be eating

1252
01:09:20,720 --> 01:09:24,280
Speaker 1: steak that doesn't need steak sauce on it. She was

1253
01:09:24,320 --> 01:09:27,320
Speaker 1: trying to speak at the Young Americans Foundation conference and

1254
01:09:27,640 --> 01:09:31,679
Speaker 1: something happened that caused the Curby Your Enthusiasm theme song

1255
01:09:32,080 --> 01:09:36,480
Speaker 1: Circus music and audio of Linda being called corrupt herself

1256
01:09:36,800 --> 01:09:39,400
Speaker 1: kept playing over her trying to speak quickly?

1257
01:09:39,560 --> 01:09:44,080
Speaker 7: Is people understood what his working style was and he

1258
01:09:44,200 --> 01:09:48,400
Speaker 7: understood you know them so, but this time, you know,

1259
01:09:48,439 --> 01:09:50,479
Speaker 7: he knew, he knew the story, he knew how to

1260
01:09:51,800 --> 01:09:53,400
Speaker 7: you know, how to make things work, how to make

1261
01:09:53,439 --> 01:09:55,559
Speaker 7: things run. He had the people coming in that he

1262
01:09:55,640 --> 01:09:58,920
Speaker 7: really wanted to work with him, and and then he'd

1263
01:09:59,000 --> 01:10:02,800
Speaker 7: had this little you know sometimes kids in college take

1264
01:10:02,840 --> 01:10:07,200
Speaker 7: a gap here, well, he didn't voluntarily take a gap term,

1265
01:10:07,240 --> 01:10:09,680
Speaker 7: but I think it turned out to be an incredible

1266
01:10:09,720 --> 01:10:10,360
Speaker 7: thing for him.

1267
01:10:10,680 --> 01:10:14,479
Speaker 1: Jab's kiss, no notes. Whoever came up with this, I

1268
01:10:14,479 --> 01:10:17,400
Speaker 1: think it's what there are big good.

1269
01:10:17,760 --> 01:10:18,720
Speaker 3: We'll leave you with this.

1270
01:10:23,960 --> 01:10:26,000
Speaker 1: Got a story about an interesting thing in tech, or

1271
01:10:26,080 --> 01:10:27,920
Speaker 1: just want to say hi? You can reach us at

1272
01:10:27,920 --> 01:10:30,639
Speaker 1: Hello at tangodi dot com. You can also find transcripts

1273
01:10:30,640 --> 01:10:33,120
Speaker 1: for today's episode at tengody dot com. There Are No

1274
01:10:33,200 --> 01:10:35,240
Speaker 1: Girls on the Internet was created by me Bridget Tod.

1275
01:10:35,640 --> 01:10:39,080
Speaker 1: It's a production of iHeartRadio, an unbossed creative. Jonathan Strickland

1276
01:10:39,080 --> 01:10:41,800
Speaker 1: is our executive producer. Terry Harrison is our producer and

1277
01:10:41,880 --> 01:10:45,639
Speaker 1: sound engineer. Michael Almado is our contributing producer. I'm your host,

1278
01:10:45,640 --> 01:10:48,439
Speaker 1: Bridget Todd. If you want to help us grow, rate

1279
01:10:48,520 --> 01:10:49,080
Speaker 1: and review.

1280
01:10:48,920 --> 01:10:50,040
Speaker 4: Us on Apple podcasts.

1281
01:10:50,680 --> 01:10:53,519
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1282
01:10:53,560 --> 01:10:55,600
Speaker 1: Apple Podcasts, or wherever you get your podcasts.

1283
01:11:00,280 --> 01:11:06,519
Speaker 7: Love be well, oh well, and well lived.