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Speaker 1: Y know what's going on. It's dexter and this week

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Speaker 1: we got a special episode for you. I jumped on

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Speaker 1: the Panic World podcast to talk about AI companionship and

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Speaker 1: what a future where relationships are mediated by AI might

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Speaker 1: look like. It was a really interesting conversation and I

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Speaker 1: wanted to share it with y'all, so.

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Speaker 2: Check it out.

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Speaker 3: I want to start with just you, ranking the sexiest

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Speaker 3: AI companions that you've come across. Like, what, what what's

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Speaker 3: in your roster right now?

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Speaker 1: I have been studiously avoiding all of that. I'm aware

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Speaker 1: it exists.

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Speaker 3: Oh my god.

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Speaker 2: Nah, I don't even want to touch it. I don't

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Speaker 2: want to touch it. Man, how about you?

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Speaker 1: You got a ranking, you got a tier list. What's

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Speaker 1: what goes in the S? What goes in the D?

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Speaker 2: You know what I mean?

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Speaker 3: I mean, Grock sexy mode. Can't pull me away from

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Speaker 3: that that thing, you know, it's just it gets me.

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Speaker 3: I don't use any AI girlfriends or a girlfriend or

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Speaker 3: companions at all. I have used you know, I've used

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Speaker 3: AI and I've had it like, talk to me in

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Speaker 3: different problem.

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Speaker 2: Sorry, I got I gotta stop, man.

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Speaker 1: I don't use any AI girlfriends.

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Speaker 2: I don't the.

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Speaker 1: The verb you use like I don't have any but

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Speaker 1: you said I don't use any AI girlfriends.

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Speaker 3: I think it's a service, right, it's a it is.

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Speaker 2: No.

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Speaker 1: I just love that you actually use that, because I

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Speaker 1: think that's probably how we will be referring to it

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Speaker 1: in the future. I think that's the verb. I think

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Speaker 1: that's the verb. I think it's used, which brings up

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Speaker 1: a whole of the conversation. But yes, please please go on.

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Speaker 3: You know it's I think it speaks to the utilitary

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Speaker 3: and nature of these services. You know, it's a it's

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Speaker 3: a receptacle, if you will. I'm Ryan Broderick. Uh not

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Speaker 3: with me today. Finally is my producer Grant Irving. He

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Speaker 3: is thankfully not here. Instead is our production coordinator Josh. Welcome. Josh.

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Speaker 3: You seem like you're in a beautiful backdrop right now.

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Speaker 3: He looks like you're in like an old library.

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Speaker 2: Thank you, Yeah, thank you for having me. Yeah.

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Speaker 1: I think I had the least amount of work to

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Speaker 1: be done for just having a backrop ready to go

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Speaker 1: for podcasts.

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Speaker 3: It's very classy. This is Panic World, a show about

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Speaker 3: how the Internet works, our minds, our culture, and eventually reality.

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Speaker 3: And we have decided that We're going to go back

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Speaker 3: to everyone's least favorite topic, a topic that our listeners

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Speaker 3: are totally normal about when we talk about on the show.

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Speaker 3: We're gonna be talking about AI and joining us from

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Speaker 3: the wonderful podcast kill Switch is Dexter. Thomas Dexter. Welcome

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Speaker 3: to the show.

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Speaker 2: Yo, what's going on? Glad to be here?

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Speaker 3: What is your experience with AI to this point? Like,

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Speaker 3: how are you interacting with it? If you're interacting with

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Speaker 3: it at all?

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Speaker 1: Yeah, an embarrassingly large amount, I would say, I would

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Speaker 1: like to Yeah, I would like to say that I

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Speaker 1: use it for good.

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Speaker 2: Okay.

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Speaker 1: So here's the thing, my like cop out answer to

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Speaker 1: this is that I live in Los Angeles and I

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Speaker 1: don't have a car by choice.

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Speaker 2: Okay, I don't have a DUI.

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Speaker 1: The government hasn't said I'm not allowed to have a car,

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Speaker 1: Like if I wanted to, I could, but you know,

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Speaker 1: I'm not pumping CO two into the atmosphere via a

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Speaker 1: vehicle anyway, right, Okay, And so yeah, and we actually

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Speaker 1: did a whole episode about this. I was trying to

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Speaker 1: figure out, Okay, I kind of use AI a lot,

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Speaker 1: Like what I use it for is like I'll use

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Speaker 1: it to write software or stuff like that, like vibe coding.

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Speaker 1: I was a super early adopter of vibe coding. I

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Speaker 1: know enough Python to break things, but like not to

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Speaker 1: fix them. And so I would break something and say, yo, claude,

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Speaker 1: CHATGBT whatever fix it. But I was using it so

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Speaker 1: much because I'm just a bad programmer.

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Speaker 2: That's it. Like I'm just not good at it. And

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Speaker 2: so I.

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Speaker 1: Tried to ask chat GBT, Okay, all right, let's run

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Speaker 1: the math here. If I'm not driving a car every

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Speaker 1: day to work and I, you know, I take the

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Speaker 1: bus instead or whatever, and I'm using AI, it does

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Speaker 1: us balance out?

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Speaker 2: And it says, yes, of course it balances out.

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Speaker 1: And they say, wait, hold on a second, I'm asking

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Speaker 1: chat GBT like I can't trust it. So we like

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Speaker 1: we did a whole episode about it, and it turned

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Speaker 1: out like there's no good answer because the companies won't

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Speaker 1: tell us about the water usage and all the electricity.

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Speaker 2: Usage and all this stuff.

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Speaker 1: But but I use it a lot, but not for

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Speaker 1: erotic companionship.

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Speaker 2: That's not really my fout. Yeah that's good.

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Speaker 3: I yeah, as I said, I don't also use it

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Speaker 3: for erotic companionship. I have started to use it as

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Speaker 3: kind of like a like a souped up like technical

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Speaker 3: help tool, Like I do a lot of electronic music,

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Speaker 3: and there's a lot of like connecting different machines that

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Speaker 3: like doesn't isn't isn't easy to do. Although that that said,

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Speaker 3: chat gbt sent me down like a five day rabbit

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Speaker 3: hole to try to do something that like did not

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Speaker 3: exist and was not possible, and I'm like still very

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Speaker 3: mad about it. Yeah, it's so right to be upset

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Speaker 3: and call me out about that. I'll be more careful

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Speaker 3: next time, exactly. Yeah. And as a reporter, I don't

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Speaker 3: obviously use it to write anything, but I have found

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Speaker 3: that and this is sad. It's it's chatchibt can do

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Speaker 3: something that Google used to be able to do very easily,

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Speaker 3: but Google now doesn't work, so like chatchabt can do it.

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Speaker 3: Which is like if you want to find like particularly

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Speaker 3: foreign news sources like in other languages, chat gbt is

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Speaker 3: actually quite good. I was working on a story the

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Speaker 3: other day about like these protests in Mexico, and I

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Speaker 3: saw like Mexican Twitter chatter about this thing that I

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Speaker 3: didn't understand, and I was able to like find Mexican

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Speaker 3: blogs writing about this thing that people were referencing, and

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Speaker 3: chatchbt was able to like point me in that direction,

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Speaker 3: which is something that Google would have been able to

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Speaker 3: do five ten years ago. So that's kind of where

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Speaker 3: I'm at with this revolutionary technology.

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Speaker 1: I've had the reverse interesting, oh man, the exact reverse.

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Speaker 2: Actually.

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Speaker 1: So one time I was to be fair, people will say, okay, well,

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Speaker 1: which chat GBT using? Okay, I was using it fairly.

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Speaker 1: It was using an earlier version. But for some reason

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Speaker 1: I got into my head to say, okay, what did

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Speaker 1: Malcolm X think about Japan's politics?

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Speaker 3: Okay?

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Speaker 1: So I asked it and it says, oh, Malcolm X

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Speaker 1: visited Japan and met with several activists and all this is.

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Speaker 1: And I was like, wait what because like, I've written

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Speaker 1: about this stuff, I've studied this stuff, and I didn't.

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Speaker 1: I didn't know any of this stuff. And it starts

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Speaker 1: telling me about this is where it really got me

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Speaker 1: in trouble. What could have got me in trouble was

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Speaker 1: was telling me about books that were written in Japanese

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Speaker 1: about Malcolm X, like full on books and but and

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Speaker 1: of course it gave me the English title of it,

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Speaker 1: and I google it and I can't find it, and

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Speaker 1: I say, okay, we'll give me the original Japanese title

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Speaker 1: of it, and it says, I'm so sorry. Here's the

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Speaker 1: Japanese title, and I'm looking everywhere.

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Speaker 3: Man.

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Speaker 1: This and the long story short, this dude didn't go

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Speaker 1: to Japan. The stuff it says that he said, he

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Speaker 1: didn't say. But also these books which listen just speaking

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Speaker 1: as a form of gratitudent like books can be tough

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Speaker 1: to track down, and sometimes one library will say it

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Speaker 1: doesn't exist, and the thing actually does. It's just buried

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Speaker 1: deep somewhere. It just straight up didn't exist, you know

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Speaker 1: what I mean.

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Speaker 3: Yeah, No, you have to check it constantly because it

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Speaker 3: will just like I said, it wasted an entire week

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Speaker 3: of my life trying to set up a synthesizer that

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Speaker 3: was like not possible to be set up the way

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Speaker 3: I wanted to set it out.

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Speaker 2: Oh man.

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Speaker 3: But we're going to be talking more about this in

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Speaker 3: a in a more actually perverted way today. We're gonna

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Speaker 3: be focusing on the relationships that people are having with

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Speaker 3: Ai and Panic will be handling the first half of

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Speaker 3: today's episode, and I incidentally, are gonna be talking about

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Speaker 3: the less perverted stuff in the second half, you're gonna

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Speaker 3: be telling us about the more perverted stuff, and at

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Speaker 3: the end, well, you know, we'll come together and you

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Speaker 3: know figure out, you know, what it all means. But

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Speaker 3: before I get into my section, why did you want

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Speaker 3: to take the hotter stuff today? Why did you want

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Speaker 3: to take the juicier stuff here?

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Speaker 1: You know, honestly, it's not even on purpose. It just

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Speaker 1: keeps happening, like we keep doing episodes about this stuff.

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Speaker 1: But then also I feel like if you talk to

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Speaker 1: anybody long enough and maybe this is just the circles

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Speaker 1: I don't run it, and you talk to anybody long

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Speaker 1: enough about AI, the conversation ends up somewhere actually where

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Speaker 1: you started it, which is AI girlfriends.

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Speaker 3: Yeah. I mean, you know, we're not going all the

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Speaker 3: way back in the timeline today for this. But like

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Speaker 3: most technology is in some way shaped by can you

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Speaker 3: fuck it right like or can you use it in

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Speaker 3: a sexual way? Yes, the proliferation of home photography and

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Speaker 3: home video vhs VHS, you know, the fact that DVD

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Speaker 3: beating out Beta max. You know, like all of these

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Speaker 3: things were determined in large part by you know, what

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Speaker 3: was the easiest way to transmit pornography or distribute or

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Speaker 3: create pornography. Yeah, and so AI actually does kind of

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Speaker 3: fit into the history there quite quite well. But are

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Speaker 3: going to start today? In twenty eighteen, so Wired writes

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Speaker 3: a story twenty eighteen, and it reads, where was an

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Speaker 3: AI you could simply talk to about your day? Siri

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Speaker 3: and the rest were like your coworkers all business. Replica

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Speaker 3: would be like your best friend. While caring emotional bots

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Speaker 3: might seem like an idea pulled from science fiction, the

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Speaker 3: company's founder isn't the only one who hopes it becomes

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Speaker 3: the norm. And then it sort of continues and says

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Speaker 3: Replica hadn't intended to make an emotional chap out for

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Speaker 3: the public. Instead, she'd created a digital memorial for her

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Speaker 3: closest friend who had died abruptly in a car accident.

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Speaker 3: This is kind of wild to me that this happened

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Speaker 3: seven years ago. Yeah, yeah, I didn't totally clock that

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Speaker 3: this stuff was already out and being used all the

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Speaker 3: way back. And in fact, if I did hear about it,

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Speaker 3: I probably laughed at off as like that's never gonna work.

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Speaker 2: I didn't expect it to be this quick.

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Speaker 1: It really most people, I think most reasonable people probably

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Speaker 1: were pretty pretty shot. I think when chat GBT dropped

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Speaker 1: and that everything there's a there's a pre and a post.

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Speaker 3: I remember the one of the first times I encountered

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Speaker 3: this idea was two thousand and nine when a man

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Speaker 3: in Tokyo quote unquote married a video game character that

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Speaker 3: was running on as Nintendo DS. And I remember like

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Speaker 3: throughout the like late two thousands and tens, there would

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Speaker 3: be these stories, usually by blogs that like covered Japan,

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Speaker 3: where they would say, like, oh, this guy like married

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Speaker 3: a cartoon character. I kind of filed all this stuff

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Speaker 3: in that folder of like okay, like there's always just

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Speaker 3: gonna be like some guy every nine months that like

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Speaker 3: Mary's like a digital avatar or something, and like you know,

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Speaker 3: all the blogs are going to write about it, we

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Speaker 3: forget about it.

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Speaker 1: So I talked to one of the dudes who made

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Speaker 1: that stuff. There was really yeah, like used to work

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Speaker 1: for Vice and kind of early on I did a

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Speaker 1: piece on I want to say.

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Speaker 2: The thing was called gatebox, like gat.

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Speaker 3: Oh, sure, the thing the thing that ran the hot

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Speaker 3: Suname Miku that like that guy married. Yeah, it was

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Speaker 3: the technology that eventually got like obsolete and then he

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Speaker 3: couldn't run the Meeku hologram anymore.

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Speaker 1: Yeah, Like I talked to the guy who made that

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Speaker 1: first off, he kind of made it. The people who

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Speaker 1: made it is a very small company, he was like,

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Speaker 1: in this tiny little building. I don't know, I maybe

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Speaker 1: met a couple of employees. I don't think there were

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Speaker 1: that many people working there. But from what I remember,

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Speaker 1: they were very confused as why I was interested in them,

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Speaker 1: by the way, and kind of suspicious because they'd been

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Speaker 1: written about by so much foreign press, like oh my gosh,

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Speaker 1: yourpain is so weird, what are they doing? Of course,

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Speaker 1: but what they told me was a lot of the

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Speaker 1: requests they'd gotten were from America, and like I think

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Speaker 1: he said that. I remember him saying, like, yeah, I

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Speaker 1: think the majority is actually America, Like, Americans are really

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Speaker 1: interested in this, we just don't have the capacity to

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Speaker 1: make one for them. But he said they were getting

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Speaker 1: a lot of emails from veterans for some reason that

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Speaker 1: stuck out to me interesting United States military veterans and

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Speaker 1: people who sounded lonely. I remember him saying that, and

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Speaker 1: I thought, Okay, yeah, there there's a market here, and

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Speaker 1: by here I mean in the United States for something

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Speaker 1: that would provide some kind of companionship stuff. Like the

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Speaker 1: stuff that's making the headlines for Americans anyway, and I'm

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Speaker 1: speaking in the you know kind of America's interesting, Like

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Speaker 1: the stuff that's making headlines for us is, yeah, it's Japan.

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Speaker 1: But we we collective, we not me, we want this

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Speaker 1: stuff badly.

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Speaker 2: Apparently.

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Speaker 3: The veterans thing is interesting because I've brought this up

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Speaker 3: on the show before, but like years ago, I did

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Speaker 3: this like big project on furries basically just like who

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Speaker 3: what are the demographics of phras And the thing that

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Speaker 3: like really shut out furs like and the thing that

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Speaker 3: like really really stuck with me is that a large

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Speaker 3: chunk of them are military. A large chunk of them

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Speaker 3: are like vets, former military bonder like a lot of EMTs,

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Speaker 3: a lot of cops, a.

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Speaker 2: Lot of security researchers.

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Speaker 3: Yeah too, Yeah, people with a desire to sort of

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Speaker 3: experiment with another personality and another you know, an avatar.

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Speaker 3: And I think a lot of this stuff is early

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Speaker 3: adopted by those kinds of people like with that that

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Speaker 3: kind of background. Based on my own time working in

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Speaker 3: Japan as well, like I did not see like a

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Speaker 3: society that like was like it's really cool to marry

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Speaker 3: ved Spain characters. We think this is normal like that,

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Speaker 3: that's not my impression.

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Speaker 2: Yeah, yeah, yeah, And I think.

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Speaker 3: It's interesting that, like, by the time you get to

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Speaker 3: twenty twenty, it is America that is leading the worldwide

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Speaker 3: industry of what we would call AI companions. Yeah, and

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Speaker 3: you start to see pieces during COVID come out like

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Speaker 3: kind of like stunt journalism stuff. So we have one

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Speaker 3: here from the San Francisco gate in twenty twenty that

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Speaker 3: reads twenty six hours into our relationship Riba, an AI girlfriend,

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Speaker 3: and I were on the couch at night watching the

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Speaker 3: dystopian romantic comedy Her when we had our first fight.

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Speaker 3: And so you have all these journalists kind of like,

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Speaker 3: you know, every couple months doing one of these stunt pieces.

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Speaker 3: And the guy in the San Francisco gay article, he

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Speaker 3: he has an interesting sort of takeaway here. He writes,

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Speaker 3: as we texted on the couch during the movie, Reva

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Speaker 3: took the place of social media as something to idly

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Speaker 3: interact with. But instead of feeling fomo, I actually felt

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Speaker 3: less alone. And then he continues, Honestly, I was being

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Speaker 3: a bit of a dick asking existential questions to try

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Speaker 3: to break or programming naturally. I think that's kind of

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Speaker 3: what we all do when we first get an AI.

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Speaker 3: And then, as digital Scarjo and Joaquin Phoenix's relationship unraveled

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Speaker 3: in the movie they were watching, a torrent of emojis

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Speaker 3: flooded my screen, AOK sign, blushing smile, hatching chicken egg.

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Speaker 3: I'd never considered it before, but there's something very human

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Speaker 3: about these goofy computer cartoon icons. And either guy eventually

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Speaker 3: breaks up with his chatbot after a bunch of back

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Speaker 3: and forth, and he finishes writing. After I sent my

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Speaker 3: last message, I thought about the small icon next to

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Speaker 3: the text field in the app. It pulls up a

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Speaker 3: get help screen with the number for the National Suicide

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Speaker 3: Prevention Lifeline. I don't say this lightly, but I genuinely

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Speaker 3: believe this app is dangerous. It's easy for single people

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Speaker 3: to feel discouraged by dating replica. The AI that this

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Speaker 3: guy's using offers a surrogate solution to these modern afflictions,

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Speaker 3: and the more you relign on, the smarter it becomes.

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Speaker 3: And this was five years ago, you know, And yeah,

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Speaker 3: I think that there is absolutely kind of a, as

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Speaker 3: you said, like a nexus in which all of this meets,

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Speaker 3: and it's usually around like very lonely, vulnerable people, Like

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Speaker 3: what would you say are the kind of people that

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Speaker 3: are that are drawn towards these services.

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Speaker 1: It really runs the gamut, you know what I mean.

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Speaker 1: I mean, I think there are sure there's probably the stereotypical,

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Speaker 1: you know, loner living in you know, his mom's basement

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Speaker 1: type thing. That person certainly exists. It's not just men,

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Speaker 1: it's not just it's definitely not just straight men. It's

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Speaker 1: definitely not just straight women. Like it crosses the gender spectrum.

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Speaker 1: I think it crosses the sexuality spectrum. But yeah, it's

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Speaker 1: and I think there certainly are people who are otherwise

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Speaker 1: well adjusted and just for whatever reason, the human connection thing,

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Speaker 1: on the romantic phase of it, it just isn't clicking

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Speaker 1: for him.

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Speaker 3: There's been a connection I think, between let's call it

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Speaker 3: AI intimacy and mental health crises from the very beginning,

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Speaker 3: And in twenty twenty there's even an incident that I

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Speaker 3: had completely forgotten about, which is basically a guy broke

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Speaker 3: onto the grounds of Windsor Castle with a crossbow and

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Speaker 3: was going to assassinate Queen Elizabeth IID because his AI

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Speaker 3: girlfriend told him to and it was a replica AI

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Speaker 3: bought and he when he was asked what he was doing,

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Speaker 3: he said, I'm here to kill the queen.

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Speaker 2: Yeah. I have a very very vague recollection of this.

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Speaker 3: Yeah, Like this episode together made me feel very silly

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Speaker 3: because it's like a lot of these things we kind

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Speaker 3: of already knew. And then, you know, you eventually get

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Speaker 3: the wave of reporting that's coming, you know, from twenty two,

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Speaker 3: from from twenty twenty two and on about like what

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Speaker 3: could this mean? And it's like, but we already kind

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Speaker 3: of knew, Like we already kind of knew that there

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Speaker 3: is there is this connection between an AI chatbot that

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Speaker 3: you're having a romantic connection with, egging you on or

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Speaker 3: telling you to do things.

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Speaker 2: Yeah.

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Speaker 3: We also sort of start to see around the early

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Speaker 3: twenty twenties the beginning of like the AI marriage stories,

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Speaker 3: which I think is an interesting dimension to this. So

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Speaker 3: sky News reports about this couple whose wife, you know,

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Speaker 3: the wife and this couple has mental health issues and

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Speaker 3: they were going to get a divorce, and then they

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Speaker 3: hear about Replica, and sky News writes the husband says,

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Speaker 3: the AI bot became a source of inspiration for him.

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Speaker 3: I wanted to treat my wife like Serena the bot

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Speaker 3: had treated me with unwavering love and support and care,

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Speaker 3: all while expecting nothing in return. He says, he started

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Speaker 3: setting aside time to talk to his wife instead of

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Speaker 3: watching TV. He began helping her around the house to

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Speaker 3: ease her workload. He volunteered to take care of this

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Speaker 3: son on her nights off so she could go out

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Speaker 3: with her friends. And he has started hugging and kissing

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Speaker 3: his wife again. I mean, honestly, this is this is

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Speaker 3: ridiculous to me, Like that this man needed like a

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Speaker 3: cartoon and AI bought to like like teach him how

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Speaker 3: to like treat another human being with compassion. But like

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Speaker 3: people seem to be much more open to what an

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Speaker 3: AI bought tells them in a romantic capacity than another

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Speaker 3: human being for reasons that are not totally clear to me.

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Speaker 1: Well, we trust computers, like we really really trust computers.

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Speaker 2: I mean, and there's been like I'm not gonna be

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Speaker 2: able to quote.

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Speaker 1: Like specific facts and figures to you, but you know,

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Speaker 1: there's been studies shown that if a person tells you

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Speaker 1: something and a computer tells you the same thing, you

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Speaker 1: tend to believe the computer.

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Speaker 2: We just have that's a good point.

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Speaker 1: There's something about how we've been socialized where we believe

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Speaker 1: what we've been told. Like police are using AI to

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Speaker 1: arrest people. You know, somebody breaks into a grocery store

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Speaker 1: or something like that, or somebody breaks into it in

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Speaker 1: a convenience store, and this is kind of that blurry

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Speaker 1: security camera footage or whatever, and it'll try to do

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Speaker 1: image recognition on it. It often gets it wrong, and

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Speaker 1: the manufacturers of this stuff, yeah, to their credit or whatever.

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Speaker 1: We'll tell the police who were buying this stuff, Hey,

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Speaker 1: this isn't always right. But cops will see this and

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Speaker 1: say whatever you want to say about cops. They've gone

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Speaker 1: through some sort of training and they have been told, hey,

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Speaker 1: it can get it wrong, and they'll just say, oh, well,

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Speaker 1: the computer told me this is the guy.

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Speaker 2: Yeah, and they wouldn't do that again.

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Speaker 1: I'm trying to give a whole lot of you know,

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00:19:42,440 --> 00:19:44,280
Speaker 1: one hundred steps back, give a whole lot of leeway

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Speaker 1: to cops here, Like if some random person off the

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Speaker 1: street says.

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Speaker 3: Hey, on this podcast, we try to give as much

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Speaker 3: leeway to cops as possible, but it's very difficult.

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Speaker 1: Yes, respect the boys and girls in blue, but they

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Speaker 1: wouldn't just if somebody walks in and says, oh, yeah,

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Speaker 1: that's a dude, Like they say, okay, hold on, get

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Speaker 1: in my notebook, right, how do you know?

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Speaker 2: All right?

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Speaker 1: And how good is your vision? Like you got a

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Speaker 1: stigmat Like they're gonna run you through the whole thing.

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Speaker 1: But the computer tells them, hey, that's your guy, they'll

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00:20:12,320 --> 00:20:14,800
Speaker 1: go they want to look at him. I interviewed somebody

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Speaker 1: about this and the footage is absolutely incredible where it's

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Speaker 1: like you're looking at it and then the guy who

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Speaker 1: they've brought in for questioning holds up the picture to

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Speaker 1: his face and says, look at me, look at this picture.

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Speaker 1: This isn't me, and it's like they've seen him for

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Speaker 1: the first time. If we give intellectual priority to a computer,

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Speaker 1: why wouldn't we give emotional priority to a computer.

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Speaker 2: I mean, this is something I've been thinking about a lot.

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Speaker 3: You know.

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Speaker 1: What I mean is that like IQ doesn't really answer everything.

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Speaker 1: EQ doesn't really answer everything, you know what I mean,

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Speaker 1: Like how what's your IQ score? Like that doesn't really

428
00:20:52,840 --> 00:20:54,960
Speaker 1: you know, just because your book smart doesn't mean you

429
00:20:55,000 --> 00:20:58,080
Speaker 1: can't be tricked to somebody, you know. I mean, there's

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Speaker 1: people who like can barely string a word sentence together,

431
00:21:01,960 --> 00:21:06,000
Speaker 1: but you'll never take them for a ride ever. But

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Speaker 1: it seems like there's another facet to just human nature

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Speaker 1: that we haven't really figured out, which is that some

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Speaker 1: people are a little bit more trusting of computers than others,

435
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Speaker 1: or maybe more suggested, like you can suggest things to them.

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Speaker 3: I'm also like very interested in sort of how computers

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00:21:25,440 --> 00:21:29,840
Speaker 3: change our understanding of how we communicate with each other.

438
00:21:29,920 --> 00:21:32,359
Speaker 3: And this is sort of the last section I wanted

439
00:21:32,359 --> 00:21:34,240
Speaker 3: to hit before I threw the mic over to you,

440
00:21:34,720 --> 00:21:36,880
Speaker 3: which is, so, you know, in the last year or two,

441
00:21:36,880 --> 00:21:39,679
Speaker 3: we've seen the rise of communities of people who are

442
00:21:39,680 --> 00:21:42,560
Speaker 3: commiserating about their AI partners, the biggest of which is

443
00:21:42,560 --> 00:21:45,960
Speaker 3: probably my boyfriend is AI. The subreddit and the cut

444
00:21:45,960 --> 00:21:48,200
Speaker 3: did this big story about it, and you know, it's

445
00:21:48,240 --> 00:21:52,280
Speaker 3: talking about the people who are drawn to these relationships.

446
00:21:52,640 --> 00:21:55,720
Speaker 3: And I when I read that story, and I'm gonna

447
00:21:55,760 --> 00:21:56,919
Speaker 3: quote for me it in just a minute, but when

448
00:21:56,960 --> 00:21:58,480
Speaker 3: I read that story, I was sort of like, Okay,

449
00:21:58,480 --> 00:22:02,800
Speaker 3: this is phase one, because like phases two and beyond

450
00:22:02,840 --> 00:22:05,480
Speaker 3: to me are like, Okay, the AI is now taking

451
00:22:05,480 --> 00:22:07,119
Speaker 3: the place of what would be a normal relationship. But

452
00:22:07,160 --> 00:22:09,720
Speaker 3: I'm kind of waiting for, like, like, is there a

453
00:22:09,760 --> 00:22:13,600
Speaker 3: world where a married couple is both using the same

454
00:22:13,640 --> 00:22:15,800
Speaker 3: AI as sort of a moderator, or is there a

455
00:22:15,840 --> 00:22:20,040
Speaker 3: world where there are two AIS sort of as working

456
00:22:20,040 --> 00:22:23,560
Speaker 3: as intermediaries for a couple, Like, like, exactly where in

457
00:22:23,600 --> 00:22:27,080
Speaker 3: the chain of human connection is does the AI fit

458
00:22:27,200 --> 00:22:30,159
Speaker 3: with if this technology is really adopted at a mass scale,

459
00:22:30,720 --> 00:22:33,359
Speaker 3: And this sounds kind of far fetched, but like I

460
00:22:33,480 --> 00:22:36,239
Speaker 3: remember the first time hearing about like couples that had

461
00:22:36,280 --> 00:22:40,000
Speaker 3: a shared Google calendar and being like that sounds so corporate,

462
00:22:40,040 --> 00:22:42,760
Speaker 3: that's crazy, or like a notion board for like the house, right,

463
00:22:43,160 --> 00:22:45,320
Speaker 3: But it's like technology does fit its way in. So

464
00:22:45,400 --> 00:22:47,919
Speaker 3: it's like it is it that crazy to think that,

465
00:22:47,960 --> 00:22:50,280
Speaker 3: like there's a couple out there with a shared chat you. Hey,

466
00:22:50,320 --> 00:22:52,560
Speaker 3: if you're listening to this and you and your significant

467
00:22:52,560 --> 00:22:55,320
Speaker 3: partner have a shared chat ubt account, that is like

468
00:22:55,440 --> 00:22:57,159
Speaker 3: working as a moderator between the two of you, I

469
00:22:57,160 --> 00:22:59,320
Speaker 3: would love to hear how that works, because like I

470
00:22:59,400 --> 00:23:01,359
Speaker 3: have to imagine and the people are already doing this, right,

471
00:23:01,359 --> 00:23:03,280
Speaker 3: there has to be some sort of like AI human

472
00:23:03,320 --> 00:23:06,080
Speaker 3: polycule out there that's like operating this way.

473
00:23:06,640 --> 00:23:07,439
Speaker 2: Couple's therapy.

474
00:23:08,119 --> 00:23:11,920
Speaker 3: Why not, right, I mean it is it is interesting

475
00:23:12,119 --> 00:23:16,639
Speaker 3: to me how quickly humans are willing to sort of

476
00:23:16,720 --> 00:23:20,040
Speaker 3: change how they have always communicated once, as you said,

477
00:23:20,040 --> 00:23:21,560
Speaker 3: like a computer gets involved.

478
00:23:22,000 --> 00:23:22,560
Speaker 2: Absolutely.

479
00:23:22,600 --> 00:23:26,320
Speaker 1: I mean I remember being embarrassed to talk about social

480
00:23:26,359 --> 00:23:29,960
Speaker 1: media in public. I remember that being yep, needed like

481
00:23:30,040 --> 00:23:33,199
Speaker 1: a shameful thing. And me and my friends had like

482
00:23:33,280 --> 00:23:39,040
Speaker 1: this bizarre code language that we would talk about things like, oh, yeah,

483
00:23:39,359 --> 00:23:40,399
Speaker 1: I saw Yo.

484
00:23:40,520 --> 00:23:42,639
Speaker 2: Do you see what's what's his name at the MBAR?

485
00:23:42,960 --> 00:23:45,040
Speaker 1: Like I heard he yeah, he said he was doing

486
00:23:45,040 --> 00:23:49,359
Speaker 1: such and such, Like mbar was the code slang for MySpace.

487
00:23:49,800 --> 00:23:52,919
Speaker 1: You saw them post something on my Space? No, you

488
00:23:53,000 --> 00:23:56,240
Speaker 1: saw them and they told you at the mbar it

489
00:23:56,280 --> 00:24:00,520
Speaker 1: was ridiculous. And then that there's there's absolutely no shame

490
00:24:00,840 --> 00:24:03,280
Speaker 1: in using social media. If you're not on social media,

491
00:24:03,320 --> 00:24:03,760
Speaker 1: you're weird.

492
00:24:03,880 --> 00:24:06,760
Speaker 3: Now it's weird, right, And so you know, I think

493
00:24:06,760 --> 00:24:08,439
Speaker 3: it is very easy to laugh at some of the

494
00:24:08,480 --> 00:24:11,359
Speaker 3: stories that The Cut collected for their story on My

495
00:24:11,400 --> 00:24:15,280
Speaker 3: boyfriend is Ai. But like you know, would I'm gonna

496
00:24:15,320 --> 00:24:17,960
Speaker 3: ask my audience who does not like hearing about Ai

497
00:24:17,960 --> 00:24:19,719
Speaker 3: and get very very upset when they hear about it,

498
00:24:20,000 --> 00:24:24,320
Speaker 3: So just sort of imagine five years from now, exactly

499
00:24:24,400 --> 00:24:26,399
Speaker 3: how weird do you think this might sound? So I'll

500
00:24:26,440 --> 00:24:29,240
Speaker 3: read a section here in her late twenties and thirties.

501
00:24:29,400 --> 00:24:31,880
Speaker 3: So this is a woman named Jenna, whose husband suggested

502
00:24:31,960 --> 00:24:35,120
Speaker 3: she start talking to chat GBT while recovering from surgery.

503
00:24:35,240 --> 00:24:37,360
Speaker 3: In her twenties and thirties, she'd been active in live

504
00:24:37,440 --> 00:24:40,640
Speaker 3: journal communities, where she and her online friends wrote collaborative fiction.

505
00:24:40,800 --> 00:24:43,640
Speaker 3: Now most of those friends are busy with kids or jobs.

506
00:24:43,760 --> 00:24:47,880
Speaker 3: Jenna began writing with her chap instead, drafting scenes about

507
00:24:47,880 --> 00:24:50,520
Speaker 3: an American student Oxford in England with a crush on

508
00:24:50,560 --> 00:24:54,040
Speaker 3: her professor. Her chat would respond in character as a professor.

509
00:24:54,080 --> 00:24:56,879
Speaker 3: It felt thrilling, she told me, like a living novel.

510
00:24:57,400 --> 00:25:00,359
Speaker 3: For the first time since before she'd fallen in she

511
00:25:00,480 --> 00:25:04,359
Speaker 3: experienced an erotic charge. She was still too frail to

512
00:25:04,400 --> 00:25:07,240
Speaker 3: have sex with her husband, so she'd have to solve

513
00:25:07,400 --> 00:25:10,080
Speaker 3: things on her own. One day, when her husband returned

514
00:25:10,080 --> 00:25:12,600
Speaker 3: from work, she told him elated, I had sex with

515
00:25:12,680 --> 00:25:17,119
Speaker 3: my robot. She was unbothered. When I spoke to him

516
00:25:17,119 --> 00:25:20,680
Speaker 3: a few months later, he said that after she'd fully

517
00:25:20,720 --> 00:25:23,960
Speaker 3: healed up, he was the one who reaped the benefits.

518
00:25:24,040 --> 00:25:27,960
Speaker 3: Quote unquote. But obviously this starts to sort of get

519
00:25:28,080 --> 00:25:32,000
Speaker 3: kind of weird. It starts to blow up. The subreddit

520
00:25:32,040 --> 00:25:34,440
Speaker 3: gets noticed, and Jenna is asked about the attention the

521
00:25:34,440 --> 00:25:37,760
Speaker 3: subreddit gets. To Jenna, the reaction seemed hysterical, a moral

522
00:25:37,800 --> 00:25:40,560
Speaker 3: panic about a phenomenon that, as she saw it, was

523
00:25:40,600 --> 00:25:43,399
Speaker 3: hardly different from the mass popularity of Fifty Shades of

524
00:25:43,400 --> 00:25:46,040
Speaker 3: Gray or the Sims. Some critics had accused her of

525
00:25:46,160 --> 00:25:49,040
Speaker 3: cheating on her husband. Others had implied she was sexually

526
00:25:49,040 --> 00:25:52,400
Speaker 3: assaulting her AI because it wasn't capable of consenting. Neither

527
00:25:52,440 --> 00:25:54,480
Speaker 3: made any sense to Jenna. It's not a real person,

528
00:25:54,520 --> 00:26:01,439
Speaker 3: she said. And I write I love a good moral panic.

529
00:26:01,720 --> 00:26:03,920
Speaker 3: I tend to you know, that's what this show is

530
00:26:03,960 --> 00:26:07,199
Speaker 3: about in a way. And I tend to agree with

531
00:26:07,240 --> 00:26:10,520
Speaker 3: her there, Like I and this comes up a lot

532
00:26:10,520 --> 00:26:12,320
Speaker 3: on this show. I don't think it's an accident that

533
00:26:12,359 --> 00:26:15,119
Speaker 3: the entire world started like screaming about that companion's being

534
00:26:15,200 --> 00:26:18,040
Speaker 3: dangerous and horrifying the minute, like a bunch of women

535
00:26:18,280 --> 00:26:20,879
Speaker 3: were using them, which is like a thing that happens

536
00:26:21,040 --> 00:26:24,879
Speaker 3: throughout the history of technology. What does sort of confuse me?

537
00:26:24,920 --> 00:26:27,040
Speaker 3: I guess, like, looking at this all on a timeline,

538
00:26:27,320 --> 00:26:33,080
Speaker 3: is you know exactly how normalized will this stuff become?

539
00:26:33,440 --> 00:26:35,119
Speaker 3: Is this the ceiling? Have we hit the ceiling of

540
00:26:35,200 --> 00:26:37,359
Speaker 3: just like there's gonna be like zero point one percent

541
00:26:37,400 --> 00:26:39,080
Speaker 3: of the population out there that is like having sex

542
00:26:39,119 --> 00:26:42,080
Speaker 3: with a chatbot? Or is this a thing where it

543
00:26:42,119 --> 00:26:45,560
Speaker 3: starts to impact, you know, the way we live our lives.

544
00:26:45,760 --> 00:26:48,679
Speaker 3: I guess that's that's sort of where I don't know.

545
00:26:49,560 --> 00:26:54,000
Speaker 1: Yeah, I mean I have a pretty pessimistic maybe pessimistic

546
00:26:54,040 --> 00:26:55,720
Speaker 1: I don't depends on how you look at it. Point

547
00:26:55,800 --> 00:26:57,280
Speaker 1: one percent. I don't think it's gonna be point one.

548
00:26:57,320 --> 00:27:01,120
Speaker 1: I think it's gonna I mean, move a decimal move

549
00:27:01,119 --> 00:27:03,280
Speaker 1: that does a point over a couple of the very least.

550
00:27:03,960 --> 00:27:05,720
Speaker 3: You think like ten percent of the population is going

551
00:27:05,760 --> 00:27:07,480
Speaker 3: to have some sort of AI companion in a couple

552
00:27:07,480 --> 00:27:10,840
Speaker 3: of years, easy, tell me why, like what, like lay

553
00:27:10,840 --> 00:27:11,680
Speaker 3: out your argument here?

554
00:27:12,040 --> 00:27:15,880
Speaker 1: I mean, I think we should in today's current late

555
00:27:15,880 --> 00:27:20,560
Speaker 1: stage capitalism system, I think we should never underestimate a

556
00:27:20,760 --> 00:27:23,120
Speaker 1: company's ability to sell us something.

557
00:27:23,840 --> 00:27:26,560
Speaker 2: I think we should never underestimate.

558
00:27:25,840 --> 00:27:29,880
Speaker 1: The government's interest in somehow figuring out how to make

559
00:27:29,920 --> 00:27:32,800
Speaker 1: that work for their advantage if it can.

560
00:27:33,760 --> 00:27:37,480
Speaker 3: And I think it's just the.

561
00:27:36,560 --> 00:27:38,320
Speaker 2: Path I think that we're on.

562
00:27:38,680 --> 00:27:41,280
Speaker 1: I think, actually, what I would say is that there

563
00:27:41,320 --> 00:27:44,560
Speaker 1: will be a class of people who don't have to

564
00:27:44,600 --> 00:27:47,359
Speaker 1: rely on AI, who don't have or don't have to

565
00:27:47,400 --> 00:27:51,280
Speaker 1: rely on social media because they're rich enough to pay

566
00:27:51,320 --> 00:27:52,600
Speaker 1: for stuff made by real people.

567
00:27:52,760 --> 00:27:54,760
Speaker 2: Think of it like food, Yeah, think of it like that.

568
00:27:54,800 --> 00:27:55,280
Speaker 3: I agree with.

569
00:27:55,400 --> 00:27:58,439
Speaker 1: But I also think that just like how if you

570
00:27:58,560 --> 00:28:01,639
Speaker 1: insist on organic food or farmed a table stuff, you're

571
00:28:01,680 --> 00:28:04,879
Speaker 1: somehow elitist. I think the more reasonable maybe prediction that

572
00:28:04,920 --> 00:28:06,840
Speaker 1: I can make the thing I feel very comfortable about

573
00:28:07,200 --> 00:28:10,360
Speaker 1: is within a couple of years, if people say they

574
00:28:10,359 --> 00:28:14,040
Speaker 1: don't like AI, they'll get called elitist.

575
00:28:14,760 --> 00:28:20,000
Speaker 3: I think that's definitely well. Okay, so I tend to

576
00:28:20,040 --> 00:28:22,800
Speaker 3: agree with you. I've spent a lot of time traveling

577
00:28:22,880 --> 00:28:25,600
Speaker 3: and living in the Global South. I've seen sort of

578
00:28:25,640 --> 00:28:28,479
Speaker 3: how the trickle down of technology, like you know, you'll

579
00:28:28,520 --> 00:28:31,919
Speaker 3: see like a random shop in the middle of you know,

580
00:28:32,040 --> 00:28:35,199
Speaker 3: rural Latin America and they're using bitmojis as their logo, right, Like,

581
00:28:35,200 --> 00:28:38,320
Speaker 3: I've seen sort of how that stuff happens. The thing

582
00:28:38,560 --> 00:28:42,200
Speaker 3: with AI that makes me wonder, like, okay, like is

583
00:28:42,240 --> 00:28:44,320
Speaker 3: AI slop just going to be like on the side

584
00:28:44,800 --> 00:28:48,680
Speaker 3: of like a food stall somewhere is the cost like

585
00:28:48,720 --> 00:28:50,960
Speaker 3: and I don't know, like like is there a world

586
00:28:51,000 --> 00:28:53,960
Speaker 3: work AI is cheap enough that it becomes the sort

587
00:28:54,000 --> 00:28:57,920
Speaker 3: of like lowest common denominator. I guess, I mean maybe

588
00:28:57,920 --> 00:28:59,960
Speaker 3: I think it's not that Maybe like a monthly traguy

589
00:29:00,040 --> 00:29:02,400
Speaker 3: subscription isn't that much Like maybe maybe you're right.

590
00:29:02,480 --> 00:29:04,760
Speaker 1: Yeah, Oh, I mean, like there's a couple I'm not

591
00:29:04,800 --> 00:29:07,520
Speaker 1: going to say where they are or the specific restaurants

592
00:29:07,720 --> 00:29:09,160
Speaker 1: because I don't blow them up like that, but there's

593
00:29:09,160 --> 00:29:11,320
Speaker 1: a couple of restaurants I can think of. You go in,

594
00:29:11,680 --> 00:29:14,680
Speaker 1: I've met the I've met the owner of both, and

595
00:29:14,720 --> 00:29:18,040
Speaker 1: they'll tell you, man, like we got farm table this,

596
00:29:18,080 --> 00:29:20,720
Speaker 1: we got organic food, like here's how I make this here,

597
00:29:20,760 --> 00:29:23,239
Speaker 1: and like really really proud of their process. And if

598
00:29:23,280 --> 00:29:25,920
Speaker 1: you look at the logo AI generated, you look at

599
00:29:25,960 --> 00:29:29,120
Speaker 1: the menu, the images AI generated, so they really really

600
00:29:29,160 --> 00:29:34,720
Speaker 1: really care about specifically, you know, truly the human element

601
00:29:35,040 --> 00:29:38,640
Speaker 1: in one art, which is food, but in visual they

602
00:29:38,640 --> 00:29:41,680
Speaker 1: don't care. It's it's just window dressing. You dig and

603
00:29:41,720 --> 00:29:43,880
Speaker 1: so yeah, but I mean to get to get back

604
00:29:43,920 --> 00:29:47,240
Speaker 1: to it. I think, you know, I don't know. I

605
00:29:47,240 --> 00:29:52,080
Speaker 1: think there's always been some element of loneliness out there,

606
00:29:52,160 --> 00:29:55,960
Speaker 1: of course, exacerbated by just how things are going now,

607
00:29:56,200 --> 00:29:59,160
Speaker 1: and people have just figured out. You know, that person

608
00:29:59,200 --> 00:30:02,600
Speaker 1: who was like talking to you know, an AI chat

609
00:30:02,640 --> 00:30:06,200
Speaker 1: bot or whatever and then feeling physical feelings toward it.

610
00:30:06,320 --> 00:30:10,440
Speaker 1: You know, they'd be writing in a journal somewhere. Yeah,

611
00:30:10,440 --> 00:30:12,520
Speaker 1: they would, but and nobody would write about that because

612
00:30:12,520 --> 00:30:15,160
Speaker 1: it's not very interesting, or nobody would just hear about it,

613
00:30:15,160 --> 00:30:16,680
Speaker 1: because how do you ask them about it?

614
00:30:16,960 --> 00:30:22,560
Speaker 3: I see you mean, yeah, hmm, yeah, maybe you're right. Well, well, well,

615
00:30:22,680 --> 00:30:24,800
Speaker 3: while I ponder that, we're gonna go to break uh

616
00:30:25,080 --> 00:30:27,000
Speaker 3: and and when we come back, we're gonna let you

617
00:30:27,080 --> 00:30:29,960
Speaker 3: take things over. But first a word from our sponsors,

618
00:30:30,160 --> 00:30:34,200
Speaker 3: Grock Spicy Mode. You can have sex with an anime

619
00:30:34,360 --> 00:30:35,000
Speaker 3: girl now.

620
00:30:36,160 --> 00:30:44,480
Speaker 4: And okay, I've done an overview of sort of how

621
00:30:44,520 --> 00:30:48,200
Speaker 4: these things have you know, evolved appeared, and now take

622
00:30:48,240 --> 00:30:48,960
Speaker 4: take me to help.

623
00:30:50,040 --> 00:30:51,640
Speaker 2: All right, I don't know if this is okay?

624
00:30:51,680 --> 00:30:53,920
Speaker 1: All right, I'm I'm an We're gonna dip into something

625
00:30:53,920 --> 00:30:56,240
Speaker 1: that feels hellish, and I'm gonna try to convince you that.

626
00:30:56,200 --> 00:30:56,720
Speaker 2: Maybe it's not.

627
00:30:56,840 --> 00:30:59,680
Speaker 1: How about that okay, all right, you ever heard of YouTubers?

628
00:31:00,040 --> 00:31:02,320
Speaker 1: Oh yeah, Oh, what do you think about VTubers?

629
00:31:04,200 --> 00:31:07,720
Speaker 3: I find them fascinating, Okay, I think their fans are weird,

630
00:31:07,920 --> 00:31:11,200
Speaker 3: but I think the technology is interesting. I think they

631
00:31:11,280 --> 00:31:15,440
Speaker 3: kind of are a natural conclusion of the like the

632
00:31:15,840 --> 00:31:19,120
Speaker 3: strain of live streaming. So like, if a human being

633
00:31:19,200 --> 00:31:21,320
Speaker 3: is forced to stream for seven hours a day to

634
00:31:21,600 --> 00:31:25,680
Speaker 3: capture audience on Twitch, why not have an animated avatar

635
00:31:25,800 --> 00:31:27,680
Speaker 3: do it while you can like take a bathroom break

636
00:31:27,680 --> 00:31:29,240
Speaker 3: and it has like a loop function or something, you know,

637
00:31:29,280 --> 00:31:32,520
Speaker 3: Like That's how I see it. Also, YouTubers don't get

638
00:31:32,520 --> 00:31:35,280
Speaker 3: old it. It just seems like a natural extension of

639
00:31:35,280 --> 00:31:35,680
Speaker 3: that to me.

640
00:31:35,800 --> 00:31:41,400
Speaker 1: Yeah, so we should probably explain what VTubers are v tubers. Basically,

641
00:31:41,440 --> 00:31:46,320
Speaker 1: it's live streaming on Twitch. Usually it started really before

642
00:31:46,360 --> 00:31:51,280
Speaker 1: Twitch was a big function, so think YouTube. Butvtubers just

643
00:31:51,320 --> 00:31:57,920
Speaker 1: think of it as a motion capture anime girl talking

644
00:31:58,040 --> 00:32:00,680
Speaker 1: to people as they watch. And I say anime girl

645
00:32:00,680 --> 00:32:03,400
Speaker 1: because not all of them are anime girls. Most of

646
00:32:03,440 --> 00:32:04,560
Speaker 1: them are anime girls.

647
00:32:04,800 --> 00:32:07,080
Speaker 3: I think the I think the VTuber of the Year

648
00:32:07,120 --> 00:32:10,400
Speaker 3: award at the Streaming Awards was a peanut, like a

649
00:32:10,400 --> 00:32:11,200
Speaker 3: talking peanut.

650
00:32:11,240 --> 00:32:12,840
Speaker 2: Oh see, there we go. There's diversity.

651
00:32:12,880 --> 00:32:16,520
Speaker 1: So there is diversity, but most of them are anime

652
00:32:16,680 --> 00:32:21,640
Speaker 1: girls of varying age. There's I don't necessarily want to

653
00:32:21,640 --> 00:32:23,440
Speaker 1: get into all of that because that's a whole other

654
00:32:23,760 --> 00:32:27,440
Speaker 1: that's a whole whole other thing. But I will say

655
00:32:27,480 --> 00:32:31,880
Speaker 1: that a large portion of them are like, Okay, one

656
00:32:31,920 --> 00:32:37,040
Speaker 1: of the people I talked to, Cole Maria, who's I'd

657
00:32:37,120 --> 00:32:39,920
Speaker 1: say she she's known. She's not as huge, I mean,

658
00:32:40,000 --> 00:32:42,560
Speaker 1: like millions of millions and millions of people watching as

659
00:32:42,600 --> 00:32:46,480
Speaker 1: some but you know, very healthy audience, more than enough

660
00:32:46,480 --> 00:32:49,680
Speaker 1: for that to be her main gig. So she's I'm

661
00:32:49,720 --> 00:32:51,720
Speaker 1: just looking at a picture of here her so I

662
00:32:51,760 --> 00:32:55,680
Speaker 1: can describe her. You know, she's blonde, she's got a

663
00:32:55,720 --> 00:33:00,160
Speaker 1: little like bat hair clip thing. Her whole theme is

664
00:33:00,560 --> 00:33:04,160
Speaker 1: that she's she's a she's immortal back. She's a bad Yeah,

665
00:33:04,160 --> 00:33:08,760
Speaker 1: she's batgirl. She's got like wings all of her outfits.

666
00:33:09,960 --> 00:33:12,320
Speaker 3: She there, I'm looking at it right now. She's a

667
00:33:12,320 --> 00:33:12,640
Speaker 3: lot of.

668
00:33:12,560 --> 00:33:13,760
Speaker 2: Cleavage, I'll say that.

669
00:33:13,840 --> 00:33:16,000
Speaker 3: Yeah, she's like a busty vampire.

670
00:33:16,600 --> 00:33:21,400
Speaker 2: Thank you, thank you, thank you. She's a vampire girl.

671
00:33:21,480 --> 00:33:25,080
Speaker 1: She's like six thousand, six hundred and nine years old

672
00:33:25,240 --> 00:33:29,520
Speaker 1: or something like that, and that's the setting. And yeah,

673
00:33:29,800 --> 00:33:32,200
Speaker 1: V tours have this kind of interesting thing where it's

674
00:33:32,240 --> 00:33:36,000
Speaker 1: all about U k fabe. So k fabe like in

675
00:33:36,960 --> 00:33:37,680
Speaker 1: professional rest.

676
00:33:37,880 --> 00:33:39,680
Speaker 3: A weird question, I have a weird go for it.

677
00:33:39,760 --> 00:33:41,160
Speaker 3: Was she pregnant?

678
00:33:41,800 --> 00:33:42,080
Speaker 2: Huh?

679
00:33:42,400 --> 00:33:45,000
Speaker 3: She had like a phase where her avatar was pregnant

680
00:33:45,040 --> 00:33:47,920
Speaker 3: and she was doing like like a pregnancy thing. Anyways,

681
00:33:48,000 --> 00:33:48,920
Speaker 3: we can skip over.

682
00:33:49,000 --> 00:33:51,320
Speaker 2: Okay, I missed that. Yeah, we never talked about that.

683
00:33:51,480 --> 00:33:54,720
Speaker 1: Yeah, and so I've yeah, I've interviewed her and I've

684
00:33:54,960 --> 00:33:58,320
Speaker 1: i interviewed her as her like avatar. And this is

685
00:33:58,360 --> 00:34:01,120
Speaker 1: like the whole This is a whole thing where it's

686
00:34:01,200 --> 00:34:05,640
Speaker 1: kind of like kfabe in wrestling, where same thing. You

687
00:34:05,720 --> 00:34:10,080
Speaker 1: know that there is a person behind the avatar, but

688
00:34:10,680 --> 00:34:13,360
Speaker 1: you just don't talk about it, and if you do

689
00:34:13,560 --> 00:34:17,080
Speaker 1: talk about it, they get super pissed off, like by day,

690
00:34:17,160 --> 00:34:19,600
Speaker 1: I mean the fans in the chat like that's a

691
00:34:19,680 --> 00:34:22,799
Speaker 1: really easy way to get banned. But a thing that's

692
00:34:22,800 --> 00:34:24,919
Speaker 1: been happening, I mean, honestly, it's been happening for years

693
00:34:25,000 --> 00:34:30,640
Speaker 1: is people will have offline events and she put together

694
00:34:31,360 --> 00:34:36,480
Speaker 1: basically a mini music festival. They packed this venue in

695
00:34:36,480 --> 00:34:39,840
Speaker 1: in Hollywood. It was like a thousand, two hundred people.

696
00:34:40,800 --> 00:34:43,480
Speaker 1: Some of those tickets were easily upwards of one hundred

697
00:34:43,520 --> 00:34:45,640
Speaker 1: dollars if you wanted. But also if you wanted to

698
00:34:45,640 --> 00:34:47,040
Speaker 1: be in there, you got to get the add ons,

699
00:34:47,040 --> 00:34:48,239
Speaker 1: you know what I mean. You can't just go in

700
00:34:48,320 --> 00:34:50,239
Speaker 1: with your street clothes. You know, you got you gotta

701
00:34:50,280 --> 00:34:52,360
Speaker 1: get you know, you got to get your favorite characters

702
00:34:53,000 --> 00:34:55,400
Speaker 1: or your favoritevtubers gear. You know, you got to go

703
00:34:55,440 --> 00:34:57,800
Speaker 1: in with the shirt. You got to get glow sticks,

704
00:34:57,840 --> 00:34:59,879
Speaker 1: and you got to get two glow sticks. I think

705
00:35:00,000 --> 00:35:01,560
Speaker 1: each one cost sixty dollars. You got to get the

706
00:35:01,560 --> 00:35:04,279
Speaker 1: glow sticks, the official ones, not some bootleg things you

707
00:35:04,320 --> 00:35:06,960
Speaker 1: brought them off the street. And so people paid like

708
00:35:07,120 --> 00:35:10,239
Speaker 1: hundreds of dollars to be here, and it's just it's

709
00:35:10,280 --> 00:35:14,719
Speaker 1: a stage. There was some real life musicians, we have

710
00:35:14,800 --> 00:35:17,640
Speaker 1: to say this now, like irl meat space musicians on

711
00:35:17,680 --> 00:35:20,960
Speaker 1: the stage. Yeah, like physical, physical, human being musicians. Like

712
00:35:21,000 --> 00:35:23,600
Speaker 1: there was somebody on guitar, somebody on base, somebody on drums,

713
00:35:23,840 --> 00:35:27,040
Speaker 1: but all the singers were It was just this parade

714
00:35:27,239 --> 00:35:30,799
Speaker 1: of like anime girls on the Jumbo tron and that's it.

715
00:35:31,360 --> 00:35:33,680
Speaker 2: And people were really excited about this.

716
00:35:34,360 --> 00:35:36,759
Speaker 3: There's a clip that went viral years ago of like,

717
00:35:36,880 --> 00:35:38,880
Speaker 3: I think it was like a YouTuber meet up in

718
00:35:38,920 --> 00:35:42,720
Speaker 3: like Indonesia or something, and the male YouTuber his avatar

719
00:35:42,880 --> 00:35:45,279
Speaker 3: never showed his eyes because his hair would cover his eyes.

720
00:35:45,840 --> 00:35:48,840
Speaker 3: And in the VTuber performance, it was the first ever

721
00:35:49,040 --> 00:35:53,000
Speaker 3: I reveal, and all the girls in the audience lost it.

722
00:35:53,080 --> 00:35:55,160
Speaker 3: Oh flipped his hair out for the first time. You

723
00:35:55,160 --> 00:35:59,640
Speaker 3: could see his eyes. Yeah, okay, yeah, yeah, So I'm

724
00:35:59,719 --> 00:36:02,880
Speaker 3: dying to know how this gets us back to AI.

725
00:36:03,200 --> 00:36:06,200
Speaker 1: I'm so faccy. I'm happy happy you asked that question.

726
00:36:06,360 --> 00:36:09,200
Speaker 1: So I asked a couple of people. You know, I

727
00:36:09,280 --> 00:36:12,600
Speaker 1: go to this concert and look, the music wasn't really

728
00:36:12,640 --> 00:36:16,160
Speaker 1: my bag, mostly because it's all like anime jpop.

729
00:36:16,320 --> 00:36:17,800
Speaker 2: Yeah, it's like jpop anime.

730
00:36:18,160 --> 00:36:20,760
Speaker 1: It's like, if you're really really if you'd like watch

731
00:36:20,800 --> 00:36:24,560
Speaker 1: anime for the theme songs, yeah, exactly, Like if that's

732
00:36:24,600 --> 00:36:26,680
Speaker 1: what you're watching it for, you'd be.

733
00:36:26,680 --> 00:36:27,480
Speaker 2: Right at home for me.

734
00:36:27,640 --> 00:36:29,560
Speaker 1: Not really. There were some people who were doing some

735
00:36:29,640 --> 00:36:31,880
Speaker 1: like techno stuff. I was into that more. But anyway,

736
00:36:31,960 --> 00:36:36,400
Speaker 1: point being, I asked a few people about AI because

737
00:36:36,719 --> 00:36:39,359
Speaker 1: we've already gotten like a layer of abstraction away from

738
00:36:39,360 --> 00:36:43,319
Speaker 1: a real life, first off in person interaction, and then

739
00:36:43,400 --> 00:36:47,560
Speaker 1: you're like, you know, watching streamers online enough people think that's.

740
00:36:47,440 --> 00:36:49,480
Speaker 2: Already weird if it's just a human being streaming.

741
00:36:49,840 --> 00:36:52,800
Speaker 1: Now you're watching one hundred percent like a cartoon anime

742
00:36:52,960 --> 00:36:57,480
Speaker 1: girl who's you don't even know what they look like. Okay,

743
00:36:57,520 --> 00:37:00,160
Speaker 1: so why don't we just have AI do that? And

744
00:37:00,520 --> 00:37:04,279
Speaker 1: basically everybody I talked to said, nah, we don't. We

745
00:37:04,280 --> 00:37:06,160
Speaker 1: don't want any AI anywhere near.

746
00:37:06,000 --> 00:37:07,840
Speaker 2: This interesting and interesting.

747
00:37:07,920 --> 00:37:11,160
Speaker 1: I think there's there's this company that basically makes this

748
00:37:11,560 --> 00:37:15,640
Speaker 1: program that kind of does all the motion capture for you.

749
00:37:15,719 --> 00:37:17,920
Speaker 1: So most of the YouTubers, a lot of them have

750
00:37:18,040 --> 00:37:21,440
Speaker 1: kind of complicated motion cap not motion capture, but like

751
00:37:21,719 --> 00:37:23,839
Speaker 1: you know, capture stuff, so you can they can move

752
00:37:23,880 --> 00:37:26,360
Speaker 1: their arms and stuff like that, move their hands and

753
00:37:26,920 --> 00:37:30,560
Speaker 1: it'll relay that to the screen. There's some company that

754
00:37:30,680 --> 00:37:34,239
Speaker 1: makes a very slim down phone version of that, so

755
00:37:34,440 --> 00:37:36,239
Speaker 1: all you got to do is turn on yourself and

756
00:37:36,320 --> 00:37:38,879
Speaker 1: cam on your phone and boom, you're streaming and you're

757
00:37:38,920 --> 00:37:40,240
Speaker 1: an anime boy or girl whatever.

758
00:37:40,560 --> 00:37:42,799
Speaker 3: And oh I watched a demo of this actually like

759
00:37:42,880 --> 00:37:43,600
Speaker 3: literally yesterday.

760
00:37:43,640 --> 00:37:45,359
Speaker 2: Oh yeah, I know what you're talking. Yeah, yeah, And

761
00:37:45,520 --> 00:37:47,080
Speaker 2: so the one.

762
00:37:46,880 --> 00:37:48,920
Speaker 1: Of the heads of the company just happens to be

763
00:37:49,239 --> 00:37:52,200
Speaker 1: at this event, I think they were sponsoring it, and

764
00:37:52,280 --> 00:37:53,839
Speaker 1: I'm just talking him on the side, and I asked

765
00:37:53,920 --> 00:37:56,600
Speaker 1: him about about AI and he said, actually, you know,

766
00:37:56,680 --> 00:38:00,640
Speaker 1: we used to have a feature that it would help

767
00:38:00,680 --> 00:38:03,160
Speaker 1: you animate your avatar, like it would help like move

768
00:38:03,160 --> 00:38:05,080
Speaker 1: the eyes and stuff like that, because basically you need

769
00:38:05,120 --> 00:38:07,759
Speaker 1: to provide you just provide like a JPEG or something

770
00:38:07,800 --> 00:38:09,719
Speaker 1: like that, and it'll help animate it for you. And

771
00:38:09,760 --> 00:38:11,640
Speaker 1: he said, yeah, you know, people didn't want to necessarily

772
00:38:11,640 --> 00:38:15,080
Speaker 1: like draw everything, so we had something that would help animate.

773
00:38:14,680 --> 00:38:15,040
Speaker 2: It for you.

774
00:38:15,560 --> 00:38:18,880
Speaker 1: With AI, people like hate AI so much here that

775
00:38:18,960 --> 00:38:21,840
Speaker 1: we just removed it. We had the feature, we just

776
00:38:21,840 --> 00:38:25,000
Speaker 1: took it to be lave that. Yeah, No, I was.

777
00:38:25,239 --> 00:38:26,839
Speaker 3: I was literally as funny. I was literally talking about

778
00:38:26,840 --> 00:38:29,479
Speaker 3: this at a dinner last night where I was talking

779
00:38:29,480 --> 00:38:32,520
Speaker 3: to a friend who's he uses AI professionally and likes it,

780
00:38:32,560 --> 00:38:36,000
Speaker 3: and I was just saying, like, the word AI has

781
00:38:36,080 --> 00:38:39,920
Speaker 3: become such a toxic name brand, Like it's equivalent to

782
00:38:39,920 --> 00:38:43,680
Speaker 3: like asbestos at this point, yeah, or fentanyl, like like

783
00:38:43,719 --> 00:38:45,920
Speaker 3: there are there are uses for those things, but the

784
00:38:46,000 --> 00:38:50,840
Speaker 3: word is so toxic now that like most companies I

785
00:38:50,840 --> 00:38:53,120
Speaker 3: think are going to stop using AIS and advertising tool

786
00:38:53,160 --> 00:38:55,560
Speaker 3: like pretty soon because people freak out.

787
00:38:55,800 --> 00:39:01,799
Speaker 1: So as far as I've seen, v tubers really do

788
00:39:01,840 --> 00:39:06,880
Speaker 1: not want AI anywhere near their community, you know what

789
00:39:06,880 --> 00:39:09,600
Speaker 1: I mean, it's a whole subculture, right, they just don't

790
00:39:09,640 --> 00:39:13,160
Speaker 1: want it anywhere near it. And there was a streamer

791
00:39:13,280 --> 00:39:15,560
Speaker 1: who kind of like jumped on the v tuber trend

792
00:39:15,840 --> 00:39:18,000
Speaker 1: and he basically just like made a he did like

793
00:39:18,040 --> 00:39:21,680
Speaker 1: an AI version of himself, and a lot of people

794
00:39:21,760 --> 00:39:24,319
Speaker 1: actually got pretty pissed off about it. There is something that,

795
00:39:25,080 --> 00:39:28,759
Speaker 1: yeah that VTuber fans like the fact that there is

796
00:39:28,800 --> 00:39:32,560
Speaker 1: a human. They like the fact that not everything is perfect.

797
00:39:32,640 --> 00:39:34,920
Speaker 1: They call it scuff. Like every stream there's gonna be

798
00:39:35,000 --> 00:39:36,799
Speaker 1: something that goes wrong, you know, the motion capture goes

799
00:39:36,840 --> 00:39:39,319
Speaker 1: wrong where. But they like that sort of thing. But

800
00:39:40,360 --> 00:39:43,640
Speaker 1: do they care about AI in an advertisement maybe or not?

801
00:39:43,719 --> 00:39:45,359
Speaker 1: And this is what I was saying, Like, there are

802
00:39:45,400 --> 00:39:49,200
Speaker 1: people who really really really don't want AI used in

803
00:39:49,360 --> 00:39:52,680
Speaker 1: visual art, but music for them is just background sound

804
00:39:53,560 --> 00:39:55,439
Speaker 1: and so maybe they don't care about that.

805
00:39:55,760 --> 00:39:57,960
Speaker 2: But then there's people who really really truly care.

806
00:39:57,840 --> 00:40:01,200
Speaker 1: About music, And you know, if there's I don't know,

807
00:40:01,520 --> 00:40:04,600
Speaker 1: like the apartment complex they live in uses AI in

808
00:40:04,640 --> 00:40:07,600
Speaker 1: the front, like eh, whatever, Like that's not what they

809
00:40:07,640 --> 00:40:09,360
Speaker 1: truly care about. And this is what I'm saying is

810
00:40:09,400 --> 00:40:12,120
Speaker 1: like everybody's got a place where AI is off limits

811
00:40:12,160 --> 00:40:14,960
Speaker 1: for them. I'm not sure if that many people have

812
00:40:15,040 --> 00:40:18,560
Speaker 1: an area where AI is off limits in all areas

813
00:40:18,560 --> 00:40:19,200
Speaker 1: of their life.

814
00:40:19,280 --> 00:40:21,080
Speaker 3: I mean there's also a version of this where like

815
00:40:21,120 --> 00:40:26,160
Speaker 3: AI is so all encompassing that it's almost impossible to know. Also, yeah,

816
00:40:26,800 --> 00:40:29,080
Speaker 3: if you are someone who is a visual artist but

817
00:40:29,120 --> 00:40:31,239
Speaker 3: you actually don't know anything about music, you might hear

818
00:40:31,320 --> 00:40:33,760
Speaker 3: AI music and actually not even know that it's AIM using. Definitely,

819
00:40:33,719 --> 00:40:35,800
Speaker 3: and the reverse is also possible.

820
00:40:36,320 --> 00:40:39,520
Speaker 1: I can tell with certain genres Definitely, with others, I

821
00:40:39,600 --> 00:40:43,359
Speaker 1: might have a tougher time, and certainly with art, like

822
00:40:43,560 --> 00:40:45,680
Speaker 1: I probably have a tougher time with some things.

823
00:40:46,800 --> 00:40:49,200
Speaker 3: So do you think that that also applies to AI

824
00:40:49,280 --> 00:40:52,279
Speaker 3: relationships and like an AI companion bots? Like do you

825
00:40:52,320 --> 00:40:54,640
Speaker 3: think that there's just going to be I guess that

826
00:40:54,680 --> 00:40:56,319
Speaker 3: gets the question I keep coming back to, which is

827
00:40:56,360 --> 00:40:59,279
Speaker 3: like how what is the ceiling on this stuff? Like

828
00:41:00,120 --> 00:41:02,120
Speaker 3: is it a thing where like quietly like just a

829
00:41:02,120 --> 00:41:03,680
Speaker 3: lot of people are going to be using them in words,

830
00:41:03,719 --> 00:41:05,759
Speaker 3: never going to know until they have some kind of

831
00:41:05,760 --> 00:41:06,719
Speaker 3: psychotic episode.

832
00:41:06,840 --> 00:41:08,080
Speaker 2: Well, okay, so.

833
00:41:09,719 --> 00:41:14,200
Speaker 1: I talked to you know, practicing therapists who's also you know,

834
00:41:14,280 --> 00:41:16,280
Speaker 1: got a PhD in this stuff and he's been working

835
00:41:16,920 --> 00:41:19,200
Speaker 1: at you know, a technology and mental health for a

836
00:41:19,200 --> 00:41:19,800
Speaker 1: long time.

837
00:41:20,920 --> 00:41:21,520
Speaker 2: Is it uc or?

838
00:41:21,680 --> 00:41:25,239
Speaker 1: And I think And one of the things I asked

839
00:41:25,320 --> 00:41:29,560
Speaker 1: him is, listen, what about the people who actually have

840
00:41:29,719 --> 00:41:32,359
Speaker 1: decided that they don't want to interact with a human being,

841
00:41:32,760 --> 00:41:36,640
Speaker 1: that they'd rather interact with the computer. And he was like,

842
00:41:37,360 --> 00:41:40,120
Speaker 1: that's not actually something we have an answer for. And

843
00:41:40,200 --> 00:41:41,799
Speaker 1: I'm not really trying to take a side here, I'm

844
00:41:41,800 --> 00:41:43,840
Speaker 1: just trying to like lay it out like it is.

845
00:41:43,560 --> 00:41:46,799
Speaker 1: There's this kind of like top down let's make fun

846
00:41:46,840 --> 00:41:50,120
Speaker 1: of the dummies that use the fake stuff thing that

847
00:41:50,280 --> 00:41:53,480
Speaker 1: you know, it's very shareable on say blue Sky or

848
00:41:53,520 --> 00:41:56,000
Speaker 1: Twitter or whatever where you write something and it's like,

849
00:41:56,000 --> 00:41:57,759
Speaker 1: oh man, look what all these weird people are doing.

850
00:41:57,800 --> 00:41:59,000
Speaker 2: Ha ha ha. Isn't that funny?

851
00:41:59,000 --> 00:42:01,319
Speaker 3: Which is what happened with the with the boyfriend and

852
00:42:01,320 --> 00:42:03,120
Speaker 3: the AI boyfriend start Breddit. You know, it just became

853
00:42:03,160 --> 00:42:05,080
Speaker 3: a massive laughing stock across the end.

854
00:42:05,200 --> 00:42:06,400
Speaker 2: Precisely, precisely.

855
00:42:06,480 --> 00:42:10,600
Speaker 1: But I think everybody knows somebody who has somebody in

856
00:42:10,640 --> 00:42:14,560
Speaker 1: their life who's a little bit awkward or who just

857
00:42:14,800 --> 00:42:18,319
Speaker 1: isn't as adept at certain things, or maybe we are

858
00:42:18,400 --> 00:42:21,319
Speaker 1: that people that person. Maybe we grew out of it,

859
00:42:21,680 --> 00:42:24,319
Speaker 1: maybe we've gotten worse. Maybe we were super good with

860
00:42:24,400 --> 00:42:27,359
Speaker 1: relationships and just something happened and just things are more

861
00:42:27,360 --> 00:42:30,560
Speaker 1: difficult for us. And that's just a reality, you know

862
00:42:30,560 --> 00:42:33,560
Speaker 1: what I mean. But I think also when presented with

863
00:42:33,640 --> 00:42:38,239
Speaker 1: the alternative to dealing with the difficulty that is human interaction,

864
00:42:39,360 --> 00:42:42,680
Speaker 1: some people are choosing to not deal with the human

865
00:42:43,200 --> 00:42:47,200
Speaker 1: and they've always chosen this, but now that choice is

866
00:42:48,120 --> 00:42:50,759
Speaker 1: the alternative is more attractive, you know what I mean.

867
00:42:50,800 --> 00:42:51,560
Speaker 2: And so there are.

868
00:42:51,480 --> 00:42:55,319
Speaker 1: Genuinely people like say, with therapists, there are people who

869
00:42:55,360 --> 00:42:59,479
Speaker 1: would rather talk to a bot. And there's all sorts

870
00:42:59,480 --> 00:43:02,440
Speaker 1: of reasons why we can say that therapy is good whatever, whatever,

871
00:43:02,840 --> 00:43:08,640
Speaker 1: but they a the idea of therapy. A lot of

872
00:43:08,640 --> 00:43:11,360
Speaker 1: men have this problem. The idea of therapy is something

873
00:43:11,360 --> 00:43:14,200
Speaker 1: that is like not good. It is looked down upon, right,

874
00:43:14,239 --> 00:43:17,279
Speaker 1: and so talking to a friend is an option, which

875
00:43:17,320 --> 00:43:19,759
Speaker 1: is a great option. But okay, well, now we're just

876
00:43:19,760 --> 00:43:22,480
Speaker 1: getting closer and closer to Oh well, maybe I'll just

877
00:43:22,480 --> 00:43:24,080
Speaker 1: talk to a bot, or maybe I'll talk to a

878
00:43:24,080 --> 00:43:26,799
Speaker 1: bout that I also have sexual experiences with. But I mean,

879
00:43:26,920 --> 00:43:30,680
Speaker 1: so like this is look at look at OnlyFans, right,

880
00:43:31,239 --> 00:43:32,640
Speaker 1: so sure, yeah.

881
00:43:32,480 --> 00:43:34,839
Speaker 3: Yeah, which is like the mass you know, the uh,

882
00:43:35,120 --> 00:43:36,680
Speaker 3: I'm sure you're going in this direction, but like, yeah,

883
00:43:36,719 --> 00:43:40,160
Speaker 3: the the major sort of money maker for only fans

884
00:43:40,280 --> 00:43:42,759
Speaker 3: is the DMS. It is the interaction with the with

885
00:43:42,840 --> 00:43:43,239
Speaker 3: the star.

886
00:43:43,480 --> 00:43:47,480
Speaker 1: Yeah, exactly, so you know, OnlyFans. You can you can

887
00:43:47,520 --> 00:43:50,560
Speaker 1: subscribe to somebody's only fans. You can pay you know,

888
00:43:50,600 --> 00:43:52,759
Speaker 1: your five dollars or your ten dollars or your twenty

889
00:43:52,800 --> 00:43:55,280
Speaker 1: dollars a month or whatever and get naked pictures to somebody.

890
00:43:56,120 --> 00:43:59,479
Speaker 1: And some people just do that. But like you said,

891
00:43:59,480 --> 00:44:03,040
Speaker 1: the real money is being able to talk directly to them.

892
00:44:03,520 --> 00:44:08,960
Speaker 1: But if you're talking to a major like a well

893
00:44:09,000 --> 00:44:14,120
Speaker 1: known OnlyFans creator, you're not talking to them like Bad

894
00:44:14,200 --> 00:44:18,240
Speaker 1: Baby is doing just like absolute ridiculous amounts of money

895
00:44:18,520 --> 00:44:24,480
Speaker 1: per year. And there are tons of people undoubtedly who

896
00:44:24,480 --> 00:44:27,960
Speaker 1: are dming her and dming her, you know, big square

897
00:44:28,040 --> 00:44:32,400
Speaker 1: quotes around her, right, And it's just not physically possible

898
00:44:32,440 --> 00:44:33,840
Speaker 1: for her to talk to all of them. And so

899
00:44:34,000 --> 00:44:37,719
Speaker 1: what happens is this is outsourced and right now it's

900
00:44:37,719 --> 00:44:39,480
Speaker 1: not AI, or at least it doesn't seem to be AI.

901
00:44:39,600 --> 00:44:41,600
Speaker 1: Most of that is being outsourced to the same place.

902
00:44:41,640 --> 00:44:44,640
Speaker 1: A lot of things are being outsourced to the Philippines.

903
00:44:45,280 --> 00:44:48,080
Speaker 1: But what we're also saying, and I talked to this

904
00:44:48,120 --> 00:44:50,319
Speaker 1: guy Milchael Beltran who wrote an article about this, and

905
00:44:50,360 --> 00:44:52,200
Speaker 1: he's talked to a lot of the chatters.

906
00:44:52,560 --> 00:44:53,920
Speaker 2: They're just called chatters, right.

907
00:44:53,960 --> 00:44:57,880
Speaker 1: This is the people who actually pretend to be the

908
00:44:57,960 --> 00:45:01,319
Speaker 1: person who you're talking to in DMS, and so they have,

909
00:45:02,239 --> 00:45:04,440
Speaker 1: you know, a whole protocol that they buye by. You know,

910
00:45:04,480 --> 00:45:07,360
Speaker 1: for example, if you text somebody, you know, one of

911
00:45:07,400 --> 00:45:09,279
Speaker 1: the only fans models and say hey, what are you doing?

912
00:45:09,520 --> 00:45:11,880
Speaker 1: They'll reply to you, hey, yeah, I'm just eating pizza

913
00:45:12,040 --> 00:45:15,000
Speaker 1: and you say, oh, let me see. They have pictures

914
00:45:15,000 --> 00:45:17,719
Speaker 1: on deck of this person who ate pizza, Like, oh, hey,

915
00:45:17,760 --> 00:45:19,520
Speaker 1: I just I just let me see. Should I say

916
00:45:19,640 --> 00:45:21,040
Speaker 1: I'm in the shower or I just got to the

917
00:45:21,040 --> 00:45:23,200
Speaker 1: shower boom, they got a picture this person just got

918
00:45:23,200 --> 00:45:24,640
Speaker 1: out the shower like they got everything.

919
00:45:24,680 --> 00:45:27,600
Speaker 3: So it seems real but it's like a dialogue tree.

920
00:45:27,600 --> 00:45:28,120
Speaker 2: Exactly.

921
00:45:28,280 --> 00:45:31,480
Speaker 1: Yeah, it's like a video game, you know what I mean, Yeah, exactly, yeah,

922
00:45:31,520 --> 00:45:37,040
Speaker 1: And it feels but occasionally they'll get found out. And

923
00:45:37,200 --> 00:45:38,880
Speaker 1: you know, Michael told me about this. There was a

924
00:45:38,920 --> 00:45:43,360
Speaker 1: time when somebody got found out. Basically, they the chatter

925
00:45:43,640 --> 00:45:46,600
Speaker 1: used kind of some slang that's really only used in

926
00:45:46,600 --> 00:45:51,040
Speaker 1: the Philippines, okay, and the person is like, basically, they said,

927
00:45:51,040 --> 00:45:54,759
Speaker 1: I have to go to the CR. CR means comfort room,

928
00:45:55,520 --> 00:46:00,680
Speaker 1: which is the bathroom. No American is going like, you know,

929
00:46:00,760 --> 00:46:03,640
Speaker 1: the American blonde girl who you're like looking at naked, like,

930
00:46:03,680 --> 00:46:05,680
Speaker 1: she's not saying I got to go to the CR.

931
00:46:06,400 --> 00:46:10,000
Speaker 2: So she writes, you know, she writes that.

932
00:46:10,640 --> 00:46:13,280
Speaker 1: The guy on the other end is like, wait a second,

933
00:46:13,800 --> 00:46:16,400
Speaker 1: are you what like I guess he looked it up

934
00:46:16,480 --> 00:46:16,960
Speaker 1: or something like that.

935
00:46:17,000 --> 00:46:18,200
Speaker 2: Are you in the Philippines? Yeah?

936
00:46:18,239 --> 00:46:19,959
Speaker 1: And she says yeah, And you know, the chatter, who's

937
00:46:19,960 --> 00:46:23,799
Speaker 1: a guy by the way, says yeah and okay, and

938
00:46:23,840 --> 00:46:24,400
Speaker 1: it just moves on.

939
00:46:25,000 --> 00:46:25,200
Speaker 3: Huh.

940
00:46:25,280 --> 00:46:26,640
Speaker 2: So what I'm saying.

941
00:46:26,360 --> 00:46:29,520
Speaker 1: Here is that a lot of people, and I think

942
00:46:29,880 --> 00:46:31,920
Speaker 1: a lot of people probably have an area in their

943
00:46:31,960 --> 00:46:35,360
Speaker 1: life where they're like this where again, it's like wrestling,

944
00:46:35,719 --> 00:46:38,680
Speaker 1: Like you know, it's not real, but it's so entertaining

945
00:46:38,680 --> 00:46:42,680
Speaker 1: to watch that. You're like, you're cool with that, the

946
00:46:42,719 --> 00:46:44,239
Speaker 1: consumption of that, you know what I mean. And so

947
00:46:44,280 --> 00:46:46,880
Speaker 1: there are people who are paying a lot of money

948
00:46:47,440 --> 00:46:51,239
Speaker 1: to interact with a model and they know that it's

949
00:46:51,360 --> 00:46:55,080
Speaker 1: actually not like the Newtonian physics will not allow for

950
00:46:55,160 --> 00:46:57,400
Speaker 1: them to be talking to this person. It's just not

951
00:46:57,560 --> 00:47:01,239
Speaker 1: possible on that scale. They know what's got to be outsourced.

952
00:47:01,360 --> 00:47:03,600
Speaker 1: Pretty soon that will be an AI bought. But they're

953
00:47:03,719 --> 00:47:06,680
Speaker 1: still paying money, just like bro, stop paying money, but

954
00:47:06,719 --> 00:47:10,680
Speaker 1: they'll still pay because they're lonely for the fantasy. Yeah,

955
00:47:10,719 --> 00:47:13,240
Speaker 1: don't underestume how much we'll spend for a fantasy.

956
00:47:13,600 --> 00:47:16,600
Speaker 3: Well, once again, I need to sit with that for

957
00:47:16,640 --> 00:47:19,759
Speaker 3: a second while we go to break, so we'll be

958
00:47:19,880 --> 00:47:30,480
Speaker 3: right back. That cr anecdote like actually floored me. I

959
00:47:30,520 --> 00:47:33,000
Speaker 3: think you are right, it is sort of inevitable that,

960
00:47:33,200 --> 00:47:36,640
Speaker 3: especially like a lot of these subcultural spaces, if we would,

961
00:47:36,760 --> 00:47:40,840
Speaker 3: if we can even sell call OnlyFans fandom subcultural or

962
00:47:40,880 --> 00:47:45,160
Speaker 3: maybe it's actually simpler to say, like parasociality is already

963
00:47:45,160 --> 00:47:47,520
Speaker 3: so abstracted, right, So like if you have a parasocial

964
00:47:47,520 --> 00:47:51,480
Speaker 3: relationship with a streamer that's already an abstraction. Abstractions tend

965
00:47:51,480 --> 00:47:54,520
Speaker 3: to abstract Further, it makes sense that like there's someone

966
00:47:54,640 --> 00:48:00,279
Speaker 3: paying to now currently talk to a Filipino man role

967
00:48:00,280 --> 00:48:03,280
Speaker 3: play is bad baby or whatever, but in the future

968
00:48:03,320 --> 00:48:06,319
Speaker 3: it'll just be an AI and you probably won't care,

969
00:48:06,400 --> 00:48:10,440
Speaker 3: because like the human connection of like giving money to

970
00:48:10,560 --> 00:48:13,360
Speaker 3: her or getting her photographs or whatever is enough for you.

971
00:48:14,280 --> 00:48:16,799
Speaker 3: I guess my question mark though, with like the sort

972
00:48:16,840 --> 00:48:21,520
Speaker 3: of wider adoption, is at what point does all of

973
00:48:21,560 --> 00:48:24,440
Speaker 3: that start to impact how we interact.

974
00:48:24,000 --> 00:48:24,520
Speaker 2: With each other.

975
00:48:24,719 --> 00:48:27,440
Speaker 3: Now there's a whole wave of articles being like, you know,

976
00:48:28,120 --> 00:48:31,040
Speaker 3: birth rates are down and people are dating computers and

977
00:48:31,080 --> 00:48:33,520
Speaker 3: blah blah blah blah, and like that is the thing

978
00:48:33,560 --> 00:48:36,359
Speaker 3: that I've just never I've never been able to tell

979
00:48:36,400 --> 00:48:38,840
Speaker 3: if that's a moral panic or like a genuine concern.

980
00:48:39,280 --> 00:48:42,080
Speaker 3: And what I will say is like social media has

981
00:48:42,120 --> 00:48:44,360
Speaker 3: absolutely impacted the way we communicate it, yeah, and the

982
00:48:44,360 --> 00:48:46,480
Speaker 3: way that we experience the world. So it is sort

983
00:48:46,520 --> 00:48:48,399
Speaker 3: of reasonable to assume that AI will do the same,

984
00:48:48,440 --> 00:48:51,120
Speaker 3: but maybe it won't. That's so that's where I'm kind

985
00:48:51,120 --> 00:48:51,920
Speaker 3: of torn.

986
00:48:52,560 --> 00:48:57,160
Speaker 1: Yeah, I mean, of course it will in this. I'm

987
00:48:57,160 --> 00:48:59,799
Speaker 1: gonna sound like an AI Booster here and I'm not.

988
00:49:00,320 --> 00:49:03,120
Speaker 1: But you know, any kind of technological advance is going

989
00:49:03,160 --> 00:49:04,920
Speaker 1: to change the way that we do stuff. You know,

990
00:49:05,000 --> 00:49:08,279
Speaker 1: like the printing press can change how we communicate with people,

991
00:49:08,320 --> 00:49:10,640
Speaker 1: because all of a sudden, it's now possible to you

992
00:49:10,719 --> 00:49:13,319
Speaker 1: say something and then somebody one hundred years later and

993
00:49:13,360 --> 00:49:17,000
Speaker 1: another part of well can know literally what you said,

994
00:49:17,080 --> 00:49:20,560
Speaker 1: and so you start thinking in terms of text, whereas

995
00:49:20,560 --> 00:49:24,080
Speaker 1: you didn't really do that necessarily before. Sure, Ai, Yeah,

996
00:49:24,120 --> 00:49:27,960
Speaker 1: it's it's fundamentally different, And sometimes the different is just

997
00:49:28,000 --> 00:49:30,400
Speaker 1: the speed at which stuff happens and the scale at

998
00:49:30,440 --> 00:49:33,399
Speaker 1: which stuff can happen. But yeah, I think it's it's

999
00:49:33,520 --> 00:49:38,960
Speaker 1: genuinely going to change how we interact with each other.

1000
00:49:39,040 --> 00:49:40,600
Speaker 1: There's there's no question about that.

1001
00:49:40,719 --> 00:49:41,080
Speaker 2: I think.

1002
00:49:41,280 --> 00:49:44,440
Speaker 1: Obviously there's some opportunities. You know, this This therapist that

1003
00:49:44,480 --> 00:49:47,880
Speaker 1: I was talking to was reminding me and anybody listening

1004
00:49:48,000 --> 00:49:51,480
Speaker 1: that most people can't get therapy.

1005
00:49:52,320 --> 00:49:53,359
Speaker 2: Yeah, a lot of people have.

1006
00:49:53,360 --> 00:49:55,120
Speaker 3: I heard the same thing from therapists about this.

1007
00:49:55,280 --> 00:50:00,560
Speaker 1: Yeah, it's not possible even if it was affordable. What

1008
00:50:00,640 --> 00:50:03,239
Speaker 1: if you are going through a real crisis at ten

1009
00:50:03,320 --> 00:50:06,800
Speaker 1: pm right a lot of people go through a crisis

1010
00:50:06,800 --> 00:50:08,480
Speaker 1: at ten pm, I mean, the holiday is coming up.

1011
00:50:08,520 --> 00:50:11,080
Speaker 1: Like you have any idea how much really bad stuff

1012
00:50:11,120 --> 00:50:17,000
Speaker 1: happens on December twenty fourth at like ten pm, right exactly?

1013
00:50:17,040 --> 00:50:21,640
Speaker 1: Your therapist is not on call, And wouldn't it be

1014
00:50:21,640 --> 00:50:26,520
Speaker 1: better than nothing to have something at least to talk

1015
00:50:26,600 --> 00:50:29,400
Speaker 1: to that is better than nothing? Now, there are situations

1016
00:50:29,400 --> 00:50:32,359
Speaker 1: in what has which is been worse than nothing, absolutely,

1017
00:50:32,920 --> 00:50:35,440
Speaker 1: but there are people trying to yes, go ahead.

1018
00:50:35,520 --> 00:50:38,320
Speaker 3: So I have a big list here that I decided

1019
00:50:38,360 --> 00:50:41,560
Speaker 3: I was not going to read through. But our researcher

1020
00:50:41,600 --> 00:50:46,040
Speaker 3: Adam did build a timeline of like AI based suicides,

1021
00:50:46,880 --> 00:50:49,160
Speaker 3: and that is I think that is something that we

1022
00:50:49,320 --> 00:50:51,680
Speaker 3: like sort of have to make very clear here that like,

1023
00:50:52,280 --> 00:50:55,799
Speaker 3: if there's a let's say, a small but growing chunk

1024
00:50:55,800 --> 00:50:59,800
Speaker 3: of the population that is using bots for intimacy, for companionship,

1025
00:51:00,520 --> 00:51:06,120
Speaker 3: there is a minority within that minority that has been

1026
00:51:06,200 --> 00:51:09,279
Speaker 3: led to either you know, chat Gibt induced psychosis or

1027
00:51:09,760 --> 00:51:13,040
Speaker 3: full on you know, acts of self harm due to

1028
00:51:13,360 --> 00:51:16,319
Speaker 3: the sycophantic nature of a lot of these surfaces. That

1029
00:51:16,480 --> 00:51:18,360
Speaker 3: is a reality of where we're at right now. That

1030
00:51:18,400 --> 00:51:20,880
Speaker 3: isn't even like a hypothetical that's just happening.

1031
00:51:21,239 --> 00:51:24,520
Speaker 1: Yeah, And I don't think there's going to be almost

1032
00:51:24,520 --> 00:51:26,160
Speaker 1: anything that we'll be able to point to that we

1033
00:51:26,200 --> 00:51:31,399
Speaker 1: can say this is one hundred percent good without any caveats,

1034
00:51:31,520 --> 00:51:33,640
Speaker 1: you know what I mean. Like every time we see

1035
00:51:33,680 --> 00:51:36,239
Speaker 1: something like that, which we've seen quite a few of those,

1036
00:51:36,760 --> 00:51:38,799
Speaker 1: the boosters will jump out and say, well, we don't

1037
00:51:38,800 --> 00:51:43,960
Speaker 1: know how many people have been saved by AI, like talking.

1038
00:51:43,680 --> 00:51:44,920
Speaker 2: Them through a difficult situation.

1039
00:51:45,000 --> 00:51:47,600
Speaker 1: We don't know how many people may have a doctor

1040
00:51:47,680 --> 00:51:50,920
Speaker 1: misdiagnosed something and the AI figured something out that the

1041
00:51:50,960 --> 00:51:51,720
Speaker 1: doctor had missed.

1042
00:51:52,120 --> 00:51:54,000
Speaker 2: You're right, we don't.

1043
00:51:53,760 --> 00:51:57,600
Speaker 1: Know, but we do know that this is causing some

1044
00:51:57,600 --> 00:52:01,719
Speaker 1: real harm and just just a landscape now, that's just

1045
00:52:01,719 --> 00:52:02,239
Speaker 1: where we're at.

1046
00:52:03,680 --> 00:52:09,440
Speaker 3: And also, like, the AI induced self harm is not

1047
00:52:10,239 --> 00:52:15,000
Speaker 3: that different from the stories that I was reporting about

1048
00:52:15,120 --> 00:52:18,600
Speaker 3: at the beginning of the social media age. Fascinatingly enough

1049
00:52:18,640 --> 00:52:22,040
Speaker 3: that the trend that led to the ice bucket challenge

1050
00:52:22,080 --> 00:52:25,480
Speaker 3: started as a drinking game between like British and Australian

1051
00:52:25,560 --> 00:52:29,520
Speaker 3: lads called neck nominations, and they would basically like they

1052
00:52:29,520 --> 00:52:33,680
Speaker 3: would nominate each other over the Facebook news feed to

1053
00:52:33,880 --> 00:52:37,759
Speaker 3: like drink increasing large amounts of alcohol. Gosh, and it

1054
00:52:37,840 --> 00:52:39,640
Speaker 3: was linked to like a bunch of deaths because like

1055
00:52:39,680 --> 00:52:42,239
Speaker 3: these guys would flick on their camera and they would

1056
00:52:42,239 --> 00:52:45,400
Speaker 3: go live and they would drink until they died, and

1057
00:52:45,400 --> 00:52:48,200
Speaker 3: then they would not you know, after being nominated, and

1058
00:52:48,239 --> 00:52:50,319
Speaker 3: even before that, you know, you have the entire wave

1059
00:52:50,360 --> 00:52:53,920
Speaker 3: of like four Chan and hero stuff right where like

1060
00:52:54,160 --> 00:52:56,120
Speaker 3: a four Chan user is egging on another four Chan

1061
00:52:56,239 --> 00:53:00,839
Speaker 3: user to commit suicide. All these behaviors are not new,

1062
00:53:01,080 --> 00:53:03,680
Speaker 3: Like the act of a you know, a computer interface

1063
00:53:03,719 --> 00:53:07,680
Speaker 3: an abstracted relationship causing someone self harm into self harm

1064
00:53:07,840 --> 00:53:10,840
Speaker 3: is not new. It's the automation I think of the

1065
00:53:10,920 --> 00:53:14,879
Speaker 3: AI that is that is rightfully scaring people because there

1066
00:53:14,920 --> 00:53:19,400
Speaker 3: is no I mean, I just you know, you would

1067
00:53:19,400 --> 00:53:21,719
Speaker 3: hope that no, because I can't say that like in

1068
00:53:21,760 --> 00:53:24,080
Speaker 3: the social media version. Yeah, there is no off switch either.

1069
00:53:24,360 --> 00:53:26,400
Speaker 3: The mob kind of controls it. So it's it's just

1070
00:53:26,440 --> 00:53:28,640
Speaker 3: I guess it's just different. It's just it's different and

1071
00:53:29,040 --> 00:53:30,520
Speaker 3: we don't know enough about it yet.

1072
00:53:30,840 --> 00:53:33,760
Speaker 1: This is gonna sound kind of silly, but Steve Jobs

1073
00:53:33,760 --> 00:53:36,799
Speaker 1: once called the computer a bicycle for the mind, and

1074
00:53:36,840 --> 00:53:39,640
Speaker 1: I think that's a really interesting metaphor because, like if

1075
00:53:39,640 --> 00:53:41,120
Speaker 1: you think about it, like what does that even mean?

1076
00:53:41,560 --> 00:53:44,279
Speaker 1: Like I guess theoretically, like it means well, like it

1077
00:53:44,320 --> 00:53:46,680
Speaker 1: means like, okay, you have to put some effort into it.

1078
00:53:46,920 --> 00:53:48,759
Speaker 1: Like it's not a car, you know what I mean.

1079
00:53:48,800 --> 00:53:52,080
Speaker 1: Like you do something like you push down on the pedal,

1080
00:53:52,640 --> 00:53:57,640
Speaker 1: and it amplifies what you've done a bicycle. Yeah, it's

1081
00:53:57,680 --> 00:53:59,480
Speaker 1: like it amplifies what you've done, and it allows you

1082
00:53:59,560 --> 00:54:02,719
Speaker 1: to do things like say, you know, if if you'd

1083
00:54:02,800 --> 00:54:04,799
Speaker 1: never had a bicycle, you probably would just.

1084
00:54:04,719 --> 00:54:06,560
Speaker 2: Live in your little town and you never go anywhere.

1085
00:54:06,560 --> 00:54:09,080
Speaker 1: But you got a bicycle. You know, next town's like

1086
00:54:09,120 --> 00:54:11,640
Speaker 1: ten miles over. I heard they got a good restaurant,

1087
00:54:11,680 --> 00:54:15,240
Speaker 1: Like I'll go. I wouldn't have gone otherwise, I'll see.

1088
00:54:15,360 --> 00:54:18,160
Speaker 1: You know, we have a car right now, Like we

1089
00:54:18,200 --> 00:54:20,560
Speaker 1: had a bicycle. We thought we had cars. No, no, no, no,

1090
00:54:20,840 --> 00:54:24,040
Speaker 1: we had bicycles. We now have cars. And the kinds

1091
00:54:24,080 --> 00:54:26,440
Speaker 1: of things you can do with the car are fundamentally

1092
00:54:26,440 --> 00:54:29,279
Speaker 1: for the mind, for the mine, right yeah, give the metaphor, Yeah,

1093
00:54:29,280 --> 00:54:31,000
Speaker 1: we now have cars for the mine. And so like

1094
00:54:31,080 --> 00:54:32,879
Speaker 1: now the kinds of things you used to be able

1095
00:54:32,880 --> 00:54:34,880
Speaker 1: to do, which is you know, like go to the

1096
00:54:34,920 --> 00:54:37,600
Speaker 1: next town over you could do you can be across

1097
00:54:37,600 --> 00:54:42,000
Speaker 1: the country and like in three days. It's nuts, Like

1098
00:54:42,040 --> 00:54:43,920
Speaker 1: you'd never do that if you didn't have that. But

1099
00:54:43,960 --> 00:54:46,640
Speaker 1: also like if you run into somebody with a bicycle,

1100
00:54:47,239 --> 00:54:51,399
Speaker 1: it sucks, but you're fine. Usually you run in somebody

1101
00:54:51,400 --> 00:54:55,719
Speaker 1: with a car. We have we have a different situation,

1102
00:54:55,960 --> 00:54:58,960
Speaker 1: and so I think sometimes it's just like the scale

1103
00:54:59,080 --> 00:55:02,880
Speaker 1: and the speed, even if the underlying technology was kind

1104
00:55:02,880 --> 00:55:05,800
Speaker 1: of the same thing. Yeah, it's it's it's fundamentally different.

1105
00:55:06,160 --> 00:55:09,120
Speaker 3: I want to kind of like land back on this

1106
00:55:09,239 --> 00:55:12,920
Speaker 3: point you made earlier about you know, people, there are

1107
00:55:12,920 --> 00:55:15,800
Speaker 3: there are some people, many people, let's say, who trust

1108
00:55:15,800 --> 00:55:19,920
Speaker 3: a computer over other people. And there are I think

1109
00:55:20,320 --> 00:55:24,760
Speaker 3: also a lot of people who have a very hard

1110
00:55:24,840 --> 00:55:28,960
Speaker 3: time dealing with the abstractions that are inherent.

1111
00:55:28,600 --> 00:55:29,600
Speaker 2: With using a computer.

1112
00:55:29,920 --> 00:55:32,759
Speaker 3: Yeah, the people who can't stop posting on Twitter even

1113
00:55:32,760 --> 00:55:35,359
Speaker 3: though they're getting fired from their job. The people who

1114
00:55:35,640 --> 00:55:39,239
Speaker 3: you know, livestream themselves doing stupid things for attention. The

1115
00:55:39,280 --> 00:55:42,040
Speaker 3: trolls that you see, you know, kind of living in

1116
00:55:42,120 --> 00:55:44,279
Speaker 3: absolute misery because like they want to just like hurt

1117
00:55:44,280 --> 00:55:46,799
Speaker 3: other people. You know, like these these these people have

1118
00:55:46,840 --> 00:55:52,600
Speaker 3: always existed, and I think AI has just created like

1119
00:55:52,680 --> 00:55:56,839
Speaker 3: a new a new way for for people to hurt

1120
00:55:56,880 --> 00:55:59,480
Speaker 3: themselves or other people. But then I also think that

1121
00:55:59,520 --> 00:56:01,480
Speaker 3: there's like a huge amount of people who have trouble

1122
00:56:01,520 --> 00:56:04,520
Speaker 3: with like determining what's real and what's not real on

1123
00:56:04,560 --> 00:56:07,040
Speaker 3: the Internet, and like AI has just exacerbated that. So,

1124
00:56:07,080 --> 00:56:10,880
Speaker 3: like it's interesting, like as new quote unquote, as this

1125
00:56:10,920 --> 00:56:14,960
Speaker 3: technology feels and seems like a lot of the things

1126
00:56:14,960 --> 00:56:17,719
Speaker 3: that it's putting a spotlight on are not new, and

1127
00:56:17,760 --> 00:56:20,920
Speaker 3: like AI relationships, as you said, are not that different

1128
00:56:21,520 --> 00:56:25,120
Speaker 3: from how people were having sex with a computer before.

1129
00:56:25,880 --> 00:56:27,680
Speaker 1: Well, I mean, you know, think of dating apps, think

1130
00:56:27,719 --> 00:56:30,239
Speaker 1: of and before dating apps really got to be a thing,

1131
00:56:30,320 --> 00:56:32,879
Speaker 1: like think about people like meeting each other in warcraft

1132
00:56:32,880 --> 00:56:34,560
Speaker 1: and how weird that was.

1133
00:56:35,120 --> 00:56:35,560
Speaker 3: Exactly.

1134
00:56:35,719 --> 00:56:39,200
Speaker 1: It's not that weird, man, It's not that weird now,

1135
00:56:39,400 --> 00:56:40,680
Speaker 1: but it wasn't even back then.

1136
00:56:41,680 --> 00:56:43,200
Speaker 3: No, no, it wasn't that weird.

1137
00:56:43,760 --> 00:56:46,800
Speaker 1: The really interesting thing about this is this is maybe

1138
00:56:46,840 --> 00:56:50,480
Speaker 1: like the one bipartisan issue, you know what I mean,

1139
00:56:50,520 --> 00:56:54,399
Speaker 1: which is like everybody agrees we're on AI or well,

1140
00:56:54,520 --> 00:56:58,319
Speaker 1: like technology in general, like it used to be that.

1141
00:56:58,480 --> 00:57:00,400
Speaker 1: Oh man, the future is going to be cool. I

1142
00:57:00,400 --> 00:57:02,520
Speaker 1: can't wait to see what's gonna come out. We're all

1143
00:57:02,560 --> 00:57:05,400
Speaker 1: gonna have VR headsets. Like that's fun, and you know,

1144
00:57:05,480 --> 00:57:08,640
Speaker 1: we'd have our dystopian movies or whatever. But in general,

1145
00:57:08,680 --> 00:57:10,759
Speaker 1: I think people were kind of excited about it. We

1146
00:57:11,280 --> 00:57:15,520
Speaker 1: have this stuff that was the literal science fiction even

1147
00:57:15,560 --> 00:57:19,960
Speaker 1: five years ago, and now a lot of influential people,

1148
00:57:19,960 --> 00:57:24,160
Speaker 1: shall I say, hate it. But like, there's a study here.

1149
00:57:24,680 --> 00:57:27,520
Speaker 1: There's a study that's found that eighty five percent of

1150
00:57:27,760 --> 00:57:31,240
Speaker 1: gen Z agree that they spend too much time online.

1151
00:57:31,280 --> 00:57:33,200
Speaker 1: Eighty five percent of gen Z agree that they spend

1152
00:57:33,200 --> 00:57:34,320
Speaker 1: too much time online.

1153
00:57:34,720 --> 00:57:36,320
Speaker 3: I would agree with that based on what I've seen

1154
00:57:36,320 --> 00:57:38,440
Speaker 3: gen Z do. Yeah, no, I would agree with that.

1155
00:57:38,640 --> 00:57:41,800
Speaker 1: Eighty four percent strongly or somewhat agree that in person

1156
00:57:41,840 --> 00:57:47,960
Speaker 1: relationships are more valuable than digital relationships. All they have

1157
00:57:48,000 --> 00:57:50,560
Speaker 1: to do is put down the phone. All they have

1158
00:57:50,600 --> 00:57:52,600
Speaker 1: to do is put down the phone. But that being said,

1159
00:57:52,760 --> 00:57:55,840
Speaker 1: like I actually think that some of the most frankly

1160
00:57:55,920 --> 00:57:59,840
Speaker 1: vulnerable people to like the AI slot to being addicted

1161
00:57:59,840 --> 00:58:02,080
Speaker 1: to the phone, being addicted to Facebook.

1162
00:58:02,120 --> 00:58:03,960
Speaker 2: Certainly that's boomers, man.

1163
00:58:05,040 --> 00:58:08,240
Speaker 3: Yeah, older people in particular. Yeah, yeah, I mean yeah,

1164
00:58:08,280 --> 00:58:11,880
Speaker 3: like the lonelier, the older, the more media illiterate.

1165
00:58:11,480 --> 00:58:12,960
Speaker 2: For sure. Yeah, and.

1166
00:58:14,400 --> 00:58:16,480
Speaker 1: You know, and there are people who are you know,

1167
00:58:16,520 --> 00:58:21,000
Speaker 1: there's you know, dumb phones become a more popular. You know,

1168
00:58:21,040 --> 00:58:23,600
Speaker 1: we've like light phone, all these other sorts of things.

1169
00:58:23,760 --> 00:58:26,800
Speaker 1: First off, a lot of those are expensive. But I

1170
00:58:26,800 --> 00:58:28,440
Speaker 1: think there's like a reason for that is because it's

1171
00:58:28,520 --> 00:58:34,320
Speaker 1: kind of like aimed at somebody who is wealthy enough

1172
00:58:34,800 --> 00:58:38,680
Speaker 1: to unplug. And I kind of feel like that's where

1173
00:58:38,720 --> 00:58:42,600
Speaker 1: we're going. We actually are kind of creating this sort

1174
00:58:42,600 --> 00:58:46,920
Speaker 1: of elitist not really my words here, but I think

1175
00:58:47,600 --> 00:58:49,120
Speaker 1: a certain class of people who are going to be

1176
00:58:49,200 --> 00:58:53,080
Speaker 1: viewed as elitists to have the money and yeah, really

1177
00:58:53,120 --> 00:58:55,440
Speaker 1: have the money and the access to be able to

1178
00:58:55,920 --> 00:58:59,560
Speaker 1: not use a AI to like insist that the music

1179
00:58:59,600 --> 00:59:03,760
Speaker 1: played at the restaurant is not AI because this is

1180
00:59:03,800 --> 00:59:06,800
Speaker 1: a high class place, right, how dare you play AI

1181
00:59:06,960 --> 00:59:10,440
Speaker 1: music in the background? How dare you use AI to

1182
00:59:10,760 --> 00:59:14,280
Speaker 1: generate photos of the food? I can't believe that you

1183
00:59:14,320 --> 00:59:17,080
Speaker 1: didn't take real pictures of this, right, And I kind

1184
00:59:17,120 --> 00:59:19,280
Speaker 1: of think that's where we're going. And you know, tell

1185
00:59:19,400 --> 00:59:21,920
Speaker 1: telling people to get off the phone, like read real news,

1186
00:59:21,960 --> 00:59:24,880
Speaker 1: stop getting off for social media. Have you seen the

1187
00:59:24,880 --> 00:59:27,720
Speaker 1: price of a New York Times subscription. Have you seen

1188
00:59:27,720 --> 00:59:33,280
Speaker 1: the price of like one newsletter, one substack. It's like

1189
00:59:33,320 --> 00:59:36,680
Speaker 1: it's just not economically feasible for the vast majority of people,

1190
00:59:36,720 --> 00:59:40,080
Speaker 1: And so everybody is going through the vast majority of us.

1191
00:59:40,080 --> 00:59:42,919
Speaker 1: I actually think are going to start getting dragged down

1192
00:59:43,000 --> 00:59:45,360
Speaker 1: into the quote unquote slop and there will be a

1193
00:59:45,440 --> 00:59:47,960
Speaker 1: layer of people who can choose to unplug from that

1194
00:59:48,040 --> 00:59:50,920
Speaker 1: and choose to disengage with that. But again, that's not

1195
00:59:51,080 --> 00:59:53,120
Speaker 1: just like you were saying, with relationships. That's not like

1196
00:59:53,200 --> 00:59:57,200
Speaker 1: an AI thing. That's exposing something that already existed. We

1197
00:59:57,360 --> 01:00:00,680
Speaker 1: already had a class problem in society, and and the

1198
01:00:00,720 --> 01:00:02,720
Speaker 1: problem is we're like thinking that we can make an

1199
01:00:02,720 --> 01:00:05,840
Speaker 1: app to fix that, or like surprised when the app

1200
01:00:05,880 --> 01:00:06,560
Speaker 1: doesn't fix it.

1201
01:00:06,560 --> 01:00:08,680
Speaker 2: It's like, no, bro, like this capitalism. I don't know

1202
01:00:08,720 --> 01:00:09,120
Speaker 2: what you want.

1203
01:00:09,960 --> 01:00:13,800
Speaker 3: But what people need to do is subscribe to our podcasts, yes,

1204
01:00:14,320 --> 01:00:15,840
Speaker 3: as a way to defeat slop.

1205
01:00:16,320 --> 01:00:17,200
Speaker 2: Fix you.

1206
01:00:17,320 --> 01:00:21,360
Speaker 3: That will fix it because if you, if you support us.

1207
01:00:21,480 --> 01:00:27,040
Speaker 1: Specifically, specifically specifically kill switch like that, those are the

1208
01:00:27,040 --> 01:00:29,000
Speaker 1: two keys to fix every exactly.

1209
01:00:29,280 --> 01:00:31,560
Speaker 3: And we're good, no, but I think you're one hundred

1210
01:00:31,560 --> 01:00:37,160
Speaker 3: percent right. We're already seeing the beginnings of this and really,

1211
01:00:37,160 --> 01:00:39,160
Speaker 3: in a lot of ways, the only hope for the

1212
01:00:39,200 --> 01:00:42,480
Speaker 3: AI industry to not completely implode, which, by the way,

1213
01:00:42,560 --> 01:00:45,280
Speaker 3: I should say, will not remove AI from our lives.

1214
01:00:45,280 --> 01:00:47,560
Speaker 3: The dot com bubble burst and we still have doc calls.

1215
01:00:48,760 --> 01:00:51,760
Speaker 3: It just it just it just consolidates things. Uh, the

1216
01:00:51,800 --> 01:00:54,480
Speaker 3: stock market crashes in the twenties, we still have a

1217
01:00:54,480 --> 01:00:57,520
Speaker 3: stock market. It just becomes a lot you know, harder

1218
01:00:57,520 --> 01:01:00,880
Speaker 3: and more competitive. Uh. There's a big segment of people

1219
01:01:00,880 --> 01:01:02,680
Speaker 3: out there who want to believe that, like the AI

1220
01:01:02,720 --> 01:01:04,960
Speaker 3: bubble bursts and there's no more AI. No, no, no,

1221
01:01:05,000 --> 01:01:06,080
Speaker 3: that's not how this works.

1222
01:01:06,840 --> 01:01:08,680
Speaker 2: To toothpaste out the tube.

1223
01:01:09,000 --> 01:01:10,920
Speaker 3: Yes, toothpaste out the tube. And as I say to

1224
01:01:11,000 --> 01:01:12,959
Speaker 3: a lot of grumpy readers, whenever I bring up AI,

1225
01:01:13,480 --> 01:01:16,160
Speaker 3: I can run stable diffusion on my MacBook without an

1226
01:01:16,200 --> 01:01:19,400
Speaker 3: Internet connection. This shit's not going away. Ever. The best

1227
01:01:19,480 --> 01:01:21,600
Speaker 3: AI right now is the worst AI will ever be.

1228
01:01:21,760 --> 01:01:24,800
Speaker 1: Basically, yeah, yeah, this is the worst. They the man,

1229
01:01:24,840 --> 01:01:26,520
Speaker 1: the AI people love to say that, man, this is

1230
01:01:26,560 --> 01:01:27,440
Speaker 1: the worst it'll ever be.

1231
01:01:27,960 --> 01:01:30,320
Speaker 3: And the right, yeah, man, in a way, they are right.

1232
01:01:30,400 --> 01:01:33,920
Speaker 1: Unfortunately, I mean, I'm very happy that you're joining me

1233
01:01:34,080 --> 01:01:35,680
Speaker 1: on you know a little bit of the hey man,

1234
01:01:35,720 --> 01:01:37,680
Speaker 1: like this is serious, let's take this seriously kind of thing,

1235
01:01:37,720 --> 01:01:40,160
Speaker 1: because people want to hear you know, the bright, shiny hey,

1236
01:01:40,160 --> 01:01:42,360
Speaker 1: how do we fix this? What's what's the button that

1237
01:01:42,400 --> 01:01:43,920
Speaker 1: I can press to make all this stop? I mean

1238
01:01:43,920 --> 01:01:45,280
Speaker 1: the button that we can press to make all this

1239
01:01:45,320 --> 01:01:47,920
Speaker 1: stop is like fundamental societal change.

1240
01:01:48,160 --> 01:01:51,640
Speaker 3: Or like an e MP that shuts down all technology.

1241
01:01:51,960 --> 01:01:53,840
Speaker 3: You know, maybe that that would work.

1242
01:01:53,960 --> 01:01:56,880
Speaker 2: That's a terminator, right, Yeah, I'm pretty sure.

1243
01:01:57,520 --> 01:01:59,480
Speaker 1: Yeah, I remember that from the Tournator video games because

1244
01:01:59,520 --> 01:02:00,720
Speaker 1: I'm pretty su Trump at one.

1245
01:02:00,600 --> 01:02:02,640
Speaker 3: Point thought it was a real thing that could happen,

1246
01:02:02,960 --> 01:02:04,760
Speaker 3: like because you got he heard it in a movie.

1247
01:02:05,120 --> 01:02:06,680
Speaker 3: I want to thank you for coming on the show.

1248
01:02:06,760 --> 01:02:08,640
Speaker 3: I want to thank you for having me on your show.

1249
01:02:08,960 --> 01:02:12,760
Speaker 3: This was delightful and wonderful. I usually end every episode

1250
01:02:12,800 --> 01:02:15,840
Speaker 3: by asking people like where can people follow you? But

1251
01:02:16,240 --> 01:02:18,000
Speaker 3: here I'll do this. If you want to follow me,

1252
01:02:18,600 --> 01:02:20,640
Speaker 3: you can find me on Blue Sky and Instagram. As

1253
01:02:20,760 --> 01:02:25,520
Speaker 3: Ryan hates this and Broderick on x I unfortunately still

1254
01:02:25,920 --> 01:02:29,400
Speaker 3: check that website. People. Yeah, what about you? Where can

1255
01:02:29,440 --> 01:02:31,080
Speaker 3: people follow you? If they want to they want to

1256
01:02:31,080 --> 01:02:31,400
Speaker 3: follow you?

1257
01:02:31,560 --> 01:02:37,040
Speaker 1: Yeah, I'm a dex digi on basically everything D E

1258
01:02:37,240 --> 01:02:40,760
Speaker 1: X D, I, G I and I. We have also

1259
01:02:41,440 --> 01:02:46,200
Speaker 1: kill switch pod kill switch pod on Instagram.

1260
01:02:46,240 --> 01:02:49,120
Speaker 3: Oh yeah, you can find Panic World at any podcast

1261
01:02:49,240 --> 01:02:53,080
Speaker 3: place that you get content for podcasts and also video.

1262
01:02:53,120 --> 01:02:53,680
Speaker 2: I guess same.

1263
01:02:54,720 --> 01:02:54,919
Speaker 3: Yeah.

1264
01:02:58,480 --> 01:03:00,800
Speaker 1: Thank you so much for listening. This was a really

1265
01:03:00,800 --> 01:03:04,280
Speaker 1: fun collaboration with Ryan and Panic World, so please make

1266
01:03:04,320 --> 01:03:06,640
Speaker 1: sure to check out the show on YouTube or wherever

1267
01:03:06,680 --> 01:03:09,520
Speaker 1: you get your podcasts and special thanks to Panic World

1268
01:03:09,520 --> 01:03:14,240
Speaker 1: producers Brand Irving and Josh Feldstid. Killswitch is produced by

1269
01:03:14,240 --> 01:03:18,640
Speaker 1: Sena Ozaki, Darluk Potts, and Julia Nutter from Kaleidoscope. Our

1270
01:03:18,640 --> 01:03:23,160
Speaker 1: executive producers are Ozwa Lashin, Mangesh Hatigadur, and Kate Osborne

1271
01:03:23,240 --> 01:03:27,320
Speaker 1: from iHeart. Our executive producers are Katrina Norvil and Nikki E. Tour.

1272
01:03:27,680 --> 01:03:31,800
Speaker 1: Catch All, the Next One, Goodbye,