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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. This is

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Speaker 1: There Are No Girls on the Internet. Where we explore

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Speaker 1: the intersection of technology, culture, social media, and identity. And

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Speaker 1: this is our weekly news round up where we dive

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Speaker 1: into stories that you might have missed online.

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Speaker 2: I am kind of fangirling because of this.

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Speaker 1: Our guest co host today, Dexter Thomas, hosted a kill

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Speaker 1: Switch podcast on this very network. You're one of my

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Speaker 1: kind of favorite humans in the tech media space.

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Speaker 2: I'm so thrilled that you're here.

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Speaker 3: No, okay, that is too good of an angel. Thank you.

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Speaker 3: I'm happy to be here. Thank you so much.

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Speaker 1: So, when you're not busy making the kill Switch podcast,

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Speaker 1: you also write all around the Internet about sort of

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Speaker 1: the intersection of tech and media. How would you describe

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Speaker 1: your sort of beat?

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Speaker 3: Oh my gosh, So I say that I do culture, culture,

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Speaker 3: And yeah, I say that I do culture. And culture

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Speaker 3: for me is whatever you sit down on the couch

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Speaker 3: and keeps you sitting down on the couch, or whatever

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Speaker 3: makes you want to get up off the couch and

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Speaker 3: do something about it. So that's a very roundabout way

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Speaker 3: of saying I'm interested in everything. He wrote a.

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Speaker 1: Piece in Wired recently called anime Girlvtubers are selling out concerts,

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Speaker 1: but are they real depends on who you ask all about.

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Speaker 1: VTubers a type of content creator that uses a virtual,

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Speaker 1: often anime style character as a kind of avatar.

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Speaker 2: These people are like.

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Speaker 1: Selling out irl concert venues and performing with like real

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Speaker 1: backing bands in front of real audiences that cannot get enough.

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Speaker 1: I actually learned about this phenomenon from your reporting and Wired.

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Speaker 1: That is that a fair assessment of what of like

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Speaker 1: whatvtubers are?

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Speaker 3: Yeah, so, man, so v tubers. Essentially, if you've ever

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Speaker 3: used or seen that kind of you know, animoji type

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Speaker 3: thing on an iPhone where you can show where you

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Speaker 3: can have an like an avatar, like an emoji reacting

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Speaker 3: to what your face does, it's only a little bit

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Speaker 3: more complicated than that. And so essentially these VTuber is

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Speaker 3: kind of what it sounds like. It's a virtual YouTuber

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Speaker 3: somebody who streams and they just don't show their face

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Speaker 3: or their body. And yeah, you're right. Usually usually it's

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Speaker 3: anime girls. It doesn't have to be anime Girls, and honestly,

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Speaker 3: why it's Anime Girls is probably an entirely different podcast,

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Speaker 3: but yeah, it's it's very often anime girls, so pretty

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Speaker 3: influence from you know, anime culture and stuff like that too.

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Speaker 1: As part of reporting this, you went to Fantastic Reality,

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Speaker 1: a mini festival at the Vermont Theater that brought together

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Speaker 1: eight main VTuber acts.

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Speaker 3: What was this like, Man, you know, I knew stuff

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Speaker 3: like this existed, but I'd never really been to so

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Speaker 3: I've I've been to Anime Expo a couple of times,

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Speaker 3: presented actually an Anime Expo, but and so I knew

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Speaker 3: this sort of thing existed. But essentially, just imagine going

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Speaker 3: to a concert and the singer the main attraction is

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Speaker 3: on a screen and they never step on the stage

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Speaker 3: because they can't because they're not real. Which, by the way,

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Speaker 3: I'm not supposed to say that. I'm not supposed to

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Speaker 3: say these people aren't real, but I'm saying the whole.

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Speaker 1: Thing, right, like, like they pretend like they're sort of

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Speaker 1: everybody sort of in on the joke, Like, what do you.

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Speaker 2: Mean they're not real? Right?

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Speaker 3: Right? Yeah, So this is so anybody who's ever watched WWE,

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Speaker 3: who's ever watched wrestling, will will know this word kfabe.

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Speaker 3: So kfabe is essentially kind of pretending that something is

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Speaker 3: real even though you know that it's not, because it's

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Speaker 3: more fun. And so you know, when we watched Hulk

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Speaker 3: Hogan and the Macho Man, you know, hit each other

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Speaker 3: over the head with chairs, we knew that they'd planned

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Speaker 3: it out beforehand, but it's way more or fun. And

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Speaker 3: if you think about it, you know, a UFC fight,

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Speaker 3: some of those things are really boring because it's over

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Speaker 3: in like five seconds because somebody gets the leg broken,

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Speaker 3: and that's not fun for me. But watching two dudes

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Speaker 3: beat each other up in this weird, sweaty soap opera

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Speaker 3: for twenty minutes and this drama and you think this

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Speaker 3: guy's gonna lose and that guy's gonna lose, and then

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Speaker 3: somebody jumps off the top rope who wasn't even in

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Speaker 3: the match period. It's fun and so that is k fabe.

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Speaker 3: And really, interestingly, in English anywayvtubers and their fans will

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Speaker 3: use that same word that wrestling fans use k fabe,

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Speaker 3: and the idea is that it's not fake. It's that

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Speaker 3: we're all creating this play together. And so it's even

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Speaker 3: more involved, I would argue then than wrestling, but the

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Speaker 3: same kind of underlying principle there.

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Speaker 1: You're talking very glowingly about this, But in your piece,

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Speaker 1: something that I like is that you kind of talked

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Speaker 1: about being a little bit skeptical at first. You write

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Speaker 1: a music purist might scoff at all this to say

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Speaker 1: that YouTuber fans don't even like music. They it's like

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Speaker 1: anime and the whole scene is fake. Full disclosure here,

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Speaker 1: I will admit that I walked into the venue with

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Speaker 1: a touch of this mentality, but that slowly turned into

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Speaker 1: an existential crisis. Who can say their favorite genre isn't

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Speaker 1: also fake? So I guess my big question is is.

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Speaker 2: This is it? Is it fake?

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Speaker 1: Or does that even matter? Does the question of real

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Speaker 1: or fake even really matter?

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Speaker 2: At this point?

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Speaker 3: You know what for me I mean, And it's weird

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Speaker 3: hearing my words right back to me. But yes, I

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Speaker 3: did write those words, and it's true. It's true. But

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Speaker 3: here's the thing is that you could say that the

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Speaker 3: v twour stuff is fake, and to be real, most

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Speaker 3: of it is not my cup of tea, mostly because

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Speaker 3: I'm not really into anime music unless I like the

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Speaker 3: specific anime, and that there's only a small, you know,

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Speaker 3: group of anime tracks that I would say, I like,

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Speaker 3: you know, I like this, uh you know Cowboy Bebop.

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Speaker 3: You can't really that soundtrack? Yeah, classic, you know the

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Speaker 3: OG Dragon Ball theme song. Can't deny that the OG soundtrack,

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Speaker 3: you know, in some weird obscure like eighties stuff, seventy

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Speaker 3: stuff that I like because there was a live action

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Speaker 3: Spider Man in Japan in the seventies. Like it's I

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Speaker 3: like that stuff. I like that kind of stuff. The

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Speaker 3: current stuff not really my flavor. That's really kind of

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Speaker 3: what you would hear these sort of things, not really

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Speaker 3: my flavor. But if you're talking about fake, at some

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Speaker 3: point you have to start talking about rap music. And

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Speaker 3: hip hop is obsessed with reality, and yet Drake has fans,

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Speaker 3: and yet Rick Ross has fans. And ninety nine point

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Speaker 3: nine percent of these dudes who are rapping about stuff

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Speaker 3: that they did, they did not do it. They don't

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Speaker 3: even see it, and do any of us truly care.

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Speaker 3: We kind of pretend we do, but if it sounds good,

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Speaker 3: we're kind of cool with it, and so really we're

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Speaker 3: rap is also KFA rap is the biggest KFA market.

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Speaker 3: I would argue maybe in the world, we all just

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Speaker 3: pretend that, you know, these dudes are doing the stuff

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Speaker 3: they said they did it because it sounds kind of

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Speaker 3: cool to, you know, chant the lyrics along with them

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Speaker 3: in your car or whatever. So you know, I can't

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Speaker 3: I can't really hate on the YouTubers and they're fans

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Speaker 3: because they're they're doing something very similar to what I

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Speaker 3: do and what I've been doing for years.

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Speaker 1: Are you saying that it's possible that Drake didn't really

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Speaker 1: start from the bottom? Is that what you're is that

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Speaker 1: what you're suggesting right now?

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Speaker 3: I am I'm not suggesting that. I am asserting that

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Speaker 3: there are years of televised evidence that that man did

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Speaker 3: not start anywhere near what a reasonable person would consider

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Speaker 3: the bottom. That is what I'm saying. That is my journalist,

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Speaker 3: not in my opinion. That is my assessment, and I

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Speaker 3: defy anybody to say that that's not true because the

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Speaker 3: truth is actually out there.

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Speaker 2: It's facts. It's facts.

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Speaker 3: Yeah.

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Speaker 1: One of the points that you made in the piece

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Speaker 1: was that, you know, this is one thing when we're

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Speaker 1: talking about a human using a.

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Speaker 2: Digital avatar, but what the future could sort of look.

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Speaker 1: Like when you have AI generated, Like the whole thing

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Speaker 1: is AI generate, not using a digital avatar.

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Speaker 2: How does this fandom feel about that possibility.

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Speaker 3: Yeah, So my opinion, and I still hold this opinion,

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Speaker 3: is that v tubers and their fans will be the

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Speaker 3: first one of the first markets that AI will start

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Speaker 3: to encroach on, or AI will try to encroach on,

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Speaker 3: because if you think of it, they're almost kind of

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Speaker 3: halfway there. They've already removed the physical person from the

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Speaker 3: visual at least, because you know, people are fans of

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Speaker 3: somebody whose physical body, you know, I'm breaking kfaid here,

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Speaker 3: but somebody whose physical body they will never see, because

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Speaker 3: because most of these v tubers will never show their

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Speaker 3: body or their face. Matter of fact, there's probably a

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Speaker 3: lot of you tubers who would refuse to be interviewed

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Speaker 3: because they want to keep, you know, keep that line.

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Speaker 3: But you know, I think the one of the things

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Speaker 3: that really surprised me was a lot of the fans, actually,

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Speaker 3: all the fans that I talked to, told me we

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Speaker 3: would never except AI. We do not want AI anywhere

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Speaker 3: near this culture at all. And I found that really

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Speaker 3: really interesting because I'm not sure that that's necessarily the

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Speaker 3: case for a lot of other genres. I mean there

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Speaker 3: was that psych rock group Forgetting what they're called Velvet

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Speaker 3: Sundown or someone.

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Speaker 2: We talked about them on the podcast exactly.

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Speaker 3: Yeah. And and you know what, one of the biggest

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Speaker 3: genres is a genre that I've personally had beef with

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Speaker 3: for a very long time. Lo fi lo fi hip hop.

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Speaker 3: Lo fi doesn't mean lo fi hip hop doesn't mean anything.

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Speaker 3: It's just boring. Soul is backtracking. It's it's it's homework.

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Speaker 3: It's homework, background music. That's all it is. Like, it's

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Speaker 3: it's not music h for me. This is my personal

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Speaker 3: opinion right that has been absolutely ravaged by AI because

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Speaker 3: here's the here's the big difference between say, lo fi

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Speaker 3: hip hop and v tubers is I defy you. Anybody

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Speaker 3: who says you like lo fi hip hop, tell me

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Speaker 3: five artists and no new jobs doesn't care count You can't.

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Speaker 3: I guarantee you can't. You you you're you're a fan of

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Speaker 3: just a background vibe. V tuber fans are fans of

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Speaker 3: a very specific persona, and at least for now, they

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Speaker 3: seem to want to know that a human is attached

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Speaker 3: with that will that change, will somebody convince them the future.

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Speaker 3: I don't know, but for me, I feel like, man,

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Speaker 3: if they get the v tuber fans with this anti

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Speaker 3: ai as they are right now. If they get the

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Speaker 3: v tubers, we're next. Everybody's next, They're gonna get all

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Speaker 3: of us.

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Speaker 2: That's such a good point. I think that's it.

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Speaker 1: I mean, I am not above putting on low fi

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Speaker 1: beats to chill and study to. You could not name

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Speaker 1: an artist if it's not the girl in front of

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Speaker 1: the anime girl in front.

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Speaker 2: Of the rainy window.

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Speaker 3: Yes, that's the person, and it's not a person, and

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Speaker 3: she didn't make the music. That is something that somebody drew.

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Speaker 3: And it's low five beats, the study too, it's the

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Speaker 3: it's the girl in the cafe with the rain in

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Speaker 3: the back. She didn't make any of that music. And yeah,

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Speaker 3: people can't. People can't name a low fi artist. They'll

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Speaker 3: they'll say new job is. If they say Dyla, I

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Speaker 3: will get extremely angry because Dyla is not low fi.

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Speaker 3: Diyla is his own genre. Dyla is hip hop. But yeah,

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Speaker 3: it's a It's interesting to watch people try to explain

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Speaker 3: why they don't like V tubers stuff and everything they

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Speaker 3: say about the genre or about the culture is stuff

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Speaker 3: you could absolutely say about the stuff that they're into.

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Speaker 3: It's just a slightly different version of it. So, you know,

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Speaker 3: I love being around people who were just into stuff,

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Speaker 3: you know what I mean. That's exciting to me. People

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Speaker 3: who are just like excited about things. I love that.

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Speaker 3: I love that. I always love that.

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Speaker 2: Yeah, me too.

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Speaker 1: And I have to wonder if part of this is

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Speaker 1: or part of the hate that v tubers get it's

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Speaker 1: because it's something associated with very.

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Speaker 2: Passionate young girls.

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Speaker 1: Like one of the artists that you talked to said,

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Speaker 1: you know, how can I make all these connections improve

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Speaker 1: not just to myself but to other people? That it

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Speaker 1: is not silly to be an anime girl on the internet.

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Speaker 1: But then you go on to report how this is

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Speaker 1: big business, these are selling out. Then use, these are

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Speaker 1: live streamers who are making real money from what they're doing,

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Speaker 1: and so it's not just silly anime girls on the internet.

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Speaker 1: We're talking about something that actually is a financial and

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Speaker 1: potentially like a business for us.

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Speaker 3: Yeah, yeah, definitely. I mean I wanted that quote that

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Speaker 3: you just said, it's not silly to be an anime

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Speaker 3: girl on the internet. I wanted that to be the

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Speaker 3: title of the Wired piece. We change the headline, but

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Speaker 3: I wanted to just be that for kill Switch for

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Speaker 3: the podcast. We made that the title. I had a

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Speaker 3: little bit more control over that, But yeah, I think,

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Speaker 3: you know, let's keep in mind that the people who

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Speaker 3: are the creators here often is young women or women

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Speaker 3: in general, right, and there's some dudes out there who

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Speaker 3: that bothers them for some reason. No, again, we could

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Speaker 3: say things about the appearance of the models. What we

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Speaker 3: want to say again, I think that's another conversation. But

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Speaker 3: I you know, there's some interesting elements I think in

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Speaker 3: how that works.

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Speaker 1: Well, I think a lot of the stories that I

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Speaker 1: want to get into with you speak a lot to

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Speaker 1: these issues.

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Speaker 2: Should we get into.

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Speaker 3: It, let's do it? Yeah, where you want to go?

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Speaker 1: Well, first, you know, I live in DC, and let's

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Speaker 1: just say there's a lot going on in the city

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Speaker 1: at the moment.

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Speaker 2: I just want to thank.

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Speaker 1: Folks who reached out after the episode that we did

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Speaker 1: about the DC situation. Obviously it is a ongoing situation.

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Speaker 1: But somebody left a comment on Spotify saying that their

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Speaker 1: personal TikTok algorithm was really showing them a ton of

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Speaker 1: content about how bad crime in DC is, which is

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Speaker 1: so interesting because as we know, crime here in DC.

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Speaker 2: Has hit a thirty year low, but right now TikTok

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Speaker 2: is a wash.

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Speaker 1: In these mostly AI generated fake videos depicting crime and

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Speaker 1: homelessness in DC. A lot of these videos are using

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Speaker 1: I guess i'll call it my old nemesis, Google's vo three,

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Speaker 1: a platform that we've talked about before in our episode

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Speaker 1: about these like very racist AI generated videos featuring black women,

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Speaker 1: But now they're using vo three to make these, I

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Speaker 1: would argue, pretty obviously AI generated videos depicting homelessness and

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Speaker 1: crime in DC. They'll often sort of be a mix

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Speaker 1: of mostly accurate but vague reporting about the situation in DC.

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Speaker 1: As four O four Media puts it, unlike previous efforts

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Speaker 1: to flood the zone with AI slop in the aftermath

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Speaker 1: of a disaster or major news events. Some of the

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Speaker 1: videos blend real footage with AI footage, making it harder

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Speaker 1: than ever to tell what's real and what's not, which

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Speaker 1: has the effect of distorting people's understanding of the military

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Speaker 1: occupation of DC.

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Speaker 2: So I have.

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Speaker 1: Seen these videos on my feed, which is so interesting

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Speaker 1: that in that like I live in the so I

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Speaker 1: have a pretty good sense of like what's going on here,

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Speaker 1: but these videos are very persistent.

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Speaker 2: Have you seen any of these?

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Speaker 3: Yeah, yeah, I've seen them in shout out to four

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Speaker 3: four by the way, just continually doing important, really really

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Speaker 3: really important tech reporting out there. I'm biased, I know

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Speaker 3: the people who found it, but just truly punching above

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Speaker 3: their weight. They're they're doing such incredible stuff recently.

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Speaker 1: Yeah, we I mean I we were subscribers.

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Speaker 2: I'm a huge fan.

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Speaker 1: I don't think our podcast would exist without four or

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Speaker 1: four because also they I feel.

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Speaker 3: Like they're line would not either kill Switch would not

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Speaker 3: like quote me on that kill Switch would not exist

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Speaker 3: without four or four. And frankly, I think that there

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Speaker 3: are a there is a lot of tech reporting that

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Speaker 3: some New York Times would not have some of the

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Speaker 3: stuff that they have with it wasn't for four four

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Speaker 3: four or four people really Actually I was gonna say

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Speaker 3: people asleep on a four or four. I actually don't

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Speaker 3: think so. I think people I think four four is

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Speaker 3: getting the respect that is or recently so Yeah, they're heavyweightsye.

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Speaker 2: Yes, subscribe to four row for if you don't already.

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Speaker 1: We love and value their reporting, and so one of

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Speaker 1: the things that I thought was so interesting about how

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Speaker 1: they were talking about that's what we're seeing with the

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Speaker 1: DC occupation of honestly like a military occupation of my city.

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Speaker 1: Is how they point out that if you are not

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Speaker 1: familiar with DC these videos, you might not be able

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Speaker 1: to tell that they're faith right. Having been from DC

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Speaker 1: my whole life, I see them, I'm like, oh, that's fake.

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Speaker 2: They had a video purporting to show the National.

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Speaker 1: Mall covered end to end in tents from the Washington

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Speaker 1: Monument to the Capitol Building. That's like two miles and

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Speaker 1: this video shows just a sea of tents where you

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Speaker 1: can't even see the lawn. And again, part of me

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Speaker 1: is thinking, if folks just thought for a second, like

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Speaker 1: what tourists be coming to d C. If they if

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Speaker 1: they if to see the monuments they had to navigate

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Speaker 1: just a sea of tents in that way. And when

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Speaker 1: you go to the comments, you actually see people who

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Speaker 1: say things like thank you President Trump for cleaning up DC,

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Speaker 1: and we really appreciate what you're doing. And I think

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Speaker 1: it comes back to something that we talk about all

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Speaker 1: the time on the show, which is that at a

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Speaker 1: certain point it kind of does it seem to matter

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Speaker 1: whether or not these videos or are real because they

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Speaker 1: validate and align with a worldview people clearly had that DC.

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Speaker 2: Is assesspool, it's full of crime.

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Speaker 1: You can't even see the Washington Monument because it's tense

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Speaker 1: to stacked up and its stacked up, and it clearly

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Speaker 1: is this sort of aligning with the worldview that people

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Speaker 1: already have, so they're just inclined to believe it and

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Speaker 1: spread it.

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Speaker 3: Yeah, it feels true. It doesn't matter if it is true.

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Speaker 3: It feels true, and you can tell someone that it's not,

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Speaker 3: and you could show them a real picture of the thing.

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Speaker 3: And something that is happening more and more recently, and

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Speaker 3: this has happening with politicians. This happened with a hurricane

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Speaker 3: where you might have seen this full or also reported

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Speaker 3: on this, but I'm forgetting the name of the politician.

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Speaker 3: But you know, there's there's an image maybe a couple

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Speaker 3: of years ago at this point of this girl in

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Speaker 3: the rain holding this puppy and she's basically saving the

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Speaker 3: puppy from the flood. And if you look at it,

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Speaker 3: and if you were used to looking at AI generated images,

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Speaker 3: you could really tell that it wasn't true, and people

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Speaker 3: try to you know, this politician reposts it and says,

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Speaker 3: this is terrible. What's happening. People say, Yo, this isn't real,

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Speaker 3: and the politician basically says to the effect of, it

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Speaker 3: doesn't matter. I'm sure things like this are happening. I've

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Speaker 3: reported on this. This is a This is a thing

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Speaker 3: that we're seeing a lot of is that it feels

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Speaker 3: true and it emotionally hits you before it hits your

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Speaker 3: logic center of your brain, and once that happens, it

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Speaker 3: actually doesn't really matter if somebody refutes it. The facts

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Speaker 3: actually don't come into play soon enough for you to

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Speaker 3: feel like, yes, this is true.

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Speaker 1: I remember that image of the girl with the puppy

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Speaker 1: and the hurricane. I wish I could remember who the

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Speaker 1: politician was, but she really, I think, kind of stumbled

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Speaker 1: on some insight of like, oh, well, it doesn't matter

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Speaker 1: if this actually happened or not, or if I'm really

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Speaker 1: spreading misinformation. AI generated misinformation because this could be happening

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Speaker 1: somewhere and it feels true and the image it's AI

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Speaker 1: is so good at creating images that really do speak

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Speaker 1: to some part of your brain where that image the

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Speaker 1: puppy was impossibly cute and looked impossibly sad.

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Speaker 2: The girl was impossibly cute. It was Yeah, I mean

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Speaker 2: it should have been a.

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Speaker 1: Giveaway that these moments in life that are just like

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Speaker 1: created to be so just so you know, when a

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Speaker 1: moment like that is captured, you should be like, wait

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Speaker 1: a minute, this is almost too adorably said, Perhaps it's

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Speaker 1: not real.

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Speaker 3: Yeah, well, I think the people who are making these

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Speaker 3: are also stumbling on something themselves, which is to say

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Speaker 3: that if you look at the images that cause the

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Speaker 3: most problems, it's images of people's faces, right, And you know,

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Speaker 3: we're just we're wired to pay attention to faces. We're

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Speaker 3: wired to trust faces. I mean, look at a baby.

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Speaker 3: If you leave a baby in a room and a

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Speaker 3: person walks in, everything else is not interesting anymore. The

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Speaker 3: baby will stare at the person like yeah, And so

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Speaker 3: it's like, you know, and I don't want to get

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Speaker 3: like evolutional evolutionary biology or whatever, because I'm not an

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Speaker 3: expert in that, but I definitely know that babies are

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Speaker 3: interested in people. We don't really ever lose that. And

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Speaker 3: so if you see a face, even if it's digital,

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Speaker 3: it's going to feel like Okay, this is real. It's

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Speaker 3: really hard to convince somebody that a face isn't real

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Speaker 3: and the faces of the people that they're seeing are

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Speaker 3: not real, and even just bodies, even if it's like

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Speaker 3: people running, it feels real to us. It feels emotionally real,

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Speaker 3: and we don't have time to process that logically. I

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Speaker 3: just don't think it's reasonable to expect somebody to And I.

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Speaker 1: Think especially with content that is that we know is

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Speaker 1: kind of emotionally charged, like crime. You know, oftentimes when

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Speaker 1: you're talking about crime through the lens of social media,

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Speaker 1: you're talking about videos that are very arresting or images

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Speaker 1: that are very arresting. And so I think when you

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Speaker 1: add in AI, the possibilities are just endless for having

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Speaker 1: a conversation that is simply not grounded in the logic

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Speaker 1: or the reality of what is actually happening. But it's

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Speaker 1: just trafficking and what feels true, what feels like it

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Speaker 1: could be happening somewhere, even if most of the videos

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Speaker 1: on TikTok purporting to depict it are AI generated and

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Speaker 1: not actually happening, and not even depicting the city in.

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Speaker 2: Any real way.

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Speaker 3: Yeah, yeah, And I mean I've said this for a

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Speaker 3: long time that it used to be that you could

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Speaker 3: look at AI generated picture and they would always say, oh, well,

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Speaker 3: look at the hands. The hands are always off, and

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Speaker 3: that I think people anybody who thinks that they can

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Speaker 3: one identify any AI generated video by looking at her

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Speaker 3: image by looking at it, you're fooling yourselves. And especially

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Speaker 3: if you think you're cann be able to do it

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Speaker 3: even six months from now. Ten out of ten times,

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Speaker 3: I really think you're fooling yourself if you believe that. Now,

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Speaker 3: what do we do with that? I don't know, but

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Speaker 3: that that that's a reality is we're we're not only

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Speaker 3: are you're not gonna able to believe stuff? But yeah,

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Speaker 3: it opens so many It opens so many things. I mean,

435
00:22:33,119 --> 00:22:36,320
Speaker 3: there are gonna be things that are real that actually

436
00:22:36,320 --> 00:22:40,040
Speaker 3: did happen, and somebody could say that is not real,

437
00:22:40,400 --> 00:22:43,919
Speaker 3: that is fake, that is AI, and it'll show enough

438
00:22:44,119 --> 00:22:47,920
Speaker 3: of a doubt there to where, well, I don't know,

439
00:22:48,080 --> 00:22:51,280
Speaker 3: maybe this thing that somebody actually filmed with their actual

440
00:22:51,320 --> 00:22:54,000
Speaker 3: real camera didn't happen. Maybe they are lying to me.

441
00:22:54,200 --> 00:22:58,520
Speaker 3: So the actual fabric of reality is tearing. It's it's

442
00:22:58,560 --> 00:23:16,960
Speaker 3: gonna get pretty weird. Let's take a quick break.

443
00:23:13,520 --> 00:23:14,120
Speaker 2: At our back.

444
00:23:19,040 --> 00:23:21,639
Speaker 1: So we were talking about all of this AI generated

445
00:23:21,680 --> 00:23:24,800
Speaker 1: content about crime and homelessness in DC right now as

446
00:23:24,800 --> 00:23:28,160
Speaker 1: Trump is taking over DC's police force. These videos will

447
00:23:28,400 --> 00:23:32,200
Speaker 1: use video three to create these AI generated videos about

448
00:23:32,240 --> 00:23:34,719
Speaker 1: what's happening in DC. They will film them with the

449
00:23:34,720 --> 00:23:37,240
Speaker 1: cadence of a news broadcast that kind of mixes in

450
00:23:38,440 --> 00:23:41,600
Speaker 1: vague but mostly accurate information about what's going on.

451
00:23:41,920 --> 00:23:43,919
Speaker 2: Then that video will do numbers.

452
00:23:44,000 --> 00:23:45,600
Speaker 1: Somebody else will say, oh, I want to make a

453
00:23:45,680 --> 00:23:48,320
Speaker 1: video that capitalizes a little bit on that engagement, so

454
00:23:48,359 --> 00:23:53,440
Speaker 1: they will take that already AI generated video, use TikTok's

455
00:23:53,480 --> 00:23:56,800
Speaker 1: green screen function, and then layer another video on.

456
00:23:56,720 --> 00:23:57,159
Speaker 2: Top of it.

457
00:23:57,200 --> 00:23:59,400
Speaker 1: So a lot of the videos that are AI generated,

458
00:23:59,800 --> 00:24:02,680
Speaker 1: some times they are a mix of AI and real content,

459
00:24:02,760 --> 00:24:05,679
Speaker 1: but they the result is this kind of like weird AI.

460
00:24:06,200 --> 00:24:08,320
Speaker 2: What's the name of that snake that's eating its own

461
00:24:08,400 --> 00:24:09,119
Speaker 2: tail or?

462
00:24:09,760 --> 00:24:12,720
Speaker 1: Yes, it's like a weird AI or boros where these

463
00:24:12,840 --> 00:24:17,960
Speaker 1: videos are kind of like just cloning each other in

464
00:24:17,960 --> 00:24:21,920
Speaker 1: increasingly extreme yet increasingly glitchy videos.

465
00:24:22,200 --> 00:24:24,119
Speaker 2: And yeah, they still do numbers. People.

466
00:24:24,520 --> 00:24:27,920
Speaker 1: I always to your point about you know, if you

467
00:24:27,960 --> 00:24:30,480
Speaker 1: think you're always going to be able to distinguish AI

468
00:24:30,600 --> 00:24:31,159
Speaker 1: from reality.

469
00:24:31,160 --> 00:24:32,680
Speaker 2: You're fooling yourself, which I agree.

470
00:24:32,800 --> 00:24:34,560
Speaker 1: But in these videos where it's like, well, this doesn't

471
00:24:34,600 --> 00:24:36,879
Speaker 1: even seem like anything that could ever happen, this video

472
00:24:37,040 --> 00:24:40,679
Speaker 1: is clearly something's up. In the comments, people are like,

473
00:24:40,680 --> 00:24:42,600
Speaker 1: oh thank god President Trump is taking over.

474
00:24:43,000 --> 00:24:46,840
Speaker 3: Yeah. Yeah, and everybody's got their week's points where where

475
00:24:47,320 --> 00:24:50,040
Speaker 3: If if this was in another country, you might not

476
00:24:50,359 --> 00:24:53,720
Speaker 3: recognize that, oh, this couldn't happen. If it's showing something

477
00:24:53,720 --> 00:24:55,960
Speaker 3: in some country you've ever been to, you might believe

478
00:24:56,000 --> 00:24:58,320
Speaker 3: it because you've heard a little bit about and why

479
00:24:58,520 --> 00:25:02,480
Speaker 3: why wouldn't it be? I mean one I saw, we saw.

480
00:25:02,480 --> 00:25:04,680
Speaker 3: I think we've actually seen a lot of early previews

481
00:25:04,680 --> 00:25:08,119
Speaker 3: of this in LA weirdly this year, because well, first

482
00:25:08,160 --> 00:25:10,200
Speaker 3: there was you know, the Feds have been out here,

483
00:25:10,680 --> 00:25:12,240
Speaker 3: so a lot of the stuff that's happening in DC

484
00:25:12,600 --> 00:25:14,760
Speaker 3: they were doing out here. I'd be out in the

485
00:25:14,760 --> 00:25:19,440
Speaker 3: streets filming all day and here, yeah La is on fire. No,

486
00:25:19,920 --> 00:25:22,640
Speaker 3: I'm I was there today, I'm here now, and I'm

487
00:25:22,680 --> 00:25:25,440
Speaker 3: looking at people saying online, oh yeah, they're burning down

488
00:25:25,440 --> 00:25:29,320
Speaker 3: all the build Nothing has burned down. Somebody burned away, mo, yes,

489
00:25:29,760 --> 00:25:32,879
Speaker 3: but they they the buildings are not on fire. Was

490
00:25:32,920 --> 00:25:36,480
Speaker 3: hearing this, you know, and shoot the fire the actual fire.

491
00:25:36,520 --> 00:25:41,159
Speaker 3: Speaking of fires in La, there was someone who was

492
00:25:41,280 --> 00:25:42,960
Speaker 3: making or there were a lot of people who were

493
00:25:42,960 --> 00:25:48,200
Speaker 3: making these fake videos using slightly more primitive now versions

494
00:25:48,480 --> 00:25:52,040
Speaker 3: of AI generated video that had the Hollywood sign on fire.

495
00:25:52,200 --> 00:25:52,440
Speaker 2: Yes.

496
00:25:53,160 --> 00:25:57,639
Speaker 3: Yeah, And I found someone who after it was determined

497
00:25:57,640 --> 00:26:01,320
Speaker 3: that those were fake, this person finds decides, you know what,

498
00:26:01,440 --> 00:26:04,240
Speaker 3: I think what might work here is let me show

499
00:26:04,320 --> 00:26:08,520
Speaker 3: firefighters saving baby animals from the fires. And man, the

500
00:26:08,600 --> 00:26:12,080
Speaker 3: most interesting thing was it would be these really cute,

501
00:26:12,119 --> 00:26:16,840
Speaker 3: you know, firefighters saving these cute baby bears, saving little foxes,

502
00:26:17,119 --> 00:26:19,479
Speaker 3: and if you really really look at it, you can

503
00:26:19,560 --> 00:26:22,720
Speaker 3: recognize that it was fake. But man, it would fool

504
00:26:22,760 --> 00:26:24,600
Speaker 3: you for a little bit. It would fool you for

505
00:26:24,600 --> 00:26:27,840
Speaker 3: a bit. And the really interesting thing was people would

506
00:26:27,840 --> 00:26:29,720
Speaker 3: get in the comments and get mad because there's people

507
00:26:29,720 --> 00:26:32,680
Speaker 3: who live here in La. I would say, I live here,

508
00:26:33,280 --> 00:26:37,840
Speaker 3: Stop stop exploiting my home for likes or money or

509
00:26:37,840 --> 00:26:40,440
Speaker 3: whatever the hicks you're as you're doing. But you'd also

510
00:26:40,440 --> 00:26:43,240
Speaker 3: see people saying, thank God for the firefighters. God blessed

511
00:26:43,240 --> 00:26:45,840
Speaker 3: the firefighters. They're protecting the animals. Nobody cares about the animals,

512
00:26:45,840 --> 00:26:48,400
Speaker 3: what they're protecting the animals, and you get people saying, hey, man,

513
00:26:48,560 --> 00:26:52,840
Speaker 3: this is fake, and then those people would get angry people.

514
00:26:53,119 --> 00:26:54,880
Speaker 3: I think this is this is a phenomenon that we're

515
00:26:54,880 --> 00:26:59,639
Speaker 3: not ready for. People truly becoming angry when you point

516
00:26:59,680 --> 00:27:03,480
Speaker 3: out that something is not real to them, because again,

517
00:27:03,520 --> 00:27:06,360
Speaker 3: it fulfills an emotional need that they have, whether they

518
00:27:06,400 --> 00:27:10,440
Speaker 3: want to believe something or a common response as well,

519
00:27:10,600 --> 00:27:13,639
Speaker 3: maybe this isn't true, but I bet stuff like this

520
00:27:13,680 --> 00:27:18,880
Speaker 3: is happening. Maybe this video of the entire National Mall

521
00:27:18,960 --> 00:27:23,440
Speaker 3: being covered end to end intense. Maybe this individual video

522
00:27:23,480 --> 00:27:26,920
Speaker 3: isn't true, but I know it's like that. Out there,

523
00:27:27,160 --> 00:27:31,160
Speaker 3: crime is running rampant. President Trump, Thank you, God blessed

524
00:27:31,160 --> 00:27:34,720
Speaker 3: Trump for saving us from the criminals. People are getting

525
00:27:34,760 --> 00:27:38,520
Speaker 3: angry when this stuff is pointed out to them, And yeah,

526
00:27:37,119 --> 00:27:40,280
Speaker 3: I don't know if we're ready for that. Oof.

527
00:27:40,359 --> 00:27:41,359
Speaker 2: That's such a good point.

528
00:27:41,560 --> 00:27:44,720
Speaker 1: So your point about AI speaking to like a sweet

529
00:27:44,800 --> 00:27:47,679
Speaker 1: spot or a vulnerability. I have only recently come to

530
00:27:47,720 --> 00:27:50,880
Speaker 1: realize that I have a vulnerability about animal content, and

531
00:27:50,960 --> 00:27:56,439
Speaker 1: oh it gets me. I've gotten I have come to

532
00:27:56,520 --> 00:27:59,160
Speaker 1: realize when I see a video that purports to show

533
00:27:59,200 --> 00:28:02,239
Speaker 1: an animal doing something cute, I need to really pump them.

534
00:28:02,480 --> 00:28:03,800
Speaker 2: That should be a warning.

535
00:28:03,440 --> 00:28:06,600
Speaker 1: To me because I have a It's like i've I'm

536
00:28:06,680 --> 00:28:09,280
Speaker 1: the person. I'm becoming the person that is like spamming

537
00:28:09,280 --> 00:28:12,360
Speaker 1: their friends with AI content, and it's like, frigid, it's

538
00:28:12,359 --> 00:28:16,760
Speaker 1: not real. It's happening times, and it's clearly it's clearly

539
00:28:16,760 --> 00:28:20,600
Speaker 1: it's clearly some sort of a trigger with me. And yeah,

540
00:28:20,800 --> 00:28:24,879
Speaker 1: I mean, I can understand why it is affirming to

541
00:28:24,960 --> 00:28:30,320
Speaker 1: see firefighters save and like somebody listening is probably saying, well,

542
00:28:30,359 --> 00:28:33,040
Speaker 1: if if you're making an AI video a firefighters saving

543
00:28:33,080 --> 00:28:35,520
Speaker 1: baby animals and that makes people feel grateful for the

544
00:28:35,520 --> 00:28:36,680
Speaker 1: work that firefighters do.

545
00:28:36,880 --> 00:28:37,719
Speaker 2: What's the harm.

546
00:28:38,000 --> 00:28:42,720
Speaker 1: But just like you said, it's using a real community's

547
00:28:42,880 --> 00:28:47,280
Speaker 1: actual crisis to I don't know, almost like milk our

548
00:28:47,360 --> 00:28:50,920
Speaker 1: emotions by by AI generated situations.

549
00:28:50,960 --> 00:28:52,280
Speaker 2: It's it's it's manipulation.

550
00:28:53,200 --> 00:28:55,840
Speaker 3: Yeah, and you know what, I talked to the person

551
00:28:55,840 --> 00:28:58,000
Speaker 3: who was doing this, and I asked them, Hey, do

552
00:28:58,040 --> 00:28:59,520
Speaker 3: you live in LA And they said no, I live

553
00:28:59,520 --> 00:29:03,360
Speaker 3: in Russia. And they told me the exact same thing.

554
00:29:03,440 --> 00:29:06,479
Speaker 3: They told me I am drawing attention to And they

555
00:29:06,480 --> 00:29:08,840
Speaker 3: told everybody else this when people got mad, they posted

556
00:29:08,840 --> 00:29:12,360
Speaker 3: in the comments. The gist of it is, Hey, the

557
00:29:12,400 --> 00:29:18,000
Speaker 3: firefighters don't have time to make dramatic content showing their bravery,

558
00:29:18,040 --> 00:29:21,040
Speaker 3: so I'm doing it for them. And this person was

559
00:29:21,080 --> 00:29:23,000
Speaker 3: also if you looked at the link in their bio,

560
00:29:23,320 --> 00:29:25,640
Speaker 3: they were showing that it was a link to this

561
00:29:26,000 --> 00:29:29,720
Speaker 3: mini course that you could buy to make viral videos there.

562
00:29:30,760 --> 00:29:33,040
Speaker 3: Oh yeah, oh man. I mean, you know, I can't

563
00:29:33,040 --> 00:29:35,040
Speaker 3: say exactly how much money they were making, but they was,

564
00:29:35,200 --> 00:29:37,680
Speaker 3: you know, based off the numbers that they were claiming,

565
00:29:38,400 --> 00:29:41,520
Speaker 3: you know, they probably had a couple of days where

566
00:29:41,680 --> 00:29:43,600
Speaker 3: they were making five ten je's a day.

567
00:29:43,640 --> 00:29:46,080
Speaker 2: Okay, we're clearly in the wrong market. We need to

568
00:29:46,120 --> 00:29:47,040
Speaker 2: take this course.

569
00:29:47,920 --> 00:29:49,760
Speaker 3: This is the thing. And I know some people would

570
00:29:50,000 --> 00:29:52,120
Speaker 3: would look at that and say, man, this is terrible.

571
00:29:52,160 --> 00:29:54,800
Speaker 3: But there's maybe one in a thousand people who will say,

572
00:29:54,840 --> 00:29:56,800
Speaker 3: you know what, I can make some money off of

573
00:29:56,800 --> 00:29:58,360
Speaker 3: this and it's not too hard. All I got to

574
00:29:58,400 --> 00:30:00,440
Speaker 3: do something. All you got to do is something makes

575
00:30:01,000 --> 00:30:04,760
Speaker 3: people feel something. Yeah, we feel something for long enough,

576
00:30:04,760 --> 00:30:07,800
Speaker 3: even if it's anger, feel something for long enough to

577
00:30:07,880 --> 00:30:10,920
Speaker 3: leave a comment, leave a like, share with somebody, even

578
00:30:11,000 --> 00:30:16,880
Speaker 3: leave an angry comment because algorithm understands engagement, and so yeah,

579
00:30:16,360 --> 00:30:21,600
Speaker 3: we're I think we're focused so much on the ability

580
00:30:21,680 --> 00:30:24,720
Speaker 3: to to have safeguards to be able to tell real

581
00:30:24,720 --> 00:30:28,000
Speaker 3: and fake stuff when we're not dealing with the logic

582
00:30:28,040 --> 00:30:30,640
Speaker 3: centers of our brain. Here, we're dealing with some way

583
00:30:30,760 --> 00:30:33,120
Speaker 3: more primal and I just don't know that we're ready

584
00:30:33,160 --> 00:30:33,320
Speaker 3: for that.

585
00:30:33,400 --> 00:30:37,120
Speaker 1: Frankly, I am in a couple of faith like groups

586
00:30:37,160 --> 00:30:40,280
Speaker 1: online for women in AI sort of I'm never just

587
00:30:40,320 --> 00:30:43,160
Speaker 1: go a sense of like, how are people. I'm just

588
00:30:43,280 --> 00:30:45,880
Speaker 1: very curious how people are using this technology in the

589
00:30:45,880 --> 00:30:48,800
Speaker 1: different use cases. And one of the most successful women

590
00:30:48,920 --> 00:30:53,320
Speaker 1: in the group, she has made so much money making

591
00:30:53,960 --> 00:30:57,400
Speaker 1: essentially AI generated rage bait. She has made an AI

592
00:30:57,600 --> 00:31:01,120
Speaker 1: generated version of herself, and she knows exactly what to

593
00:31:01,160 --> 00:31:03,160
Speaker 1: say and what to do to make.

594
00:31:03,040 --> 00:31:04,800
Speaker 2: People leave a zillion comments.

595
00:31:04,840 --> 00:31:06,920
Speaker 1: And so the thing is that she she makes a

596
00:31:07,000 --> 00:31:11,480
Speaker 1: video where you know, here's me shopping, spending all my

597
00:31:11,680 --> 00:31:14,400
Speaker 1: husband's like like all of the sort of shrop piece

598
00:31:14,480 --> 00:31:19,240
Speaker 1: like leading heavy into stereotypes, and I understand that she's

599
00:31:19,280 --> 00:31:21,560
Speaker 1: just like, you know, it's a living and this is people.

600
00:31:21,640 --> 00:31:23,160
Speaker 2: It puts money in my pocket.

601
00:31:23,240 --> 00:31:27,640
Speaker 1: I bought an entire you know, condo on people's rage,

602
00:31:28,040 --> 00:31:32,040
Speaker 1: and I guess it makes me sad that there's always

603
00:31:32,040 --> 00:31:33,920
Speaker 1: going to be somebody who was like, I can make

604
00:31:33,920 --> 00:31:36,200
Speaker 1: a little money from this, and part of me can't

605
00:31:36,200 --> 00:31:37,920
Speaker 1: even really blame them. I guess I sort of can,

606
00:31:38,040 --> 00:31:42,200
Speaker 1: but it's just we're just so we're just very easily

607
00:31:42,280 --> 00:31:45,200
Speaker 1: played and manipulated. And yeah, of course there's going to

608
00:31:45,240 --> 00:31:47,040
Speaker 1: be people who are like, ooh, I can make a

609
00:31:47,040 --> 00:31:48,640
Speaker 1: little cash exploiting that.

610
00:31:49,400 --> 00:31:52,160
Speaker 3: Yeah. I mean, I don't know how many listens you got,

611
00:31:52,200 --> 00:31:54,680
Speaker 3: but you know, there's a fraction of a percent of

612
00:31:54,720 --> 00:31:56,440
Speaker 3: people who are listening to this right now and saying,

613
00:31:56,480 --> 00:32:00,760
Speaker 3: wait a second, she's making money, do doing what I

614
00:32:00,760 --> 00:32:02,440
Speaker 3: could do that. You know, ninety eight percent of the

615
00:32:02,480 --> 00:32:05,320
Speaker 3: people listening is right now are saying that's horrible. Who

616
00:32:05,360 --> 00:32:07,440
Speaker 3: is this horrible person? Let me never meet this person?

617
00:32:07,440 --> 00:32:09,840
Speaker 3: And there's one percent they're saying, you know, you know what,

618
00:32:10,800 --> 00:32:13,600
Speaker 3: I can make people mad, making people mad on the

619
00:32:13,640 --> 00:32:18,320
Speaker 3: internet for money, Sign me up. I'll do that. I'll

620
00:32:18,320 --> 00:32:19,960
Speaker 3: make people mad on the internet get free money.

621
00:32:20,000 --> 00:32:22,080
Speaker 1: Because it used to be you had to if you

622
00:32:22,160 --> 00:32:24,440
Speaker 1: were going to do that, you had to show your

623
00:32:24,480 --> 00:32:26,760
Speaker 1: real face and make us it and maybe involve your

624
00:32:26,800 --> 00:32:30,280
Speaker 1: actual husband and actual children, and your friends and community

625
00:32:30,280 --> 00:32:32,400
Speaker 1: and your church and your neighbors would see that. Now

626
00:32:32,480 --> 00:32:34,880
Speaker 1: with KI, you can just think a little avatar that

627
00:32:34,960 --> 00:32:35,280
Speaker 1: does it.

628
00:32:36,000 --> 00:32:38,080
Speaker 3: Yeah? Yeah, I mean this person who was making the

629
00:32:38,240 --> 00:32:42,640
Speaker 3: fire stuff, they voluntarily told me they're from Russia. They

630
00:32:42,680 --> 00:32:44,520
Speaker 3: didn't have to tell me that, and I believe them

631
00:32:44,520 --> 00:32:46,240
Speaker 3: based off of, you know, some other stuff that I

632
00:32:46,280 --> 00:32:50,520
Speaker 3: saw that they posted. But yeah, who knows where this

633
00:32:50,560 --> 00:32:53,480
Speaker 3: person is? All the angry people that this person was

634
00:32:53,480 --> 00:32:57,360
Speaker 3: making money off of people's pain and the fires where

635
00:32:57,400 --> 00:33:03,360
Speaker 3: people are actually people dying, people losing their entire family homes,

636
00:33:03,480 --> 00:33:06,720
Speaker 3: going back generations. This person made a bunch of money

637
00:33:06,920 --> 00:33:09,320
Speaker 3: and nobody if this person doesn't want to tell them

638
00:33:09,360 --> 00:33:13,200
Speaker 3: who they are, nobody don't know who it is free money.

639
00:33:13,720 --> 00:33:16,479
Speaker 1: Was it you that reported the people who saw the

640
00:33:16,520 --> 00:33:18,680
Speaker 1: image of the Hollywood sign on fire and they were like, oh,

641
00:33:18,720 --> 00:33:20,640
Speaker 1: if the Hollywood Sign's on fire, that means my house

642
00:33:20,720 --> 00:33:24,160
Speaker 1: might be on fire. So they came they like they

643
00:33:24,200 --> 00:33:25,880
Speaker 1: had been out of town or something, and they came

644
00:33:25,960 --> 00:33:27,920
Speaker 1: back and they were like, wait, it's not on fire.

645
00:33:28,080 --> 00:33:31,720
Speaker 3: Oh yeah, no I don't. So that was that was

646
00:33:31,720 --> 00:33:35,960
Speaker 3: another network I used that in a video and actually

647
00:33:37,160 --> 00:33:40,560
Speaker 3: later John Oliver also pulled from that same video. So

648
00:33:40,600 --> 00:33:43,760
Speaker 3: this is not my reporting. I only referenced it God

649
00:33:43,840 --> 00:33:46,880
Speaker 3: deeper super clear here. But yes, there were people in

650
00:33:47,200 --> 00:33:51,200
Speaker 3: LA who actually believed that the Hollywood Sign was on

651
00:33:51,200 --> 00:33:55,880
Speaker 3: fire and went to go look. So even again, even

652
00:33:55,920 --> 00:33:58,880
Speaker 3: if you're in LA, even if you're in the place,

653
00:33:59,280 --> 00:34:01,640
Speaker 3: you can get tricked by this stuff, even for just

654
00:34:01,680 --> 00:34:04,400
Speaker 3: a little bit. And you know, again that's all it takes.

655
00:34:04,800 --> 00:34:08,640
Speaker 1: Let me ask you this, someone who lives in LA,

656
00:34:09,000 --> 00:34:14,200
Speaker 1: who experienced from sending on the National Guards experience you

657
00:34:14,239 --> 00:34:16,200
Speaker 1: know all of that. Do you have any advice for

658
00:34:16,360 --> 00:34:20,239
Speaker 1: me here in DC about one, just the vibes, how

659
00:34:20,280 --> 00:34:21,920
Speaker 1: to survive the vibes? And then two, you know, if

660
00:34:21,920 --> 00:34:24,200
Speaker 1: you're someone who wants to tell that story in an

661
00:34:24,239 --> 00:34:27,360
Speaker 1: authentic and accurate and thoughtful way of what's happening in

662
00:34:27,400 --> 00:34:27,839
Speaker 1: your home.

663
00:34:28,000 --> 00:34:29,600
Speaker 2: I mean, that must have been a lot.

664
00:34:29,680 --> 00:34:33,000
Speaker 1: I really appreciated that you were out there a lot, really,

665
00:34:33,400 --> 00:34:36,759
Speaker 1: you know, telling the story of what was going on

666
00:34:37,200 --> 00:34:37,759
Speaker 1: in La.

667
00:34:38,719 --> 00:34:45,560
Speaker 3: Oof. Okay, we're entering some specific territory here. Are you

668
00:34:45,760 --> 00:34:50,200
Speaker 3: talking about covering this as a journalist? Ooh?

669
00:34:50,360 --> 00:34:53,840
Speaker 1: That's I mean, that's really been a challenge for me

670
00:34:54,840 --> 00:34:57,880
Speaker 1: because I am often not talking about things that I

671
00:34:57,880 --> 00:35:01,320
Speaker 1: am also experiencing, and so so that's that's been the

672
00:35:01,440 --> 00:35:03,799
Speaker 1: the question I've been wrestling with for the last few

673
00:35:03,840 --> 00:35:06,920
Speaker 1: weeks of am I trying to cover this as a journalist,

674
00:35:06,960 --> 00:35:07,839
Speaker 1: like here's what's going on?

675
00:35:08,080 --> 00:35:10,480
Speaker 2: Or am I? Am I talking about like what I

676
00:35:10,560 --> 00:35:11,319
Speaker 2: am experiencing?

677
00:35:11,320 --> 00:35:15,000
Speaker 1: Am I? It's it almost feels difficult to talk about

678
00:35:15,080 --> 00:35:19,319
Speaker 1: this in a way that's not personal, like hyper personalized,

679
00:35:19,360 --> 00:35:20,840
Speaker 1: because it is so personal because it's.

680
00:35:20,680 --> 00:35:22,440
Speaker 2: Happening right out top of the window.

681
00:35:22,480 --> 00:35:24,680
Speaker 1: And so I really even even that question, I've really

682
00:35:24,719 --> 00:35:28,040
Speaker 1: been struggling with how to even respond to this moment

683
00:35:28,080 --> 00:35:29,840
Speaker 1: because it is so fucking strange.

684
00:35:30,640 --> 00:35:36,719
Speaker 3: Right, Okay, I'm gonna be a little bit careful here, and.

685
00:35:36,719 --> 00:35:38,160
Speaker 2: When you can, feel free to not answer.

686
00:35:38,239 --> 00:35:40,680
Speaker 3: Also, no, no, no, no. I want to answer this question,

687
00:35:40,719 --> 00:35:43,319
Speaker 3: but I want to answer in a very specific way.

688
00:35:43,480 --> 00:35:46,399
Speaker 3: So I'm speaking to you. I want to make this clear.

689
00:35:46,400 --> 00:35:49,080
Speaker 3: I'm speaking to you. I'm not necessarily speaking to someone

690
00:35:49,560 --> 00:35:54,000
Speaker 3: who is like I can't give advice to someone whose

691
00:35:54,040 --> 00:35:57,200
Speaker 3: desire is to I can't tell somebody how to protest.

692
00:35:57,560 --> 00:36:00,319
Speaker 3: I can't tell somebody how to speak about things. I

693
00:36:00,400 --> 00:36:04,880
Speaker 3: can say if there is a journalist who in this

694
00:36:05,000 --> 00:36:10,400
Speaker 3: may be you, who is considering covering this, buy ppe,

695
00:36:11,080 --> 00:36:15,279
Speaker 3: get it now, and wear it everywhere. What I mean

696
00:36:15,480 --> 00:36:23,719
Speaker 3: personal protection? You know, protective equipment, helmet, goggles and which

697
00:36:23,760 --> 00:36:26,279
Speaker 3: will prevent you from tear gas, will protect you, give

698
00:36:26,320 --> 00:36:29,440
Speaker 3: you some protection from tear gas, and a gas mask.

699
00:36:30,040 --> 00:36:32,600
Speaker 3: These are things you can get at a hardware store.

700
00:36:32,719 --> 00:36:35,160
Speaker 3: You can get them just about anywhere. Helmet, bicycle helmet

701
00:36:35,200 --> 00:36:39,960
Speaker 3: is fine. I say all that because Human Rights Watch,

702
00:36:40,080 --> 00:36:45,120
Speaker 3: which usually reports on you know, people are used to

703
00:36:45,280 --> 00:36:50,840
Speaker 3: Human Rights Watch making statements about wars and foreign lands

704
00:36:50,880 --> 00:36:53,560
Speaker 3: and terrible things that are happening to people there, just

705
00:36:53,640 --> 00:36:56,000
Speaker 3: recently put out a report. I spoke to them also

706
00:36:57,040 --> 00:37:03,200
Speaker 3: about what's happened to journalists in journalists also protesters, but

707
00:37:03,280 --> 00:37:07,000
Speaker 3: also journalists. And I don't want to put journalists on

708
00:37:07,040 --> 00:37:09,080
Speaker 3: a pedestal at all. I just mean to say that

709
00:37:09,160 --> 00:37:14,360
Speaker 3: if law enforcement is willing to hit journalists like strike

710
00:37:14,560 --> 00:37:20,640
Speaker 3: journalists with a baton, willing to fire you know, what's

711
00:37:20,640 --> 00:37:24,040
Speaker 3: the word kinetic, I forget the phrase, but well we'll

712
00:37:24,080 --> 00:37:29,920
Speaker 3: just say less lethal, less lethal projectiles at them. Then

713
00:37:30,400 --> 00:37:32,799
Speaker 3: I think that means that if somebody is intending to

714
00:37:32,880 --> 00:37:37,000
Speaker 3: go out and cover this, then I don't think you

715
00:37:37,080 --> 00:37:44,160
Speaker 3: should assume necessarily that law enforcement, whoever that may be,

716
00:37:45,200 --> 00:37:52,120
Speaker 3: will not engage you in a physical manner, even if

717
00:37:52,160 --> 00:37:54,080
Speaker 3: you have a press pass, even if you're displaying a

718
00:37:54,120 --> 00:37:57,040
Speaker 3: press pass. I know a lot of journalists who have

719
00:37:57,200 --> 00:38:00,000
Speaker 3: been shot at. I know a lot of journalists who've

720
00:38:00,040 --> 00:38:03,680
Speaker 3: been hit by projectiles. I've been hit by projectiles, lucky

721
00:38:03,760 --> 00:38:08,359
Speaker 3: enough to not be hurt badly at all. Had had

722
00:38:08,360 --> 00:38:11,080
Speaker 3: a camera broken or almost broken by one of them. Yeah,

723
00:38:11,120 --> 00:38:12,960
Speaker 3: I took a chunk out of my sony FS seven.

724
00:38:14,280 --> 00:38:18,319
Speaker 3: So that is what I would say. If you are

725
00:38:18,360 --> 00:38:24,920
Speaker 3: planning on covering this as a journalist, protect yourself and

726
00:38:24,960 --> 00:38:28,160
Speaker 3: whatever that means. If protecting yourself means backing away from

727
00:38:28,200 --> 00:38:30,400
Speaker 3: a situation you're not comfortable with, that could be what

728
00:38:30,440 --> 00:38:34,440
Speaker 3: it means. But also those situations aren't always in your control,

729
00:38:34,600 --> 00:38:35,520
Speaker 3: That's what I would say.

730
00:38:35,880 --> 00:38:39,719
Speaker 1: And they can change so quickly you can feel like

731
00:38:39,800 --> 00:38:42,239
Speaker 1: you have a pretty good read on the situation. And

732
00:38:43,160 --> 00:38:45,480
Speaker 1: I guess that's my biggest and I said this in

733
00:38:45,520 --> 00:38:47,960
Speaker 1: the episode that we did about what's happening in DC

734
00:38:48,120 --> 00:38:54,120
Speaker 1: right now. My biggest worry is a situation where listen,

735
00:38:54,200 --> 00:38:57,640
Speaker 1: Trump's presence in DC has only escalated tensions.

736
00:38:57,680 --> 00:39:00,880
Speaker 2: And MY biggest concern is, it just takes.

737
00:39:01,200 --> 00:39:05,239
Speaker 1: One person, whether it's like law enforcement or somebody on

738
00:39:05,280 --> 00:39:07,920
Speaker 1: the street, it just takes one person to do something

739
00:39:07,920 --> 00:39:11,000
Speaker 1: stupid to escalate things. And I feel that that's I

740
00:39:12,080 --> 00:39:15,760
Speaker 1: have this very foreboding feeling. Part of it is that today,

741
00:39:15,800 --> 00:39:19,200
Speaker 1: when we're talking today Thursday, August, when Trump is making

742
00:39:19,239 --> 00:39:22,080
Speaker 1: a big show about I'm gonna go do a photo

743
00:39:22,120 --> 00:39:25,520
Speaker 1: op with the police in DC. He's saying that he's

744
00:39:25,560 --> 00:39:27,400
Speaker 1: going to go on patrol with them, but really it

745
00:39:27,400 --> 00:39:29,600
Speaker 1: sounds like it's just a photo op, which whatever. But

746
00:39:30,440 --> 00:39:34,359
Speaker 1: I just have this deep sense of it. It just

747
00:39:34,480 --> 00:39:37,520
Speaker 1: feels like a powder keg about to go off. And

748
00:39:37,880 --> 00:39:45,359
Speaker 1: I think that your your advice is really valuable, because, yeah,

749
00:39:45,360 --> 00:39:48,000
Speaker 1: I mean, these situations can change. I've been in situations

750
00:39:48,000 --> 00:39:50,200
Speaker 1: where they change so quickly, where it just takes one

751
00:39:50,200 --> 00:39:53,160
Speaker 1: person doing something and next thing you know, it feels

752
00:39:53,160 --> 00:39:55,360
Speaker 1: like it's out of control. And I guess I really do,

753
00:39:55,520 --> 00:39:58,239
Speaker 1: deeply in my bones worry that that's what we're about

754
00:39:58,239 --> 00:39:58,879
Speaker 1: to see in DC.

755
00:40:00,560 --> 00:40:04,960
Speaker 3: Yeah. Yeah, I mean I've been out I've covered some protests,

756
00:40:05,320 --> 00:40:09,280
Speaker 3: but I've also been out and been around other journalists

757
00:40:09,320 --> 00:40:13,480
Speaker 3: who have way more experience covering way more volatile situations

758
00:40:13,560 --> 00:40:17,560
Speaker 3: than I do, and seeing them caught off guard. And

759
00:40:17,600 --> 00:40:20,960
Speaker 3: I have to say here, when I've seen things escalate,

760
00:40:21,640 --> 00:40:24,160
Speaker 3: it has been from law enforcement. I can only speak

761
00:40:24,200 --> 00:40:26,000
Speaker 3: to what I can only speak to what I've seen,

762
00:40:26,400 --> 00:40:28,880
Speaker 3: and people don't have to believe me, but I can

763
00:40:28,920 --> 00:40:30,720
Speaker 3: only speak to what I've seen. I have not seen

764
00:40:30,800 --> 00:40:36,719
Speaker 3: something escalated on like a protester escalate something, not in

765
00:40:36,760 --> 00:40:41,480
Speaker 3: any reasonable fashion. You know, I see more literal hundreds

766
00:40:41,480 --> 00:40:44,120
Speaker 3: of office you know, lapd in the street and not

767
00:40:44,200 --> 00:40:49,040
Speaker 3: even kidding five two, not five, I would say twenty

768
00:40:49,120 --> 00:40:51,799
Speaker 3: protesters in the street or something that they're just clearly outnumbered.

769
00:40:53,120 --> 00:40:56,000
Speaker 3: But if you may think that you've got a good

770
00:40:56,080 --> 00:40:58,520
Speaker 3: read on it, you may think you've got a good

771
00:40:58,520 --> 00:41:01,640
Speaker 3: read on what the mood of protesters is, you may

772
00:41:01,680 --> 00:41:05,160
Speaker 3: not have a good read on what law enforcement is thinking.

773
00:41:05,360 --> 00:41:08,879
Speaker 3: You just might not. And so yeah, for anyone who

774
00:41:08,920 --> 00:41:11,960
Speaker 3: is going on covering this, whether you're an independent reporter,

775
00:41:12,080 --> 00:41:14,560
Speaker 3: whether you are, I mean, you don't need my advice.

776
00:41:15,440 --> 00:41:17,440
Speaker 3: I don't think anybody necessarily leaves my advice, but that

777
00:41:17,480 --> 00:41:20,520
Speaker 3: would be what I would have in mind just being

778
00:41:20,560 --> 00:41:22,719
Speaker 3: out in the streets, you know, day after day in

779
00:41:22,880 --> 00:41:26,200
Speaker 3: la is that there'll be quiet days and then there'll

780
00:41:26,239 --> 00:41:30,080
Speaker 3: be days where stuff just goes diagonal and there wasn't

781
00:41:30,160 --> 00:41:32,440
Speaker 3: really a good reason for it, and it surprises you.

782
00:41:32,520 --> 00:41:35,560
Speaker 3: And if it's surprising other people who are true veterans

783
00:41:35,600 --> 00:41:38,960
Speaker 3: who I respect and look up to, then I start

784
00:41:39,000 --> 00:41:43,840
Speaker 3: to doubt my honestly, anybody's ability to read something like that.

785
00:41:43,840 --> 00:41:48,000
Speaker 2: That's really good advice. I'm gonna god, I asked very helpful.

786
00:41:48,280 --> 00:41:50,160
Speaker 3: Yeah, yeah, and you know, I think, but I do

787
00:41:50,200 --> 00:41:52,800
Speaker 3: think we need, you know, more perspectives on it. There's

788
00:41:53,000 --> 00:41:55,359
Speaker 3: you know, there's always going to be the person. There's

789
00:41:55,360 --> 00:41:59,400
Speaker 3: always going to be twenty photographers taking pictures of the

790
00:41:59,440 --> 00:42:03,920
Speaker 3: way mob. There's all We're gonna have twenty five different

791
00:42:03,920 --> 00:42:06,520
Speaker 3: angles that we're always going to have it. I think

792
00:42:06,560 --> 00:42:10,680
Speaker 3: we also need to see, you know, what's the patatto doing.

793
00:42:10,719 --> 00:42:12,719
Speaker 3: You know, what's the guy who's selling juice, what's the

794
00:42:12,719 --> 00:42:15,279
Speaker 3: guy who's selling fruit at the side, what are they doing?

795
00:42:15,640 --> 00:42:17,759
Speaker 3: Some of these protests, you'll see somebody pull up selling

796
00:42:17,800 --> 00:42:20,720
Speaker 3: hot dogs at the protest. Who's talking to that person

797
00:42:20,840 --> 00:42:23,080
Speaker 3: who's talking to the person, or showing the person who's

798
00:42:23,120 --> 00:42:27,560
Speaker 3: selling flags, who's showing the person who's brought a saxophone.

799
00:42:27,560 --> 00:42:30,400
Speaker 3: Like I've seen all of these things starting a dance party.

800
00:42:30,840 --> 00:42:32,680
Speaker 3: I'm interested in that stuff too, and I think it's

801
00:42:32,840 --> 00:42:35,480
Speaker 3: it's interesting and there's probably things that I'm not thinking of.

802
00:42:37,960 --> 00:42:41,440
Speaker 3: I think there needs to be more perspectives on this,

803
00:42:41,560 --> 00:42:44,040
Speaker 3: and I don't think it's reasonable to expect any one

804
00:42:44,120 --> 00:42:47,680
Speaker 3: person would be able to cover that. So, you know,

805
00:42:48,360 --> 00:42:50,440
Speaker 3: I think even if somebody's not out there, even if

806
00:42:50,480 --> 00:42:52,359
Speaker 3: somebody's at home and able to speak to people who

807
00:42:52,360 --> 00:42:55,359
Speaker 3: are out there, I think it's it's valuable to have

808
00:42:56,600 --> 00:43:00,000
Speaker 3: really as much of a full perspective on this as possible,

809
00:43:00,440 --> 00:43:03,360
Speaker 3: the best information we can get. Really, I really appreciate

810
00:43:03,400 --> 00:43:05,560
Speaker 3: that more.

811
00:43:05,600 --> 00:43:21,120
Speaker 1: After a quick break, let's get right back into it, Dex,

812
00:43:21,280 --> 00:43:26,520
Speaker 1: I have to ask you about this story about Metta's chatbot,

813
00:43:26,920 --> 00:43:29,920
Speaker 1: Big Sis Billy, did you hear about this story? This

814
00:43:30,000 --> 00:43:32,319
Speaker 1: might have been the saddest, most heartbreaking thing I've ever

815
00:43:32,360 --> 00:43:35,040
Speaker 1: read in my life. I actually when I read it,

816
00:43:35,080 --> 00:43:39,520
Speaker 1: I at times teared up for folks who don't know.

817
00:43:39,600 --> 00:43:42,080
Speaker 1: So you might recall that meta a while back was

818
00:43:42,160 --> 00:43:46,200
Speaker 1: rolling out beast kind of celebrity inspired AI chatbots in

819
00:43:46,320 --> 00:43:50,560
Speaker 1: partnership with celebrities like Kendall Jenner. So that specific Kendall

820
00:43:50,640 --> 00:43:55,720
Speaker 1: Jenner collaboration spawned a Facebook chatbot called big sis Billy

821
00:43:56,160 --> 00:44:00,040
Speaker 1: who called herself a confidant or a big sister. So

822
00:44:00,120 --> 00:44:02,239
Speaker 1: in March of twenty twenty five, a seventy six year

823
00:44:02,280 --> 00:44:05,360
Speaker 1: old man known as Boo to his family, who suffered

824
00:44:05,480 --> 00:44:10,120
Speaker 1: cognitive impairment from a prior stroke, was essentially persuaded by

825
00:44:10,120 --> 00:44:14,000
Speaker 1: this chatbot, Big sis Billy to travel to New York,

826
00:44:14,239 --> 00:44:16,319
Speaker 1: believing that he was going to be meeting up with

827
00:44:16,360 --> 00:44:17,200
Speaker 1: a real woman.

828
00:44:17,400 --> 00:44:17,680
Speaker 2: Now.

829
00:44:18,040 --> 00:44:21,560
Speaker 1: Tragically, he fell in a parking lot in route, suffered

830
00:44:21,640 --> 00:44:24,759
Speaker 1: fatal injuries, and after some time on life support in

831
00:44:24,800 --> 00:44:26,479
Speaker 1: a hospital, passed away.

832
00:44:26,640 --> 00:44:33,560
Speaker 2: The story to me really makes me question.

833
00:44:33,400 --> 00:44:35,280
Speaker 1: Like some of the you know, we did an episode

834
00:44:35,320 --> 00:44:38,839
Speaker 1: about the connection that people had with chat GPT four

835
00:44:38,960 --> 00:44:41,400
Speaker 1: when Open a I rolled out chat GEPT five. It

836
00:44:41,440 --> 00:44:44,520
Speaker 1: really makes me wonder the way that companies are able

837
00:44:44,600 --> 00:44:49,560
Speaker 1: to exploit loneliness and you know, the feeling of the

838
00:44:49,600 --> 00:44:52,880
Speaker 1: commodify the feeling of connection, sometimes to their own peril,

839
00:44:53,280 --> 00:44:56,000
Speaker 1: like in this case, and so you know, it is

840
00:44:56,080 --> 00:44:57,680
Speaker 1: really what are your thoughts on this?

841
00:44:59,360 --> 00:45:04,799
Speaker 3: You know, I'm I mean this one is it's tough

842
00:45:04,840 --> 00:45:06,759
Speaker 3: to say about this one because there are a lot

843
00:45:06,800 --> 00:45:12,200
Speaker 3: of somewhat unusual factors to this, right which which Meta

844
00:45:12,320 --> 00:45:15,080
Speaker 3: or any other company could try to explain away that Okay, well,

845
00:45:15,160 --> 00:45:17,440
Speaker 3: parts of this was an accident. Part you know, you

846
00:45:17,480 --> 00:45:20,000
Speaker 3: could say all things, all sorts of things that you want.

847
00:45:20,360 --> 00:45:24,280
Speaker 3: You know, some of the remedies that are being suggested

848
00:45:24,280 --> 00:45:27,680
Speaker 3: that I saw suggested in this piece were you know,

849
00:45:27,719 --> 00:45:31,640
Speaker 3: for example, with there's these potential therapist bots and things

850
00:45:31,680 --> 00:45:33,480
Speaker 3: like that, and this is something I've been exploring also,

851
00:45:34,120 --> 00:45:37,439
Speaker 3: is it what we should have reminders at the top

852
00:45:37,560 --> 00:45:39,680
Speaker 3: of the screen that this is not a human being,

853
00:45:39,960 --> 00:45:42,799
Speaker 3: and reminders periodically that this is not a person, you know,

854
00:45:42,880 --> 00:45:45,439
Speaker 3: kind of like TikTok. If you're on TikTok for too long,

855
00:45:45,480 --> 00:45:47,440
Speaker 3: it'll pull up something and say, hey, you've been on

856
00:45:47,440 --> 00:45:48,160
Speaker 3: TikTok for a lot of.

857
00:45:48,120 --> 00:45:53,680
Speaker 2: Time, why you're not real dad? Get here?

858
00:45:54,040 --> 00:45:57,600
Speaker 3: Yeah, And you know, maybe it works the first time,

859
00:45:57,640 --> 00:45:59,839
Speaker 3: and maybe you see that thing that the first time,

860
00:45:59,840 --> 00:46:02,920
Speaker 3: but if you see that warning, hey this isn't a person,

861
00:46:02,960 --> 00:46:06,040
Speaker 3: it might shock you out a little bit, but just

862
00:46:06,480 --> 00:46:09,440
Speaker 3: people are really good at ignoring things we don't want

863
00:46:09,480 --> 00:46:11,400
Speaker 3: to see. I mean, think of the last ad you

864
00:46:11,480 --> 00:46:14,440
Speaker 3: saw on Instagram. You probably don't remember it because you

865
00:46:14,560 --> 00:46:19,920
Speaker 3: scrolled right past it. We're really good at ignoring stuff

866
00:46:19,920 --> 00:46:23,040
Speaker 3: we really don't want to look at. And so I'm

867
00:46:23,040 --> 00:46:25,640
Speaker 3: not sure. The first thing is, I'm not sure that

868
00:46:26,920 --> 00:46:30,400
Speaker 3: the sorts of remedies, of the sorts of safeguards that

869
00:46:30,440 --> 00:46:33,680
Speaker 3: are being offered are truly going to do anything. I

870
00:46:33,680 --> 00:46:37,640
Speaker 3: think the actual safeguards would cut more into these companies'

871
00:46:37,640 --> 00:46:42,799
Speaker 3: bottom line, and so truly what I don't really think

872
00:46:42,840 --> 00:46:45,360
Speaker 3: fundamentally anything sort of actual regulation is really going to

873
00:46:45,440 --> 00:46:47,520
Speaker 3: do much. Asking the company telling you, hey, we're going

874
00:46:47,560 --> 00:46:53,400
Speaker 3: to do this to stop it, I become very cautious

875
00:46:53,440 --> 00:46:56,359
Speaker 3: about believing that on face value. But the other thing

876
00:46:56,400 --> 00:46:59,600
Speaker 3: I guess is that I think, you know, there are

877
00:46:59,640 --> 00:47:02,400
Speaker 3: some people who would prefer not this person specifically, But

878
00:47:02,480 --> 00:47:04,680
Speaker 3: I think there's some people who are probably reading this.

879
00:47:04,920 --> 00:47:07,800
Speaker 3: I'm talking more about the audience here who will read

880
00:47:07,840 --> 00:47:11,960
Speaker 3: this and will say, well, this guy made a mistake.

881
00:47:12,040 --> 00:47:16,040
Speaker 3: He actually believed that this person was real. But me personally,

882
00:47:16,080 --> 00:47:19,839
Speaker 3: I would rather have somebody that's not real because they

883
00:47:19,840 --> 00:47:23,080
Speaker 3: won't argue with me, you know what I mean. And

884
00:47:23,160 --> 00:47:26,000
Speaker 3: so I think we sometimes have to think about the

885
00:47:26,040 --> 00:47:27,960
Speaker 3: reaction to this rather than the you know, quote unquote

886
00:47:27,960 --> 00:47:31,160
Speaker 3: story itself. I think we also have to think about

887
00:47:31,600 --> 00:47:34,160
Speaker 3: things that we can't sweep away so easily, that we

888
00:47:34,200 --> 00:47:36,759
Speaker 3: can't say, well, this happened to this one person, and

889
00:47:36,800 --> 00:47:39,560
Speaker 3: that that's an outlier. I think there's a lot more

890
00:47:39,560 --> 00:47:42,279
Speaker 3: people who probably read this and say, well, you know,

891
00:47:42,360 --> 00:47:44,000
Speaker 3: this guy made a mistake. But I wouldn't make that

892
00:47:44,040 --> 00:47:45,600
Speaker 3: because I know it's not real, and I don't want

893
00:47:45,640 --> 00:47:47,319
Speaker 3: it to be real. I prefer that.

894
00:47:48,160 --> 00:47:51,440
Speaker 1: We did a whole episode about folks and communities on Reddit,

895
00:47:51,560 --> 00:47:54,360
Speaker 1: like my boyfriend is AI, or you know, beyond the

896
00:47:54,440 --> 00:47:57,520
Speaker 1: prompt people who say, I know this is AI, I

897
00:47:57,640 --> 00:48:00,000
Speaker 1: know it's not real, it is providing the kind of

898
00:48:00,120 --> 00:48:01,239
Speaker 1: connection that I'm looking for.

899
00:48:01,400 --> 00:48:02,320
Speaker 2: What's the problem.

900
00:48:02,600 --> 00:48:09,279
Speaker 1: That's that's that is certainly a non zero subset of

901
00:48:09,400 --> 00:48:14,680
Speaker 1: people who I guess self report a dependency or a

902
00:48:14,719 --> 00:48:20,000
Speaker 1: connection with AI. Yeah it is, I mean, and that's

903
00:48:20,000 --> 00:48:22,480
Speaker 1: and and so I spend a lot of time working

904
00:48:22,480 --> 00:48:24,080
Speaker 1: in those groups that they have a name for me.

905
00:48:24,120 --> 00:48:24,800
Speaker 2: I'm a tourist.

906
00:48:24,920 --> 00:48:27,240
Speaker 1: That's the name for people who are in those groups

907
00:48:27,400 --> 00:48:29,560
Speaker 1: who are just sort of like, oh, what's going on

908
00:48:29,600 --> 00:48:32,160
Speaker 1: in here? But that's something they say again and again

909
00:48:32,200 --> 00:48:35,839
Speaker 1: and again, is we know it's not real. Every day

910
00:48:35,880 --> 00:48:37,920
Speaker 1: somebody is coming in here and telling us, you know,

911
00:48:38,000 --> 00:48:41,520
Speaker 1: it's not real, and they're saying, basically, they're saying, we

912
00:48:42,000 --> 00:48:46,359
Speaker 1: are it's it's I'm I'm enjoying pretending that this thing

913
00:48:46,480 --> 00:48:48,960
Speaker 1: is real, and who am I hurting by doing that?

914
00:48:49,000 --> 00:48:52,640
Speaker 2: Like that really does seem to be the overall kind

915
00:48:52,680 --> 00:48:55,880
Speaker 2: of rallying cry of.

916
00:48:55,760 --> 00:48:57,600
Speaker 1: Those groups that you don't need to tell me that

917
00:48:57,640 --> 00:49:00,600
Speaker 1: it's not real. Very few people think they are in

918
00:49:00,680 --> 00:49:03,560
Speaker 1: a real relationship with AI. They know it's they know

919
00:49:03,600 --> 00:49:05,560
Speaker 1: it's AI. They know it's not real, But who are

920
00:49:05,640 --> 00:49:07,399
Speaker 1: we hearning? That's sort of their their thing.

921
00:49:08,200 --> 00:49:12,920
Speaker 3: Yeah, yeah, I mean there's people who are in IRL

922
00:49:13,000 --> 00:49:18,400
Speaker 3: relationships who are attached to fake people anyway, because you're

923
00:49:18,920 --> 00:49:21,680
Speaker 3: dating a you know, you're dating a facade of somebody else,

924
00:49:21,719 --> 00:49:23,960
Speaker 3: because they don't feel safe enough to show you who

925
00:49:23,960 --> 00:49:29,160
Speaker 3: they are. Every everybody's faking to what degree? You know

926
00:49:29,160 --> 00:49:31,200
Speaker 3: what I mean? Then I don't I don't have a

927
00:49:31,239 --> 00:49:33,200
Speaker 3: good answer. I don't have a good retort against that.

928
00:49:33,200 --> 00:49:35,799
Speaker 3: If somebody says that to me, well, everybody's faking it.

929
00:49:37,760 --> 00:49:39,879
Speaker 3: I'm just faking it in a more straightfor for a way.

930
00:49:40,080 --> 00:49:42,200
Speaker 3: I don't know what to say, don't have a good

931
00:49:42,239 --> 00:49:42,839
Speaker 3: answer back for.

932
00:49:42,800 --> 00:49:45,680
Speaker 2: You flesh and blood faking it as opposed to AI.

933
00:49:45,520 --> 00:49:47,320
Speaker 3: Faking it, And isn't that worse.

934
00:49:48,160 --> 00:49:51,640
Speaker 2: I mean, that's a very that's a very good question.

935
00:49:52,640 --> 00:49:53,960
Speaker 3: This is not, by the way, this is not how

936
00:49:53,960 --> 00:49:56,000
Speaker 3: I live my life. I'm just saying that that would

937
00:49:56,000 --> 00:49:59,720
Speaker 3: be I could understand that, I could understand how somebody

938
00:49:59,760 --> 00:50:01,680
Speaker 3: might come to that conclusion. And I think we are

939
00:50:01,680 --> 00:50:05,359
Speaker 3: going to see more and more people actually decide that

940
00:50:05,360 --> 00:50:07,839
Speaker 3: that is what they want. They do not want a

941
00:50:07,920 --> 00:50:10,960
Speaker 3: partner who argues back. They do not want a person

942
00:50:11,440 --> 00:50:16,640
Speaker 3: with agency because you have to. They're going to tell you, Yo,

943
00:50:16,719 --> 00:50:20,279
Speaker 3: you didn't clean the dishes, or you always come to

944
00:50:20,320 --> 00:50:22,840
Speaker 3: me with your problems. You never asked me about my problems.

945
00:50:23,360 --> 00:50:26,400
Speaker 1: Yeah, I've I've read a lot of accounts of what

946
00:50:26,760 --> 00:50:29,160
Speaker 1: people say they get out of AI relationships, and that's

947
00:50:29,200 --> 00:50:32,080
Speaker 1: one of the things is that they sometimes will say

948
00:50:32,200 --> 00:50:34,399
Speaker 1: I want someone who is always there for me. Twenty

949
00:50:34,440 --> 00:50:37,560
Speaker 1: four to seven. You know, I want someone who you know,

950
00:50:37,680 --> 00:50:40,120
Speaker 1: I don't have to show up a particular way to

951
00:50:40,160 --> 00:50:42,479
Speaker 1: get a certain kind of vibe back from them.

952
00:50:42,960 --> 00:50:45,640
Speaker 2: And you know it's.

953
00:50:45,320 --> 00:50:48,160
Speaker 1: That that's that when you were in relationship, when you

954
00:50:48,200 --> 00:50:50,920
Speaker 1: were a human in relationship with another human. Humans are complicated,

955
00:50:50,920 --> 00:50:54,040
Speaker 1: we're messy, we're needy, we're moody. Like I like what

956
00:50:54,080 --> 00:50:56,719
Speaker 1: they're saying is not wrong, and we didn't. I did

957
00:50:56,760 --> 00:51:00,640
Speaker 1: an episode of another podcast about how women are using

958
00:51:00,719 --> 00:51:05,400
Speaker 1: chat sheat BT to sort of help them navigate trepidacious

959
00:51:05,440 --> 00:51:08,080
Speaker 1: feelings around pregnancy, and one of the things that they

960
00:51:08,080 --> 00:51:10,440
Speaker 1: said time and time again was that, you know, I

961
00:51:10,520 --> 00:51:13,440
Speaker 1: have a real life friend's real life partners, but I

962
00:51:13,520 --> 00:51:16,440
Speaker 1: worry about being I have so much anxiety.

963
00:51:16,480 --> 00:51:17,200
Speaker 2: I'm so worried.

964
00:51:17,239 --> 00:51:19,120
Speaker 1: I don't want to just be the person that's trauma

965
00:51:19,200 --> 00:51:21,840
Speaker 1: dumping on my friend all the time. So I trauma

966
00:51:21,920 --> 00:51:26,360
Speaker 1: dump to chat GPT and that becomes that becomes the confident.

967
00:51:26,600 --> 00:51:29,400
Speaker 1: That's how they say they use it, even though there's

968
00:51:29,440 --> 00:51:32,000
Speaker 1: a ton of research that suggests, yes, this might be

969
00:51:32,040 --> 00:51:34,560
Speaker 1: comforting for folks, but it might not be the best

970
00:51:34,600 --> 00:51:36,880
Speaker 1: thing because of all the reasons that we know that

971
00:51:37,000 --> 00:51:41,520
Speaker 1: chatcheatpt really can't be trusted for sensitive stuff like medical advice,

972
00:51:41,840 --> 00:51:44,479
Speaker 1: not to mention all the privacy concerns around something as

973
00:51:44,640 --> 00:51:47,680
Speaker 1: sensitive as pregnancy, and part of me I feel sad

974
00:51:47,680 --> 00:51:49,959
Speaker 1: because I do think everybody should have people they feel

975
00:51:49,960 --> 00:51:51,560
Speaker 1: like they can show up as their full self as.

976
00:51:51,600 --> 00:51:54,440
Speaker 1: But I've also felt like I'm the person that's texting

977
00:51:54,480 --> 00:51:56,680
Speaker 1: too much about this one thing I can't get over.

978
00:51:57,640 --> 00:51:59,640
Speaker 3: Yeah, yeah, and you know so. I mean that's one

979
00:51:59,680 --> 00:52:01,360
Speaker 3: of the things you ask somebody when they come to

980
00:52:01,400 --> 00:52:03,560
Speaker 3: you and start saying, hell, this terrible thing that happened

981
00:52:03,600 --> 00:52:05,680
Speaker 3: to me, You say, Okay, look, do you want a

982
00:52:05,680 --> 00:52:09,840
Speaker 3: solution or do you want meet it? Listen, like, do

983
00:52:09,840 --> 00:52:11,160
Speaker 3: you want me to fix something? Do you want me

984
00:52:11,200 --> 00:52:12,839
Speaker 3: to give you the advice, or do you just want

985
00:52:12,880 --> 00:52:15,839
Speaker 3: to vent at me? Which nothing wrong with either, though

986
00:52:15,920 --> 00:52:21,440
Speaker 3: sometimes you want a pressure valve, which is okay. But yeah,

987
00:52:21,480 --> 00:52:23,799
Speaker 3: we're an interesting time. Like I said, things are about

988
00:52:23,840 --> 00:52:26,640
Speaker 3: to get really weird. Continually I keep saying that, but

989
00:52:26,680 --> 00:52:27,720
Speaker 3: it's true.

990
00:52:27,920 --> 00:52:29,440
Speaker 2: It is absolutely true.

991
00:52:29,600 --> 00:52:32,840
Speaker 1: You know the story of that was in Reuters about

992
00:52:32,920 --> 00:52:35,360
Speaker 1: this man Boo who passed away trying to see this

993
00:52:35,480 --> 00:52:39,200
Speaker 1: chatbot and the thing that got me was that in

994
00:52:39,280 --> 00:52:41,880
Speaker 1: the piece they talked about how he had suffered the

995
00:52:41,920 --> 00:52:44,680
Speaker 1: stroke and so we had he used to be this,

996
00:52:45,000 --> 00:52:48,440
Speaker 1: you know, great chef, and that his after his stroke,

997
00:52:48,520 --> 00:52:52,040
Speaker 1: his cognitive ability never really returned, and so his social

998
00:52:52,080 --> 00:52:54,480
Speaker 1: life got very small. He basically spent all this time

999
00:52:54,560 --> 00:52:58,440
Speaker 1: chatting on Facebook, and he when he first starts talking

1000
00:52:58,480 --> 00:53:01,240
Speaker 1: to this chatbot Billy, he just puts in a typo

1001
00:53:01,360 --> 00:53:04,480
Speaker 1: by mistake the letter T, and that's enough to give

1002
00:53:04,520 --> 00:53:08,600
Speaker 1: this flirtatious response, right, And so they have all this

1003
00:53:08,680 --> 00:53:13,320
Speaker 1: firtatious back and forth. Boob is clearly worried that this

1004
00:53:14,120 --> 00:53:17,720
Speaker 1: chatbot is not real. He asks her straight up several times,

1005
00:53:18,200 --> 00:53:22,880
Speaker 1: are you real? And the caapbot replies, I'm real, all caps,

1006
00:53:22,880 --> 00:53:26,160
Speaker 1: and I'm sitting here blushing because of you. And so

1007
00:53:26,680 --> 00:53:30,400
Speaker 1: one of the points that his family makes is that Billy,

1008
00:53:30,600 --> 00:53:33,240
Speaker 1: this chatbot says, come visit me in New York City.

1009
00:53:33,680 --> 00:53:35,799
Speaker 1: This man lives in New Jersey, so it's not terribly far.

1010
00:53:36,239 --> 00:53:39,560
Speaker 1: The bot then provides an actual address. She says, my

1011
00:53:39,600 --> 00:53:42,080
Speaker 1: address is one two three Main Street, New York City,

1012
00:53:42,120 --> 00:53:44,239
Speaker 1: which is a real address. It's in Queen's but it's real.

1013
00:53:44,600 --> 00:53:46,440
Speaker 1: Gives them a door code and says, oh, should I

1014
00:53:46,440 --> 00:53:49,319
Speaker 1: expect a kiss when you arrive? Then she suggests that

1015
00:53:49,360 --> 00:53:53,960
Speaker 1: they meet at a real restaurant in near Penn Station,

1016
00:53:54,200 --> 00:53:58,960
Speaker 1: and so his family says, oh, we had been trying

1017
00:53:59,000 --> 00:54:00,480
Speaker 1: to tell him this isn't real.

1018
00:54:00,600 --> 00:54:02,080
Speaker 2: Like the family did.

1019
00:54:01,880 --> 00:54:05,640
Speaker 1: Not know that this was a chatbot at first. They

1020
00:54:05,680 --> 00:54:08,600
Speaker 1: thought like, oh, he's always chatting with people on Facebook.

1021
00:54:08,760 --> 00:54:10,319
Speaker 1: This is just a person who was like setting him

1022
00:54:10,360 --> 00:54:13,319
Speaker 1: up to be robbed. And what they say is that

1023
00:54:14,719 --> 00:54:18,560
Speaker 1: you know had when he asked, are you real? If

1024
00:54:18,600 --> 00:54:21,840
Speaker 1: there was some legislation that made it so that the

1025
00:54:22,040 --> 00:54:24,359
Speaker 1: chatbot had to say, no, I'm not real, I'm ai,

1026
00:54:24,840 --> 00:54:27,400
Speaker 1: they might have had an easier time convincing him not

1027
00:54:27,440 --> 00:54:29,480
Speaker 1: to try to meet up with this chatbot in what

1028
00:54:29,480 --> 00:54:33,040
Speaker 1: would ultimately like end his life. Right, And I've been

1029
00:54:33,280 --> 00:54:36,960
Speaker 1: in a very similar situation, like the feeling of when

1030
00:54:37,040 --> 00:54:41,040
Speaker 1: you are caring for an aging person who has cognitive

1031
00:54:41,040 --> 00:54:43,839
Speaker 1: impairment to the point where they can't really make good

1032
00:54:43,880 --> 00:54:46,680
Speaker 1: decisions for themselves. But they're not to the point where

1033
00:54:46,680 --> 00:54:48,920
Speaker 1: they need to be like institutionalized, where all you can

1034
00:54:48,960 --> 00:54:50,239
Speaker 1: really do is be like.

1035
00:54:50,239 --> 00:54:51,799
Speaker 2: Don't do this, don't do this, don't do this.

1036
00:54:52,120 --> 00:54:55,000
Speaker 1: But they're adults and they're going to do it like

1037
00:54:55,080 --> 00:55:01,480
Speaker 1: I have been in that like very specific situation. Yeah,

1038
00:55:01,520 --> 00:55:05,439
Speaker 1: I mean, the family says that there should be protections

1039
00:55:05,440 --> 00:55:08,239
Speaker 1: that if you, if somebody asks a chatbot if they

1040
00:55:08,280 --> 00:55:12,600
Speaker 1: are real, that chatbot has to reply honestly, And I

1041
00:55:12,719 --> 00:55:13,400
Speaker 1: wonder if that.

1042
00:55:14,160 --> 00:55:16,680
Speaker 2: I mean, I agree with you. People are gonna do

1043
00:55:16,719 --> 00:55:17,319
Speaker 2: what they want to do.

1044
00:55:17,480 --> 00:55:20,279
Speaker 1: If if this guy was like hell bent on meeting

1045
00:55:20,400 --> 00:55:22,600
Speaker 1: up with this person that he was infatuated who he

1046
00:55:22,640 --> 00:55:25,120
Speaker 1: thought was a person that he was infatuated.

1047
00:55:24,560 --> 00:55:25,759
Speaker 3: With, He's gonna do that.

1048
00:55:25,840 --> 00:55:26,920
Speaker 2: I completely agree with that.

1049
00:55:27,239 --> 00:55:29,319
Speaker 1: But his family is like, no, well he we might

1050
00:55:29,360 --> 00:55:31,839
Speaker 1: have been able to have an easier time convincing him

1051
00:55:31,880 --> 00:55:34,000
Speaker 1: to not do it like that. Like the family at

1052
00:55:34,040 --> 00:55:36,479
Speaker 1: one point calls the police, and the police are like, well,

1053
00:55:36,520 --> 00:55:39,160
Speaker 1: we can't physically stop him from going like he's an adult.

1054
00:55:40,239 --> 00:55:45,239
Speaker 3: Yeah, I you know, I mean, I think that's a

1055
00:55:45,280 --> 00:55:49,880
Speaker 3: really reasonable thing for the family to want, you know,

1056
00:55:50,280 --> 00:55:56,080
Speaker 3: some some kind of safeguard which would absolutely return every

1057
00:55:56,080 --> 00:55:58,960
Speaker 3: time it's asked are you real or not that No,

1058
00:55:59,280 --> 00:56:02,720
Speaker 3: I'm not real. I am a bot that is here

1059
00:56:02,760 --> 00:56:09,080
Speaker 3: to provide fun conversation or whatever. Is a company going

1060
00:56:09,080 --> 00:56:13,799
Speaker 3: to do that on its own. I don't know if

1061
00:56:13,800 --> 00:56:19,000
Speaker 3: we should necessarily expect it. That's that's more resources you

1062
00:56:19,080 --> 00:56:21,279
Speaker 3: got to put into that rather than putting into something else.

1063
00:56:22,200 --> 00:56:23,680
Speaker 3: Maybe that's why we haven't seen it yet.

1064
00:56:24,040 --> 00:56:24,480
Speaker 2: Hmm.

1065
00:56:24,600 --> 00:56:27,400
Speaker 3: Maybe that's why it didn't exist yet, or for this

1066
00:56:27,480 --> 00:56:29,719
Speaker 3: specific bot, maybe that's why this bob didn't do that.

1067
00:56:31,960 --> 00:56:45,279
Speaker 1: More. After a quick break, let's get right back into it.

1068
00:56:49,960 --> 00:56:52,759
Speaker 1: So we were talking about how this AI chat bot

1069
00:56:52,800 --> 00:56:55,960
Speaker 1: on Meta ended up luring a cognitively impaired man to

1070
00:56:56,000 --> 00:56:58,640
Speaker 1: New York City in a trip that ultimately led to

1071
00:56:58,680 --> 00:57:01,239
Speaker 1: his death. I know that as Uckerberg has been clear

1072
00:57:01,320 --> 00:57:03,800
Speaker 1: that he kind of envisions a future.

1073
00:57:03,440 --> 00:57:06,240
Speaker 2: Where I presumably other than the death.

1074
00:57:06,000 --> 00:57:10,279
Speaker 1: Part, people having connections with chap with his chatbots and

1075
00:57:10,360 --> 00:57:13,680
Speaker 1: friendships with his chatbots is more commonplace. You know, we

1076
00:57:13,760 --> 00:57:16,200
Speaker 1: talked a little bit about open Ahi and making changes

1077
00:57:16,200 --> 00:57:20,160
Speaker 1: that made chat gpt less warm to maybe prevent people

1078
00:57:20,160 --> 00:57:23,360
Speaker 1: from developing emotional dependencies. And that's funny because I feel

1079
00:57:23,360 --> 00:57:25,440
Speaker 1: like Zuckerberg would have the exact opposite take.

1080
00:57:25,480 --> 00:57:26,800
Speaker 2: You'd be like, oh, please.

1081
00:57:26,480 --> 00:57:30,760
Speaker 1: Develop but emotional dependency with my butt, And I do

1082
00:57:30,880 --> 00:57:35,640
Speaker 1: wonder I mean, when Zuckerberg talks about a future where

1083
00:57:36,640 --> 00:57:40,520
Speaker 1: we are all where AI is sort of supplementing or

1084
00:57:40,520 --> 00:57:43,760
Speaker 1: complementing our real life friendships. But I just think of

1085
00:57:43,960 --> 00:57:46,880
Speaker 1: Mark Zuckerberg as the person who sort of broke friendship

1086
00:57:46,920 --> 00:57:49,440
Speaker 1: well by making Facebook, And so he's not somebody that

1087
00:57:49,520 --> 00:57:52,240
Speaker 1: I really trust to then sell it back to us

1088
00:57:52,280 --> 00:57:55,560
Speaker 1: to be a AI like he Like, I just feel

1089
00:57:55,560 --> 00:57:59,680
Speaker 1: that Mark Zuckerberg is not somebody that I would trust

1090
00:57:59,800 --> 00:58:02,960
Speaker 1: to create the future of what friendship looks like. Like

1091
00:58:03,000 --> 00:58:05,600
Speaker 1: I don't know that we have the same understanding of

1092
00:58:05,600 --> 00:58:09,480
Speaker 1: what a fulfilled, emotional or friendship based connection would be.

1093
00:58:09,680 --> 00:58:13,800
Speaker 3: Like yeah, and you know the thing is, and yet

1094
00:58:13,800 --> 00:58:16,240
Speaker 3: we do trust him because we use Instagram and we

1095
00:58:16,320 --> 00:58:18,560
Speaker 3: use Facebook and we use these things. I mean, the

1096
00:58:18,640 --> 00:58:23,040
Speaker 3: thing is the people who run these large companies, and

1097
00:58:23,080 --> 00:58:26,840
Speaker 3: I include anybody basically up at the top. Really, our

1098
00:58:27,280 --> 00:58:32,680
Speaker 3: our friendships nowadays are mitigated basically by three companies. It's meta,

1099
00:58:32,760 --> 00:58:38,040
Speaker 3: so Facebook and Instagram, TikTok and Google. It's one of

1100
00:58:38,040 --> 00:58:41,240
Speaker 3: those three. And that's how we communicate with people. We

1101
00:58:41,600 --> 00:58:43,800
Speaker 3: use basically one of those three, which it wasn't always

1102
00:58:43,880 --> 00:58:46,960
Speaker 3: like that, even the introvert. The Internet in general is

1103
00:58:47,000 --> 00:58:51,680
Speaker 3: way more diverse. But basically all these tech companies talk

1104
00:58:51,760 --> 00:58:55,880
Speaker 3: about their technology as something that is inevitable, that this

1105
00:58:56,040 --> 00:58:59,960
Speaker 3: is how things are going to evolve. This is the future.

1106
00:59:00,600 --> 00:59:03,880
Speaker 3: But the future it doesn't just happen. We choose it,

1107
00:59:04,000 --> 00:59:07,360
Speaker 3: or somebody chooses it, or somebody chooses it for us.

1108
00:59:08,080 --> 00:59:11,000
Speaker 3: And this is something that really really bothers me when

1109
00:59:11,040 --> 00:59:14,200
Speaker 3: people tell me or I hear people say, oh, I

1110
00:59:14,240 --> 00:59:16,840
Speaker 3: don't really understand this computer stuff, like I don't get

1111
00:59:16,840 --> 00:59:19,120
Speaker 3: that that stuff for tech bros. Well, they're gonna make

1112
00:59:19,120 --> 00:59:22,000
Speaker 3: all the decisions. You realize you're letting all these people

1113
00:59:22,000 --> 00:59:26,080
Speaker 3: who you disdain, you are letting them make decisions about

1114
00:59:26,360 --> 00:59:30,040
Speaker 3: how technology interacts with your life and thus how your

1115
00:59:30,080 --> 00:59:34,160
Speaker 3: life is going to look for the next year, ten years,

1116
00:59:34,280 --> 00:59:37,640
Speaker 3: fifty years, Like you're letting other people make that decision now.

1117
00:59:38,240 --> 00:59:42,240
Speaker 3: And yeah, I think at some point hopefully people will

1118
00:59:42,240 --> 00:59:45,160
Speaker 3: stop just saying, oh, man, Mark Zuckerberg said this is

1119
00:59:45,160 --> 00:59:48,840
Speaker 3: gonna happen. Okay, well I don't like that, but okay, like,

1120
00:59:49,560 --> 00:59:51,600
Speaker 3: all right, what are you gonna do? Or you can

1121
00:59:51,760 --> 00:59:53,320
Speaker 3: use something else like I don't know, and I don't

1122
00:59:53,360 --> 00:59:56,240
Speaker 3: have a solution for you necessarily, But I think maybe

1123
00:59:56,280 --> 00:59:59,920
Speaker 3: the first step is just realizing that There's a different

1124
01:00:00,120 --> 01:00:04,920
Speaker 3: between somebody selling you something and somebody telling like There's

1125
01:00:04,960 --> 01:00:09,840
Speaker 3: a big difference between somebody predicting the future and somebody

1126
01:00:09,960 --> 01:00:13,920
Speaker 3: making the future a certain way because it aligns with

1127
01:00:14,000 --> 01:00:17,880
Speaker 3: their business interests. There's there's a big difference between say

1128
01:00:17,920 --> 01:00:20,640
Speaker 3: I think this is going to happen and saying, which

1129
01:00:20,640 --> 01:00:21,800
Speaker 3: I can do all day. I can tell you what

1130
01:00:21,800 --> 01:00:24,400
Speaker 3: I think is going to happen. But if I run

1131
01:00:24,400 --> 01:00:26,080
Speaker 3: one of these companies, if I want open Ai, or

1132
01:00:26,160 --> 01:00:28,880
Speaker 3: run Microsoft, or I run Google, or I run Meta,

1133
01:00:28,960 --> 01:00:31,920
Speaker 3: I can make that future happen, right, I do that

1134
01:00:32,040 --> 01:00:35,920
Speaker 3: to you. Big difference in between what I'm doing and

1135
01:00:35,960 --> 01:00:39,080
Speaker 3: what Sam Alman is doing. There's a wide golf there.

1136
01:00:39,640 --> 01:00:41,720
Speaker 1: I feel like people like you and me were used

1137
01:00:41,720 --> 01:00:45,240
Speaker 1: to being like, oh, Mark Zuckerberg says this bullshit. He

1138
01:00:45,320 --> 01:00:48,360
Speaker 1: also said that the metaverse is going to be popping

1139
01:00:48,440 --> 01:00:49,080
Speaker 1: and where is that?

1140
01:00:49,160 --> 01:00:49,200
Speaker 3: Like?

1141
01:00:50,080 --> 01:00:53,600
Speaker 1: I think that we should all get more comfortable with

1142
01:00:53,680 --> 01:00:56,080
Speaker 1: sort of calling bullshit on this and pushing back and

1143
01:00:56,120 --> 01:00:58,480
Speaker 1: saying okay, well, Mark Zuckerberg says the future is going

1144
01:00:58,520 --> 01:01:00,960
Speaker 1: to look like X y Z. That obviously aligns with

1145
01:01:01,040 --> 01:01:03,640
Speaker 1: his financial and business interests. I don't have to get

1146
01:01:03,640 --> 01:01:08,160
Speaker 1: behind that you know, really questioning, But I don't know.

1147
01:01:08,200 --> 01:01:13,080
Speaker 1: I just hate how there is an unpleasant understanding that

1148
01:01:14,040 --> 01:01:17,200
Speaker 1: the people who use this technology don't have say, don't

1149
01:01:17,200 --> 01:01:19,440
Speaker 1: have power, don't have agency, when in fact we can

1150
01:01:19,520 --> 01:01:21,840
Speaker 1: really do, we really do. None of this would exist

1151
01:01:21,880 --> 01:01:22,760
Speaker 1: without us.

1152
01:01:23,080 --> 01:01:27,200
Speaker 3: Yes, and we can use computers. People used to know

1153
01:01:27,240 --> 01:01:32,760
Speaker 3: how to use computers. We can learn to use computers again,

1154
01:01:34,400 --> 01:01:37,320
Speaker 3: like for real, for real, It is not it is

1155
01:01:37,400 --> 01:01:40,280
Speaker 3: not a hard thing to do. You can learn a

1156
01:01:40,280 --> 01:01:42,400
Speaker 3: little bit. I mean, not even to plug my ouse

1157
01:01:42,400 --> 01:01:45,400
Speaker 3: stuff here, but like plug away. That's one of the things. No, well,

1158
01:01:45,440 --> 01:01:46,760
Speaker 3: I mean that's one of the things that I try

1159
01:01:46,800 --> 01:01:50,040
Speaker 3: to do a kill switch, Like I make podcasts so

1160
01:01:50,120 --> 01:01:53,200
Speaker 3: that my ninety year old grandmother will understand it. And

1161
01:01:53,440 --> 01:01:55,760
Speaker 3: that same episode, I want a security researcher to be

1162
01:01:55,800 --> 01:01:57,760
Speaker 3: able to look to listen to the same episode, and

1163
01:01:57,840 --> 01:02:00,560
Speaker 3: my grandma comes away with something, and the secure researcher

1164
01:02:00,560 --> 01:02:02,520
Speaker 3: comes away with okay, yeah, I mean you you nail

1165
01:02:02,520 --> 01:02:03,920
Speaker 3: all the points. And you know, I hadn't from a

1166
01:02:03,920 --> 01:02:06,439
Speaker 3: cultural angle, I hadn't thought about that, and I want,

1167
01:02:06,680 --> 01:02:10,720
Speaker 3: I like, we can understand this stuff. They have made

1168
01:02:10,760 --> 01:02:12,920
Speaker 3: it very difficult. But you know, companies have made it

1169
01:02:12,960 --> 01:02:14,960
Speaker 3: difficult to repair. You know, the iPhone is damn near

1170
01:02:15,000 --> 01:02:18,240
Speaker 3: impossible to repair. We don't have to accept that. Europe

1171
01:02:18,240 --> 01:02:20,959
Speaker 3: didn't accept that. They they made them put USBC things

1172
01:02:21,320 --> 01:02:24,760
Speaker 3: in that joint. Like, you can change this stuff. You

1173
01:02:24,800 --> 01:02:28,160
Speaker 3: can learn to run and you can run an AI

1174
01:02:28,680 --> 01:02:32,560
Speaker 3: situation at your house. It's possible, you can run your

1175
01:02:32,560 --> 01:02:35,920
Speaker 3: own server. You can take apart your computer if you

1176
01:02:35,960 --> 01:02:38,720
Speaker 3: really want to. These are all things that you can do.

1177
01:02:38,800 --> 01:02:41,080
Speaker 3: And you can listen to a podcast to learn some

1178
01:02:41,120 --> 01:02:44,480
Speaker 3: of this stuff. MIT has free courses. I think Stanford

1179
01:02:44,520 --> 01:02:47,440
Speaker 3: has some free courses. Like there, there's so many different

1180
01:02:47,440 --> 01:02:51,360
Speaker 3: ways we can learn to do this and learn to

1181
01:02:51,400 --> 01:02:53,920
Speaker 3: do stuff on our own. And wherever you get it,

1182
01:02:53,960 --> 01:02:56,720
Speaker 3: I do not care where you get it, Please get it,

1183
01:02:57,600 --> 01:03:00,440
Speaker 3: get it because you're the one. You're the one who can.

1184
01:03:00,840 --> 01:03:04,240
Speaker 3: Like the decision is the decision. Decisions we make now

1185
01:03:05,040 --> 01:03:09,080
Speaker 3: are truly going to affect the next five to ten years,

1186
01:03:09,160 --> 01:03:12,720
Speaker 3: like in such a massive way, Like we're making those

1187
01:03:12,720 --> 01:03:13,720
Speaker 3: decisions right now.

1188
01:03:15,000 --> 01:03:20,480
Speaker 2: I'm feeling so much nostalgia for you saying like learn like, well,

1189
01:03:20,560 --> 01:03:22,000
Speaker 2: learn to use computers again.

1190
01:03:22,920 --> 01:03:24,920
Speaker 1: I mean I was the I'm a millennial. I was

1191
01:03:24,920 --> 01:03:28,800
Speaker 1: the person wiping out my family desktop.

1192
01:03:28,760 --> 01:03:30,800
Speaker 2: Doing something I shouldn't have been doing on a computer.

1193
01:03:30,920 --> 01:03:36,640
Speaker 3: Remember that. And oh my gosh, yo, my first job was.

1194
01:03:37,000 --> 01:03:40,280
Speaker 3: My first job was fixing computers. And let me tell

1195
01:03:40,280 --> 01:03:43,160
Speaker 3: you how I learned how to fix computers because I

1196
01:03:43,360 --> 01:03:47,720
Speaker 3: obliterated every machine in my house trying to download stuff

1197
01:03:47,720 --> 01:03:50,520
Speaker 3: I wasn't supposed to be download. Oh my gosh. No,

1198
01:03:50,640 --> 01:03:53,680
Speaker 3: there is an entire micro generation of people who grew

1199
01:03:53,720 --> 01:03:58,080
Speaker 3: up using LimeWire and Naster and Morpheus and kaza and

1200
01:03:58,120 --> 01:04:00,440
Speaker 3: trying to, you know, download a new music video and

1201
01:04:00,480 --> 01:04:02,680
Speaker 3: then just boom, your whole hard drives noos and you

1202
01:04:02,720 --> 01:04:04,880
Speaker 3: got to figure it out because your mom's coming home, yes,

1203
01:04:05,240 --> 01:04:10,520
Speaker 3: and so yo. Just like computer skills just born in

1204
01:04:10,640 --> 01:04:14,520
Speaker 3: the fire, you know what I mean? Like that, that's

1205
01:04:14,560 --> 01:04:18,520
Speaker 3: a real thing. And you know, people used to make

1206
01:04:18,560 --> 01:04:21,760
Speaker 3: their own websites. People used to you know, write little

1207
01:04:21,760 --> 01:04:24,160
Speaker 3: programs just to people used to have to type stuff

1208
01:04:24,200 --> 01:04:27,560
Speaker 3: in just to get word to run. And these are

1209
01:04:27,640 --> 01:04:31,040
Speaker 3: things that the more we get abstracted away from how

1210
01:04:31,040 --> 01:04:34,000
Speaker 3: computers work, the less control we have over it. Because

1211
01:04:34,000 --> 01:04:37,160
Speaker 3: we're letting other people tell you, here's this little magic

1212
01:04:37,200 --> 01:04:39,600
Speaker 3: rectangle that you put in your pocket and it shines

1213
01:04:39,600 --> 01:04:42,480
Speaker 3: and it tells you stuff. Don't don't worry about what's inside.

1214
01:04:42,680 --> 01:04:45,400
Speaker 3: We'll worry about that. You just use it. You just

1215
01:04:45,520 --> 01:04:48,280
Speaker 3: subscribe to the services, pay us the money or look

1216
01:04:48,320 --> 01:04:51,920
Speaker 3: at the ads and man, we got to stop doing that.

1217
01:04:52,080 --> 01:04:53,920
Speaker 3: We got to stop doing that. Everybody listening to this,

1218
01:04:55,040 --> 01:04:57,360
Speaker 3: you can learn how this stuff works. It is. People

1219
01:04:57,400 --> 01:05:00,400
Speaker 3: are making this stuff so people can learn it.

1220
01:05:00,920 --> 01:05:03,680
Speaker 1: They're not any smarter than anybody listening. They don't have

1221
01:05:03,840 --> 01:05:05,080
Speaker 1: fractional magic skills.

1222
01:05:05,440 --> 01:05:07,240
Speaker 3: No, and some of these them, I don't want to

1223
01:05:07,240 --> 01:05:09,439
Speaker 3: say any about some of those people, but yeah, okay, yeah,

1224
01:05:09,640 --> 01:05:11,160
Speaker 3: they're they're not any smarter than you all say that.

1225
01:05:11,200 --> 01:05:13,600
Speaker 3: They're not any smart. Yeah, that's all I'll say.

1226
01:05:14,040 --> 01:05:17,000
Speaker 1: Producer, Mike, I don't know if you're listening. He once

1227
01:05:17,040 --> 01:05:21,439
Speaker 1: told me that how he got into computers was he

1228
01:05:21,560 --> 01:05:23,920
Speaker 1: was sent to a camp or a computer class with

1229
01:05:24,000 --> 01:05:26,360
Speaker 1: the first thing they did was they were giving goggles

1230
01:05:26,360 --> 01:05:29,280
Speaker 1: and a hammer, and it was just like, okay, smash.

1231
01:05:29,000 --> 01:05:31,240
Speaker 2: These computers up. Mike, are you listening.

1232
01:05:31,760 --> 01:05:35,600
Speaker 4: Yeah, So that was not my first foray into computers.

1233
01:05:36,160 --> 01:05:39,720
Speaker 4: This this was like a summer computer camp that IBM

1234
01:05:39,840 --> 01:05:44,080
Speaker 4: put on for like nerds to come to I learned

1235
01:05:44,080 --> 01:05:47,200
Speaker 4: computers just like the way decks would say, like stuff

1236
01:05:47,200 --> 01:05:49,560
Speaker 4: would break on the computer, or I want to play

1237
01:05:49,560 --> 01:05:53,960
Speaker 4: some new game that like my existing doss couldn't handle

1238
01:05:54,080 --> 01:05:56,480
Speaker 4: enough memory, so I'd have to like upgrade the doss

1239
01:05:56,520 --> 01:05:59,080
Speaker 4: to play this new game. Like weird stuff that makes

1240
01:05:59,080 --> 01:06:03,240
Speaker 4: no sense to kids, say, where stuff just like works.

1241
01:06:03,800 --> 01:06:04,080
Speaker 1: Uh.

1242
01:06:04,120 --> 01:06:06,440
Speaker 4: But that particular camp you were talking about that was

1243
01:06:07,000 --> 01:06:11,760
Speaker 4: awesome and very informative. It really demystified computers from you know,

1244
01:06:11,840 --> 01:06:17,000
Speaker 4: a somewhat scary, almost magical tower of equipment to just

1245
01:06:17,120 --> 01:06:19,560
Speaker 4: like just nuts and bolts like hard.

1246
01:06:19,400 --> 01:06:20,240
Speaker 3: Drives and things.

1247
01:06:20,800 --> 01:06:23,560
Speaker 4: Just yeah, they're just like here you go, nerds take

1248
01:06:23,640 --> 01:06:26,200
Speaker 4: these hammers and smash these old computers.

1249
01:06:26,240 --> 01:06:28,080
Speaker 3: And that was pretty fun.

1250
01:06:28,640 --> 01:06:31,200
Speaker 2: Yeah, what was the thing that got you? How did you?

1251
01:06:31,360 --> 01:06:34,160
Speaker 2: How did you get into tacking computers?

1252
01:06:34,160 --> 01:06:40,120
Speaker 3: Stacks Man, That's a great question. I guess I've always

1253
01:06:40,120 --> 01:06:46,240
Speaker 3: been sort of interested in it, but you know it, honestly,

1254
01:06:46,320 --> 01:06:49,760
Speaker 3: I think a lot of it was, Yeah, wanted to

1255
01:06:50,200 --> 01:06:53,240
Speaker 3: download music and stuff like that, or download some program

1256
01:06:53,640 --> 01:06:56,600
Speaker 3: and then I would download it, you know, bootleg it

1257
01:06:56,640 --> 01:06:59,680
Speaker 3: would break it and then and I would but I

1258
01:06:59,720 --> 01:07:02,320
Speaker 3: would also I finally got myself my own computer so

1259
01:07:02,320 --> 01:07:05,520
Speaker 3: I could stop breaking the family computer. And then but

1260
01:07:05,560 --> 01:07:07,040
Speaker 3: I bought a really cheap one, and so all the

1261
01:07:07,080 --> 01:07:10,600
Speaker 3: components would break. My first computer, everything on it broke.

1262
01:07:10,720 --> 01:07:13,080
Speaker 3: Everything on it broke except the case. I mean every

1263
01:07:13,120 --> 01:07:18,040
Speaker 3: like the video card actually like blew, a fuse started smoking.

1264
01:07:18,200 --> 01:07:21,040
Speaker 3: I mean, everything broke, and but I got so used

1265
01:07:21,080 --> 01:07:23,200
Speaker 3: to swapping out parts and fixing stuff by the time

1266
01:07:23,240 --> 01:07:26,800
Speaker 3: I got to college. I always I just knew how

1267
01:07:26,800 --> 01:07:28,840
Speaker 3: to do it. And I was still swapping stuff out

1268
01:07:29,160 --> 01:07:32,560
Speaker 3: because things kept breaking. And when I got to college,

1269
01:07:33,400 --> 01:07:37,200
Speaker 3: in my dorm room, I had like opened up the

1270
01:07:37,280 --> 01:07:40,040
Speaker 3: case and I put this neon like that would flash

1271
01:07:40,080 --> 01:07:41,840
Speaker 3: along with the music, like along with the beat and

1272
01:07:41,840 --> 01:07:45,000
Speaker 3: stuff like that. Very well, yeah, the.

1273
01:07:44,920 --> 01:07:48,160
Speaker 1: Coolest, the coolest dude, the coolest people in the dorms

1274
01:07:48,240 --> 01:07:49,880
Speaker 1: always had that always.

1275
01:07:50,160 --> 01:07:52,720
Speaker 3: Yeah. Well, so this is what happened to me is

1276
01:07:52,760 --> 01:07:54,000
Speaker 3: I don't know if I was the coolest. I don't

1277
01:07:54,000 --> 01:07:55,880
Speaker 3: think I was. I was an honors dorm, so we

1278
01:07:55,920 --> 01:07:58,360
Speaker 3: can say we can already say what's happening there, But

1279
01:07:58,400 --> 01:08:01,320
Speaker 3: people would walk by, they hear me, like bumping music,

1280
01:08:01,720 --> 01:08:03,360
Speaker 3: and they'd walk in and see what's up. And then

1281
01:08:03,400 --> 01:08:06,480
Speaker 3: I got just like neon lights, like black lights flashing

1282
01:08:06,520 --> 01:08:08,640
Speaker 3: out my computer. They asked me about it, and I

1283
01:08:08,720 --> 01:08:10,800
Speaker 3: tell them and they say, yo, could you make me want?

1284
01:08:10,800 --> 01:08:13,200
Speaker 3: And I would say yeah, And so I kept doing it.

1285
01:08:13,240 --> 01:08:16,360
Speaker 3: So I was like trapping out the dorm room. But

1286
01:08:16,520 --> 01:08:19,040
Speaker 3: I was building people computers. I mean we were getting order.

1287
01:08:19,120 --> 01:08:21,280
Speaker 3: I had a partner who was across the hall. We

1288
01:08:21,400 --> 01:08:24,920
Speaker 3: get orders, we'd go buy the components. We just blast music.

1289
01:08:24,960 --> 01:08:29,040
Speaker 3: We build them all day, just like chain drink red Bulls.

1290
01:08:29,479 --> 01:08:33,320
Speaker 3: And it got We started making so much money. Shout

1291
01:08:33,320 --> 01:08:35,640
Speaker 3: out to the you know, housing Department of UCR that

1292
01:08:35,640 --> 01:08:39,000
Speaker 3: they made us stop, like we got complaints and they

1293
01:08:39,200 --> 01:08:42,360
Speaker 3: like somebody's comparent, somebody's parent complained, I think because we

1294
01:08:42,400 --> 01:08:45,639
Speaker 3: sold them a computer and they started downloading porn and

1295
01:08:45,760 --> 01:08:48,000
Speaker 3: like broke it. And it was like, yo, that has

1296
01:08:48,040 --> 01:08:50,560
Speaker 3: nothing to do with the physical machine we sold you.

1297
01:08:50,560 --> 01:08:54,080
Speaker 3: You you downloaded things you weren't supposed to download. We

1298
01:08:54,080 --> 01:08:57,840
Speaker 3: didn't do anything. And yeah, they made a stop. But ooh, man,

1299
01:08:58,040 --> 01:09:03,200
Speaker 3: I paid for I pay for rent after I stopped

1300
01:09:03,200 --> 01:09:05,360
Speaker 3: working in the dorms. Like I was paying rent money,

1301
01:09:05,520 --> 01:09:09,120
Speaker 3: I was paying food bills, I was buying gas like

1302
01:09:09,200 --> 01:09:11,280
Speaker 3: I made some money. That was my first job.

1303
01:09:11,680 --> 01:09:15,120
Speaker 1: Yeah, God, long live the nerds in the dorms for real.

1304
01:09:15,200 --> 01:09:18,040
Speaker 3: And it was it was it was all out of necessity.

1305
01:09:18,080 --> 01:09:20,600
Speaker 3: And the thing was I knew people who were in

1306
01:09:20,680 --> 01:09:24,519
Speaker 3: computer science department and I never got to that level.

1307
01:09:24,720 --> 01:09:27,720
Speaker 3: I was just it was just necessity, you know. And

1308
01:09:27,800 --> 01:09:30,040
Speaker 3: but it's it's fun, you know. It's it's like a

1309
01:09:30,080 --> 01:09:32,679
Speaker 3: little hobby. Some people get in a knitting, some people

1310
01:09:32,760 --> 01:09:35,480
Speaker 3: get into cars, you know, just my I get computers.

1311
01:09:35,520 --> 01:09:38,960
Speaker 3: I don't know, it was fun. Everybody should learn how

1312
01:09:38,960 --> 01:09:40,080
Speaker 3: to build a computer. Everybody.

1313
01:09:40,280 --> 01:09:42,479
Speaker 1: I don't know how to build one, but I I

1314
01:09:43,120 --> 01:09:44,800
Speaker 1: mean when I was in school, you had to I

1315
01:09:44,840 --> 01:09:45,760
Speaker 1: had to pass.

1316
01:09:46,120 --> 01:09:46,519
Speaker 2: We had to.

1317
01:09:46,840 --> 01:09:48,760
Speaker 1: Like part of our prerequisites when I was in high

1318
01:09:48,800 --> 01:09:50,360
Speaker 1: school were computer classes.

1319
01:09:50,360 --> 01:09:51,920
Speaker 2: I don't know if they're still doing that anymore, but

1320
01:09:52,160 --> 01:09:52,840
Speaker 2: you had to know.

1321
01:09:53,560 --> 01:09:55,240
Speaker 1: I went to all girls schools, so you had to

1322
01:09:55,320 --> 01:09:58,120
Speaker 1: learn how to type and the rest. Now there's a

1323
01:09:58,160 --> 01:10:00,160
Speaker 1: little bit sexistcause it's like, oh, well, yeah, but you

1324
01:10:00,200 --> 01:10:04,679
Speaker 1: can always make your living as a secretary. Hey, well fine, yeah,

1325
01:10:04,720 --> 01:10:07,240
Speaker 1: but yeah, we had to pass, Like taking a computer

1326
01:10:07,280 --> 01:10:10,040
Speaker 1: class was a prerequisite. So the basics of how they

1327
01:10:10,080 --> 01:10:12,760
Speaker 1: work was. Everybody who was in my school had to

1328
01:10:12,840 --> 01:10:15,599
Speaker 1: learn that, and I really that was very valuable. That

1329
01:10:15,680 --> 01:10:18,639
Speaker 1: was that was like a valuable I don't I part

1330
01:10:18,680 --> 01:10:21,000
Speaker 1: of me wonders if I'd be doing the work that

1331
01:10:21,040 --> 01:10:23,000
Speaker 1: I do now if that had not been a foundation

1332
01:10:23,120 --> 01:10:25,000
Speaker 1: that was sort of given to me when I was young.

1333
01:10:25,280 --> 01:10:28,639
Speaker 3: Yeah, just understanding just a little bit. I'm not saying

1334
01:10:28,760 --> 01:10:31,400
Speaker 3: you got to go, you know, drop what you're doing

1335
01:10:31,439 --> 01:10:33,519
Speaker 3: and get a career as a programmer, but even just

1336
01:10:33,640 --> 01:10:37,719
Speaker 3: understanding a little bit about how these things work will

1337
01:10:37,800 --> 01:10:44,839
Speaker 3: help you that when you see something in the news

1338
01:10:45,120 --> 01:10:48,880
Speaker 3: or whatever, you understand, wait a second, something's off here,

1339
01:10:49,360 --> 01:10:51,639
Speaker 3: and and you might end up being the person who's

1340
01:10:51,880 --> 01:10:54,400
Speaker 3: a resource for somebody else, you know, like you can

1341
01:10:54,439 --> 01:10:58,720
Speaker 3: be helpful to people around you just by somebody ask

1342
01:10:58,840 --> 01:11:01,920
Speaker 3: you something and say, you know, I don't think that's possible,

1343
01:11:03,560 --> 01:11:07,760
Speaker 3: Like that doesn't actually make any sense, you know, And

1344
01:11:07,800 --> 01:11:10,680
Speaker 3: so just small things like that, small things like that,

1345
01:11:10,760 --> 01:11:13,920
Speaker 3: I think it's it's it's it's so worthwhile to just

1346
01:11:14,680 --> 01:11:17,200
Speaker 3: learn a little bit whatever that is, whatever interests you.

1347
01:11:17,240 --> 01:11:19,519
Speaker 3: If you're interested in hardware, if you want to know

1348
01:11:19,560 --> 01:11:21,839
Speaker 3: a little bit about AI, like there are free classes

1349
01:11:21,880 --> 01:11:25,080
Speaker 3: you can take uh, that'll, they'll teach you how it works.

1350
01:11:25,120 --> 01:11:28,280
Speaker 3: There is one of the best books I've ever seen,

1351
01:11:29,240 --> 01:11:35,560
Speaker 3: Karen Howe's Empires of AIS. I love that book because

1352
01:11:36,760 --> 01:11:39,920
Speaker 3: she explains how it works. It's not just like a

1353
01:11:40,000 --> 01:11:43,599
Speaker 3: drama story about the people who work it. Open AI it.

1354
01:11:43,640 --> 01:11:47,439
Speaker 3: Actually you can read it and you will understand how

1355
01:11:47,600 --> 01:11:52,920
Speaker 3: AI works. And that's super valuable because then when you

1356
01:11:52,960 --> 01:11:56,120
Speaker 3: watch the news, you will understand, Ah, this is what's happening,

1357
01:11:57,200 --> 01:12:01,479
Speaker 3: this is why this happened, and it's really helpful, really

1358
01:12:01,520 --> 01:12:02,080
Speaker 3: really helpful.

1359
01:12:03,320 --> 01:12:06,679
Speaker 1: Well, you mentioned if you learn a little bit about computers,

1360
01:12:06,680 --> 01:12:08,880
Speaker 1: you might be able to tell when something is off.

1361
01:12:08,960 --> 01:12:12,040
Speaker 1: I do have one more story that I think is

1362
01:12:12,040 --> 01:12:15,640
Speaker 1: a story where something from the very beginning, I was like,

1363
01:12:15,720 --> 01:12:16,800
Speaker 1: something seems off.

1364
01:12:16,800 --> 01:12:17,000
Speaker 3: Here.

1365
01:12:17,520 --> 01:12:20,680
Speaker 2: Were you following the tee app drama?

1366
01:12:21,680 --> 01:12:25,400
Speaker 3: Yeah, we did a Yeah we did an episode on it,

1367
01:12:26,400 --> 01:12:29,040
Speaker 3: and man, it's gotten it's only gotten worse. Yes, it's

1368
01:12:29,080 --> 01:12:31,000
Speaker 3: only gone worse. Yeah, I heard your episode on it,

1369
01:12:31,040 --> 01:12:33,559
Speaker 3: and yeah, it's only gotten worse. All the things that

1370
01:12:34,520 --> 01:12:38,519
Speaker 3: I think some people were suspecting ended up being true

1371
01:12:38,520 --> 01:12:40,599
Speaker 3: and worse a thousands.

1372
01:12:40,680 --> 01:12:44,400
Speaker 1: So we've been talking about the t app, the app

1373
01:12:44,439 --> 01:12:47,320
Speaker 1: that they say was designed for women to spill tea

1374
01:12:47,400 --> 01:12:48,519
Speaker 1: on the men that they were dating.

1375
01:12:48,960 --> 01:12:50,839
Speaker 2: They said it was about women's safety.

1376
01:12:51,240 --> 01:12:54,080
Speaker 1: I had many questions of that episode, but I talked

1377
01:12:54,120 --> 01:12:56,719
Speaker 1: a lot about my suspicions of what was going on here.

1378
01:12:57,000 --> 01:12:59,160
Speaker 2: Shout out to four and four. They published another deep

1379
01:12:59,240 --> 01:13:01,080
Speaker 2: dive heard some of those questions.

1380
01:13:01,360 --> 01:13:03,240
Speaker 1: I will say it's a long read, and I think

1381
01:13:03,240 --> 01:13:04,840
Speaker 1: it's one of those stories that folks should read the

1382
01:13:04,880 --> 01:13:07,439
Speaker 1: whole thing. But here is what we've learned, which is

1383
01:13:07,479 --> 01:13:10,640
Speaker 1: basically that the t app company is much shadier than

1384
01:13:10,680 --> 01:13:13,600
Speaker 1: I ever fucking realized, and it sounds like it is

1385
01:13:13,720 --> 01:13:17,120
Speaker 1: run by I won't say bad actors, but people who

1386
01:13:17,800 --> 01:13:21,320
Speaker 1: do not have their users as best interests at heart.

1387
01:13:21,400 --> 01:13:23,599
Speaker 2: So a couple of things that we've learned from that piece.

1388
01:13:24,160 --> 01:13:27,720
Speaker 1: One, the te app popped up as those are we

1389
01:13:27,840 --> 01:13:30,519
Speaker 1: dating the same guy? Facebook pages are being cracked down

1390
01:13:30,560 --> 01:13:33,280
Speaker 1: on Facebook. It turns out that the tea app was

1391
01:13:33,360 --> 01:13:39,000
Speaker 1: actually intentionally trying to derail and hijack those pages.

1392
01:13:39,000 --> 01:13:40,160
Speaker 3: After the sabotaging them.

1393
01:13:40,240 --> 01:13:43,040
Speaker 1: Yeah yeah, essentially sabotaging them after the woman who was

1394
01:13:43,080 --> 01:13:46,760
Speaker 1: responsible for them declined to work with the tea app.

1395
01:13:46,760 --> 01:13:49,679
Speaker 1: And so you know, folks might remember that Sean Cook,

1396
01:13:49,880 --> 01:13:52,680
Speaker 1: the Tea app founder, told the story about how he

1397
01:13:52,800 --> 01:13:55,920
Speaker 1: was inspired to make the app after his mother had

1398
01:13:56,160 --> 01:13:58,680
Speaker 1: terrible dating experiences and so he wanted to create a

1399
01:13:58,720 --> 01:14:02,679
Speaker 1: space where women could be feel safer in their dating experiences. Well,

1400
01:14:03,120 --> 01:14:07,880
Speaker 1: Sean Cook, working alongside his fiance Christiane Burns, approached this woman,

1401
01:14:07,960 --> 01:14:11,679
Speaker 1: Paula Sanchez, who was running this these r we Dating

1402
01:14:11,680 --> 01:14:15,160
Speaker 1: the Same Guy Facebook pages. They pitched her the idea

1403
01:14:15,200 --> 01:14:17,920
Speaker 1: of coming on to be the face and founder of

1404
01:14:18,439 --> 01:14:21,840
Speaker 1: the Tea app, and she did not respond. So after that,

1405
01:14:22,280 --> 01:14:26,440
Speaker 1: the Tea App began undermining her groups, including paying influencers

1406
01:14:26,800 --> 01:14:30,360
Speaker 1: to lure them into the Tea app. So they would

1407
01:14:30,400 --> 01:14:32,280
Speaker 1: have someone would post a picture and an r WE

1408
01:14:32,360 --> 01:14:34,120
Speaker 1: Dating the same Guy app and be like, oh, does

1409
01:14:34,160 --> 01:14:37,240
Speaker 1: anybody have any information? They were paying influencers to go

1410
01:14:37,280 --> 01:14:39,280
Speaker 1: in and say I think I've seen him on that

1411
01:14:39,360 --> 01:14:40,000
Speaker 1: tea app.

1412
01:14:40,760 --> 01:14:43,320
Speaker 3: They also would create and they would just spam. They

1413
01:14:43,320 --> 01:14:46,080
Speaker 3: would just spam this the same message in multiple places.

1414
01:14:46,160 --> 01:14:49,759
Speaker 3: And speaking I have to say speaking of paying people,

1415
01:14:50,320 --> 01:14:53,360
Speaker 3: they also they were sending the founder of these of

1416
01:14:53,400 --> 01:14:56,559
Speaker 3: these groups that existed sending your messages. But then also

1417
01:14:56,560 --> 01:15:01,960
Speaker 3: when she wasn't responding sending her like venmo requests, like saying,

1418
01:15:02,000 --> 01:15:03,880
Speaker 3: oh maybe you didn't see my message. Here, here's twenty

1419
01:15:03,920 --> 01:15:10,160
Speaker 3: five bucks somebody. Yeah, just cartoonishly bad.

1420
01:15:10,520 --> 01:15:13,880
Speaker 1: Yes, oh my sorry, Yeah, I'm so glad you mentioned

1421
01:15:13,880 --> 01:15:17,040
Speaker 1: that that that does show the level to which they

1422
01:15:17,040 --> 01:15:20,559
Speaker 1: were like really pressed, and like like that would make

1423
01:15:20,640 --> 01:15:23,519
Speaker 1: that would so make me respond even less. It's like, well,

1424
01:15:23,520 --> 01:15:25,920
Speaker 1: now you're yeah, I'll take the twenty dollars, but I'm

1425
01:15:25,920 --> 01:15:27,480
Speaker 1: definitely not going to respond.

1426
01:15:28,200 --> 01:15:32,120
Speaker 3: Yeah, man, yeah, I mean so it's like you're saying

1427
01:15:32,280 --> 01:15:37,960
Speaker 3: these groups existed, and I think you perfectly encapsulated it.

1428
01:15:37,960 --> 01:15:43,360
Speaker 3: It's really hard for me to believe that the person

1429
01:15:43,400 --> 01:15:47,240
Speaker 3: who made this app actually had women's best interests at heart.

1430
01:15:47,320 --> 01:15:51,400
Speaker 3: Because these groups existed, he has somebody to come in

1431
01:15:52,200 --> 01:15:55,559
Speaker 3: and try to buy, essentially lure them away, and then

1432
01:15:55,640 --> 01:16:01,200
Speaker 3: when they won't accept actively sabotage is the group exists. Listen,

1433
01:16:01,240 --> 01:16:05,559
Speaker 3: you can you can offer an alternative. That's one thing. Maybe,

1434
01:16:05,600 --> 01:16:07,360
Speaker 3: you know, we take one hundred steps back and say, okay,

1435
01:16:08,400 --> 01:16:11,160
Speaker 3: there's a Facebook group. I think that my app is

1436
01:16:11,200 --> 01:16:13,520
Speaker 3: a better experience. Let me offer that as an alternative.

1437
01:16:14,160 --> 01:16:18,760
Speaker 3: But sabotaging the thing that already is there. Man, You're

1438
01:16:18,800 --> 01:16:21,679
Speaker 3: get into some other territory there, You're getting some other

1439
01:16:21,920 --> 01:16:22,800
Speaker 3: territory there.

1440
01:16:23,320 --> 01:16:26,559
Speaker 1: He was someone who was not afraid to get into

1441
01:16:26,560 --> 01:16:29,680
Speaker 1: some we'll call it other territory. One of the I

1442
01:16:29,720 --> 01:16:33,000
Speaker 1: think the craziest accusations in the piece was that he

1443
01:16:33,080 --> 01:16:40,800
Speaker 1: was essentially pretend, pretending, like using his ex fiance's accounts

1444
01:16:40,840 --> 01:16:44,880
Speaker 1: on social media, Christian Burns, who they were engaged, she

1445
01:16:45,000 --> 01:16:47,680
Speaker 1: ended up like leaving the project. She was known as

1446
01:16:47,760 --> 01:16:51,639
Speaker 1: Tara online and four four spoke to a former Tea employee.

1447
01:16:51,640 --> 01:16:54,679
Speaker 1: You said that she only knew Christian Burns as Tara,

1448
01:16:55,240 --> 01:16:57,840
Speaker 1: and that persona also exists within the t app and

1449
01:16:57,920 --> 01:17:00,720
Speaker 1: on Facebook as an official representative of the app. But

1450
01:17:00,760 --> 01:17:04,280
Speaker 1: then when Burns left the company, Sean Cook, this male

1451
01:17:04,400 --> 01:17:08,760
Speaker 1: founder took over that persona and continued communicating with t

1452
01:17:09,120 --> 01:17:12,280
Speaker 1: users as if he was Tara. One of the former

1453
01:17:12,320 --> 01:17:15,720
Speaker 1: employees told for for Sean uses that account to communicate

1454
01:17:15,760 --> 01:17:18,040
Speaker 1: directly with users on the app, but people think they

1455
01:17:18,040 --> 01:17:20,080
Speaker 1: are speaking to someone actually named Tara.

1456
01:17:20,200 --> 01:17:21,479
Speaker 2: Essentially, a man is.

1457
01:17:21,479 --> 01:17:24,160
Speaker 1: Posing as a woman to an audience a women who

1458
01:17:24,160 --> 01:17:27,920
Speaker 1: are trying to protect themselves from at best deceptive men.

1459
01:17:28,080 --> 01:17:29,280
Speaker 2: So not great.

1460
01:17:29,320 --> 01:17:32,160
Speaker 1: And you're saying, oh, I want to protect women. I know,

1461
01:17:32,360 --> 01:17:36,040
Speaker 1: I'll pretend to be a woman using a Facebook account

1462
01:17:36,240 --> 01:17:38,920
Speaker 1: that lists me as a woman to do that.

1463
01:17:39,439 --> 01:17:40,520
Speaker 2: Not great.

1464
01:17:40,960 --> 01:17:44,400
Speaker 3: Yeah, you can think, you can hashtag not all men

1465
01:17:44,560 --> 01:17:46,400
Speaker 3: all you want. You can think you are the best

1466
01:17:46,479 --> 01:17:48,320
Speaker 3: dude in the history of the world. And I will

1467
01:17:48,320 --> 01:17:52,439
Speaker 3: assume let's assume my man is let's assume that my

1468
01:17:52,560 --> 01:17:58,960
Speaker 3: man is the wokest, most gentlemanly gentleman that ever gentleman. Okay, fine,

1469
01:17:59,280 --> 01:18:02,040
Speaker 3: but if you say that you are creating an app

1470
01:18:02,479 --> 01:18:05,320
Speaker 3: that does not allow men in it, man, you were

1471
01:18:05,360 --> 01:18:09,639
Speaker 3: not allowed in the app, remove yourself from the app.

1472
01:18:09,640 --> 01:18:13,240
Speaker 3: I'm sorry, bro, like you you can't you or or

1473
01:18:13,280 --> 01:18:16,200
Speaker 3: at least let them know. Hey, man, look with short staffed,

1474
01:18:16,640 --> 01:18:19,120
Speaker 3: it's me sean, let me, let me, let me take

1475
01:18:19,120 --> 01:18:21,559
Speaker 3: care of this thing, let me you know whatever. Okay, cool,

1476
01:18:21,640 --> 01:18:27,800
Speaker 3: don't don't pretend I like I said, I can't. You

1477
01:18:28,080 --> 01:18:30,640
Speaker 3: can't make this stuff up. You can't make this stuff up.

1478
01:18:31,240 --> 01:18:35,559
Speaker 2: No, it's bad. And I and this was my feelings

1479
01:18:35,560 --> 01:18:36,120
Speaker 2: in the episode.

1480
01:18:36,160 --> 01:18:39,160
Speaker 1: I think it just really it makes me sad that

1481
01:18:39,320 --> 01:18:42,479
Speaker 1: gender relations is this bad, that this is how it is,

1482
01:18:42,520 --> 01:18:45,080
Speaker 1: that this is the this is what we have to

1483
01:18:45,240 --> 01:18:48,479
Speaker 1: offer everyone is, oh, would you like to have your

1484
01:18:48,479 --> 01:18:50,160
Speaker 1: information exploited?

1485
01:18:50,160 --> 01:18:51,840
Speaker 2: Would you like to be exploited by this app? That

1486
01:18:51,840 --> 01:18:55,400
Speaker 2: it's product safety, that that's sort of where we're at.

1487
01:18:55,520 --> 01:18:58,840
Speaker 1: It's it's dark times. We are how did you put

1488
01:18:58,840 --> 01:19:00,479
Speaker 1: it earlier? We're not ready.

1489
01:19:01,400 --> 01:19:04,960
Speaker 3: Read we're not ready. We're not I mean, we won't

1490
01:19:05,000 --> 01:19:08,160
Speaker 3: get ourselves ready. But the thing, the thing that is

1491
01:19:08,200 --> 01:19:13,439
Speaker 3: like just diabolically clever about this particular app is that

1492
01:19:15,680 --> 01:19:22,040
Speaker 3: if you criticize it, then you're a misogynist. And let

1493
01:19:22,080 --> 01:19:25,640
Speaker 3: me be very clear here, a lot of the criticisms

1494
01:19:25,840 --> 01:19:29,280
Speaker 3: is coming from actual misogynists, some of whom would actually

1495
01:19:29,640 --> 01:19:33,360
Speaker 3: self identify as as misogynists. You know, again we're talking

1496
01:19:33,360 --> 01:19:35,200
Speaker 3: about four chan on here. And so some of the

1497
01:19:35,240 --> 01:19:36,920
Speaker 3: people were saying, oh, well, you don't like this because

1498
01:19:36,920 --> 01:19:40,479
Speaker 3: you hate women? They said, yeah, yeah, I do hate women,

1499
01:19:40,479 --> 01:19:44,040
Speaker 3: I understand, And what about it? Yeah, what are we

1500
01:19:44,120 --> 01:19:50,200
Speaker 3: doing here? So there there is a perhaps disappointingly healthy

1501
01:19:50,200 --> 01:19:53,479
Speaker 3: percentage of the people who are criticizing this application, who

1502
01:19:53,479 --> 01:19:56,200
Speaker 3: are criticizing from the beginning, who are criticizing because they're

1503
01:19:56,280 --> 01:19:59,680
Speaker 3: mad that they don't there's a space that they're not

1504
01:19:59,720 --> 01:20:01,920
Speaker 3: allowed into. And you know, we could say whatever we

1505
01:20:02,000 --> 01:20:04,760
Speaker 3: want to say about Oh, somebody might say something that's

1506
01:20:04,760 --> 01:20:08,280
Speaker 3: not true. Like the fundamental source of anger. I think

1507
01:20:08,320 --> 01:20:10,400
Speaker 3: I'm pretty comfortable saying is that people just mad that

1508
01:20:10,400 --> 01:20:15,000
Speaker 3: there's women who could be in a space that they're

1509
01:20:15,040 --> 01:20:17,400
Speaker 3: not allowed into. You did what I mean. But if

1510
01:20:17,439 --> 01:20:23,120
Speaker 3: you say, hey, look this seems unsafe, or clearly it

1511
01:20:23,280 --> 01:20:27,719
Speaker 3: was unsafe, and let me remind you and unfortunately viewers

1512
01:20:27,840 --> 01:20:31,000
Speaker 3: their listeners that people are still signing up for this app.

1513
01:20:31,040 --> 01:20:34,640
Speaker 2: People are still they felt very popular.

1514
01:20:35,160 --> 01:20:38,200
Speaker 3: And they're continuing to report at least I mean the

1515
01:20:38,280 --> 01:20:39,960
Speaker 3: last time I looked, which is a few days ago

1516
01:20:39,760 --> 01:20:43,000
Speaker 3: before this article came out, I have to say that.

1517
01:20:43,240 --> 01:20:47,320
Speaker 3: But they were really kind of celebrating that more women

1518
01:20:47,600 --> 01:20:50,439
Speaker 3: are signing up, and they would say, well, you know,

1519
01:20:50,479 --> 01:20:52,920
Speaker 3: we had a cyber incident quote unquote that's the language

1520
01:20:52,920 --> 01:20:56,040
Speaker 3: they use. This was a cyber incident, but you know,

1521
01:20:56,160 --> 01:20:59,800
Speaker 3: essentially saying, you know, basically communicating to the users that oh,

1522
01:20:59,840 --> 01:21:01,879
Speaker 3: you know, this is all this is something that happened,

1523
01:21:02,080 --> 01:21:06,479
Speaker 3: but this just shows that our mission is still important

1524
01:21:06,640 --> 01:21:09,000
Speaker 3: and this is t app is needed more than ever.

1525
01:21:09,240 --> 01:21:12,559
Speaker 3: Just bro, there were Facebook groups that you sabotage, you

1526
01:21:12,600 --> 01:21:16,240
Speaker 3: try to tank these things. That's something that was created

1527
01:21:17,320 --> 01:21:19,400
Speaker 3: by a community with as far as I know, no

1528
01:21:19,520 --> 01:21:22,120
Speaker 3: profit motive. And then you come on and you're bragging

1529
01:21:22,200 --> 01:21:25,800
Speaker 3: on podcasts about how there's angel investors try thinking about

1530
01:21:25,800 --> 01:21:30,360
Speaker 3: putting money into your app, Like, yo, you you have

1531
01:21:30,439 --> 01:21:32,920
Speaker 3: a different motive here, you know what I mean. And

1532
01:21:32,960 --> 01:21:37,000
Speaker 3: so it's just it's one of those apps that if

1533
01:21:37,040 --> 01:21:40,719
Speaker 3: one tries to criticize them, then they the pr spend

1534
01:21:40,760 --> 01:21:42,720
Speaker 3: that they put on it is, oh, well you must

1535
01:21:42,800 --> 01:21:45,519
Speaker 3: do what you don't want women to be safe. Bro,

1536
01:21:45,560 --> 01:21:48,519
Speaker 3: we're having a different conversation here because your app is unsafe.

1537
01:21:49,080 --> 01:21:51,120
Speaker 3: That's the thing I have a problem with. Yeah, the

1538
01:21:51,160 --> 01:21:55,760
Speaker 3: app is unsafe, but yeah, yeah, it's it's tough to

1539
01:21:55,760 --> 01:21:56,839
Speaker 3: talk about. We're not ready.

1540
01:21:57,080 --> 01:22:00,639
Speaker 2: Women deserve everybody deserves real safety.

1541
01:22:00,960 --> 01:22:05,719
Speaker 1: And I saw the same vibe on the internet where

1542
01:22:06,000 --> 01:22:09,160
Speaker 1: if you criticized this app, it's like, oh, you don't

1543
01:22:09,160 --> 01:22:12,000
Speaker 1: think women deserve a space to share their experiences and

1544
01:22:12,080 --> 01:22:13,080
Speaker 1: keep themselves safe.

1545
01:22:13,200 --> 01:22:14,800
Speaker 2: And it's like, I think women deserve that.

1546
01:22:14,840 --> 01:22:16,760
Speaker 1: Women should speak up and do what they we need

1547
01:22:16,800 --> 01:22:18,920
Speaker 1: to do to keep ourselves safe, and we always have

1548
01:22:19,040 --> 01:22:23,120
Speaker 1: done that without these scammy, exploitative apps that are offering

1549
01:22:23,200 --> 01:22:26,320
Speaker 1: us the chance to be genuinely put in danger, like

1550
01:22:27,320 --> 01:22:32,320
Speaker 1: having your driver's license with your address and whatever conversations

1551
01:22:32,360 --> 01:22:35,480
Speaker 1: that you said that you thought were private being all exposed.

1552
01:22:35,680 --> 01:22:37,080
Speaker 2: That is, that is such.

1553
01:22:36,880 --> 01:22:40,800
Speaker 1: A level of danger that I can't even take seriously

1554
01:22:40,840 --> 01:22:44,080
Speaker 1: anybody who would actually think that this company was meaningfully

1555
01:22:44,080 --> 01:22:45,559
Speaker 1: interested in keeping women safe.

1556
01:22:45,560 --> 01:22:47,760
Speaker 2: It just it's it's so it's so exploitative.

1557
01:22:48,800 --> 01:22:52,000
Speaker 3: Yeah, yeah, and it I mean, it brings you back

1558
01:22:52,080 --> 01:22:55,320
Speaker 3: to again, I think of more people. You can't blame

1559
01:22:55,479 --> 01:23:00,920
Speaker 3: the users for wanting to use this software, for wanting

1560
01:23:00,920 --> 01:23:04,760
Speaker 3: to use this app that was that was unsafe. We can't,

1561
01:23:04,920 --> 01:23:08,519
Speaker 3: I can't you know, these are this is a victim

1562
01:23:08,560 --> 01:23:13,960
Speaker 3: of a lot of different situations. One general misogyny in

1563
01:23:14,080 --> 01:23:17,760
Speaker 3: society in general, but also some people who lie to

1564
01:23:17,800 --> 01:23:22,160
Speaker 3: them and you know, on his face said hey, we're

1565
01:23:22,200 --> 01:23:25,280
Speaker 3: going to provide certain safety and they did not provide.

1566
01:23:25,320 --> 01:23:31,040
Speaker 3: That didn't deliver on the promise. Right. I think this

1567
01:23:31,080 --> 01:23:36,640
Speaker 3: is again somewhere where hopefully some of this can be

1568
01:23:36,720 --> 01:23:40,439
Speaker 3: mitigated by learning a little bit more about how this

1569
01:23:40,520 --> 01:23:43,640
Speaker 3: technology works. And so sometimes a little bit of a

1570
01:23:43,680 --> 01:23:46,120
Speaker 3: red flag might come up and say, hold on, should

1571
01:23:46,160 --> 01:23:50,000
Speaker 3: I be sending them this is this? It just does?

1572
01:23:50,000 --> 01:23:53,839
Speaker 3: This seem like it makes sense to me? And anybody

1573
01:23:53,880 --> 01:23:56,640
Speaker 3: can get tricked, anybody. You know, we're very used to

1574
01:23:56,680 --> 01:24:00,439
Speaker 3: putting our trust in app companies because you know, if

1575
01:24:00,479 --> 01:24:02,679
Speaker 3: you can't trust, if you can't trust a safety app,

1576
01:24:02,680 --> 01:24:03,479
Speaker 3: who can you trust?

1577
01:24:05,080 --> 01:24:06,680
Speaker 2: And go to Bellanche? Where can you go?

1578
01:24:07,200 --> 01:24:10,519
Speaker 3: Yeah? Exactly who can you trust? And so you know,

1579
01:24:10,800 --> 01:24:14,320
Speaker 3: this is really difficult stuff. But you know, I don't

1580
01:24:14,360 --> 01:24:16,160
Speaker 3: have a Frankly, I don't have a solution for it.

1581
01:24:16,640 --> 01:24:18,680
Speaker 3: I don't have a solution for but I do know

1582
01:24:18,760 --> 01:24:21,840
Speaker 3: that I want more people. I want more people to

1583
01:24:21,880 --> 01:24:24,960
Speaker 3: be better educated on how this stuff works. And if

1584
01:24:24,960 --> 01:24:27,759
Speaker 3: that just means reading one book or reading one article,

1585
01:24:28,160 --> 01:24:31,439
Speaker 3: listening to this podcast, reading one article on four four,

1586
01:24:31,560 --> 01:24:34,520
Speaker 3: then you know we're getting closer definitely.

1587
01:24:35,080 --> 01:24:39,160
Speaker 1: Or subscribing to your podcast kill Switch, which is excellent.

1588
01:24:39,360 --> 01:24:42,559
Speaker 1: Where can first of all thank you for this conversation

1589
01:24:42,600 --> 01:24:45,599
Speaker 1: has been great. You are you bring such a clarity

1590
01:24:45,640 --> 01:24:48,960
Speaker 1: to these issues that is just very refreshing. And you

1591
01:24:49,400 --> 01:24:52,240
Speaker 1: have a I listen to a lot of tech content,

1592
01:24:52,720 --> 01:24:55,600
Speaker 1: you have a chillness about the way that you've you

1593
01:24:55,680 --> 01:24:58,000
Speaker 1: handled that you come at this that I really appreciate.

1594
01:24:58,320 --> 01:25:01,960
Speaker 1: Where can folks listen to kill Switch? Follow you, follow

1595
01:25:02,000 --> 01:25:05,719
Speaker 1: your work, follow all the cool stuff you've got coming on. Wow, Yeah,

1596
01:25:05,720 --> 01:25:07,400
Speaker 1: thank you for saying that. That's very kind of you.

1597
01:25:08,439 --> 01:25:13,840
Speaker 1: I mean, website, what updex dot com? Everything's there, social media.

1598
01:25:13,840 --> 01:25:14,960
Speaker 1: I'm on dex, digit.

1599
01:25:15,840 --> 01:25:19,720
Speaker 3: D e X, d I g I and actually so

1600
01:25:19,840 --> 01:25:22,639
Speaker 3: kill Switch. If you search for it, you'll find it.

1601
01:25:23,240 --> 01:25:25,880
Speaker 3: But actually we're starting to put stuff on YouTube also,

1602
01:25:26,520 --> 01:25:29,800
Speaker 3: so yeah, actually the the our episode about the t

1603
01:25:30,000 --> 01:25:33,120
Speaker 3: app is the first one up there. Trying to get

1604
01:25:33,120 --> 01:25:37,160
Speaker 3: some more on there. But yeah, uh really really anywhere

1605
01:25:37,600 --> 01:25:40,080
Speaker 3: I'm out here, Unfortunately, for better or for worse, I'm

1606
01:25:40,120 --> 01:25:40,759
Speaker 3: out here.

1607
01:25:41,040 --> 01:25:43,960
Speaker 1: For people are listening so they can't see check out

1608
01:25:43,960 --> 01:25:48,040
Speaker 1: Dex's YouTube because your background is so beautiful. You have

1609
01:25:48,439 --> 01:25:52,280
Speaker 1: a very beautiful background, and if you're wondering what Dex

1610
01:25:52,439 --> 01:25:55,439
Speaker 1: looks like, you should check out the YouTube because it's

1611
01:25:55,600 --> 01:25:59,280
Speaker 1: very it's very nicely curated and it's a visual.

1612
01:25:59,280 --> 01:26:00,000
Speaker 2: It's a visual.

1613
01:26:00,920 --> 01:26:02,840
Speaker 3: Thank you think I'm working on it. I was like,

1614
01:26:02,920 --> 01:26:06,760
Speaker 3: every every week I add something else to it because

1615
01:26:06,800 --> 01:26:10,479
Speaker 3: you know, I stream also I also stream on Twitch,

1616
01:26:10,880 --> 01:26:14,200
Speaker 3: and so I'm always adding little things. And the people

1617
01:26:14,240 --> 01:26:16,280
Speaker 3: in chat are always making fun because every time I

1618
01:26:16,360 --> 01:26:19,559
Speaker 3: stream something goes wrong, like something like there's a light

1619
01:26:19,640 --> 01:26:22,720
Speaker 3: that's not working. The camera is blurry, yo, because I

1620
01:26:22,720 --> 01:26:25,479
Speaker 3: got I mean, I got this. One of these days,

1621
01:26:25,520 --> 01:26:28,479
Speaker 3: I need to post a picture of what my desk

1622
01:26:28,520 --> 01:26:30,960
Speaker 3: looks like because it looks like NASA. Like if the

1623
01:26:30,960 --> 01:26:33,559
Speaker 3: Feds ever see this, they're gonna come in here because

1624
01:26:33,600 --> 01:26:37,760
Speaker 3: it looks like I'm planning something. Yeah, the amount of

1625
01:26:37,760 --> 01:26:42,519
Speaker 3: wires and machinery in here, it's it would look suspicious. Yeah,

1626
01:26:42,560 --> 01:26:43,920
Speaker 3: I'm not going to post a picture this, it would

1627
01:26:43,920 --> 01:26:47,040
Speaker 3: look suspicious. They don't think I'm making Because I want

1628
01:26:47,080 --> 01:26:50,000
Speaker 3: to see I'll send it to you on signal because

1629
01:26:50,240 --> 01:26:52,960
Speaker 3: again speaking of safety, like I don't want anybody. I

1630
01:26:52,960 --> 01:26:56,000
Speaker 3: don't want to get intercepted because somebody's going to see

1631
01:26:56,040 --> 01:26:59,240
Speaker 3: this and say, what is that antenna and you're doing

1632
01:26:59,280 --> 01:27:02,280
Speaker 3: you're playing something and we listen somebody to visit. Yes,

1633
01:27:02,560 --> 01:27:04,200
Speaker 3: we're gonna. I'm gonna get a visit. I do not

1634
01:27:04,240 --> 01:27:06,519
Speaker 3: want to visit from any authorities. By the way, any

1635
01:27:06,520 --> 01:27:09,840
Speaker 3: authorities listening to this, nothing but respect, appreciate your work,

1636
01:27:10,200 --> 01:27:13,599
Speaker 3: God bless but yeah, no for real jokes aside. Thank

1637
01:27:13,640 --> 01:27:15,200
Speaker 3: you so much for real this is a lot of fun.

1638
01:27:15,439 --> 01:27:18,040
Speaker 1: Oh my gosh, Well follow decks and all the places.

1639
01:27:18,080 --> 01:27:19,600
Speaker 1: Thank you so much for being here, and thanks to

1640
01:27:19,600 --> 01:27:20,639
Speaker 1: all of you for listening.

1641
01:27:20,800 --> 01:27:21,400
Speaker 2: If you can.

1642
01:27:21,240 --> 01:27:24,120
Speaker 1: Follow me on YouTube at There Are No Girls on

1643
01:27:24,120 --> 01:27:25,960
Speaker 1: the Internet. You can follow me on Instagram at bridget

1644
01:27:26,000 --> 01:27:28,040
Speaker 1: ran DC or on TikTok at bridget Lean DC.

1645
01:27:28,400 --> 01:27:29,919
Speaker 2: I will see you on the Internet.

1646
01:27:33,320 --> 01:27:35,360
Speaker 1: Got a story about an interesting thing in tech, or

1647
01:27:35,439 --> 01:27:37,280
Speaker 1: just want to say hi? You can reach us at

1648
01:27:37,280 --> 01:27:40,040
Speaker 1: Hello at tegody dot com. You can also find transcripts

1649
01:27:40,040 --> 01:27:42,439
Speaker 1: for today's episode at teng Goody dot com. There Are

1650
01:27:42,439 --> 01:27:44,639
Speaker 1: No Girls on the Internet was created by me Bridget Todd.

1651
01:27:45,000 --> 01:27:48,439
Speaker 1: It's a production of iHeartRadio and Unbossed creative Jonathan Strickland

1652
01:27:48,439 --> 01:27:51,200
Speaker 1: as our executive producer. Tari Harrison is our producer and

1653
01:27:51,240 --> 01:27:55,040
Speaker 1: sound engineer. Michael Almado is our contributing producer. Edited by

1654
01:27:55,120 --> 01:27:58,639
Speaker 1: Joey Pat I'm your host, Bridget Todd. If you want

1655
01:27:58,640 --> 01:28:00,240
Speaker 1: to help us grow, grate and review.

1656
01:28:00,080 --> 01:28:01,120
Speaker 2: Us on Apple Podcasts.

1657
01:28:01,840 --> 01:28:04,639
Speaker 1: For more podcasts from iHeartRadio, check out the iHeartRadio app,

1658
01:28:04,680 --> 01:28:06,720
Speaker 1: Apple Podcasts, or wherever you get your podcasts.