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Speaker 1: All media.

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Speaker 2: Hey everybody, Robert Evans here, and I wanted to let

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Speaker 2: you know this is a compilation episode. So every episode

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Speaker 2: of the week that just happened is here in one

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Speaker 2: convenient and with somewhat less ads package for you to

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Speaker 2: listen to in a long stretch if you want. If

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Speaker 2: you've been listening to the episodes every day this week,

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Speaker 2: there's going to be nothing new here for you, but

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Speaker 2: you can make your own decisions.

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Speaker 3: Hi.

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Speaker 4: Everyone, it's James coming at you. We're pretty nasty cold here.

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Speaker 4: I wanted to share with you that Wildfast has swept

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Speaker 4: through Los Angeles in the last couple of days. While

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Speaker 4: I'm recording this. Thousands of people have been displaced. Five

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Speaker 4: people have died that we know of so far, thousands

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Speaker 4: of structures have been burned, and many many people in

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Speaker 4: I will be finding themselves out of their homes with

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Speaker 4: nowhere to go, with very few resources. If you'd like

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Speaker 4: to help, We've come up with some mutual aid groups

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Speaker 4: who you can donate to, and we'll be interviewing one

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Speaker 4: of them on this show next week. So if you

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Speaker 4: like to help, the three places where we suggest you

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Speaker 4: would donate some cash, The Sidewalk Project, that's the Sidewalk

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Speaker 4: Project dot org K Town for All. That's that's a

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Speaker 4: K T O W N F O R A L

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Speaker 4: L dot O r G and ETNA Street Solidarity. You

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Speaker 4: can find them on Venmo or I think on Instagram

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Speaker 4: as well. That's a E T N A S T

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Speaker 4: R E E T S O L I D A

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Speaker 4: R I T Y.

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Speaker 2: All right, I'm gonna go rest my voice order in

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Speaker 2: the court order in the court Justice Robert Evans presiding,

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Speaker 2: I see we have a fine jury here to take

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Speaker 2: questions from the audience of our of our daily news show,

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Speaker 2: which is also my court room. Everybody, everybody get it,

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Speaker 2: because I'm a judge now, really, because that's how the

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Speaker 2: legal system works.

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Speaker 5: All those rubers finally have true.

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Speaker 2: No municipal Judge Garrison.

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Speaker 5: That's okay, okay, that's get You're right, You're right, You're

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Speaker 5: right here.

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Speaker 2: I will now for the rest of my life be

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Speaker 2: able to say when people ask questions, well as a

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Speaker 2: man of the law, which I'm very much looking forward to.

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Speaker 4: No, only able to say, Robert quite likely.

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Speaker 2: To say, anyway, that's all I got.

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Speaker 1: All right, this is the it could happen here. A

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Speaker 1: Q and A episode. We've got what what are we

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Speaker 1: calling you now? Robert Evans? What's your title?

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Speaker 2: The Honorable Robert Evans and I I actually all the

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Speaker 2: judge who made me a judge sent me a gabble,

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Speaker 2: but I didn't grab it for this one. So I

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Speaker 2: just used I have a the the barrel and lower

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Speaker 2: receiver from an antique sought off shotgun that belonged to

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Speaker 2: a bootlegger, and I just sort of slammed that into

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Speaker 2: my table.

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Speaker 5: I'm sure editor will love that.

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Speaker 4: Yeah, yeah, but before we broadcast, so you have a

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Speaker 4: sort of shotgun.

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Speaker 2: It's not a it's not functional, it's been destroyed.

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Speaker 4: I see, I see good. Didn't want a little really

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Speaker 4: rich moment.

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Speaker 1: Yeah, we've got Mio Wong, Garrison Davis, James Stout and

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Speaker 1: the dishonorable Robert Evans.

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Speaker 5: And Sophie Lickterman. Oh yes, it mean yeah, we're.

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Speaker 1: Gonna do the We're gonna do some questions we posted

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Speaker 1: in on our Blue Sky. If you not following us

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Speaker 1: on Blue Sky, we are on there.

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Speaker 2: Blue Ski one does not post on Blue Sky. So

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Speaker 2: if you want.

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Speaker 1: Skeets, I really hope that's not true, because that's really.

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Speaker 2: Embarrassing Unfortunately, they really tried to get that off the ground.

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Speaker 2: I don't see anyone actually using skeat.

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Speaker 4: I saw someone using it in French and it was

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Speaker 4: a real moment. Hi are you Garrison.

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Speaker 5: Instead of saying send a tweet, now, I just say

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Speaker 5: send skeet in conversation. Everyone loves it. M hm.

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Speaker 2: Do you re skeet? Is that a thing? Yeah?

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Speaker 5: I guess you do. I guess you do, mm hm.

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Speaker 1: And we're moving on. I'm just gonna throw out some

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Speaker 1: of the questions we received on online. I'm not even

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Speaker 1: gonna say the name of the app again because I'm

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Speaker 1: afraid being labeled as an old Garrison's embarrassed by me.

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Speaker 1: I can tell I didn't say that, but you thought it.

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Speaker 5: But you thought it. I didn't think that you did.

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Speaker 1: Any advice for someone with a desire to do some

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Speaker 1: hobby or freelance journalism in the coming few years, I

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Speaker 1: want to actively fight for equality. Also, thank you for

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Speaker 1: your questions. Everyone.

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Speaker 2: Hm, I don't thank you for your questions. I'm actively

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Speaker 2: angry at you for your question.

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Speaker 1: Yeah, that's why you're the dishonorable.

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Speaker 4: Yeah, start rich if you want to be a freelance journalist.

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Speaker 4: Because you'll progressively become poorer.

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Speaker 2: It's my I have funded my journal I get I

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Speaker 2: love whatever people ask me questions like how did you

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Speaker 2: convince Krack to send you to Iraq? I didn't. I

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Speaker 2: bought plane tickets, Like being an entertainer has always been

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Speaker 2: what's funded my journalism.

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Speaker 5: I guess my advice would be get really autistic about

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Speaker 5: something problematic, just like one thing, this one thing I

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Speaker 5: get like RELLI into it to the point where it

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Speaker 5: kind of takes over your life. Your personal life starts

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Speaker 5: fading away, it kind of blends into your whole state

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Speaker 5: of existence. And only then will you actually get good

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Speaker 5: at that thing. Yep, that's my advice. And then you

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Speaker 5: just take one thing at a time and every few

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Speaker 5: years you kind of change the scope of the thing

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Speaker 5: you're getting really autistic about. But that's kind of how

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Speaker 5: I've rolled, and it's been it's been okay.

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Speaker 2: Yeah, you just finished thirty six hours of digging into

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Speaker 2: the life of a school shooter. And I also built

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Speaker 2: the back of my career spending hours and hours digging

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Speaker 2: through the online lives of mass shooters. And you don't

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Speaker 2: have to do that, but you do have to do

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Speaker 2: that thing, which is Yeah, exactly what Garrison said. You

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Speaker 2: have to pick a very narrow thing and make it

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Speaker 2: your life, and not just a random thing, but like

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Speaker 2: a thing that you think is important. Yeah, and that

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Speaker 2: people don't other people don't understand how important it is.

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Speaker 2: And if you make yourself there's a fella. His blog

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Speaker 2: is called We Hunted the Mammoth. They've Futrell who's been

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Speaker 2: covering what we call the manosphere for like more than

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Speaker 2: a decade before anybody else in journalism was taking it seriously. Yep,

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Speaker 2: you got to do that kind of thing. If you

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Speaker 2: do that kind of thing, you build a name for yourself,

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Speaker 2: and that can allow you when that the thing that

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Speaker 2: you're obsessed on becomes a big story, being first to

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Speaker 2: have something meaningful to say about it can provide you

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Speaker 2: eventually with the opportunity to cover other things. Yeah, and

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Speaker 2: it's good advice.

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Speaker 4: I would say if you want to get started freelancing,

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Speaker 4: it's a good idea to join that IWW Freelance Journalist Union.

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Speaker 4: You can learn a lot from people who are freelancing there.

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Speaker 4: You can learn who not to pitch, which editors are

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Speaker 4: toxic as fuck, which is a surprisingly large amount. Yeah,

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Speaker 4: you can learn which email to send your pitches to

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Speaker 4: and how to pitch if you're not familiar with how

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Speaker 4: to pitch. I also teach sometimes journalism workshops at a

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Speaker 4: community college, So if you have a community college near you,

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Speaker 4: you might be able to get some either free or

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Speaker 4: very cheap sort of advice and the real like nuts

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Speaker 4: and bolts of journalism like ending pitches and stuff like that.

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Speaker 2: Cool.

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Speaker 1: What is the consensus on what the next Trump administration

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Speaker 1: will do? On the first day or first week, all

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Speaker 1: of us just look like we're in pain.

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Speaker 5: Oh fus like it's chaos of the Yeah.

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Speaker 2: I'm not.

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Speaker 5: I'm not for seeing good things. There'll be a lot

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Speaker 5: of executive orders that are you know, probably bad, you know,

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Speaker 5: things that aren't great.

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Speaker 2: Yeah, I think that, Uh, he's going to try to

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Speaker 2: do as much of what he's promised to do in

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Speaker 2: terms of particular not in terms of everything he's prounded,

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Speaker 2: but in terms of going after immigrants. Yeah, he's going

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Speaker 2: to do as much of what he's promised to do

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Speaker 2: as he possibly can't. Now that doesn't mean he's going

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Speaker 2: to actually deport millions of people. There are like some

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Speaker 2: just practical limitations based on the capacity of the institutions

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Speaker 2: will be using to do this and he could get

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Speaker 2: there's a very good chance things will get bogged down

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Speaker 2: and whatnot, but like he will try. Yeah, that's my take. Yeah,

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Speaker 2: I think.

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Speaker 6: I think the other thing that's going to happen pretty

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Speaker 6: quickly is I think he's gonna start moving on tariffs

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Speaker 6: very very fast.

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Speaker 2: Yeah. If you're planning to buy a computer, go ahead

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Speaker 2: and grab that fucker now if you can.

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Speaker 5: If you're getting anything from overseas, you should get it

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Speaker 5: in the few weeks that you still can.

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Speaker 2: Yeah, it has a battery.

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Speaker 3: It made here.

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Speaker 1: I had my annual physical today because otherwise our insurance

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Speaker 1: screws us over. And my doctor was like, you should

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Speaker 1: try to get as many prescriptions filled before the end

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Speaker 1: of the year before things come up, just in case.

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Speaker 1: There you go, and you know that's not terrible advice.

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Speaker 3: Yeah.

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Speaker 4: I think in terms of executive orders, he will try

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Speaker 4: and further restrict access to asylum, try and further change that.

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Speaker 4: There are things he can do by executive order with

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Speaker 4: ICE and CBP in terms of how they operate that

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Speaker 4: he will try and do. It's not impossible that they

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Speaker 4: will try and again immediately mobilize public health law against

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Speaker 4: migrants like he did in twenty twenty. Right, Yeah, those

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Speaker 4: things could all be done without congressional support. We might

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Speaker 4: hope on us about this, but Stephen Miller suggested that

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Speaker 4: they might do some of those things.

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Speaker 2: So, yeah, not impossible. Party won't be a great day.

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Speaker 1: Somebody's getting fired the first week, probably first day.

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Speaker 2: Yeah. I mean, I've seen the fact that the FBI

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Speaker 2: director is stepping down pushed as like an act of

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Speaker 2: resistance because it means that Trump now has to actually

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Speaker 2: go through like Congress to get it done. I don't

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Speaker 2: know if how much I buy that, how much I

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Speaker 2: think that. I think a lot of what I'm seeing

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Speaker 2: right now from establishment people. And maybe this isn't true

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Speaker 2: of Ray because I did find some of the arguments

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Speaker 2: they're compelling, But a lot of what I've seen from

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Speaker 2: establishment people in politics is they're scared and just really

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Speaker 2: try and not to make waves. Yeah, and I think

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Speaker 2: that's what you're going to see overwhelmingly. I think that

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Speaker 2: he's going to probably will not immediately act against the press,

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Speaker 2: and in a legal sense, as the president, they will

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Speaker 2: do that, but I think he's going to He's already

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Speaker 2: suing differently and I think that that's going to be

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Speaker 2: kind of his his focus there for a while, just

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Speaker 2: because there's a lot on his plate. But I think

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Speaker 2: here there will be attempts like the fuck with libel

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Speaker 2: laws and stuff, especially as things go on.

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Speaker 1: Okay, several of you have asked about the Android ad

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Speaker 1: free version subscription channel, and I want you all to

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Speaker 1: know that it will happen next year. I have been

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Speaker 1: trying to get this to happen for two years now,

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Speaker 1: and for unforeseen reasons, it just keeps getting roadblocked. But

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Speaker 1: it is happening. We're just waiting on a couple of

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Speaker 1: final things to get into place, so that will be happening,

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Speaker 1: hopefully very soon into twenty twenty five. I will update

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Speaker 1: everybody as soon as that's possible. And I'm so sorry

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Speaker 1: it's taken so long. I want you to know I

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Speaker 1: have worked so unbelievably hard on this, miserably hard.

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Speaker 2: Yeah, we've seen it, Sophie has. It's been a nightmare,

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Speaker 2: Harder than I have worked on anything else this year. Like,

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Speaker 2: it's been nuts. Yeah, And here's the thing that sucks

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Speaker 2: for no reason, no reason, but not that there's no

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Speaker 2: reason to launch the app. There's a great reason. There's

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Speaker 2: no reason it should have taken this long. Correct, But

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Speaker 2: we can't say anymore for reasons that are also equally frustrating.

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Speaker 2: I'd like to say in general, folks, there's a few

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Speaker 2: things that get brought up a lot. It's like, why

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Speaker 2: haven't they done this yet? Why haven't they done this yet?

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Speaker 2: We're talking like technical things or like you know, things

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Speaker 2: like a like a paid subscription, and they're like, why

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Speaker 2: haven't they gotten around to it yet? And the answer

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Speaker 2: is always some infuriating bullshit based.

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Speaker 1: On some bureaucracy bullshit.

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Speaker 2: Some be rock some legal shit where you're like, you

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Speaker 2: don't actually realize it's illegal to do this if you

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Speaker 2: do it this way or whatever, like some sort of

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Speaker 2: bullshit that makes it impossible. It's not that we want

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Speaker 2: to make this is it is as possible for people

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Speaker 2: to have the best listening experience that we can afford

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Speaker 2: to provide them. But there's a lot of annoying bullshit

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Speaker 2: that exists for reasons beyond our comprehension.

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Speaker 1: Sorry, anyways, here's ads. Unless you have an iPhone and

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Speaker 1: subscribe to color Zone Media on Apple. All right, we're back.

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Speaker 1: How do you each motivate yourself to write or do

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Speaker 1: your jobs. I get asked that question all the time,

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Speaker 1: but I'll let each of you tackle it. While this

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Speaker 1: is a communally hosted show, I feel like each of

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Speaker 1: you do very different things, so your answers are going

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Speaker 1: to be all over the place. So Garrison, Oh.

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Speaker 5: Well, I mean paying rents a great motivator.

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Speaker 2: Sure, yes, yes, understated, this is a big thing that

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Speaker 2: a lot of people who want to be writers but

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Speaker 2: have never done it for a living. Miss is that

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Speaker 2: all of your favorite writers who do it for a living,

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Speaker 2: a big part of how they get over fucking writer's

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Speaker 2: block is they have to pay rent or mortgage. Yeah,

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Speaker 2: it turns out that helps.

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Speaker 5: It's it's it's a quite compelling motivator, and sometimes it

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Speaker 5: has required the assistance of you know, caffeine or other things.

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Speaker 5: I have a variety of playlists to help me in

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Speaker 5: when I'm in like different moods. I definitely will about

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Speaker 5: you know, maybe twice a month, I just do a complete,

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Speaker 5: like a complete body check to my sleep schedule to

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Speaker 5: get a special project finished. And that's just kind of

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Speaker 5: part of the deal, at least in terms of how

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Speaker 5: I work, and not everyone does it this way. Though

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Speaker 5: maybe maybe people are more healthy than me.

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Speaker 2: Yeah for me.

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Speaker 6: Okay, So the easiest way something gets done is just

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Speaker 6: pure rage. I can just do it like.

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Speaker 2: It just comes out. The anger is a great motivator.

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Speaker 6: The other fun one is pure joy as something funny happening,

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Speaker 6: Like this is the shinzo Abe assassination. The easiest writing

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Speaker 6: I've ever done in my lifetimes.

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Speaker 2: It just flows.

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Speaker 7: Yeah.

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Speaker 6: Other times it's just like there's a deadline and everyone

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Speaker 6: is counting on me, and I have to get it out,

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Speaker 6: and I've gotten to the right level of sleep deprivation

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Speaker 6: where I can just do it.

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Speaker 5: That's right, That's right.

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Speaker 2: Yeah.

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Speaker 6: But I also think, you know, there's obviously like health insurance,

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Speaker 6: which is sort of a joke given our health insurance.

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Speaker 6: But yeah, and then the last thing, and this is

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Speaker 6: the sort of the serious one, is that like this,

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Speaker 6: you know, I mean I do some organizing stuff too,

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Speaker 6: but like this, this is the thing that I have

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Speaker 6: to do that can materially affect the world. Which is

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Speaker 6: a very very weird thing to say about a podcast.

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Speaker 6: But I've seen it happen, right, I've seen all of

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Speaker 6: you go and do things that wouldn't have happened and

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Speaker 6: I've had. You know, it's a weird situation, right because

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Speaker 6: my my motivation for doing this stuff is the chance

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Speaker 6: that you will make the world better. But I've seen

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Speaker 6: it happen, and I have to continue to believe that

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Speaker 6: the thing that I've been doing for all these years

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Speaker 6: is project of building a very large hammer and deploying

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Speaker 6: it against our enemies can work and will work. And

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Speaker 6: that is you know, that's how I get out of

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Speaker 6: bed every morning, is we're building the hammer and we're

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Speaker 6: swinging it.

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Speaker 2: Yeah, that's a great way to put it.

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Speaker 4: Very large hammer will be a banging name for a podcast.

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Speaker 1: I agree, Yeah, I agree.

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Speaker 2: Yeah, there's a there's a great speech in the comic

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Speaker 2: series trans Metropolitan about how journalism is a gun that

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Speaker 2: you you wire up to your eyes and your ears

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Speaker 2: and several other organs in order to shoot at the world.

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Speaker 2: And that's I think a good way to keep yourself

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Speaker 2: doing it when it feels like you're just shouting into

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Speaker 2: a void.

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Speaker 8: Yeah.

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Speaker 2: I really like the process of writing.

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Speaker 4: I like telling stories like that makes me happy and

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Speaker 4: I feel.

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Speaker 2: So lucky I can do it for my job.

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Speaker 4: I don't ticularly like like receiving trauma, which I also

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Speaker 4: do for yob but.

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Speaker 2: Like, really it can be sometimes I can't sleep.

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Speaker 4: So many people trusted me with their stories, especially this year,

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Speaker 4: that they didn't have to and sometimes a great personal risk,

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Speaker 4: and it's a massive privilege that they trusted me with

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Speaker 4: those stories, and I think I owe it to them

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Speaker 4: to do my best to tell those stories as well

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Speaker 4: as I can. Yeah, and like as Mia said, it

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Speaker 4: has materially changed the world, like the amount of people

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Speaker 4: who listened to our podcasts and came to the border

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Speaker 4: to help last year when we really desperately needed help,

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Speaker 4: people who just like on Sunday Night, gave their money,

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Speaker 4: which I know none of us have enough money right

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Speaker 4: now to help people who are displaced in Rushaba. Like

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Speaker 4: all that stuff really makes it feel like if you

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Speaker 4: tell a good enough story, people will care. That's always

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Speaker 4: what I felt like, if you could just get people

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Speaker 4: to see it, if people could be there, they would care,

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Speaker 4: and if they care enough, they'll do something. Then I've

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Speaker 4: seen that be true with people who listen to the show,

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Speaker 4: and that really makes me happy, So I want to

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Speaker 4: keep doing that.

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Speaker 1: Yeah, for me, it's two part answer. The first part

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Speaker 1: is that I genuinely give a shit about everything that

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Speaker 1: we put out and what we do is not really

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Speaker 1: while it is a job, it matters so much. And

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Speaker 1: the second part is if I don't do my job,

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Speaker 1: the amount of people's lives that that impacts is a

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Speaker 1: lot of fucking people, and I give a shit about

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Speaker 1: each and every one of them. So I'm gonna keep

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Speaker 1: doing my job so that everybody else can keep doing

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Speaker 1: their job and maybe we make a difference in this world,

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Speaker 1: this fucked up, crumbly world. Robert, did you have anything

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Speaker 1: to add? You were speaking and then nice talked, I.

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Speaker 2: Have did I already not give an answer?

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Speaker 1: You gave an answer, that's why, but you were starting

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Speaker 1: to speak.

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Speaker 2: Oh yeah, I do it for the fame, baby.

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Speaker 1: Great. Next, What episode or episodes were your favorite this

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Speaker 1: year to make or otherwise?

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Speaker 4: Yeah?

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Speaker 1: My favorite this year We're definitely James's series from the

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Speaker 1: Darien Gap. That was an incredible series. I'm so unbelievably

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Speaker 1: proud of it. Yeah, James had been trying to do

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Speaker 1: that work for a long time, and I'm I'm happy

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Speaker 1: that we were able to fund it and James was

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Speaker 1: able to do the incredible reporting that he did. I'm

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Speaker 1: also quite proud of Robert Garrison and I surviving the

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Speaker 1: RNC and d n C and C.

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Speaker 2: Was a good time, like legitimately.

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Speaker 1: Great time, pulling the worst people in the world.

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Speaker 2: It was the d n C that fucked me up. Yeah, yeah, same.

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Speaker 5: I was like destroyed emotionally after the d see.

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Speaker 1: Yeah, the DNC was really a huge bummer. And then

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Speaker 1: MIAs covered some of the most important labor stories that

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Speaker 1: like nobody covers absolutely yeah, and like without those genuinely

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Speaker 1: like nobody covers like small labor stories or big labor stories,

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Speaker 1: and she's always on top of that beat. And yeah,

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Speaker 1: I also really just like Robert's don't Panic episode something

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Speaker 1: some great writing, my friend, I answered, Now everybody else

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Speaker 1: has to, Well, I'll start with Miyah.

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Speaker 6: There's weirdly a few this year cool I normally isn't

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Speaker 6: I like the Boeing ones that was fun.

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Speaker 5: Yeah.

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Speaker 6: The one that was most emotionally impactful for me was

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Speaker 6: getting to interview doctor Julia Serrano, who if you haven't

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Speaker 6: listened to that episode, go listen to it.

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Speaker 1: Great book.

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Speaker 6: Yeah, Whipping Girl is the book that literally created a

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Speaker 6: bunch of the like like the concept of misgendering is

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Speaker 6: from that book, right, Like like the language that we

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Speaker 6: used to talk about Transnist today is directly her and

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Speaker 6: so few people who ever read the book, so a

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Speaker 6: few people even know who she is, and getting a

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Speaker 6: chance to talk to her was like incredible. And I'm

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Speaker 6: also really happy about the organizing one that I did,

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Speaker 6: because I've gotten so many messages from people who were

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Speaker 6: just like, I, oh, wait, my knitting is useful to organizing,

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Speaker 6: And I'm like, yes, yes it is, you're knitting. You're

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Speaker 6: so incredible, staggeringly useful.

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Speaker 2: Yeah, so I'm proud of that one.

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Speaker 1: Yeah, let's take a quick break then, Garrison, Robert James,

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Speaker 1: you can answer that question and we're back. James, how

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Speaker 1: about you.

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Speaker 4: I'm proud of doing the Darien ones. I think, like,

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Speaker 4: I'm so happy that we finally got to a place

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Speaker 4: where like we could do that, where we could fund that,

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Speaker 4: Like I've been trying to do that, like I said,

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Speaker 4: for nearly.

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Speaker 2: A decade, and.

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Speaker 4: Yeah, it's been hard, and it continues to be hard,

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Speaker 4: like one of the people you heard from in those

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Speaker 4: episodes got deported last week then, so like it continues

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Speaker 4: to kind of be emotionally difficult. But I really liked

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Speaker 4: how many people messaged me and were like, I sent

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Speaker 4: this to my father, uncle, not just dudes aunts and

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Speaker 4: their mum's too, I'm sure, and think non binary relatives,

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Speaker 4: but like, well maybe not because they sent it to

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Speaker 4: their right wing relatives and they like learned some compassion.

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Speaker 4: That's always what you want to do, Like I said before,

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Speaker 4: you want people to see it so that they care

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Speaker 4: and so they understand it and they don't just get

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Speaker 4: this stupid Fox News bullshit racism stuff, and.

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Speaker 2: So yeah, that may be really happy.

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Speaker 4: The reason we're all different on this, by the way,

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Speaker 4: is because we have not done a Come twenty twenty

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Speaker 4: four episode, and if we had, this would have been

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Speaker 4: a much much shorter segment.

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Speaker 2: James, let me just tell you, I think we can

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Speaker 2: all look forward to all white Christmas this year.

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Speaker 1: Jesus mother.

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Speaker 2: Set him up. It's my own fault.

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Speaker 5: Wow, I guess I'll go now. I'll just short clean

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Speaker 5: out the aftertaste of that.

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Speaker 2: So it's worse.

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Speaker 5: I think I started out pretty strong with police drones,

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Speaker 5: even more topical as we record this now as New

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Speaker 5: Jersey is about to get completely abducted I think by

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Speaker 5: alien aircraft.

442
00:22:35,600 --> 00:22:37,399
Speaker 2: Yeah, there's no one left in New Jersey.

443
00:22:37,440 --> 00:22:41,320
Speaker 5: Now they've all been taken away by all these unidentified drone.

444
00:22:41,000 --> 00:22:43,359
Speaker 2: That actually happened three days ago. It just took a

445
00:22:43,400 --> 00:22:45,320
Speaker 2: long time for the rest of the country to notice

446
00:22:45,400 --> 00:22:49,320
Speaker 2: or care. Breece Springsteen hasn't made a song about it.

447
00:22:49,680 --> 00:22:50,720
Speaker 2: We have no way of known.

448
00:22:51,280 --> 00:22:54,480
Speaker 5: Besides the mass hysteria of the New Jersey drone panic.

449
00:22:54,960 --> 00:22:57,239
Speaker 5: Police drones are a real problem, and those are going

450
00:22:57,320 --> 00:23:00,359
Speaker 5: to be increasingly so. I was happy with my porting

451
00:23:01,080 --> 00:23:04,080
Speaker 5: on that at CES, and then I guess I mean

452
00:23:04,400 --> 00:23:07,719
Speaker 5: to echo Sophie. I had a great time at the RNC.

453
00:23:08,280 --> 00:23:11,399
Speaker 5: It's fun, a sentence I never thought I would say, yeah,

454
00:23:11,440 --> 00:23:13,920
Speaker 5: And particularly the R and C Grinder episode, I still

455
00:23:13,920 --> 00:23:15,480
Speaker 5: think is Bruce pretty is pretty good.

456
00:23:15,680 --> 00:23:19,439
Speaker 1: It's pretty great. The amount of places that Garrison and

457
00:23:19,480 --> 00:23:22,840
Speaker 1: I snuck into at the rn C a time.

458
00:23:23,560 --> 00:23:25,879
Speaker 5: It was really dangerous too, because I was having to

459
00:23:25,960 --> 00:23:28,760
Speaker 5: like do my RNC research next to Robert and Sophie

460
00:23:28,800 --> 00:23:31,520
Speaker 5: the whole time, and oh boy, it's like a minefield

461
00:23:31,600 --> 00:23:32,200
Speaker 5: scrolling through.

462
00:23:32,240 --> 00:23:37,840
Speaker 1: That app effects an experience, to say the least. Any

463
00:23:37,920 --> 00:23:40,679
Speaker 1: thoughts on the proposed twenty twenty eight general strike, How

464
00:23:40,720 --> 00:23:42,160
Speaker 1: are people feeling about that?

465
00:23:42,640 --> 00:23:45,240
Speaker 6: I'll start with Mia, Yeah, I mean it's it's a

466
00:23:45,240 --> 00:23:48,880
Speaker 6: pretty good idea, Like there's definitely sort of and I'm

467
00:23:48,920 --> 00:23:51,440
Speaker 6: immediately going into this nacying a little bit. There's definitely

468
00:23:51,760 --> 00:23:53,720
Speaker 6: problems with it. It's going to be extremely hard to

469
00:23:53,800 --> 00:23:56,879
Speaker 6: execute because we just don't have a modern history of

470
00:23:56,920 --> 00:23:58,320
Speaker 6: doing that in the US, and even some of the

471
00:23:58,320 --> 00:24:00,919
Speaker 6: success so ones in the last decade that people have

472
00:24:00,960 --> 00:24:04,240
Speaker 6: pulled off haven't been that effective. But on the other hand,

473
00:24:04,280 --> 00:24:06,359
Speaker 6: as something that we you know, a concrete thing that

474
00:24:06,400 --> 00:24:10,239
Speaker 6: we have to organize towards that has a bunch of

475
00:24:10,600 --> 00:24:14,879
Speaker 6: like pretty large unions behind it. Already, I did an

476
00:24:14,880 --> 00:24:17,760
Speaker 6: episode about that a few weeks ago. I don't know,

477
00:24:17,800 --> 00:24:19,600
Speaker 6: a couple months ago. I don't remember when I did

478
00:24:19,600 --> 00:24:22,760
Speaker 6: this episode. I'm sorry, I can't remember anything you've ever done.

479
00:24:23,359 --> 00:24:26,719
Speaker 6: But I think I think it's a good opportunity to

480
00:24:26,760 --> 00:24:29,280
Speaker 6: connect a whole bunch of different kinds of organizing together,

481
00:24:30,080 --> 00:24:32,160
Speaker 6: both in terms of sort of labor and in terms

482
00:24:32,200 --> 00:24:35,240
Speaker 6: of the support work you need for that. So yeah,

483
00:24:35,240 --> 00:24:36,280
Speaker 6: cautiously optimistic.

484
00:24:36,680 --> 00:24:38,359
Speaker 1: Anyone else have anything they want to add?

485
00:24:38,680 --> 00:24:41,000
Speaker 5: The time to start figuring out those logistics, like is

486
00:24:41,040 --> 00:24:43,520
Speaker 5: now it's it's not waiting till twenty twenty seven.

487
00:24:43,720 --> 00:24:47,199
Speaker 2: Yeah, I agree, Garrison. I think that the fact that

488
00:24:47,240 --> 00:24:51,320
Speaker 2: there are serious people who represent serious unions talking about

489
00:24:51,320 --> 00:24:54,200
Speaker 2: it is part of way. It's one of the things

490
00:24:54,200 --> 00:24:56,480
Speaker 2: that does give me a degree of hope. We're going

491
00:24:56,520 --> 00:24:59,080
Speaker 2: to have to start working now towards it. It's not

492
00:24:59,160 --> 00:25:01,560
Speaker 2: going to be so in any way, shape or form.

493
00:25:01,640 --> 00:25:04,159
Speaker 2: If they see it coming, they are going to start

494
00:25:04,200 --> 00:25:08,359
Speaker 2: trying to criminalize things preemptively. If it is something that

495
00:25:08,400 --> 00:25:11,640
Speaker 2: even looks like a real possibility, they're going to come

496
00:25:11,680 --> 00:25:13,679
Speaker 2: after it with everything they've got. And it's one of

497
00:25:13,680 --> 00:25:20,240
Speaker 2: those things where maybe if the midterms go well for Democrats,

498
00:25:20,480 --> 00:25:24,040
Speaker 2: maybe Democrats stop that, But it's just as plausible and

499
00:25:24,080 --> 00:25:27,280
Speaker 2: probably more plausible, that Democrats line up with Republicans to

500
00:25:27,320 --> 00:25:29,040
Speaker 2: attempt to criminalize something like that.

501
00:25:29,440 --> 00:25:33,360
Speaker 4: Yeah, it's strained to be seeing something like this organized

502
00:25:33,400 --> 00:25:34,480
Speaker 4: so far off like it.

503
00:25:35,080 --> 00:25:37,280
Speaker 2: Yes, it's not something where any of us are familiar with,

504
00:25:37,400 --> 00:25:39,720
Speaker 2: which it has to be to be clear. Yeah, yeah, Yeah,

505
00:25:39,720 --> 00:25:40,200
Speaker 2: it has to.

506
00:25:40,160 --> 00:25:43,520
Speaker 4: Be barring, like an actual coup. That's the only way

507
00:25:43,560 --> 00:25:45,960
Speaker 4: you get a general strike, right like, either something so

508
00:25:47,160 --> 00:25:50,280
Speaker 4: earth shattering that everyone so that everyone's ready to risk

509
00:25:50,280 --> 00:25:52,520
Speaker 4: it because they're already in danger or yeah, you take

510
00:25:52,560 --> 00:25:54,040
Speaker 4: the time when you plan that you do it properly.

511
00:25:54,400 --> 00:25:56,080
Speaker 4: But it's just not something we're familiar with. I love

512
00:25:56,080 --> 00:25:58,439
Speaker 4: the general strike. I'm always going to support a general strike.

513
00:25:59,080 --> 00:26:01,720
Speaker 4: I'm excited to see a general strike. But yeah, we

514
00:26:01,760 --> 00:26:02,800
Speaker 4: have to put in the work now.

515
00:26:03,400 --> 00:26:07,879
Speaker 2: Yeah, the only responsible way to characterize the organized left

516
00:26:07,960 --> 00:26:10,880
Speaker 2: in the United States is a complete and utter failure.

517
00:26:11,200 --> 00:26:14,080
Speaker 2: Like it has been a calamity for the causes that

518
00:26:14,160 --> 00:26:18,960
Speaker 2: it seeks to represent. And a lot of that is

519
00:26:19,000 --> 00:26:22,960
Speaker 2: because of like fucking bullshit online clicktivism. You know, we're

520
00:26:22,960 --> 00:26:25,400
Speaker 2: all going to do a general strike. Everybody, get ready

521
00:26:25,480 --> 00:26:27,400
Speaker 2: next week, We're going to do it. You know, shit

522
00:26:27,600 --> 00:26:31,239
Speaker 2: like that is. It's just so deeply unseerious. And if

523
00:26:31,240 --> 00:26:34,240
Speaker 2: we're going to take the momentum and the energy that

524
00:26:34,359 --> 00:26:37,719
Speaker 2: exists in the number of people who are angry and

525
00:26:37,760 --> 00:26:40,000
Speaker 2: who you know, and that number of people will be

526
00:26:40,080 --> 00:26:45,080
Speaker 2: increasing as the consequences of conservative policies hit home by

527
00:26:45,119 --> 00:26:49,439
Speaker 2: twenty twenty eight, Like, it has to be something taken

528
00:26:49,920 --> 00:26:53,960
Speaker 2: deadly seriously by very serious people who are thinking through

529
00:26:54,720 --> 00:26:58,040
Speaker 2: the consequences and what's necessary in order to make this feasible.

530
00:26:58,119 --> 00:27:02,919
Speaker 1: You know, and last, do each of you have you

531
00:27:02,960 --> 00:27:06,440
Speaker 1: know a movie or a book or something you would

532
00:27:06,480 --> 00:27:07,280
Speaker 1: like to recommend.

533
00:27:08,119 --> 00:27:10,760
Speaker 4: In twenty twenty five, when I finished my books, you

534
00:27:10,760 --> 00:27:12,600
Speaker 4: should buy it, yes, but.

535
00:27:12,760 --> 00:27:13,800
Speaker 2: Read General Strike.

536
00:27:13,840 --> 00:27:17,280
Speaker 4: I've been reading a book called Platentive, which is in English,

537
00:27:17,320 --> 00:27:20,119
Speaker 4: but it's about how San Francisco doc workers block to

538
00:27:20,200 --> 00:27:24,200
Speaker 4: shipment of weapons to El Salvador and it just seems

539
00:27:24,240 --> 00:27:27,080
Speaker 4: a very relevant book. And they did it to Pinochet

540
00:27:27,119 --> 00:27:30,159
Speaker 4: as well. It's easy to read and like. It just

541
00:27:30,240 --> 00:27:32,720
Speaker 4: reminded me how important labor organizing is going to be

542
00:27:32,800 --> 00:27:34,600
Speaker 4: in the next four years and how powerful it can

543
00:27:34,680 --> 00:27:36,840
Speaker 4: be too. So I'll give that one a little plug.

544
00:27:37,200 --> 00:27:37,680
Speaker 2: Excellent.

545
00:27:38,000 --> 00:27:40,200
Speaker 4: There's a film called The End Will Be Spectacular which

546
00:27:40,280 --> 00:27:44,320
Speaker 4: is about the Kurdish use movement in Northern Kurdistan in Turkey.

547
00:27:44,680 --> 00:27:47,639
Speaker 4: It's a really good film. I think of understand to

548
00:27:47,680 --> 00:27:50,400
Speaker 4: help you understand the Kurdish freedom movement, and it's worth

549
00:27:50,400 --> 00:27:52,600
Speaker 4: a watch. It's not like a necessarily a happy, feel

550
00:27:52,600 --> 00:27:55,000
Speaker 4: good film, but I think it's worth a watch. Fifty

551
00:27:55,160 --> 00:27:57,399
Speaker 4: if you've recently become interested in that because of what

552
00:27:57,440 --> 00:27:58,440
Speaker 4: you've heard on the podcast.

553
00:27:58,960 --> 00:28:02,679
Speaker 6: Yeah, yeah, I have a couple, so I'm trans fiction pilled.

554
00:28:02,840 --> 00:28:06,080
Speaker 6: Right now, you've given you fiction for trans authors.

555
00:28:05,960 --> 00:28:08,480
Speaker 2: Would you say you're transfixed? Wow?

556
00:28:09,200 --> 00:28:09,840
Speaker 5: I walked.

557
00:28:09,920 --> 00:28:15,480
Speaker 6: I walked right into that one, like drove directly into it,

558
00:28:15,560 --> 00:28:17,119
Speaker 6: like JFK's head into that bullet.

559
00:28:17,320 --> 00:28:18,520
Speaker 5: Oh my god.

560
00:28:20,520 --> 00:28:20,760
Speaker 2: Wow.

561
00:28:20,840 --> 00:28:22,560
Speaker 1: We spent a lot of time with each other.

562
00:28:25,800 --> 00:28:26,040
Speaker 2: Yeah.

563
00:28:26,040 --> 00:28:28,280
Speaker 6: The first one I wanted to talk about is The

564
00:28:28,280 --> 00:28:30,800
Speaker 6: gun Runner and Her Hound by Maria Ying, which is

565
00:28:30,800 --> 00:28:33,800
Speaker 6: the pen name of a couple of authors. Okay, so

566
00:28:33,920 --> 00:28:36,920
Speaker 6: it's this is a This is an absolutely unhinged lesbian

567
00:28:36,960 --> 00:28:39,560
Speaker 6: book about a lesbian crime ward and here a do

568
00:28:39,640 --> 00:28:42,840
Speaker 6: bodyguard who is also a lesbian, and it rules. Uh,

569
00:28:43,000 --> 00:28:46,320
Speaker 6: there's a whole sort of like post apocalypse US thing

570
00:28:46,360 --> 00:28:48,720
Speaker 6: going on, but they're still in like civilized Hong Kong.

571
00:28:49,240 --> 00:28:52,400
Speaker 6: It's awesome, it's great. It's you need you need war

572
00:28:52,480 --> 00:28:55,960
Speaker 6: on hinge lesbians in your life, go read this. The

573
00:28:55,960 --> 00:28:58,720
Speaker 6: the other one is One of the Boys. This is

574
00:28:58,920 --> 00:29:02,520
Speaker 6: Forthcoming is going to release May thirteen, twenty twenty five,

575
00:29:03,240 --> 00:29:07,000
Speaker 6: by Victoria Zeller, and it's about a trans girl who's

576
00:29:07,040 --> 00:29:09,920
Speaker 6: like the kicker on her football team and she has

577
00:29:09,920 --> 00:29:12,160
Speaker 6: to like leave the team because she transitions, but then

578
00:29:12,440 --> 00:29:14,440
Speaker 6: the team needs her back. They don't have a kicker,

579
00:29:14,760 --> 00:29:17,160
Speaker 6: and it's it's fun, it's it's a good time, so

580
00:29:17,200 --> 00:29:18,880
Speaker 6: you should get that when it comes out.

581
00:29:19,240 --> 00:29:21,720
Speaker 2: Yeah. So I'm actually right now in the middle of

582
00:29:21,720 --> 00:29:23,760
Speaker 2: a book that I found myself surprised by how much

583
00:29:23,800 --> 00:29:26,800
Speaker 2: I've liked. It's called When Paris Went Dark, and it

584
00:29:26,880 --> 00:29:30,640
Speaker 2: is a history of the occupation of Paris under the Nazis.

585
00:29:30,840 --> 00:29:35,440
Speaker 2: That is a really fascinating social history by Ronald rose

586
00:29:35,480 --> 00:29:39,280
Speaker 2: Bottom that I found very like, emotionally affecting, especially in

587
00:29:39,400 --> 00:29:43,040
Speaker 2: light of, you know, some things going on, and yeah,

588
00:29:43,160 --> 00:29:46,880
Speaker 2: just kind of a fascinating look at the psychology of

589
00:29:46,920 --> 00:29:50,520
Speaker 2: a people, of like of a of an entire people

590
00:29:51,040 --> 00:29:54,240
Speaker 2: kind of grappling with what's about to happen to them

591
00:29:54,400 --> 00:29:56,760
Speaker 2: in the wake of the failure of the French army

592
00:29:56,840 --> 00:29:59,680
Speaker 2: and then what happens next. And then I would also

593
00:29:59,720 --> 00:30:03,640
Speaker 2: reckon Men Setting the Desert on Fire by James Barr,

594
00:30:03,960 --> 00:30:07,840
Speaker 2: which is one of the books about te Lawrence that

595
00:30:07,920 --> 00:30:12,479
Speaker 2: I cited in the Te Lawrence episodes. If you are

596
00:30:12,520 --> 00:30:15,480
Speaker 2: at all interested in the realities of needing to fight

597
00:30:15,560 --> 00:30:16,440
Speaker 2: an insurgent war.

598
00:30:17,040 --> 00:30:20,720
Speaker 5: Here, I guess just two recent things I've enjoyed. Finally

599
00:30:20,960 --> 00:30:27,440
Speaker 5: finished the Steppenwolf by Herman Hess. Yes, I enjoyed that deeply.

600
00:30:28,200 --> 00:30:31,000
Speaker 5: It kind of it kind of my picked my twin

601
00:30:31,000 --> 00:30:33,880
Speaker 5: Peaks the Return Brain. So that was that was pleasant

602
00:30:34,560 --> 00:30:39,600
Speaker 5: for and for a more recent release, Luca Guardaghino's new

603
00:30:39,680 --> 00:30:43,880
Speaker 5: movie Queer, adapting the short story by William S.

604
00:30:43,880 --> 00:30:44,240
Speaker 2: Burrows.

605
00:30:44,520 --> 00:30:49,680
Speaker 5: I found this movie to be utterly fascinating and transfixing,

606
00:30:49,840 --> 00:30:53,440
Speaker 5: to use the term from me, Robert. I don't have

607
00:30:53,520 --> 00:30:55,440
Speaker 5: much else to say about it because I would rather

608
00:30:55,480 --> 00:30:58,080
Speaker 5: people just watch it and take away what they want

609
00:30:58,120 --> 00:31:00,520
Speaker 5: to themselves. But it got me thinking a lot about

610
00:31:00,960 --> 00:31:05,640
Speaker 5: the lack of meaning inherent to identity and why I

611
00:31:05,680 --> 00:31:08,920
Speaker 5: hate the term queer bodies. So yeah, good.

612
00:31:08,720 --> 00:31:13,000
Speaker 1: Movie, awesome. I just have one movie to recommend, and

613
00:31:13,040 --> 00:31:16,120
Speaker 1: it's one of my favorite movies of all time, the

614
00:31:16,720 --> 00:31:21,200
Speaker 1: original nineteen seventy three, seventy two, so seventy three, seventy three,

615
00:31:21,680 --> 00:31:25,280
Speaker 1: The Wicker Man, not the fucking Nicholas Cage version, the

616
00:31:25,400 --> 00:31:29,520
Speaker 1: original version. And if you have a local theater that

617
00:31:29,560 --> 00:31:32,440
Speaker 1: plays old movies a lot of times, they'll play it

618
00:31:32,480 --> 00:31:36,080
Speaker 1: in theaters, and I highly recommend that experience. It's really fun,

619
00:31:36,160 --> 00:31:39,360
Speaker 1: especially at the end. I see it in theaters or

620
00:31:39,400 --> 00:31:41,360
Speaker 1: watch it at least once or twice a year and

621
00:31:42,400 --> 00:31:44,760
Speaker 1: vibes are good. Yeah, that's it for a Q and

622
00:31:44,800 --> 00:32:04,520
Speaker 1: A episode. Thanks for submitting, and goodbye, Welcome to it

623
00:32:04,560 --> 00:32:08,000
Speaker 1: could happen here. This is our twenty twenty five Predictions episode.

624
00:32:08,080 --> 00:32:11,760
Speaker 1: We were starting to bicker off Bike about what we

625
00:32:11,800 --> 00:32:13,680
Speaker 1: predicted last year, and I was talking about the things

626
00:32:13,680 --> 00:32:16,360
Speaker 1: we predicted, and one of the things I predicted early on,

627
00:32:16,400 --> 00:32:19,560
Speaker 1: I was like, I think Kim Kardashian will be part

628
00:32:19,600 --> 00:32:25,320
Speaker 1: of the Trump cabinet, and like, honestly goals at this point,

629
00:32:25,720 --> 00:32:29,960
Speaker 1: but I'm not that far off though, because essentially what

630
00:32:30,120 --> 00:32:32,320
Speaker 1: he has done is he's basically tried to go for

631
00:32:32,400 --> 00:32:34,800
Speaker 1: people that are good on TV. It's true, It's true,

632
00:32:35,000 --> 00:32:39,120
Speaker 1: and like going off of that reality TV energy.

633
00:32:40,040 --> 00:32:42,760
Speaker 4: Finally we will acknowledge the Armenian genocide.

634
00:32:43,920 --> 00:32:46,280
Speaker 1: I was I was vibing, okay, James, I.

635
00:32:46,280 --> 00:32:50,520
Speaker 4: Was vibing genocide.

636
00:32:50,640 --> 00:32:57,760
Speaker 1: Just James, all right, Viba side God all right? Mia

637
00:32:57,800 --> 00:33:01,360
Speaker 1: woggs here, I'm Bred Garrison's here, James Stout's here, and

638
00:33:01,440 --> 00:33:03,640
Speaker 1: the dishonorable Robert Evans is also here.

639
00:33:04,040 --> 00:33:06,880
Speaker 2: I judge that nickname bad Jesus Christ.

640
00:33:07,000 --> 00:33:11,080
Speaker 5: Wow, let's let's go over some of our terrible twenty

641
00:33:11,120 --> 00:33:15,200
Speaker 5: twenty four predictions just briefly. Now, Unfortunately there was a

642
00:33:15,200 --> 00:33:17,520
Speaker 5: lot of election ones which were very sad to listen to.

643
00:33:17,760 --> 00:33:22,560
Speaker 5: Oh no, now, we were correct about many things we did.

644
00:33:22,600 --> 00:33:25,600
Speaker 5: We did talk about how Harris would probably be a

645
00:33:25,640 --> 00:33:28,640
Speaker 5: really bad candidate to run against Trump. Totally forgot about that.

646
00:33:29,240 --> 00:33:34,800
Speaker 2: We did. We did huge for us. Yeah, for the country.

647
00:33:35,880 --> 00:33:38,320
Speaker 2: That brief period of time when Biden stepped down, it

648
00:33:38,360 --> 00:33:40,560
Speaker 2: really felt like it might be I mean, she did

649
00:33:40,560 --> 00:33:43,080
Speaker 2: better than he would have done. Yeah.

650
00:33:43,120 --> 00:33:45,120
Speaker 5: Well, I think that's just because we were still just

651
00:33:45,160 --> 00:33:49,400
Speaker 5: reeling from that debate, because so bad that like anything

652
00:33:49,560 --> 00:33:51,280
Speaker 5: was like, oh my god, there's like a life.

653
00:33:51,280 --> 00:33:54,200
Speaker 2: Look at how she can walk thirty forty feet at

654
00:33:54,200 --> 00:33:57,640
Speaker 2: a time, exact exact sentence. Good God.

655
00:33:58,040 --> 00:34:01,000
Speaker 5: None of us picked Vance specifically at that point in time,

656
00:34:01,040 --> 00:34:04,960
Speaker 5: but we did pinpoint Trump's orbit and his like campaign

657
00:34:05,080 --> 00:34:10,000
Speaker 5: like hmmm crew pretty well, like Mia predicted that RFK

658
00:34:10,160 --> 00:34:12,480
Speaker 5: Junior could be a Trump VP pick, and though he

659
00:34:12,520 --> 00:34:15,279
Speaker 5: didn't become VP, he essentially kind of took over the

660
00:34:15,400 --> 00:34:18,279
Speaker 5: VP like campaigning role from Vans in.

661
00:34:18,200 --> 00:34:21,239
Speaker 2: Like August Ye was so bad at it.

662
00:34:22,000 --> 00:34:24,439
Speaker 5: We all decided that like Vivec was simply like way

663
00:34:24,480 --> 00:34:27,000
Speaker 5: too loud and like obnoxious. So Trump would like find

664
00:34:27,000 --> 00:34:29,839
Speaker 5: some other spot for him. Stand by that, And that's

665
00:34:29,840 --> 00:34:31,640
Speaker 5: what happened. He's still in the orbit, but he's not

666
00:34:31,680 --> 00:34:35,560
Speaker 5: super close. So if he talked about possibly Christine nome

667
00:34:36,280 --> 00:34:39,520
Speaker 5: As is getting LinkedIn with Trump maybe for VP. Now

668
00:34:39,520 --> 00:34:43,200
Speaker 5: that didn't happen for VP, but christineoam is in the cabinet.

669
00:34:43,560 --> 00:34:44,759
Speaker 1: Good job passed me.

670
00:34:45,120 --> 00:34:47,520
Speaker 5: Yeah, and Robert said that he would not be shocked

671
00:34:47,600 --> 00:34:56,240
Speaker 5: if Trump got close with Tulca Gabbert and other less

672
00:34:56,239 --> 00:34:59,759
Speaker 5: good predictions I predicted that a daily wire host would

673
00:34:59,760 --> 00:35:04,560
Speaker 5: get unfortunately did not come to pass. There's still time.

674
00:35:04,640 --> 00:35:06,840
Speaker 6: It's still twenty twenty fours, right.

675
00:35:07,000 --> 00:35:08,640
Speaker 2: Not when this airs, not when this air.

676
00:35:10,160 --> 00:35:13,920
Speaker 5: Yes, Kim Kardashian getting into politics didn't really happen, she

677
00:35:14,000 --> 00:35:16,880
Speaker 5: kind of stated at her regular coast level. Sorry, Sophie

678
00:35:17,360 --> 00:35:17,839
Speaker 5: so far.

679
00:35:18,160 --> 00:35:21,480
Speaker 1: Trust me. She did all those things when Trump was

680
00:35:21,480 --> 00:35:23,360
Speaker 1: elected the first time, where all of a sudden she was,

681
00:35:23,440 --> 00:35:26,440
Speaker 1: like with other lawyers trying to get people out of

682
00:35:26,520 --> 00:35:29,040
Speaker 1: jail by utilizing Trump.

683
00:35:29,200 --> 00:35:31,080
Speaker 5: Yeah, I mean, and she was doing that with the

684
00:35:31,120 --> 00:35:34,240
Speaker 5: Biden campaign as well, not as visible a Harris campaign.

685
00:35:34,600 --> 00:35:37,360
Speaker 5: She was meeting with Harris multiple times. She kind of

686
00:35:37,360 --> 00:35:40,040
Speaker 5: stayed at this like distant but like talkative place.

687
00:35:40,640 --> 00:35:43,680
Speaker 1: That's the Kardashian way, distant and talkative.

688
00:35:45,280 --> 00:35:48,799
Speaker 5: Speaking of speaking of your other prediction was that people

689
00:35:48,800 --> 00:35:52,040
Speaker 5: would start forgetting about the Nazi stuff and Kanye would

690
00:35:52,040 --> 00:35:57,160
Speaker 5: put out a well received album, which kind of happened. Yeah, yeah, yeah,

691
00:35:57,200 --> 00:35:57,680
Speaker 5: a little bit.

692
00:35:58,000 --> 00:36:01,080
Speaker 1: God, I haven't thought about Kanye so many months. It

693
00:36:01,160 --> 00:36:05,480
Speaker 1: was really nice, well, really nice, thanks Garrison.

694
00:36:06,000 --> 00:36:10,480
Speaker 5: Lastly, my failed prediction is that if Trump won the election,

695
00:36:10,600 --> 00:36:12,879
Speaker 5: that there would be two solid weeks of writing which

696
00:36:12,920 --> 00:36:13,920
Speaker 5: simply did not happen.

697
00:36:14,120 --> 00:36:15,400
Speaker 2: Yes, nothing happened.

698
00:36:15,600 --> 00:36:18,640
Speaker 5: I think it's actually kind of interesting, and we will

699
00:36:18,680 --> 00:36:21,880
Speaker 5: maybe unpack that in the coming months as Trump's second

700
00:36:21,960 --> 00:36:24,560
Speaker 5: term kind of settles in. I'm sure we will kind

701
00:36:24,600 --> 00:36:28,240
Speaker 5: of revisit why we think this did not happen. Certainly,

702
00:36:28,280 --> 00:36:30,960
Speaker 5: I'm curious about what inauguration Day will look like. But

703
00:36:30,960 --> 00:36:35,920
Speaker 5: but yeah, that was a lot so sorry. Morsey is

704
00:36:35,920 --> 00:36:39,560
Speaker 5: still alive, David David Scavenger is still alive, Putin is

705
00:36:39,640 --> 00:36:42,920
Speaker 5: still alive, and though James did say that Asad would

706
00:36:42,960 --> 00:36:46,879
Speaker 5: eat it, and though a sawd didn't die, he kind

707
00:36:46,880 --> 00:36:47,640
Speaker 5: of did eat it.

708
00:36:49,480 --> 00:36:52,480
Speaker 2: Well, yeah, I mean, James, Yeah, that's that's not gonna

709
00:36:52,520 --> 00:36:56,399
Speaker 2: be the biggest dub of the year. Yeah, that's right.

710
00:36:56,680 --> 00:37:00,560
Speaker 4: Damn I forgot no one about that. Really happy with myself.

711
00:37:00,160 --> 00:37:02,240
Speaker 1: Now, James, I'm so proud of you, buddy.

712
00:37:02,840 --> 00:37:05,839
Speaker 2: You got to pick another one this year. Yeah, may

713
00:37:05,960 --> 00:37:07,800
Speaker 2: on long, baby, he's next.

714
00:37:09,000 --> 00:37:12,480
Speaker 5: Let's I guess let's start with some kind of dictator predictions.

715
00:37:12,600 --> 00:37:15,000
Speaker 5: What do we think will happen to like a dictator

716
00:37:15,440 --> 00:37:16,960
Speaker 5: in twenty twenty five.

717
00:37:17,120 --> 00:37:18,880
Speaker 4: Which is Hace is gonna die? Do we think go

718
00:37:19,000 --> 00:37:20,480
Speaker 4: just general dictator predictions?

719
00:37:20,600 --> 00:37:24,279
Speaker 5: Dictator predictions. It can be maybe we get a new one,

720
00:37:24,360 --> 00:37:26,200
Speaker 5: you know, maybe we get a new fancy one.

721
00:37:26,280 --> 00:37:28,920
Speaker 2: Yeah, well I don't know. Yeah, something's happening in January.

722
00:37:29,600 --> 00:37:31,879
Speaker 6: I have two well one of them, I mean it's

723
00:37:31,920 --> 00:37:33,520
Speaker 6: kind of a hack one, but I don't think. I

724
00:37:33,520 --> 00:37:35,560
Speaker 6: don't think the June to memr makes out of twenty

725
00:37:35,600 --> 00:37:39,000
Speaker 6: twenty five. Yeah, I think not in the version is today. Yeah,

726
00:37:39,040 --> 00:37:41,640
Speaker 6: that's the hack one. The the other one is another

727
00:37:41,640 --> 00:37:44,000
Speaker 6: assad one. Is I think someone actually does assassinate a

728
00:37:44,080 --> 00:37:46,560
Speaker 6: sod Well, he's like like he he gets too full

729
00:37:46,600 --> 00:37:48,239
Speaker 6: of himself and he goes to Abi Dabi and some

730
00:37:48,280 --> 00:37:50,000
Speaker 6: Mosan brotherhood guy just wax him.

731
00:37:50,239 --> 00:37:51,440
Speaker 2: Yep, okay.

732
00:37:51,680 --> 00:37:54,520
Speaker 5: May Assaud prediction is he becomes a Russia Today host.

733
00:37:54,920 --> 00:37:56,640
Speaker 5: That's my assad prediction.

734
00:37:57,000 --> 00:37:58,480
Speaker 1: Oh my gosh.

735
00:37:58,680 --> 00:38:02,120
Speaker 4: Yeah, he's going to open his ophthalmology clinic.

736
00:38:02,760 --> 00:38:05,120
Speaker 2: No, I mean I think he's going to get signed

737
00:38:05,520 --> 00:38:09,040
Speaker 2: to host a podcast by a little a little network

738
00:38:09,080 --> 00:38:13,400
Speaker 2: you might have heard of called cool Zone Media. Congratulations, guys,

739
00:38:13,640 --> 00:38:15,319
Speaker 2: let's bring him on. So if you get him on

740
00:38:15,360 --> 00:38:16,960
Speaker 2: the get him on the zoom, tell him you can

741
00:38:17,120 --> 00:38:24,719
Speaker 2: kick an hop in the room. Now for sure, baby.

742
00:38:23,120 --> 00:38:26,240
Speaker 5: We are merging with Tennant Media to bring up our friend.

743
00:38:28,400 --> 00:38:33,399
Speaker 2: Yeah, welcome to the Podsha. He's actually doing a whole

744
00:38:33,440 --> 00:38:35,839
Speaker 2: media tour with the Pod Save guys next week. That's

745
00:38:35,840 --> 00:38:36,879
Speaker 2: got to be fascinating.

746
00:38:38,360 --> 00:38:42,640
Speaker 4: Pod Save bathis Siria the most cursed podcast in the world.

747
00:38:44,040 --> 00:38:48,160
Speaker 1: My Dictator slash world Leader prediction is that despite being

748
00:38:48,200 --> 00:38:52,160
Speaker 1: Nen Yahoo's I was thinking, yeah, last ride.

749
00:38:52,120 --> 00:38:56,440
Speaker 2: From your mouth to whatever fucking clot is working its

750
00:38:56,480 --> 00:38:59,800
Speaker 2: way through his coronary sy in a year.

751
00:39:00,280 --> 00:39:03,640
Speaker 1: I really fucking hope. I'm really fucking hope. I'm right,

752
00:39:05,400 --> 00:39:05,880
Speaker 1: we all do.

753
00:39:05,920 --> 00:39:07,280
Speaker 2: I don't know what else to say there?

754
00:39:07,440 --> 00:39:11,319
Speaker 4: Yeah, yeah, yeah, yeah, yeah, that's a big thing for

755
00:39:11,360 --> 00:39:11,640
Speaker 4: the world.

756
00:39:12,280 --> 00:39:12,480
Speaker 2: Yeah.

757
00:39:12,520 --> 00:39:15,640
Speaker 5: I mean we are verging into not doing predictions, just

758
00:39:15,640 --> 00:39:16,640
Speaker 5: doing hopes and dreams.

759
00:39:16,880 --> 00:39:19,000
Speaker 4: Yeah. Well I did Morrissey like that last year. We

760
00:39:19,040 --> 00:39:20,040
Speaker 4: didn't get it, and I'm sack.

761
00:39:20,160 --> 00:39:22,200
Speaker 5: We need some hopes and dreams out in the world.

762
00:39:22,239 --> 00:39:24,040
Speaker 1: I fair enough.

763
00:39:24,400 --> 00:39:28,240
Speaker 5: Yeah, do you know what else? We need team money

764
00:39:28,440 --> 00:39:40,840
Speaker 5: from these advertisers. That's right, and we.

765
00:39:40,920 --> 00:39:44,320
Speaker 1: Are back, all right, Garrison, what's next?

766
00:39:44,680 --> 00:39:48,000
Speaker 5: So usually in the middle of these prediction episodes, I

767
00:39:48,400 --> 00:39:53,080
Speaker 5: like doing our like third annual death segment, who do

768
00:39:53,080 --> 00:39:54,920
Speaker 5: we think will die? And I guess we kin we

769
00:39:55,000 --> 00:39:57,120
Speaker 5: kind of touched on this briefly, but I don't think

770
00:39:57,160 --> 00:39:59,240
Speaker 5: we we actually secure death for any of those people

771
00:39:59,480 --> 00:40:01,480
Speaker 5: in our prediction, just that they would, you know, have

772
00:40:01,600 --> 00:40:05,719
Speaker 5: circumstances change, though for this year's death segment we have.

773
00:40:05,880 --> 00:40:09,360
Speaker 5: We have a bit of a twist. So it turns

774
00:40:09,360 --> 00:40:13,920
Speaker 5: out about two years ago, on Spotify Wrapped Day, we

775
00:40:14,040 --> 00:40:17,800
Speaker 5: all woke up to the news that both Angela BATTLEMENTI

776
00:40:17,920 --> 00:40:21,759
Speaker 5: was embarrassingly my number one Spotify artist that year, but

777
00:40:21,960 --> 00:40:28,280
Speaker 5: also that Henry Kissinger died honey, and this Spotify rap. Today,

778
00:40:29,200 --> 00:40:32,880
Speaker 5: we will come to the news that the United Healthcare

779
00:40:33,239 --> 00:40:39,200
Speaker 5: CEO was gunned down in New York City. So Spotify

780
00:40:39,360 --> 00:40:41,960
Speaker 5: rapped twenty twenty five Who's dying?

781
00:40:42,040 --> 00:40:43,759
Speaker 2: Who's dying on.

782
00:40:45,960 --> 00:40:49,040
Speaker 5: Spotify rap to day? So this is what late November,

783
00:40:49,160 --> 00:40:55,480
Speaker 5: early December, we don't really know. Spotify rapped death day predictions.

784
00:40:55,520 --> 00:40:59,960
Speaker 1: So long, farewell, Love Eider saying goodbye, Mitch McCall.

785
00:41:00,600 --> 00:41:05,520
Speaker 2: Oh, that's a good one. That's an easy one. But okay,

786
00:41:05,800 --> 00:41:06,640
Speaker 2: I'll give it to you.

787
00:41:06,880 --> 00:41:09,879
Speaker 5: I'm thinking, like, who's got to get through most of

788
00:41:09,920 --> 00:41:13,280
Speaker 5: the year but not finish it out. You know, it's tough.

789
00:41:13,719 --> 00:41:16,560
Speaker 2: I'm gonna make my call tye up resip air to one.

790
00:41:16,719 --> 00:41:19,960
Speaker 2: You know. That's that's that's my hope. That's a long shot.

791
00:41:20,000 --> 00:41:21,960
Speaker 2: I know, yes, he doesn't seem like he's in bad health,

792
00:41:22,040 --> 00:41:22,959
Speaker 2: but that's a big one.

793
00:41:23,400 --> 00:41:26,360
Speaker 5: Kissinger was a long shot too, because he was like

794
00:41:26,520 --> 00:41:27,760
Speaker 5: arguably immortals.

795
00:41:27,800 --> 00:41:29,960
Speaker 2: He'd kept living for so fucking long.

796
00:41:30,320 --> 00:41:36,960
Speaker 1: Ah, so long, farewell, Love Eater, say goodbye Musk.

797
00:41:37,600 --> 00:41:39,840
Speaker 2: I was gonna say that I think he might die.

798
00:41:40,040 --> 00:41:41,719
Speaker 5: You think we're finally gonna get that drug over do.

799
00:41:41,880 --> 00:41:44,920
Speaker 4: So I just he just seems to be spiraling so

800
00:41:45,120 --> 00:41:45,840
Speaker 4: hard right.

801
00:41:45,680 --> 00:41:47,120
Speaker 1: Now, the spiral's mad real.

802
00:41:47,239 --> 00:41:49,520
Speaker 2: Yeah, he's getting everything he wants though, but I mean

803
00:41:49,520 --> 00:41:50,480
Speaker 2: that that also.

804
00:41:50,320 --> 00:41:51,400
Speaker 5: It's It's true.

805
00:41:52,280 --> 00:41:56,680
Speaker 2: Sometimes that's dangerous, Yeah, especially if you are addicted to

806
00:41:56,840 --> 00:41:59,960
Speaker 2: a drug that you can get in unlimited peer quantities

807
00:42:00,080 --> 00:42:01,880
Speaker 2: and no one will ever say no to handing it

808
00:42:01,920 --> 00:42:02,120
Speaker 2: to you.

809
00:42:02,600 --> 00:42:05,520
Speaker 5: We have some more must predictions for later on the episode. Okay,

810
00:42:05,960 --> 00:42:08,840
Speaker 5: but I can see of some, you know, like fantously

811
00:42:08,920 --> 00:42:12,479
Speaker 5: the Secret Service, you know, not not great at hiding

812
00:42:12,520 --> 00:42:15,440
Speaker 5: there own drug problems. I can I can see possibly

813
00:42:15,680 --> 00:42:18,440
Speaker 5: with Musk entering a new level of comfort, maybe the

814
00:42:18,520 --> 00:42:21,480
Speaker 5: spiraling a little, a little too far out of his control.

815
00:42:21,600 --> 00:42:24,319
Speaker 2: He and two Secret Service ations are found dead with

816
00:42:24,440 --> 00:42:25,760
Speaker 2: fitnyl infected blow.

817
00:42:28,920 --> 00:42:35,920
Speaker 5: Maybe a SpaceX launch goes really wrong. Who's to say?

818
00:42:36,440 --> 00:42:39,960
Speaker 5: Who's to say? Damn? I gotta think of who? Who?

819
00:42:40,040 --> 00:42:42,520
Speaker 5: My who? My Spotify Rapped Day death is.

820
00:42:42,719 --> 00:42:47,320
Speaker 6: I have a long shot. Oh yeah, My long shot

821
00:42:47,520 --> 00:42:51,760
Speaker 6: is that sometime on Spotify rap Day, JK Rolling sees

822
00:42:51,800 --> 00:42:54,279
Speaker 6: a trans woman just like existing and gets so mad,

823
00:42:54,360 --> 00:42:55,560
Speaker 6: he has an aneurysm and dies.

824
00:42:55,560 --> 00:42:59,960
Speaker 5: No, she's looking through the Spotify wraps and she knows

825
00:43:00,440 --> 00:43:03,200
Speaker 5: that trans would make the best music, and she she

826
00:43:03,320 --> 00:43:06,719
Speaker 5: sees it, gets so mad she just she just kills over.

827
00:43:08,080 --> 00:43:12,239
Speaker 4: She transvestigates every single female artist on the Spotify rap

828
00:43:12,280 --> 00:43:14,759
Speaker 4: list and dies of sleep deprivation doing so.

829
00:43:15,000 --> 00:43:17,160
Speaker 6: Her her own fans start transvestigating her.

830
00:43:17,920 --> 00:43:20,360
Speaker 2: This is the edge.

831
00:43:20,840 --> 00:43:24,120
Speaker 5: Okay, I have a real long shot here, but I

832
00:43:24,160 --> 00:43:27,080
Speaker 5: can see how it could happen. So we're we're in.

833
00:43:27,160 --> 00:43:29,840
Speaker 5: We're in like what like month, month ten eleven of

834
00:43:29,920 --> 00:43:35,120
Speaker 5: Trump term two. Right, the right wing nazi content creators

835
00:43:35,120 --> 00:43:38,600
Speaker 5: are settling, are settling into their into their kind of groove.

836
00:43:38,920 --> 00:43:41,840
Speaker 5: Some of them aren't really happy at Trump, not like

837
00:43:42,320 --> 00:43:44,319
Speaker 5: you know, carrying on all of all of his big

838
00:43:44,360 --> 00:43:49,920
Speaker 5: lofty promises. And one one disgruntled fan of Nick Fuentes

839
00:43:50,920 --> 00:43:56,000
Speaker 5: does something crazy on Spotify rapped day, and that's that's

840
00:43:56,040 --> 00:43:59,800
Speaker 5: that's my prediction is that somehow something like really weird,

841
00:43:59,840 --> 00:44:05,120
Speaker 5: like like stalker or fan does something to to mister Fuentes.

842
00:44:05,520 --> 00:44:08,360
Speaker 5: Just pure prediction on like just like what would be

843
00:44:08,360 --> 00:44:11,680
Speaker 5: the oddest, oddest thing to happen, but something that could

844
00:44:11,719 --> 00:44:14,279
Speaker 5: totally make sense. Maybe it's like an old like Kanye fan,

845
00:44:14,440 --> 00:44:16,320
Speaker 5: you know from Kanye and Nick.

846
00:44:16,360 --> 00:44:17,360
Speaker 2: From his nasra.

847
00:44:17,880 --> 00:44:20,960
Speaker 5: Yeah, Yeah, I don't know. I feel like it's it's

848
00:44:21,480 --> 00:44:24,120
Speaker 5: his fandom's getting close enough to pull some like weird

849
00:44:24,160 --> 00:44:26,239
Speaker 5: crazy shit like that on, like a weird like on,

850
00:44:26,320 --> 00:44:30,040
Speaker 5: like a on, like a deeply parasocially destructive level, like

851
00:44:30,080 --> 00:44:34,319
Speaker 5: Stephen King's misery. A misery happens to Nick Quintes, but

852
00:44:34,360 --> 00:44:36,520
Speaker 5: he doesn't but he doesn't make it, he doesn't make

853
00:44:36,560 --> 00:44:39,400
Speaker 5: it out. That's that's my Spotify wrapped prediction.

854
00:44:40,000 --> 00:44:43,120
Speaker 2: I have said for years Nick Fuintes is going to

855
00:44:43,239 --> 00:44:47,320
Speaker 2: go down live probably maybe maybe live. He's gonna go

856
00:44:47,360 --> 00:44:50,000
Speaker 2: down like George Lincoln Rockwell. It is not going to

857
00:44:50,080 --> 00:44:52,279
Speaker 2: be like an enemy of his, that does it. It's

858
00:44:52,320 --> 00:44:55,760
Speaker 2: going to be a result of his incredibly messy personal life. Yeah,

859
00:44:55,840 --> 00:44:59,160
Speaker 2: like someone is going to take him down. H Like

860
00:44:59,280 --> 00:45:02,239
Speaker 2: it's it's that Yeah, Yeah, that feels right.

861
00:45:03,040 --> 00:45:06,680
Speaker 1: Do we have a non categorized predictions? Is it that

862
00:45:06,719 --> 00:45:07,320
Speaker 1: time yet?

863
00:45:07,440 --> 00:45:07,760
Speaker 2: Sure?

864
00:45:08,080 --> 00:45:11,239
Speaker 5: Now that we have, we have finished our Spotify wrapped predictions,

865
00:45:11,600 --> 00:45:13,799
Speaker 5: And I do not know who my top artists will be.

866
00:45:14,640 --> 00:45:18,880
Speaker 5: This last year it was Trent Resnor so salute that's like, okay,

867
00:45:19,040 --> 00:45:23,240
Speaker 5: Garrison Challenger soundtrack, that thing fucking bops.

868
00:45:23,440 --> 00:45:26,160
Speaker 1: I tried to make Robert watch that on the way

869
00:45:26,200 --> 00:45:28,120
Speaker 1: to Oh is it the d n C or the

870
00:45:28,239 --> 00:45:31,200
Speaker 1: r n C. I don't remember, and but he wouldn't

871
00:45:31,239 --> 00:45:33,880
Speaker 1: watch it with headphones, and so it was just on

872
00:45:33,880 --> 00:45:35,800
Speaker 1: on the plane. I think it was the DNC.

873
00:45:36,560 --> 00:45:37,320
Speaker 5: That's terrible.

874
00:45:37,680 --> 00:45:39,880
Speaker 2: Yeah, I think I think I was reading a Nick Land.

875
00:45:43,280 --> 00:45:47,160
Speaker 5: Honestly, that's a vibe that actually pairs quite well.

876
00:45:47,440 --> 00:45:52,439
Speaker 2: I landed completely deranged. It was great, Ready to work.

877
00:45:53,560 --> 00:45:56,520
Speaker 1: A prediction. A prediction that I have is that like

878
00:45:57,400 --> 00:46:01,200
Speaker 1: Trump basically tries to move a lot of the main

879
00:46:01,640 --> 00:46:06,520
Speaker 1: time he spends to mar Alago versus the White House. Like,

880
00:46:06,640 --> 00:46:09,560
Speaker 1: I feel like he's going to make mar A Lago

881
00:46:09,760 --> 00:46:13,799
Speaker 1: some like national monument type shit so that he can

882
00:46:13,880 --> 00:46:16,560
Speaker 1: take whatever the fuck documents he wants from the White

883
00:46:16,600 --> 00:46:18,560
Speaker 1: House to mar A Lago and spend as much time

884
00:46:18,560 --> 00:46:22,000
Speaker 1: there as he wants and make that like a national

885
00:46:22,080 --> 00:46:23,160
Speaker 1: residence or some shit.

886
00:46:23,840 --> 00:46:28,600
Speaker 5: Went to White House, Yeah, the whiter House, we could

887
00:46:28,640 --> 00:46:29,120
Speaker 5: call it.

888
00:46:29,239 --> 00:46:36,360
Speaker 2: Yeah, so true, true Harrison. No, I'm kind of interested

889
00:46:36,440 --> 00:46:39,719
Speaker 2: to watch what happens with AOC over the next year,

890
00:46:39,880 --> 00:46:43,840
Speaker 2: because she has definitely become to a lot of folks

891
00:46:43,880 --> 00:46:46,399
Speaker 2: that progressive and on the left, like a villain over

892
00:46:46,440 --> 00:46:51,040
Speaker 2: the last year, And I kind of wouldn't be surprised if, like,

893
00:46:51,320 --> 00:46:55,680
Speaker 2: in assuming there's still politics in twenty years. When we're

894
00:46:55,719 --> 00:46:59,600
Speaker 2: talking to young people, they think of her like Pelosi

895
00:46:59,719 --> 00:47:02,640
Speaker 2: and like, oh, you've got to understand when things started out,

896
00:47:02,640 --> 00:47:05,960
Speaker 2: this was a very different person. Yeah, yeah, Yeah. And

897
00:47:06,040 --> 00:47:08,440
Speaker 2: I'm not saying that's a fair way to characterize her

898
00:47:08,480 --> 00:47:10,400
Speaker 2: now or where she'll go. I'm just saying, like, I

899
00:47:10,440 --> 00:47:12,719
Speaker 2: wouldn't be shocked if that's the way a lot of

900
00:47:12,719 --> 00:47:15,600
Speaker 2: folks are looking at it in fucking a few years,

901
00:47:15,600 --> 00:47:17,279
Speaker 2: because I'm saying I'm hearing a lot of that now. Yeah,

902
00:47:17,320 --> 00:47:21,399
Speaker 2: peop are very angry at her over largely Gaza. But yeah,

903
00:47:21,480 --> 00:47:23,799
Speaker 2: also the fact that she and Bernie both tried to

904
00:47:23,800 --> 00:47:30,200
Speaker 2: back Biden kind of yeah late in his uh centicence. Yeah, Okay.

905
00:47:30,200 --> 00:47:32,319
Speaker 6: My My big one for the year is this is

906
00:47:32,320 --> 00:47:34,560
Speaker 6: the this is the year the economy finally collapses, Like,

907
00:47:34,600 --> 00:47:36,600
Speaker 6: this is the year you find out that no company

908
00:47:36,600 --> 00:47:40,000
Speaker 6: has made any fucking money in a decade. It's all

909
00:47:40,040 --> 00:47:44,200
Speaker 6: been being pumped up by like a deranged combination of

910
00:47:44,239 --> 00:47:48,160
Speaker 6: interest rate bullshit, a bunch of fucking money from like

911
00:47:48,239 --> 00:47:51,640
Speaker 6: overnight repo purchases, keeping the banks propped up. And I

912
00:47:51,880 --> 00:47:53,279
Speaker 6: don't know if it's gonna be the trade war that

913
00:47:53,320 --> 00:47:55,280
Speaker 6: fucking blows it up, although I think that will instantly

914
00:47:55,280 --> 00:47:56,800
Speaker 6: detonate everything.

915
00:47:56,880 --> 00:47:57,160
Speaker 2: I don't know.

916
00:47:57,200 --> 00:47:58,920
Speaker 6: Maybe it's maybe he's a Chinese housing bubble, maybe the

917
00:47:58,920 --> 00:48:01,080
Speaker 6: tech bubble finally collapse. Maybe all three of them hit

918
00:48:01,120 --> 00:48:03,040
Speaker 6: at the same time. This is the year fucking goes.

919
00:48:03,480 --> 00:48:05,920
Speaker 6: I've never actually put my name down down on this

920
00:48:06,000 --> 00:48:07,680
Speaker 6: on the show. On any other fucking year. This is

921
00:48:07,719 --> 00:48:11,520
Speaker 6: the year the zombie economy will fall over dead. The

922
00:48:11,560 --> 00:48:12,840
Speaker 6: necromancy cannot hold.

923
00:48:14,160 --> 00:48:16,560
Speaker 2: I guess my prediction is that the economy is going

924
00:48:16,600 --> 00:48:20,960
Speaker 2: to be basically identical to the Biden economy in that

925
00:48:21,120 --> 00:48:25,040
Speaker 2: we're going to get like fucked up inflation and people

926
00:48:25,040 --> 00:48:27,560
Speaker 2: are going to be very angry, and the number will

927
00:48:27,560 --> 00:48:29,680
Speaker 2: continue to go up on the stock market because that's

928
00:48:29,719 --> 00:48:32,000
Speaker 2: kind of what it's designed to do. That's my theory.

929
00:48:32,239 --> 00:48:34,759
Speaker 1: And the housing market will still be trash.

930
00:48:35,080 --> 00:48:38,120
Speaker 2: Yeah, we will never afford times and the housing's just

931
00:48:38,160 --> 00:48:41,360
Speaker 2: gonna get more expensive. It will be interesting to see

932
00:48:41,840 --> 00:48:44,800
Speaker 2: Trump's entire all of his backers and his whole media

933
00:48:45,080 --> 00:48:47,120
Speaker 2: like one thing that will be easier for the left

934
00:48:47,600 --> 00:48:51,120
Speaker 2: is really hitting conservatives on inflation as it gets horrible

935
00:48:51,160 --> 00:48:55,480
Speaker 2: again or continues to suck, because that's, you know, at

936
00:48:55,480 --> 00:49:00,160
Speaker 2: this point, just a factor of the economy working as intended. Yeah,

937
00:49:00,200 --> 00:49:03,000
Speaker 2: that they all have to pretend, isn't. Yeah.

938
00:49:03,120 --> 00:49:04,680
Speaker 1: And before we go to a break, I just want

939
00:49:04,680 --> 00:49:06,799
Speaker 1: to say the price of eggs will go up.

940
00:49:07,239 --> 00:49:09,479
Speaker 2: I need to get chickens now. Oh yeah, this bird

941
00:49:09,480 --> 00:49:11,799
Speaker 2: flu thing is not gonna help with eggs. Oh boy,

942
00:49:12,520 --> 00:49:15,200
Speaker 2: oh boy, get your eggs now, by one hundred, by

943
00:49:15,280 --> 00:49:16,760
Speaker 2: thousands of dollars of eggs.

944
00:49:16,800 --> 00:49:19,880
Speaker 4: Now, there was some kind of device to make eggs

945
00:49:19,920 --> 00:49:22,640
Speaker 4: and you could have in your own God, oh.

946
00:49:22,440 --> 00:49:31,920
Speaker 1: My goodness, it's time for ads.

947
00:49:35,480 --> 00:49:38,680
Speaker 5: I guess to piggyback off of Robert and MEA's predictions

948
00:49:38,719 --> 00:49:42,560
Speaker 5: there in the economy. My prediction is that once I

949
00:49:42,800 --> 00:49:46,120
Speaker 5: finally launched cool zone coin this year, I'm gonna make

950
00:49:46,120 --> 00:49:52,000
Speaker 5: a big If the economy is gonna go down, I

951
00:49:52,040 --> 00:49:54,359
Speaker 5: am gonna be going up. Everyone's gonna start buying coole

952
00:49:54,440 --> 00:49:58,239
Speaker 5: zone coin because the US dollar becomes worthless. Bitcoin's gonna

953
00:49:58,239 --> 00:50:01,720
Speaker 5: crash too. It's fake, but cool zone coin has real

954
00:50:01,920 --> 00:50:02,960
Speaker 5: fungible value.

955
00:50:03,480 --> 00:50:05,799
Speaker 2: Well, yeah, the thing about cool zone coin. That makes

956
00:50:05,840 --> 00:50:08,640
Speaker 2: it different from all of the other crypto coins is

957
00:50:08,680 --> 00:50:11,800
Speaker 2: that it is really based on a fundamentally limited and

958
00:50:11,920 --> 00:50:14,840
Speaker 2: valuable resource, which is movies from the nineties that I

959
00:50:14,960 --> 00:50:18,879
Speaker 2: showed Garrison and they actually liked. So, you know, there's

960
00:50:19,080 --> 00:50:22,480
Speaker 2: there's only so many cool Zone coins that can be

961
00:50:22,520 --> 00:50:23,359
Speaker 2: in circulation.

962
00:50:24,719 --> 00:50:26,799
Speaker 5: We're lucky I was in Portland this Christmas because we

963
00:50:26,840 --> 00:50:29,040
Speaker 5: really stocked up a few more of those nineties classics

964
00:50:29,040 --> 00:50:30,960
Speaker 5: to bump up the price of cool Zone coin going

965
00:50:31,000 --> 00:50:31,879
Speaker 5: into twenty twenty five.

966
00:50:31,920 --> 00:50:36,120
Speaker 4: That's right, everybody, Wow, sell your house by cool Zone coin.

967
00:50:36,800 --> 00:50:40,160
Speaker 2: Have you seen Hook? Garrison? I have seen Hook. I like,

968
00:50:40,480 --> 00:50:43,040
Speaker 2: of course, good, Yeah, a classic.

969
00:50:43,560 --> 00:50:45,799
Speaker 1: Have you seen Wickerman nineteen seventy three?

970
00:50:46,320 --> 00:50:48,520
Speaker 5: You know, I actually haven't. I've been waiting to catch

971
00:50:48,560 --> 00:50:49,320
Speaker 5: it in the theater.

972
00:50:49,680 --> 00:50:52,359
Speaker 1: We will make this happen at some point necessary.

973
00:50:52,680 --> 00:50:54,719
Speaker 5: I would I would love too. I would love too.

974
00:50:55,280 --> 00:50:58,760
Speaker 4: I bet one thing. I think it's very predictable border stuff.

975
00:50:58,800 --> 00:51:02,399
Speaker 4: They will stunt on an the caravan of migrants, and

976
00:51:02,440 --> 00:51:04,400
Speaker 4: I think it's pretty easy for them to kind of

977
00:51:04,480 --> 00:51:06,840
Speaker 4: organize that and make that happen, and it will be

978
00:51:07,280 --> 00:51:10,480
Speaker 4: a way for Trump to flex his border fascism yeah,

979
00:51:10,560 --> 00:51:12,359
Speaker 4: much like he did in twenty eighteen. Maybe they'll wait

980
00:51:12,400 --> 00:51:15,240
Speaker 4: till the midterms again. There's always a fun border disaster

981
00:51:15,320 --> 00:51:16,080
Speaker 4: for the midterms.

982
00:51:16,320 --> 00:51:19,040
Speaker 1: Could I just do one? That might not be a prediction,

983
00:51:19,160 --> 00:51:20,520
Speaker 1: but like a Sophie Hope.

984
00:51:20,800 --> 00:51:22,600
Speaker 2: Sure, yeah, get it.

985
00:51:22,719 --> 00:51:25,440
Speaker 1: Something has to happen to those Paul brothers.

986
00:51:25,480 --> 00:51:29,120
Speaker 2: Oh, Sophie, Oh yeah, that's possible. Yeah. My prediction for

987
00:51:29,200 --> 00:51:31,719
Speaker 2: the Paul brothers is that one of them dies within

988
00:51:31,760 --> 00:51:33,520
Speaker 2: the next five years, and one of them lives to

989
00:51:33,560 --> 00:51:34,560
Speaker 2: be one hundred and seven.

990
00:51:34,719 --> 00:51:36,960
Speaker 5: Oh that tracks sure.

991
00:51:37,040 --> 00:51:39,040
Speaker 4: Yeah, they decide to take on Bob Dylan in a

992
00:51:39,080 --> 00:51:41,560
Speaker 4: boxing match and only one of them survives.

993
00:51:41,600 --> 00:51:43,600
Speaker 2: I think Bob Dylan'll live in this next year.

994
00:51:43,680 --> 00:51:47,120
Speaker 4: But I've just found Bob Dylan's tweets the purest thing.

995
00:51:47,920 --> 00:51:49,480
Speaker 4: He just tweets about what he's doing.

996
00:51:51,080 --> 00:51:51,680
Speaker 2: What a hero.

997
00:51:52,520 --> 00:51:57,680
Speaker 1: Netflix paying Jake Paul to billions of dollars to fight

998
00:51:57,840 --> 00:52:01,280
Speaker 1: nine hundred year old Mike Tyson and then Jake Paul

999
00:52:01,800 --> 00:52:06,920
Speaker 1: coming in on like a vintage car and spraying his

1000
00:52:07,160 --> 00:52:11,719
Speaker 1: product and it having higher streaming numbers than the Super Bowl?

1001
00:52:11,920 --> 00:52:12,399
Speaker 5: Is that real?

1002
00:52:12,600 --> 00:52:12,839
Speaker 1: Yes?

1003
00:52:13,719 --> 00:52:15,960
Speaker 6: To be fair, that was a rancid super Bowl.

1004
00:52:17,000 --> 00:52:21,160
Speaker 1: Rancid Super Bowl. This this cannot this cannot be.

1005
00:52:21,640 --> 00:52:23,319
Speaker 4: Most of us just turned in on the off chane,

1006
00:52:23,360 --> 00:52:25,000
Speaker 4: So Jake Paul would die.

1007
00:52:25,040 --> 00:52:25,960
Speaker 5: Yes, that is true.

1008
00:52:26,120 --> 00:52:29,160
Speaker 4: That is true, or at least get bitten.

1009
00:52:29,440 --> 00:52:32,760
Speaker 1: Like Yeah, all of us were hoping that Mike Tyson

1010
00:52:33,080 --> 00:52:36,400
Speaker 1: was not in fact sixty years old. But he is

1011
00:52:36,640 --> 00:52:42,320
Speaker 1: sixty years old. So uh yeah some god, yeah something,

1012
00:52:42,520 --> 00:52:44,919
Speaker 1: something's gotta give. Oh, and there won't be a left

1013
00:52:44,960 --> 00:52:46,359
Speaker 1: wing Joe Rogan, thank you so much.

1014
00:52:46,440 --> 00:52:47,800
Speaker 2: Oh, I don't know, Sophie.

1015
00:52:47,480 --> 00:52:50,279
Speaker 5: I can as soon as we logicals so good, I

1016
00:52:50,320 --> 00:52:50,879
Speaker 5: think we can.

1017
00:52:51,000 --> 00:52:51,239
Speaker 2: Really.

1018
00:52:51,600 --> 00:52:55,960
Speaker 1: Oh, there'll be there'll be somebody trying to be.

1019
00:52:56,840 --> 00:52:59,560
Speaker 2: Oh, Sorphie, there already. By the way, it's time for

1020
00:52:59,600 --> 00:53:02,239
Speaker 2: me to do our new ad plug. You've heard of

1021
00:53:02,239 --> 00:53:04,719
Speaker 2: how good elk meat is for you, and you've heard

1022
00:53:04,719 --> 00:53:08,400
Speaker 2: of how liver is a superfood. Well now try new

1023
00:53:08,560 --> 00:53:12,200
Speaker 2: elk liver steaks. It's just just ground up liver shoved

1024
00:53:12,239 --> 00:53:14,959
Speaker 2: inside a steak. I send it through the mail through

1025
00:53:14,960 --> 00:53:19,960
Speaker 2: FedEx five day delivery. It is not refrigerated in any way.

1026
00:53:19,880 --> 00:53:22,759
Speaker 5: No refrigeration. It's better at roop temp, better at.

1027
00:53:22,760 --> 00:53:37,640
Speaker 4: ROOPTEMP to get the healthy bacteria it gives you mystical powers.

1028
00:53:33,880 --> 00:53:37,200
Speaker 5: Of One of my I guess more hopes and still

1029
00:53:37,200 --> 00:53:40,400
Speaker 5: partial predictions is that National Guard gets into a scuffle

1030
00:53:40,440 --> 00:53:42,520
Speaker 5: with border patrol in some kind of blue state.

1031
00:53:42,680 --> 00:53:43,880
Speaker 2: Yeah, yeah, good chance.

1032
00:53:43,960 --> 00:53:47,840
Speaker 5: We have some brave and strong governor is gonna is

1033
00:53:47,840 --> 00:53:51,640
Speaker 5: gonna salute the troops and send out our proud National

1034
00:53:51,640 --> 00:53:55,160
Speaker 5: Guard boys to fight off ice. That's just a battle

1035
00:53:55,160 --> 00:53:57,120
Speaker 5: I would love to see. I've wanted to see that

1036
00:53:57,160 --> 00:53:59,920
Speaker 5: ever since Portland twenty twenty. I've wanted to see National

1037
00:54:00,080 --> 00:54:03,160
Speaker 5: Guard troops fight against federal forces.

1038
00:54:02,760 --> 00:54:04,759
Speaker 2: Two groups of men who don't really know how to

1039
00:54:04,880 --> 00:54:09,719
Speaker 2: use their guns, using their guns, No, it's gonna be amazing.

1040
00:54:09,920 --> 00:54:12,400
Speaker 5: So battle, I've wanted to see you for like five years.

1041
00:54:12,560 --> 00:54:15,680
Speaker 2: Who's plate carriers at the top, closer to their nipples?

1042
00:54:17,120 --> 00:54:18,200
Speaker 2: It's anyone's game.

1043
00:54:19,400 --> 00:54:21,560
Speaker 5: I need to see it. I need to see it.

1044
00:54:22,040 --> 00:54:22,399
Speaker 5: Come on.

1045
00:54:23,040 --> 00:54:24,759
Speaker 4: I would like to see it from a distance, because

1046
00:54:24,760 --> 00:54:26,359
Speaker 4: that would be a shit show.

1047
00:54:26,440 --> 00:54:28,200
Speaker 2: Yeah, from a sizable distance.

1048
00:54:28,520 --> 00:54:30,279
Speaker 5: Yeah, General Whitmer, let's go.

1049
00:54:30,440 --> 00:54:34,359
Speaker 2: Let's go expensing a fucking telescope for that firefight. Yeah, yeah,

1050
00:54:34,800 --> 00:54:38,239
Speaker 2: a periscope. Maybe I trust the Iraqi Army more than

1051
00:54:38,280 --> 00:54:39,680
Speaker 2: either of those sides.

1052
00:54:41,239 --> 00:54:43,520
Speaker 4: I've seen a lot of dudes five guns while ducking

1053
00:54:43,560 --> 00:54:44,799
Speaker 4: behind a KB holding the gun.

1054
00:54:44,960 --> 00:54:45,400
Speaker 1: I love you.

1055
00:54:45,480 --> 00:54:47,359
Speaker 2: And then it does look fun. It does look fun.

1056
00:54:47,960 --> 00:54:48,239
Speaker 5: It is.

1057
00:54:48,360 --> 00:54:51,000
Speaker 4: Yeah, definitely, I would like to do that. That they kicked

1058
00:54:51,000 --> 00:54:53,760
Speaker 4: me out the range every time because of woke how sad?

1059
00:54:53,880 --> 00:54:57,200
Speaker 5: Well, not anymore, James, Yeah, that's also the casualties yet,

1060
00:54:57,239 --> 00:54:58,800
Speaker 5: not any more, James. Wolke is beaten.

1061
00:55:00,160 --> 00:55:02,359
Speaker 2: That's right. Yeah, they wait, went, they went broke.

1062
00:55:02,440 --> 00:55:04,879
Speaker 4: I'm going to buy the range, that's right, and we'll

1063
00:55:04,920 --> 00:55:06,920
Speaker 4: all fall from behind the bentress.

1064
00:55:06,560 --> 00:55:09,240
Speaker 5: Now, oh mama.

1065
00:55:09,520 --> 00:55:09,680
Speaker 3: Yeah.

1066
00:55:09,760 --> 00:55:10,160
Speaker 2: Yeah.

1067
00:55:10,440 --> 00:55:13,840
Speaker 4: Other predictions, maybe we'll get a good, solid couple of

1068
00:55:13,840 --> 00:55:15,919
Speaker 4: weeks of writing again, and that Garrison said, like, maybe

1069
00:55:15,920 --> 00:55:17,600
Speaker 4: it'll let me take a year or two this time.

1070
00:55:18,600 --> 00:55:20,360
Speaker 5: I don't think that anymore.

1071
00:55:20,560 --> 00:55:23,799
Speaker 2: Something will have to change. Yeah, there will have to

1072
00:55:23,840 --> 00:55:30,720
Speaker 2: be a material change in either organizing or social conditions,

1073
00:55:31,160 --> 00:55:34,200
Speaker 2: because people will need to either be vastly more desperate

1074
00:55:34,239 --> 00:55:36,279
Speaker 2: than they are right now, or they will need to

1075
00:55:36,280 --> 00:55:38,680
Speaker 2: have a specific reason to think, well, this time, getting

1076
00:55:38,719 --> 00:55:41,880
Speaker 2: out in the street might do something. Yeah.

1077
00:55:41,960 --> 00:55:45,520
Speaker 5: I think we're gonna kind of continue the trends that

1078
00:55:45,520 --> 00:55:48,440
Speaker 5: that we've been seeing, which which points towards a bit

1079
00:55:48,480 --> 00:55:52,960
Speaker 5: of an apathy towards like like big popular mobilizations and

1080
00:55:53,120 --> 00:55:57,799
Speaker 5: more towards kind of bizarre lone wolf attacks, something that

1081
00:55:57,880 --> 00:56:00,680
Speaker 5: you know could be slight even a slightly probable or

1082
00:56:00,719 --> 00:56:03,480
Speaker 5: you know, possibly darker productions. I think we'll have like

1083
00:56:03,520 --> 00:56:10,520
Speaker 5: a really bad Luigi copycat within the next like four months. Sure, yes,

1084
00:56:10,880 --> 00:56:13,239
Speaker 5: the years of Luigi, Like, it's not gonna be good.

1085
00:56:13,600 --> 00:56:16,239
Speaker 2: It's not gonna be good. There's probably gonna be a

1086
00:56:16,280 --> 00:56:19,200
Speaker 2: situation where some guy either gets the best case is

1087
00:56:19,200 --> 00:56:22,080
Speaker 2: that he gets killed immediately by the dude's security. The

1088
00:56:22,120 --> 00:56:24,840
Speaker 2: worst case is there's a big public firefight and a

1089
00:56:24,880 --> 00:56:26,239
Speaker 2: whole fuck let of people get hit.

1090
00:56:26,400 --> 00:56:28,279
Speaker 4: Yeah, didn't I predict that there would be a big

1091
00:56:28,320 --> 00:56:30,520
Speaker 4: public crime with a three D printed gun last year?

1092
00:56:30,920 --> 00:56:33,400
Speaker 5: I think that was the year before we talked about that.

1093
00:56:34,120 --> 00:56:37,400
Speaker 2: Oh damn, okay, so close and.

1094
00:56:37,920 --> 00:56:40,399
Speaker 5: Yeah, you know, I mean this, this certainly does kind

1095
00:56:40,400 --> 00:56:43,359
Speaker 5: of fit that mold. We'll see how much that like

1096
00:56:43,719 --> 00:56:46,640
Speaker 5: gets focused on in the trial and like continued reporting.

1097
00:56:47,080 --> 00:56:50,120
Speaker 4: Yeah, and in that legislation too, I missed a death.

1098
00:56:50,160 --> 00:56:52,959
Speaker 4: We can also include it in the hope section. Matthew Iglesias,

1099
00:56:53,040 --> 00:56:58,280
Speaker 4: that motherfucker, motherfucker has been standing bullshit for twenty years.

1100
00:56:58,719 --> 00:57:01,600
Speaker 4: It just it cannot can he knew he's lost a

1101
00:57:01,680 --> 00:57:03,520
Speaker 4: juice a little bit. I think he ses on the

1102
00:57:03,520 --> 00:57:03,879
Speaker 4: way out.

1103
00:57:04,120 --> 00:57:07,040
Speaker 2: All Right, Something very funny did just happen that we

1104
00:57:07,040 --> 00:57:11,440
Speaker 2: should talk about as a team. Senator Doug Mastriano taught,

1105
00:57:11,920 --> 00:57:14,640
Speaker 2: a thirty year US Army veteran who taught at the

1106
00:57:14,680 --> 00:57:19,680
Speaker 2: War College, just tweeted an indignant, furious tweet about the

1107
00:57:19,800 --> 00:57:23,040
Speaker 2: US government not being honest with Americans about like what's

1108
00:57:23,040 --> 00:57:25,480
Speaker 2: happening with these drones? And yeah, and the picture of

1109
00:57:25,560 --> 00:57:28,720
Speaker 2: the crash drone is a tie fighter. That's like a

1110
00:57:28,720 --> 00:57:30,880
Speaker 2: model tie fighter on the bed of a flatbed.

1111
00:57:31,880 --> 00:57:36,680
Speaker 5: Yes, we've all lost our mind.

1112
00:57:36,920 --> 00:57:41,360
Speaker 2: Hot at the US Army War College, they're not sending

1113
00:57:41,400 --> 00:57:46,360
Speaker 2: their best people. Oh fuck, that's funny, amazing stuff. That's

1114
00:57:46,400 --> 00:57:51,000
Speaker 2: one of the best things I've seen out here. Oh

1115
00:57:51,320 --> 00:57:51,880
Speaker 2: good me.

1116
00:57:53,760 --> 00:57:56,200
Speaker 5: Finally, I like the closer predictions a little bit on

1117
00:57:56,480 --> 00:57:59,680
Speaker 5: Trump's cabinet. I think it's pretty pretty safe to say,

1118
00:57:59,680 --> 00:58:03,000
Speaker 5: becausidering his last presidency, we'll have at least one third

1119
00:58:03,120 --> 00:58:06,080
Speaker 5: cabinet turnover by the end of the year. Yeah, this

1120
00:58:06,120 --> 00:58:08,280
Speaker 5: is something that we've been talking about a lot. When

1121
00:58:08,320 --> 00:58:11,000
Speaker 5: do we think Musk is gonna get the boot and

1122
00:58:11,440 --> 00:58:13,520
Speaker 5: based on the way Trump's kind of positioned him, I'm

1123
00:58:13,520 --> 00:58:14,840
Speaker 5: not sure if it's gonna be as soon as what

1124
00:58:14,880 --> 00:58:17,360
Speaker 5: we all kind of initially thought, because Trump has kept

1125
00:58:17,440 --> 00:58:21,360
Speaker 5: him out of his inner orbit but pretty solidly in

1126
00:58:21,440 --> 00:58:25,919
Speaker 5: his middle orbit. Like he's not in any like real position, right, Yeah,

1127
00:58:25,960 --> 00:58:27,400
Speaker 5: he has Doge, Like, come on.

1128
00:58:27,800 --> 00:58:29,160
Speaker 2: It just came out that he's not going to be

1129
00:58:29,200 --> 00:58:31,680
Speaker 2: able to get a high the highest security clearance. There

1130
00:58:31,720 --> 00:58:34,200
Speaker 2: you go, that's funny.

1131
00:58:34,240 --> 00:58:36,880
Speaker 1: But like he has him sitting next to his family

1132
00:58:36,960 --> 00:58:38,280
Speaker 1: at Thanksgiving totally.

1133
00:58:38,400 --> 00:58:41,800
Speaker 5: Yeah, yeah, no, no, totally, And especially in like the

1134
00:58:42,000 --> 00:58:44,680
Speaker 5: three weeks after the election, they were like they were

1135
00:58:44,720 --> 00:58:48,439
Speaker 5: like honeymoon, right, they were neck and neck and something

1136
00:58:48,480 --> 00:58:51,480
Speaker 5: that's gonna like start dissipating. Musk can't get fully booted

1137
00:58:51,480 --> 00:58:55,120
Speaker 5: out because like you know, the federal government needs SpaceX

1138
00:58:55,200 --> 00:58:58,960
Speaker 5: and and unlike Musk's other like technologies, so like they

1139
00:58:58,960 --> 00:59:01,080
Speaker 5: will remain friendly, but like you're not going to be

1140
00:59:01,120 --> 00:59:04,040
Speaker 5: in the close position that they are now. I initially

1141
00:59:04,200 --> 00:59:07,600
Speaker 5: I put that date for being March twentieth, twenty twenty five,

1142
00:59:07,760 --> 00:59:10,760
Speaker 5: you know, a two months after inauguration day. It's it's

1143
00:59:10,840 --> 00:59:13,200
Speaker 5: enough time to get, you know, for for someone like

1144
00:59:13,200 --> 00:59:16,760
Speaker 5: Trump to get tired of Musk's like personality. But I

1145
00:59:17,000 --> 00:59:19,479
Speaker 5: think I might stretch that out a little bit more

1146
00:59:19,600 --> 00:59:22,600
Speaker 5: now than my initial prediction. I think I think they

1147
00:59:22,680 --> 00:59:24,680
Speaker 5: might do a little bit more of a long term

1148
00:59:24,880 --> 00:59:27,760
Speaker 5: game here. But that also means that that Musk maybe

1149
00:59:27,760 --> 00:59:31,240
Speaker 5: will not have as much like constant influence as what

1150
00:59:31,280 --> 00:59:33,000
Speaker 5: it was first looking like in those like, you know,

1151
00:59:33,040 --> 00:59:34,640
Speaker 5: three months after the election.

1152
00:59:34,720 --> 00:59:37,760
Speaker 2: I think that RFK Junior is probably at pushed out

1153
00:59:37,760 --> 00:59:39,200
Speaker 2: of the picture before Musk is.

1154
00:59:39,880 --> 00:59:41,840
Speaker 1: Yeah, if he tries to get rid of the fucking

1155
00:59:41,960 --> 00:59:45,880
Speaker 1: polio vaccine, it's gonna be a real quick trip to

1156
00:59:45,920 --> 00:59:47,919
Speaker 1: the unemployment line for Bobby Boy.

1157
00:59:48,920 --> 00:59:53,760
Speaker 2: Yeah, I really, I don't think Trump's that reckquest No,

1158
00:59:54,040 --> 00:59:56,960
Speaker 2: like that that would be quite quite a line to

1159
00:59:57,360 --> 00:59:59,440
Speaker 2: get rid of the polio vaccine.

1160
00:59:59,680 --> 01:00:03,800
Speaker 4: Trump, I'm still so old, like he remembers he's old.

1161
01:00:04,920 --> 01:00:07,560
Speaker 1: But if, but if ORFK Junior could get the wheat

1162
01:00:07,800 --> 01:00:10,800
Speaker 1: ingredient out of the McDonald's fries, I'd be most obliged.

1163
01:00:11,280 --> 01:00:13,960
Speaker 5: Oh yeah, no, I'm I'm sure that he's gonna he's

1164
01:00:13,960 --> 01:00:17,160
Speaker 5: gonna reverse one hundred years of corn subsidies. And get

1165
01:00:17,200 --> 01:00:19,920
Speaker 5: corn served out of our Coca Cola. I believe in RFK.

1166
01:00:20,280 --> 01:00:24,000
Speaker 2: Yeah, I feel pretty good about the continuing legality of

1167
01:00:24,080 --> 01:00:26,240
Speaker 2: Kraton as long as he's the HHS head.

1168
01:00:26,720 --> 01:00:27,280
Speaker 5: There you go.

1169
01:00:27,320 --> 01:00:29,680
Speaker 2: All it's gonna take, is one of Joe Rogan's friends,

1170
01:00:29,760 --> 01:00:30,720
Speaker 2: speaking in his ear.

1171
01:00:32,360 --> 01:00:35,400
Speaker 5: We'll be all right, We're gonna have legally required DMT

1172
01:00:35,640 --> 01:00:37,120
Speaker 5: for everyone in the country.

1173
01:00:38,400 --> 01:00:41,200
Speaker 2: Yeah, why not? I think we need and I've been

1174
01:00:41,240 --> 01:00:43,480
Speaker 2: I've been saying this for years. We need to put

1175
01:00:43,480 --> 01:00:45,640
Speaker 2: the lithium back in the water. We also need to

1176
01:00:45,720 --> 01:00:48,360
Speaker 2: use those crop dusting planes and just like fill them

1177
01:00:48,360 --> 01:00:54,320
Speaker 2: with xanax. Just just just calm everyone down, take everything

1178
01:00:54,360 --> 01:00:55,600
Speaker 2: back a couple of steps.

1179
01:00:56,400 --> 01:00:59,440
Speaker 1: All right, I'm gonna go pet some dogs. So the

1180
01:00:59,480 --> 01:01:03,720
Speaker 1: podcast over. Happy New Year, everyone, Happy, Happy new everyone.

1181
01:01:04,080 --> 01:01:06,760
Speaker 1: I do want everyone to pick one thing that that

1182
01:01:06,760 --> 01:01:09,360
Speaker 1: that they're gonna do this year that will improve their life,

1183
01:01:09,720 --> 01:01:10,560
Speaker 1: however small.

1184
01:01:10,840 --> 01:01:13,400
Speaker 5: For me, I'm gonna get a new mirror. We're gonna

1185
01:01:13,400 --> 01:01:15,960
Speaker 5: all pick one thing. We call that Project twenty twenty five.

1186
01:01:16,240 --> 01:01:18,439
Speaker 5: It's what one thing we can do to improve our

1187
01:01:18,520 --> 01:01:21,480
Speaker 5: lives and you know, and then by extension, the lives

1188
01:01:21,520 --> 01:01:23,680
Speaker 5: of everyone else around us. So make sure everyone has

1189
01:01:23,720 --> 01:01:26,200
Speaker 5: their own personal project twenty twenty five going into this

1190
01:01:26,240 --> 01:01:28,040
Speaker 5: next year. I think we will need it.

1191
01:01:28,120 --> 01:01:31,200
Speaker 1: Yeah, I'm holding my Project twenty twenty five in my

1192
01:01:31,360 --> 01:01:31,960
Speaker 1: arms right now.

1193
01:01:32,040 --> 01:01:34,160
Speaker 5: Your new dog, your new dog I adopted.

1194
01:01:34,320 --> 01:01:37,960
Speaker 1: I adopted Anderson a sibling, and her name is Truman.

1195
01:01:37,880 --> 01:01:41,320
Speaker 2: Lovely after our greatest US president, after not.

1196
01:01:41,520 --> 01:01:44,280
Speaker 1: The greatest US president. I would never name a child

1197
01:01:44,280 --> 01:01:45,600
Speaker 1: of mine after our president.

1198
01:01:46,160 --> 01:01:49,320
Speaker 5: After the sheriff in Twin Peaks, that's right. Also, no,

1199
01:01:49,480 --> 01:01:50,840
Speaker 5: all right, well we love that.

1200
01:01:51,600 --> 01:01:54,800
Speaker 4: After the house did viv who grew up in the

1201
01:01:54,840 --> 01:01:58,000
Speaker 4: Truman Shows Gate is Gates?

1202
01:01:58,080 --> 01:01:59,520
Speaker 2: Yeah, named you're after Matt Gates.

1203
01:01:59,560 --> 01:02:03,920
Speaker 5: Tildhoods is like totally unoven job. Now that's so funny.

1204
01:02:04,000 --> 01:02:07,200
Speaker 1: It's very funny. It's very very funny, and I feel

1205
01:02:07,240 --> 01:02:09,800
Speaker 1: like I feel like we should end on that note.

1206
01:02:09,840 --> 01:02:15,880
Speaker 1: So ha to Matt Gates. Anyways, Anderson Truman to the

1207
01:02:15,880 --> 01:02:16,640
Speaker 1: fuck out.

1208
01:02:16,440 --> 01:02:33,360
Speaker 2: Of here, welcome back to it could happen here, A

1209
01:02:33,440 --> 01:02:36,520
Speaker 2: podcast about it, which in this week's case is the

1210
01:02:36,560 --> 01:02:41,160
Speaker 2: Consumer Electronics Show is happening here. And yeah, we're here

1211
01:02:41,200 --> 01:02:43,680
Speaker 2: to talk about things falling apart. And again, in this

1212
01:02:43,760 --> 01:02:47,200
Speaker 2: case that's the tech industry because The story this CES,

1213
01:02:47,320 --> 01:02:49,440
Speaker 2: as it has been for the last several CES, is

1214
01:02:49,440 --> 01:02:52,960
Speaker 2: is that the continuing degradation of big tech as it

1215
01:02:53,080 --> 01:02:56,960
Speaker 2: seeks more places to get money from while providing less

1216
01:02:57,000 --> 01:02:59,080
Speaker 2: and less utility to the people that it needs to

1217
01:02:59,120 --> 01:03:02,080
Speaker 2: give it money. And every CEES at some point I

1218
01:03:02,120 --> 01:03:04,800
Speaker 2: find myself face to face with something that makes me say,

1219
01:03:05,000 --> 01:03:08,840
Speaker 2: I've now seen the silliest thing I've ever seen. And

1220
01:03:08,920 --> 01:03:11,920
Speaker 2: this year, that experience happened for the first time within

1221
01:03:12,080 --> 01:03:14,680
Speaker 2: thirty minutes of the first half day. And I'm going

1222
01:03:14,720 --> 01:03:17,680
Speaker 2: to talk about that and show some videos to my

1223
01:03:18,440 --> 01:03:21,560
Speaker 2: panelists here, which of course are the great ed Zeitron,

1224
01:03:21,720 --> 01:03:24,560
Speaker 2: It's me, I'm here, the pretty good Garrison Davis.

1225
01:03:24,640 --> 01:03:29,680
Speaker 5: Okay, thanks, okay, all right, all right, Boddy them.

1226
01:03:28,960 --> 01:03:32,280
Speaker 2: And the supernumerate supernumerary. I'm sorry, I messed up the

1227
01:03:32,320 --> 01:03:34,400
Speaker 2: word I was using as a superlative to praise you.

1228
01:03:35,000 --> 01:03:36,440
Speaker 5: I'll take at longways.

1229
01:03:36,480 --> 01:03:40,280
Speaker 2: So Junior, thanks Ed, Thank you so much for joining us. Everybody,

1230
01:03:40,320 --> 01:03:42,560
Speaker 2: are you ready to see some of the dumbest AI

1231
01:03:42,640 --> 01:03:43,560
Speaker 2: generated videos?

1232
01:03:43,600 --> 01:03:45,960
Speaker 7: Sure sell me with more pleasure?

1233
01:03:46,160 --> 01:03:47,120
Speaker 2: Excellent, excellent.

1234
01:03:47,240 --> 01:03:48,400
Speaker 8: Nothing fills me with pleasure.

1235
01:03:48,520 --> 01:03:51,000
Speaker 2: So the first panel I sat down today with at

1236
01:03:51,040 --> 01:03:54,480
Speaker 2: ten am in the Goddamn Morning Jesus was the Hollywood

1237
01:03:54,480 --> 01:03:58,400
Speaker 2: Trajectory Generative AI Timeline twenty twenty five to twenty thirty.

1238
01:03:58,480 --> 01:04:01,200
Speaker 5: Oh boy, I am fascinated for what they think will

1239
01:04:01,200 --> 01:04:02,480
Speaker 5: happen in twenty thirty.

1240
01:04:03,400 --> 01:04:06,720
Speaker 2: Everything's just gonna get better Garrison. This panel featured a

1241
01:04:06,800 --> 01:04:10,600
Speaker 2: number of luminary thinkers, including Mary Hamilton, a managing director

1242
01:04:10,640 --> 01:04:13,960
Speaker 2: at a Centsure, who announced her company's three billion dollar

1243
01:04:14,040 --> 01:04:16,200
Speaker 2: investment in AI by dropping this gym.

1244
01:04:16,600 --> 01:04:17,880
Speaker 8: I have a digital twin.

1245
01:04:17,760 --> 01:04:21,200
Speaker 5: And she's constantly evolving and how she gets used and

1246
01:04:21,240 --> 01:04:22,360
Speaker 5: what she says, and.

1247
01:04:23,120 --> 01:04:25,880
Speaker 9: You know there's you know, big educations around that.

1248
01:04:26,040 --> 01:04:28,720
Speaker 5: So I think this is a really exciting space to

1249
01:04:28,800 --> 01:04:29,480
Speaker 5: be thinking.

1250
01:04:29,200 --> 01:04:32,440
Speaker 7: That to any like that she just stole Hurley Herndon's thing.

1251
01:04:32,520 --> 01:04:37,360
Speaker 10: But okay, they've probably said that to a dolta they

1252
01:04:37,440 --> 01:04:38,560
Speaker 10: think I had a concussion.

1253
01:04:38,600 --> 01:04:41,400
Speaker 2: The shere would this person needs?

1254
01:04:41,400 --> 01:04:42,640
Speaker 5: Like psychologically, Yeah, you.

1255
01:04:42,640 --> 01:04:43,600
Speaker 2: Should be allowed to drive.

1256
01:04:44,960 --> 01:04:46,480
Speaker 11: You need a trying kid.

1257
01:04:46,560 --> 01:04:49,240
Speaker 8: Okay, let's get you, let's get you sit down, and.

1258
01:04:49,440 --> 01:04:52,720
Speaker 2: We're taking the phone away from you. Now. I think

1259
01:04:52,720 --> 01:04:55,120
Speaker 2: this is very silly because again I think, yeah, it's

1260
01:04:55,160 --> 01:04:57,680
Speaker 2: just a fundamental mismatch and what people might want from

1261
01:04:57,680 --> 01:05:00,160
Speaker 2: an AI agent, and like the way in w which

1262
01:05:00,200 --> 01:05:01,040
Speaker 2: they get talked about.

1263
01:05:01,080 --> 01:05:03,400
Speaker 8: But also they use digital twin, which is cement to

1264
01:05:03,440 --> 01:05:04,160
Speaker 8: prise software.

1265
01:05:04,240 --> 01:05:07,320
Speaker 2: Shit. Yeah, oh my god, yeah it's it's it's I'm

1266
01:05:07,360 --> 01:05:09,800
Speaker 2: excited to go see some digital twin technology that I'm

1267
01:05:09,800 --> 01:05:13,080
Speaker 2: sure we'll make a cheap egg code switching.

1268
01:05:14,080 --> 01:05:15,800
Speaker 5: This was this is the first thing I reported on

1269
01:05:15,880 --> 01:05:18,760
Speaker 5: at CEES was there was the digital twin. Like back

1270
01:05:18,800 --> 01:05:21,640
Speaker 5: in like twenty twenty two, twenty one, there was like

1271
01:05:21,760 --> 01:05:23,800
Speaker 5: one single company and all of c Yes, I was

1272
01:05:23,800 --> 01:05:26,120
Speaker 5: promising like a digital twin, and now it's like every

1273
01:05:26,120 --> 01:05:26,720
Speaker 5: other company.

1274
01:05:27,080 --> 01:05:28,800
Speaker 2: Yees. It means so many different things.

1275
01:05:28,840 --> 01:05:31,240
Speaker 8: It means literally a digital representation of anything.

1276
01:05:31,280 --> 01:05:32,720
Speaker 2: It doesn't even mean an AI agent.

1277
01:05:32,760 --> 01:05:34,240
Speaker 8: The fact that they're using it in the wrong place

1278
01:05:34,320 --> 01:05:35,280
Speaker 8: is very annoying to me.

1279
01:05:35,440 --> 01:05:37,280
Speaker 2: Yeah. I keep seeing like they can now make an

1280
01:05:37,400 --> 01:05:40,360
Speaker 2: an AI chatbot trained off of your social media presence.

1281
01:05:40,360 --> 01:05:42,040
Speaker 2: That's eighty five percent accurate.

1282
01:05:42,120 --> 01:05:45,040
Speaker 12: Oh, as all twins are.

1283
01:05:45,400 --> 01:05:47,760
Speaker 2: And I want to say I know they can't. But

1284
01:05:47,800 --> 01:05:50,160
Speaker 2: then you talk to the average person at CEES or

1285
01:05:50,200 --> 01:05:53,479
Speaker 2: the average panelist on this particular panel, I'm like, yes,

1286
01:05:53,520 --> 01:05:55,600
Speaker 2: I do believe in fact, everyone on that panel you

1287
01:05:55,640 --> 01:05:58,880
Speaker 2: could accurately, you could accurately get eighty five percent of

1288
01:05:58,880 --> 01:06:02,160
Speaker 2: their personality with the I bought for a bit, you know,

1289
01:06:03,960 --> 01:06:08,040
Speaker 2: maybe a lot higher improvement. Yeah. Yeah, So I will say,

1290
01:06:08,120 --> 01:06:11,080
Speaker 2: like that was silly. That's not the silliest thing I saw.

1291
01:06:11,720 --> 01:06:14,280
Speaker 2: The silliest thing I saw came courtesy of another panelist,

1292
01:06:14,440 --> 01:06:19,040
Speaker 2: Jason Zada, founder of Secret Level and COO of the company.

1293
01:06:19,400 --> 01:06:22,360
Speaker 2: The videos that Jason came to CEES to brag about

1294
01:06:22,480 --> 01:06:25,880
Speaker 2: were a collection of the laziest AI slop ever to

1295
01:06:25,920 --> 01:06:29,440
Speaker 2: stain human eyeballs. His most recent big success that you

1296
01:06:29,440 --> 01:06:31,920
Speaker 2: could just see radiating off of him how proud he

1297
01:06:32,040 --> 01:06:35,720
Speaker 2: was of this was Coca Cola's annual Christmas ad, which

1298
01:06:35,920 --> 01:06:39,560
Speaker 2: last year was produced for the first time entirely with AI.

1299
01:06:40,040 --> 01:06:42,120
Speaker 2: And I'm just gonna if you haven't seen this, who

1300
01:06:42,200 --> 01:06:43,920
Speaker 2: hears seen Coca Cola's AI.

1301
01:06:46,520 --> 01:06:49,800
Speaker 5: Guess yeah, I've seen pictures. I think I've watched one.

1302
01:06:50,600 --> 01:06:55,160
Speaker 2: Okay, well let's let's let watch a few times. We're

1303
01:06:55,160 --> 01:06:57,200
Speaker 2: gonna play. There's three different versions of this.

1304
01:06:57,160 --> 01:06:59,200
Speaker 8: So why we're just gonna I mean, that's that's what

1305
01:06:59,240 --> 01:06:59,680
Speaker 8: it's about.

1306
01:06:59,680 --> 01:07:01,840
Speaker 10: Out Oh my God, if there's three different versions that

1307
01:07:01,760 --> 01:07:03,560
Speaker 10: that's just they saved the from.

1308
01:07:09,440 --> 01:07:18,880
Speaker 8: Everyone is the same length of shark.

1309
01:07:20,360 --> 01:07:24,120
Speaker 2: Can you believe this song's AI generated? I can't believe

1310
01:07:24,880 --> 01:07:27,280
Speaker 2: the cou Could they teach a computer to write the lyrics?

1311
01:07:27,280 --> 01:07:28,320
Speaker 2: Holidays are going?

1312
01:07:29,720 --> 01:07:31,800
Speaker 10: I just can't believe we finally have the technology to

1313
01:07:31,840 --> 01:07:33,880
Speaker 10: have three trucks driving somewhere.

1314
01:07:33,680 --> 01:07:37,560
Speaker 2: And there wagging its tail with a dead eye too

1315
01:07:37,640 --> 01:07:44,920
Speaker 2: horrible girls move trucks with Coca cola and them driving

1316
01:07:44,960 --> 01:07:47,400
Speaker 2: down not a street. Raccoons.

1317
01:07:48,000 --> 01:07:50,360
Speaker 8: What the why is there a satellite?

1318
01:07:50,360 --> 01:07:51,960
Speaker 10: Are they going to drop the iron cannon on the

1319
01:07:52,000 --> 01:07:57,680
Speaker 10: polar bears?

1320
01:07:58,920 --> 01:08:02,840
Speaker 2: It's all clearly a it's all glowing like the city

1321
01:08:02,880 --> 01:08:07,280
Speaker 2: shots of like snow colored villages with that, as we're

1322
01:08:07,320 --> 01:08:09,920
Speaker 2: going to see in later videos. A. I loves putting

1323
01:08:09,960 --> 01:08:13,080
Speaker 2: smoke and random fires where there should not be smoking.

1324
01:08:13,200 --> 01:08:16,840
Speaker 5: Random fire. Chris, that's such a bad omen for four

1325
01:08:16,920 --> 01:08:19,720
Speaker 5: more years of a Trump presidency. It's a bleak that

1326
01:08:19,800 --> 01:08:23,639
Speaker 5: we have like even uglier Thomas Kincaid esque artwork.

1327
01:08:24,080 --> 01:08:27,360
Speaker 2: That's all every frame looks like animated.

1328
01:08:27,920 --> 01:08:30,639
Speaker 5: It's like they just generated a Thomas Kincaid like frame

1329
01:08:30,720 --> 01:08:32,400
Speaker 5: and then like badly animated and.

1330
01:08:32,320 --> 01:08:34,800
Speaker 10: The way that they move is very weird, like it

1331
01:08:34,880 --> 01:08:37,320
Speaker 10: looks kind of right but kind of right, looks very strong.

1332
01:08:37,400 --> 01:08:39,080
Speaker 2: It does that all of the scenes because it's like

1333
01:08:39,120 --> 01:08:41,120
Speaker 2: showing you a bunch of you see like a polar bear,

1334
01:08:41,200 --> 01:08:43,519
Speaker 2: obviously it's a Coca Cola Christmas ad. You see like

1335
01:08:43,560 --> 01:08:46,599
Speaker 2: a fucking reindeer, you see squirrels, you see a dog.

1336
01:08:46,800 --> 01:08:49,280
Speaker 2: But it always is like this very ai shot where

1337
01:08:49,320 --> 01:08:52,719
Speaker 2: it just pans across the animal and it's like glowing

1338
01:08:52,760 --> 01:08:57,720
Speaker 2: and kind of glossy and steering much. But they're not

1339
01:08:57,840 --> 01:08:59,080
Speaker 2: going anywhere with the movement.

1340
01:08:59,120 --> 01:09:01,280
Speaker 8: It's just like they doing something and that's it.

1341
01:09:01,520 --> 01:09:01,760
Speaker 2: Yeah.

1342
01:09:01,880 --> 01:09:03,880
Speaker 10: You think in ten years they're still gonna have these commercials.

1343
01:09:04,080 --> 01:09:06,400
Speaker 10: Yeah no, because where's the snoke. It's just a polo

1344
01:09:06,400 --> 01:09:07,280
Speaker 10: bez walking around like.

1345
01:09:08,240 --> 01:09:11,640
Speaker 2: System one, which tests emotional responses to ads, claims that

1346
01:09:11,680 --> 01:09:14,919
Speaker 2: the initial response to their Christmas ad was overwhelmingly positive.

1347
01:09:15,040 --> 01:09:17,720
Speaker 5: I don't think they're lying about that. I think if

1348
01:09:17,760 --> 01:09:20,000
Speaker 5: you walked up to someone like randomly on the street

1349
01:09:20,000 --> 01:09:22,200
Speaker 5: and showed them this, I think they'd be like, oh, yeah,

1350
01:09:22,400 --> 01:09:22,960
Speaker 5: it looks fine.

1351
01:09:24,600 --> 01:09:24,840
Speaker 2: Yeah.

1352
01:09:25,080 --> 01:09:28,240
Speaker 8: No one's watching a Coca Cola RDE and being like, yeah, wow,

1353
01:09:28,720 --> 01:09:30,080
Speaker 8: I've never had one of these before.

1354
01:09:30,240 --> 01:09:33,200
Speaker 2: Yeah, it's never a new experience. Not yet.

1355
01:09:33,439 --> 01:09:34,759
Speaker 7: We need an ad man.

1356
01:09:34,800 --> 01:09:37,559
Speaker 8: Need an ad man for the coke holdouts, we.

1357
01:09:37,520 --> 01:09:38,839
Speaker 7: Need an AI Don Draper.

1358
01:09:39,040 --> 01:09:41,000
Speaker 5: Yeah, well, do not give them ideas.

1359
01:09:42,640 --> 01:09:43,280
Speaker 2: What if a.

1360
01:09:43,160 --> 01:09:44,479
Speaker 8: Company lost five billion dollars.

1361
01:09:44,600 --> 01:09:45,880
Speaker 2: It's just an air that doesn't work.

1362
01:09:45,880 --> 01:09:47,840
Speaker 7: Instead of going to the movies like Don Draper does

1363
01:09:47,880 --> 01:09:48,439
Speaker 7: throughout all.

1364
01:09:48,360 --> 01:09:50,479
Speaker 12: Of mad Man, it just doesn't work and respond to

1365
01:09:50,479 --> 01:09:51,360
Speaker 12: any of your queries.

1366
01:09:51,479 --> 01:09:55,720
Speaker 2: Just Don Draper spending hours watching that looping Christmas video.

1367
01:09:56,280 --> 01:09:57,600
Speaker 8: Staring at nothingness.

1368
01:09:57,920 --> 01:10:01,280
Speaker 2: Yeah. So there was like an immediate, pretty immediate backlash

1369
01:10:01,320 --> 01:10:03,559
Speaker 2: to this, like all of the responses. If you go

1370
01:10:03,560 --> 01:10:05,960
Speaker 2: to any of like where these things live on YouTube,

1371
01:10:06,000 --> 01:10:09,720
Speaker 2: it's just people shitting on them, which he did acknowledge

1372
01:10:09,840 --> 01:10:12,520
Speaker 2: Jason by saying the video was very debated.

1373
01:10:13,000 --> 01:10:14,920
Speaker 8: Yes, classic thing with commercials.

1374
01:10:15,120 --> 01:10:16,520
Speaker 2: We love debating commercials.

1375
01:10:16,560 --> 01:10:18,920
Speaker 5: Many things are very debated these days.

1376
01:10:18,680 --> 01:10:19,799
Speaker 7: A lot of people are saying.

1377
01:10:19,880 --> 01:10:22,360
Speaker 2: And then he showed us next an AI generated video,

1378
01:10:22,479 --> 01:10:26,160
Speaker 2: the Heist, which was entirely made by a tech script

1379
01:10:26,160 --> 01:10:29,240
Speaker 2: that itself was mostly written by chat GPT. And here's

1380
01:10:29,240 --> 01:10:32,400
Speaker 2: how Jason describes the workflow for what you're about to see.

1381
01:10:32,840 --> 01:10:35,519
Speaker 2: It took thousands of generations to get the final film,

1382
01:10:35,560 --> 01:10:39,560
Speaker 2: but I'm absolutely blown away by the quality, the consistency,

1383
01:10:39,920 --> 01:10:43,599
Speaker 2: and adherence to the original prompt when I described gritty

1384
01:10:43,680 --> 01:10:47,840
Speaker 2: New York City in the eighties, it delivered in spades consistently.

1385
01:10:48,280 --> 01:10:51,800
Speaker 2: While this is not perfect, it is hands down the

1386
01:10:51,840 --> 01:10:56,200
Speaker 2: best video generation model out there by a long shot. Additionally,

1387
01:10:56,479 --> 01:11:00,160
Speaker 2: it's important no VFX, no cleanup, no color correction has

1388
01:11:00,160 --> 01:11:03,639
Speaker 2: been added. Everything is straight out of VO two Google

1389
01:11:03,680 --> 01:11:06,759
Speaker 2: deep mind. So what is the model vo two Google

1390
01:11:06,760 --> 01:11:08,040
Speaker 2: deep mind? I think is what he's saying.

1391
01:11:08,080 --> 01:11:09,880
Speaker 8: It is, so I thought that I had another one.

1392
01:11:10,000 --> 01:11:11,960
Speaker 8: By the way, I'm sure what you're about to show

1393
01:11:12,040 --> 01:11:13,200
Speaker 8: me looks like a dog's.

1394
01:11:12,960 --> 01:11:15,240
Speaker 2: As it looks like, yeah, New York, exactly like New

1395
01:11:15,320 --> 01:11:18,840
Speaker 2: York at Juliani right before he came in clean it up.

1396
01:11:20,560 --> 01:11:23,360
Speaker 5: So this is like the competitor to Sora. I guess

1397
01:11:23,400 --> 01:11:26,559
Speaker 5: that's the other big like video generation brand new.

1398
01:11:26,600 --> 01:11:29,639
Speaker 2: I don't buy for a fucking and I'm not impressed.

1399
01:11:29,680 --> 01:11:31,080
Speaker 2: But we'll see what you guys think. Okay, I don't

1400
01:11:31,080 --> 01:11:33,400
Speaker 2: want to poison your I wouldn't.

1401
01:11:34,840 --> 01:11:37,720
Speaker 8: Oh god, okay, there is fire in.

1402
01:11:37,680 --> 01:11:39,800
Speaker 2: This The last time you're gonna see the sack full

1403
01:11:39,840 --> 01:11:41,760
Speaker 2: of money. It does not shine again.

1404
01:11:41,840 --> 01:11:44,360
Speaker 5: It's a lot of a lot of fire, random fire

1405
01:11:44,439 --> 01:11:45,599
Speaker 5: and go.

1406
01:11:45,560 --> 01:11:47,120
Speaker 8: Backwards when they're driving forwards.

1407
01:11:47,880 --> 01:11:51,040
Speaker 2: Wheel again another straight fire.

1408
01:11:52,120 --> 01:11:53,800
Speaker 8: I would love to do freeze frames on this.

1409
01:11:54,320 --> 01:11:58,960
Speaker 2: Actually it's in Gosthel. Why is there so many fires?

1410
01:11:59,040 --> 01:12:00,760
Speaker 7: Just all right, let's take a shot every time?

1411
01:12:01,400 --> 01:12:04,040
Speaker 2: Oh my god, and also take a shut every time.

1412
01:12:04,280 --> 01:12:07,280
Speaker 2: He is wearing different clothing and has a clearly different face.

1413
01:12:07,280 --> 01:12:10,080
Speaker 2: The car has changed. Column he's praising the consistency and

1414
01:12:10,120 --> 01:12:13,360
Speaker 2: it is a he is dressed completely differently every scene.

1415
01:12:14,800 --> 01:12:17,680
Speaker 5: His jacket has has has changed since the last one.

1416
01:12:17,760 --> 01:12:21,000
Speaker 2: Yeah, yeah, and again the cop car the cars. When

1417
01:12:21,000 --> 01:12:23,519
Speaker 2: it shows the cars dragging across the screen, they're kind

1418
01:12:23,520 --> 01:12:26,200
Speaker 2: of doing the same thing usually that the animals doing

1419
01:12:26,240 --> 01:12:26,839
Speaker 2: the coke.

1420
01:12:26,680 --> 01:12:29,839
Speaker 7: And minimal motion at the best.

1421
01:12:30,720 --> 01:12:34,880
Speaker 2: Yeah. I also love this. Can you believe this music?

1422
01:12:36,439 --> 01:12:38,519
Speaker 10: I also want to just say when he swoved hit

1423
01:12:38,600 --> 01:12:40,920
Speaker 10: that thing, he was driving like half a mile on it.

1424
01:12:41,200 --> 01:12:43,679
Speaker 5: Yeah, that's how I run.

1425
01:12:43,840 --> 01:12:46,439
Speaker 2: Yeah, look an obviously different man.

1426
01:12:46,560 --> 01:12:52,040
Speaker 5: That's by the way he runs was like he had

1427
01:12:52,040 --> 01:12:52,400
Speaker 5: his arms.

1428
01:12:52,560 --> 01:12:56,680
Speaker 2: Looks cops are three kes actually, look how they run.

1429
01:12:57,360 --> 01:12:58,519
Speaker 5: The running is very funny.

1430
01:12:58,600 --> 01:13:02,040
Speaker 2: Yeah, the bond his seat Yeah different, okay.

1431
01:13:01,840 --> 01:13:04,240
Speaker 5: What is going on with his feet and that, different

1432
01:13:04,320 --> 01:13:09,720
Speaker 5: levels of facial hair, different different jackets, he's wearing different colors, jackets.

1433
01:13:09,360 --> 01:13:12,960
Speaker 12: Vaguely definiteness and in this SA just move.

1434
01:13:13,000 --> 01:13:15,639
Speaker 2: What the fuck is going on? Oh my god, I

1435
01:13:15,680 --> 01:13:19,599
Speaker 2: got me? Yeah, directed by Jason's Odd in big flaming

1436
01:13:19,680 --> 01:13:22,840
Speaker 2: words because again the AI only knows how to put

1437
01:13:22,920 --> 01:13:23,840
Speaker 2: random fires on.

1438
01:13:24,000 --> 01:13:26,200
Speaker 10: Wow, I'm so glad that we have the technology that

1439
01:13:26,240 --> 01:13:28,040
Speaker 10: there a thing where a guy gets chased by the place.

1440
01:13:28,240 --> 01:13:30,479
Speaker 2: Yeah, we couldn't. This would have been impossible before.

1441
01:13:30,520 --> 01:13:32,880
Speaker 10: As he runs at anywhere from one to one hundred

1442
01:13:32,880 --> 01:13:33,479
Speaker 10: miles an hour.

1443
01:13:33,680 --> 01:13:36,439
Speaker 5: I assume they just trained they like this was specifically

1444
01:13:36,479 --> 01:13:39,360
Speaker 5: like pulling on like Scorsese movies a lot.

1445
01:13:39,800 --> 01:13:42,080
Speaker 10: I just want to know about these thousands of generations

1446
01:13:42,080 --> 01:13:43,160
Speaker 10: of script because.

1447
01:13:42,920 --> 01:13:43,599
Speaker 5: That is interesting.

1448
01:13:43,680 --> 01:13:46,000
Speaker 10: I am very curious because I just don't believe that

1449
01:13:46,040 --> 01:13:49,320
Speaker 10: for did he just uh read there?

1450
01:13:49,400 --> 01:13:51,320
Speaker 12: Yeah no, that's the opening crawl to just like some

1451
01:13:51,600 --> 01:13:55,840
Speaker 12: uh generated Star Wars.

1452
01:13:56,560 --> 01:13:58,800
Speaker 5: It seems like shot by shot, right, each each shot

1453
01:13:58,880 --> 01:13:59,880
Speaker 5: is going to require a lot of.

1454
01:13:59,800 --> 01:14:01,559
Speaker 2: Like iteration the script.

1455
01:14:02,280 --> 01:14:05,160
Speaker 5: It's just yeah, I mean again, like it unpacking. What

1456
01:14:05,240 --> 01:14:07,560
Speaker 5: he actually is saying is unclear.

1457
01:14:07,280 --> 01:14:09,960
Speaker 2: Because I went to the YouTube video for this and

1458
01:14:10,120 --> 01:14:13,040
Speaker 2: the first five or four comments are looks like we

1459
01:14:13,120 --> 01:14:15,439
Speaker 2: found the new king of video. Jesus Christ, give it

1460
01:14:15,479 --> 01:14:19,320
Speaker 2: a rest close change in every shot. Four to six

1461
01:14:19,400 --> 01:14:22,000
Speaker 2: year old boys are gonna love it and still acts

1462
01:14:22,080 --> 01:14:25,440
Speaker 2: character and vehicle consistency. But we're getting close.

1463
01:14:27,520 --> 01:14:28,719
Speaker 5: Which is which is the exact?

1464
01:14:30,640 --> 01:14:33,839
Speaker 2: By twenty thirty, you'll make a man wear the same

1465
01:14:33,920 --> 01:14:36,280
Speaker 2: clothes for an entire video. Oh this is This has

1466
01:14:36,320 --> 01:14:37,920
Speaker 2: happened before with Sora.

1467
01:14:38,040 --> 01:14:40,439
Speaker 10: When they put Sora out there, like check out airhead

1468
01:14:41,160 --> 01:14:45,240
Speaker 10: on your man God, and the balloon changes every single shot.

1469
01:14:45,280 --> 01:14:47,719
Speaker 8: It's a different size and color each time.

1470
01:14:47,920 --> 01:14:50,800
Speaker 10: There are just people running in the background sometimes and

1471
01:14:50,840 --> 01:14:52,519
Speaker 10: then they made a new one. You're like, oh, this

1472
01:14:52,560 --> 01:14:55,280
Speaker 10: is gonna be good. It was worse and less consistent,

1473
01:14:55,560 --> 01:14:57,800
Speaker 10: and this is what they think of us. They're like,

1474
01:14:57,840 --> 01:14:59,400
Speaker 10: these pigs will slop up anything.

1475
01:14:59,760 --> 01:14:59,800
Speaker 4: You.

1476
01:15:00,000 --> 01:15:03,720
Speaker 2: I can't expect technology to do something as complicated as

1477
01:15:03,800 --> 01:15:05,880
Speaker 2: dress a man in clothing and have him stay in

1478
01:15:05,920 --> 01:15:08,800
Speaker 2: that same clothing over multiple scenes. Hollywood never figured it

1479
01:15:09,040 --> 01:15:09,599
Speaker 2: so cool.

1480
01:15:09,439 --> 01:15:12,360
Speaker 10: That this costs like so much money as well just

1481
01:15:12,400 --> 01:15:16,240
Speaker 10: burning there's some fucking gpu melting and a day just

1482
01:15:16,360 --> 01:15:17,520
Speaker 10: enter in Arizona.

1483
01:15:17,120 --> 01:15:19,480
Speaker 2: The strain learning North Carolina.

1484
01:15:19,880 --> 01:15:22,959
Speaker 12: It is also there's gonna be like thirty forty companies

1485
01:15:23,000 --> 01:15:26,200
Speaker 12: trying to recreate the same misshapen wheel you know, for

1486
01:15:26,280 --> 01:15:27,200
Speaker 12: the next five days.

1487
01:15:27,240 --> 01:15:30,400
Speaker 10: Also, the little pigs that watch Star Wars, including myself,

1488
01:15:30,600 --> 01:15:33,519
Speaker 10: they'll notice every miner inconsistency. Do you think that they're

1489
01:15:33,560 --> 01:15:37,680
Speaker 10: going to tolerate Luke Skywalker's and Watteau and all their favorite.

1490
01:15:37,360 --> 01:15:39,360
Speaker 8: Characters they're going to drive? Do you think that they're

1491
01:15:39,360 --> 01:15:41,200
Speaker 8: going to be happy office with a cyber truck?

1492
01:15:41,560 --> 01:15:45,120
Speaker 2: That's a cyber truck situation. You I think the issues

1493
01:15:45,160 --> 01:15:47,479
Speaker 2: are twofold, which is like number one. In order to

1494
01:15:47,520 --> 01:15:50,000
Speaker 2: make this shiit sell to the people who watch movies,

1495
01:15:50,040 --> 01:15:53,599
Speaker 2: you have to dramatically reduce the average intelligence of people

1496
01:15:53,640 --> 01:15:56,519
Speaker 2: watching movies. You have to give everyone brain damage, which

1497
01:15:56,520 --> 01:15:57,599
Speaker 2: except they are.

1498
01:15:57,439 --> 01:15:59,000
Speaker 7: Working in Giant Yeah.

1499
01:15:59,320 --> 01:16:01,240
Speaker 2: And the other thing is the models have to get

1500
01:16:01,400 --> 01:16:03,320
Speaker 2: much better. And Jason made a point that like, look,

1501
01:16:03,400 --> 01:16:05,519
Speaker 2: every time people would like talk about the criticism and

1502
01:16:05,600 --> 01:16:08,240
Speaker 2: be like, look, this is the worst it's gonna look guys,

1503
01:16:08,840 --> 01:16:12,040
Speaker 2: And I was just looking into it. GPT four took

1504
01:16:12,160 --> 01:16:15,720
Speaker 2: fifty times as many resources in like fifty times as

1505
01:16:15,760 --> 01:16:19,680
Speaker 2: much energy to train as GPT three did. So this

1506
01:16:19,760 --> 01:16:22,720
Speaker 2: is these are the kind of like exponential increases that

1507
01:16:22,760 --> 01:16:25,120
Speaker 2: we're looking at. So like, if it took them so

1508
01:16:25,880 --> 01:16:29,320
Speaker 2: many millions, billions of dollars of investment to get to

1509
01:16:29,360 --> 01:16:31,519
Speaker 2: the point where they can make this shitty video, to

1510
01:16:31,600 --> 01:16:35,040
Speaker 2: make anything close to watchable. You're talking about again just

1511
01:16:35,080 --> 01:16:39,000
Speaker 2: like lighting on fire, billions of dollars to do what

1512
01:16:39,240 --> 01:16:41,640
Speaker 2: to make a scene that you could already get like

1513
01:16:41,680 --> 01:16:44,519
Speaker 2: a twenty six year old dude who grew up watching

1514
01:16:44,520 --> 01:16:48,519
Speaker 2: fucking Quentin Tarantino movies and taking cocaine, And you could

1515
01:16:48,520 --> 01:16:50,880
Speaker 2: give them sixty thousand dollars and he'll film that shit

1516
01:16:50,920 --> 01:16:52,439
Speaker 2: for you with an old car, Like.

1517
01:16:52,479 --> 01:16:55,040
Speaker 5: Yeah, I mean you could. You could even like animate it.

1518
01:16:55,520 --> 01:16:57,559
Speaker 12: M I mean, look, you give me a PS four

1519
01:16:57,760 --> 01:17:00,160
Speaker 12: and somebody's grandmother and I will make them think that

1520
01:17:00,200 --> 01:17:01,000
Speaker 12: they're watching that.

1521
01:17:01,160 --> 01:17:03,280
Speaker 5: No, seriously, seriously that six.

1522
01:17:03,520 --> 01:17:05,479
Speaker 10: But also this, I just want to read out some

1523
01:17:05,520 --> 01:17:08,120
Speaker 10: of the fucking people that use this model. We started

1524
01:17:08,160 --> 01:17:10,960
Speaker 10: working with creatives like Donald Glover, who I said was

1525
01:17:10,960 --> 01:17:11,880
Speaker 10: washed ten years ago.

1526
01:17:11,960 --> 01:17:13,200
Speaker 2: I'm fucking sick of people.

1527
01:17:13,840 --> 01:17:16,120
Speaker 7: My Love was a was a good album.

1528
01:17:16,240 --> 01:17:18,120
Speaker 5: America is an objectively bad song.

1529
01:17:18,680 --> 01:17:20,200
Speaker 2: It's a bad song with a great video.

1530
01:17:20,400 --> 01:17:22,600
Speaker 8: Yeaheah, I thought he's like kind of bar and be

1531
01:17:22,680 --> 01:17:23,080
Speaker 8: stuff is.

1532
01:17:23,120 --> 01:17:26,080
Speaker 10: Very interesting anyway, moving and of course the week in

1533
01:17:26,200 --> 01:17:32,080
Speaker 10: it so weekend and someone called great. I'll work with

1534
01:17:32,160 --> 01:17:34,800
Speaker 10: creators on VO one and form the development of VO two,

1535
01:17:34,840 --> 01:17:36,760
Speaker 10: and we look forward to working with trusted testers and

1536
01:17:36,760 --> 01:17:38,519
Speaker 10: creators to get feedback on this new model.

1537
01:17:38,600 --> 01:17:40,759
Speaker 8: How long are you going to get fucking feedback? It stinks.

1538
01:17:41,080 --> 01:17:44,160
Speaker 2: We've got some feedback from Yeah, I got a few thoughts.

1539
01:17:44,200 --> 01:17:47,080
Speaker 5: Hopefully those people are are just getting paid to tell

1540
01:17:47,120 --> 01:17:49,360
Speaker 5: them words and be like yeah, sure, I'll take your money.

1541
01:17:49,680 --> 01:17:52,200
Speaker 2: Yeah, if they can be twenty million dollars, I'm flipping

1542
01:17:52,240 --> 01:17:55,200
Speaker 2: the hole like just no, I will turn on a dime.

1543
01:17:55,640 --> 01:17:58,759
Speaker 2: Speaking of turning on a dime for money, here's.

1544
01:17:58,600 --> 01:18:11,960
Speaker 13: Ads Ah, we're back.

1545
01:18:12,960 --> 01:18:16,439
Speaker 2: So the next video that our friend I now feel

1546
01:18:16,479 --> 01:18:19,120
Speaker 2: he's like a brother to me, Jason puts on was

1547
01:18:19,160 --> 01:18:22,880
Speaker 2: of an AI generated fictional elderly rock star talking about

1548
01:18:22,960 --> 01:18:29,879
Speaker 2: death to do this plastic and incapable of dynamic expression

1549
01:18:29,880 --> 01:18:32,599
Speaker 2: as he guzzles randomly from bottles of liquor that flash

1550
01:18:32,680 --> 01:18:35,200
Speaker 2: in and out of existence. Sometimes he lies on his

1551
01:18:35,280 --> 01:18:38,160
Speaker 2: back in empty streets while talking about all the all

1552
01:18:38,200 --> 01:18:41,000
Speaker 2: of the cgi featureless women that he has loved in

1553
01:18:41,080 --> 01:18:45,040
Speaker 2: his exciting life. Other Times he plays stadium shows while

1554
01:18:45,080 --> 01:18:48,280
Speaker 2: obvious GPT written dialogue about aging and death drones on.

1555
01:18:48,680 --> 01:18:51,680
Speaker 2: When the video ends, everybody in the room claps, And

1556
01:18:51,720 --> 01:18:54,439
Speaker 2: as you watch this, I need to imagine seeing the

1557
01:18:54,479 --> 01:18:56,640
Speaker 2: thing that I'm about to show you all and a

1558
01:18:56,720 --> 01:18:59,479
Speaker 2: room with like two hundred people in it, all clapping

1559
01:18:59,560 --> 01:19:04,320
Speaker 2: in thusiastically. I don't think I did. I did it.

1560
01:19:04,560 --> 01:19:07,599
Speaker 2: I did. I said, come the fuck on, as flat

1561
01:19:07,640 --> 01:19:11,759
Speaker 2: as I could rise, a skywalk up. Yeah. So here's

1562
01:19:12,120 --> 01:19:16,320
Speaker 2: fade out and an old man. Yea, it looks a

1563
01:19:16,360 --> 01:19:19,559
Speaker 2: little bit like George car It's the end. Okay, three?

1564
01:19:20,040 --> 01:19:23,840
Speaker 6: Can you chest like the world's just you, God damn

1565
01:19:23,880 --> 01:19:29,600
Speaker 6: big and you're just to go passing through them?

1566
01:19:29,640 --> 01:19:30,280
Speaker 2: What's he doing?

1567
01:19:30,439 --> 01:19:34,679
Speaker 10: He carried my heart concerts, Granddad, calm down, It scattered.

1568
01:19:34,920 --> 01:19:38,080
Speaker 10: I love these slash cuts, the fast cuts, these fosse cuts,

1569
01:19:38,120 --> 01:19:39,680
Speaker 10: because the next frame was unusable.

1570
01:19:40,760 --> 01:19:41,960
Speaker 5: Yes, actually yes.

1571
01:19:42,000 --> 01:19:44,360
Speaker 2: Like that he drank and the bottle changed in his hand.

1572
01:19:44,400 --> 01:19:47,680
Speaker 2: You could see it starting to happen? What is just

1573
01:19:47,760 --> 01:19:52,360
Speaker 2: anonymous with destroyed It? Just a beautiful music. Listen to

1574
01:19:52,400 --> 01:19:53,760
Speaker 2: that lived It to the bow?

1575
01:19:54,200 --> 01:19:55,320
Speaker 5: Could you believe.

1576
01:19:56,640 --> 01:19:59,840
Speaker 2: By firing a Roman candles time?

1577
01:20:01,040 --> 01:20:01,760
Speaker 5: I like so?

1578
01:20:01,880 --> 01:20:05,200
Speaker 10: The old man does look very different each time, very

1579
01:20:05,200 --> 01:20:05,880
Speaker 10: different old man.

1580
01:20:06,400 --> 01:20:08,160
Speaker 5: That's a different that's a different guy.

1581
01:20:08,680 --> 01:20:15,840
Speaker 2: Yeah, that's the Emperor from the first Cladiators, trotting get running.

1582
01:20:15,560 --> 01:20:18,920
Speaker 5: Away from this the way this model generates running diesel.

1583
01:20:20,880 --> 01:20:25,040
Speaker 2: There he is drinking on the fire, old rock star,

1584
01:20:25,160 --> 01:20:29,720
Speaker 2: drinking in front of a flaming house, the a I

1585
01:20:29,760 --> 01:20:30,679
Speaker 2: loves burning building.

1586
01:20:30,760 --> 01:20:33,360
Speaker 5: What is this voice? I would love to track his

1587
01:20:33,479 --> 01:20:35,000
Speaker 5: tattoos from three.

1588
01:20:35,320 --> 01:20:37,880
Speaker 10: We'll say he's about to eat the micro different, I've

1589
01:20:37,920 --> 01:20:40,720
Speaker 10: done it, yum.

1590
01:20:40,840 --> 01:20:47,719
Speaker 2: Now he's sleeping and a broken Mustang, the classic Ferrari

1591
01:20:47,800 --> 01:20:51,479
Speaker 2: Mustang Mustang. It's in like a pool in front of

1592
01:20:51,479 --> 01:20:53,400
Speaker 2: a mansion, but he clearly isn't questioned to it. The

1593
01:20:53,400 --> 01:20:58,080
Speaker 2: car is hovering slightly over the pool like I love this,

1594
01:20:58,240 --> 01:21:00,639
Speaker 2: I love this, I love him, And he tells us.

1595
01:21:00,680 --> 01:21:02,560
Speaker 2: He tells us during this as if we're supposed to

1596
01:21:02,560 --> 01:21:06,960
Speaker 2: be impressed that Chatchypt wrote seventy five and that's fucking hell.

1597
01:21:08,800 --> 01:21:10,280
Speaker 5: I can't believe that.

1598
01:21:10,439 --> 01:21:16,360
Speaker 12: Frankly, as a bartender, I regret walking into the room

1599
01:21:16,400 --> 01:21:17,559
Speaker 12: to see if people want drinks.

1600
01:21:17,600 --> 01:21:19,120
Speaker 8: This is a bartender.

1601
01:21:19,240 --> 01:21:22,519
Speaker 2: I apologize. I apologize that you had to hit a drink.

1602
01:21:22,680 --> 01:21:24,760
Speaker 5: I also would like, actually, can I have a drink too?

1603
01:21:24,960 --> 01:21:26,519
Speaker 2: We are in the line.

1604
01:21:26,360 --> 01:21:28,920
Speaker 10: Ces Sweet and we're all drinking because I just want

1605
01:21:29,000 --> 01:21:32,040
Speaker 10: to say, I'm fucking disassociating after that, I'm so fucking

1606
01:21:32,080 --> 01:21:35,400
Speaker 10: saying every a year of doing this nonsense, and I

1607
01:21:35,479 --> 01:21:37,720
Speaker 10: look at these chit eaters and they show us that,

1608
01:21:37,760 --> 01:21:39,360
Speaker 10: and they like slope down the slop.

1609
01:21:39,600 --> 01:21:43,439
Speaker 2: Oh my god, it's it's it's hitting the easiest.

1610
01:21:42,960 --> 01:21:45,240
Speaker 8: Things to find an old man that drinks.

1611
01:21:44,920 --> 01:21:46,920
Speaker 2: For an idea of like how real this company is.

1612
01:21:46,920 --> 01:21:48,800
Speaker 2: Obviously they were one of the companies. They were not

1613
01:21:48,800 --> 01:21:50,559
Speaker 2: the only people who made that Coca Cola add They

1614
01:21:50,560 --> 01:21:52,400
Speaker 2: were one of like three or four companies.

1615
01:21:52,439 --> 01:21:55,479
Speaker 5: It takes four companies to take companies to make that.

1616
01:21:55,600 --> 01:21:59,080
Speaker 2: Can't believe it. They have six hundred and twenty two

1617
01:21:59,080 --> 01:22:02,080
Speaker 2: followers on Twitter, Hell yes or not? Twitter, on YouTube,

1618
01:22:02,160 --> 01:22:04,840
Speaker 2: on YouTube, on YouTube, on YouTube, I don't know than

1619
01:22:05,320 --> 01:22:08,000
Speaker 2: I post this karaoke song and this this fade out

1620
01:22:08,040 --> 01:22:10,800
Speaker 2: is there or Sorry. The Heist is their most successful video,

1621
01:22:10,840 --> 01:22:13,960
Speaker 2: with fifty six thousand views. Fade Out, which we just watched,

1622
01:22:14,000 --> 01:22:17,200
Speaker 2: has less than five thousand views. They're not ready, so

1623
01:22:17,240 --> 01:22:19,080
Speaker 2: they're not they're not white.

1624
01:22:19,120 --> 01:22:20,360
Speaker 5: It's only going to get better.

1625
01:22:20,479 --> 01:22:22,640
Speaker 2: Yeah, it's only going to get get only going to

1626
01:22:22,720 --> 01:22:23,080
Speaker 2: get better.

1627
01:22:23,160 --> 01:22:25,439
Speaker 5: Obviously, things will only get.

1628
01:22:25,280 --> 01:22:28,120
Speaker 7: Read floor for a small price of one billion dollars.

1629
01:22:28,200 --> 01:22:30,440
Speaker 8: It's just like one hundred thousand dollars to compute.

1630
01:22:30,880 --> 01:22:33,599
Speaker 2: Yeah, imagine how good it would be much a billion

1631
01:22:33,720 --> 01:22:38,320
Speaker 2: will only get worth more. I mean, I get now, Garrison,

1632
01:22:38,680 --> 01:22:40,599
Speaker 2: I do think you should invest all of your salary.

1633
01:22:40,600 --> 01:22:42,960
Speaker 8: I just did a sixteenth minute about talking this.

1634
01:22:43,640 --> 01:22:46,160
Speaker 10: I think I would rather hook To has a more

1635
01:22:46,560 --> 01:22:49,519
Speaker 10: obvious use case than this ship. Hey, do you want

1636
01:22:49,560 --> 01:22:51,679
Speaker 10: to spend way more money to get something way worse?

1637
01:22:51,760 --> 01:22:54,920
Speaker 10: I actually can't get over the seventy five percent check GPTO.

1638
01:22:55,720 --> 01:22:57,679
Speaker 2: That should be more twenty.

1639
01:22:57,920 --> 01:23:00,679
Speaker 8: No, it should be Theoretically it should be it should

1640
01:23:00,680 --> 01:23:00,880
Speaker 8: be one.

1641
01:23:00,840 --> 01:23:04,000
Speaker 10: Hundred, should be hundred percent, which means that a quarter

1642
01:23:04,439 --> 01:23:06,680
Speaker 10: a quarture of it was just fucking unusable.

1643
01:23:06,800 --> 01:23:07,000
Speaker 2: No.

1644
01:23:07,360 --> 01:23:10,519
Speaker 5: Absolutely, they're generating like individual shots that they're like stitching

1645
01:23:10,560 --> 01:23:13,320
Speaker 5: together and like who knows how how long it takes

1646
01:23:13,320 --> 01:23:15,400
Speaker 5: to like get like the prompt right for that shot

1647
01:23:15,439 --> 01:23:15,880
Speaker 5: to work.

1648
01:23:16,280 --> 01:23:18,760
Speaker 2: However long it takes, it was too long because it

1649
01:23:18,800 --> 01:23:20,920
Speaker 2: looks like, shit, we're gonna watch a video I haven't

1650
01:23:20,920 --> 01:23:22,960
Speaker 2: seen yet, or at least of course, because it's five minutes,

1651
01:23:22,960 --> 01:23:23,800
Speaker 2: so we're not watching all this.

1652
01:23:23,840 --> 01:23:24,400
Speaker 5: Oh my god.

1653
01:23:24,479 --> 01:23:27,360
Speaker 2: It's two hundred and fifty two views and came out

1654
01:23:27,360 --> 01:23:29,640
Speaker 2: a week ago. It's called miniminade.

1655
01:23:30,680 --> 01:23:34,400
Speaker 5: What Yeah, it's a word.

1656
01:23:34,600 --> 01:23:36,840
Speaker 10: Now it's like when you find your cat's momented on

1657
01:23:36,880 --> 01:23:37,639
Speaker 10: the floor again.

1658
01:23:38,240 --> 01:23:41,040
Speaker 2: So first we see a diner called Minimonade that appears

1659
01:23:41,080 --> 01:23:42,360
Speaker 2: to be both on the fire.

1660
01:23:42,200 --> 01:23:44,200
Speaker 8: Light runner yeah, light runner, oh god.

1661
01:23:44,360 --> 01:23:46,720
Speaker 2: And an old lady Rice is up out of a

1662
01:23:46,760 --> 01:23:49,479
Speaker 2: pile of ashes. That's how mouths work.

1663
01:23:51,680 --> 01:23:52,679
Speaker 5: Where am I.

1664
01:23:54,120 --> 01:23:55,160
Speaker 2: Great? AI voice?

1665
01:23:55,240 --> 01:23:59,479
Speaker 8: What is this pantasticgoria or all voice acting? It's me

1666
01:23:59,640 --> 01:24:06,400
Speaker 8: Harris Forward. The funk is going on with the tea.

1667
01:24:06,920 --> 01:24:09,840
Speaker 2: What I think this is death? This old lady is dead.

1668
01:24:10,479 --> 01:24:10,920
Speaker 2: That's how I.

1669
01:24:14,160 --> 01:24:14,320
Speaker 5: Now.

1670
01:24:14,400 --> 01:24:20,000
Speaker 2: She's tripping on tomatoes. The decaying Sandy diner that exploded

1671
01:24:20,040 --> 01:24:24,320
Speaker 2: has turned into a lively fifties diner off off.

1672
01:24:24,160 --> 01:24:25,679
Speaker 8: Dennis phil Villa News.

1673
01:24:25,760 --> 01:24:27,000
Speaker 7: This a segregated diner.

1674
01:24:28,720 --> 01:24:32,240
Speaker 2: I only see why going back to the good old days.

1675
01:24:32,320 --> 01:24:33,640
Speaker 5: Yeah, yeah, I know.

1676
01:24:33,640 --> 01:24:35,920
Speaker 2: There's a little Indian book he is. He is the

1677
01:24:36,000 --> 01:24:43,360
Speaker 2: help though. Yeah mm hmmm, oh that's not. The little

1678
01:24:43,400 --> 01:24:45,439
Speaker 2: kid just fell down, and the way it shows falling

1679
01:24:45,520 --> 01:24:47,439
Speaker 2: is that he just sort of deflays.

1680
01:24:48,680 --> 01:24:52,719
Speaker 7: And he's up again. The action is staring at.

1681
01:24:52,720 --> 01:24:55,600
Speaker 2: Well, that's terrible. We don't need to watch anymore of that.

1682
01:24:55,960 --> 01:24:57,840
Speaker 2: No one, no one, no one want to watch watch

1683
01:24:57,880 --> 01:24:59,080
Speaker 2: this and have a positive reaction.

1684
01:24:59,160 --> 01:25:01,080
Speaker 8: They should, They should keep you in a holding cell.

1685
01:25:01,200 --> 01:25:04,439
Speaker 2: Yeah, I'm deeply unhappy at the time we already spent watching.

1686
01:25:04,560 --> 01:25:06,320
Speaker 8: Yeah, like, we don't know what you're gonna do next.

1687
01:25:06,680 --> 01:25:08,200
Speaker 7: We're building a facility for you.

1688
01:25:08,439 --> 01:25:12,519
Speaker 2: Yeah. The phrase reality distortion field gets used a lot

1689
01:25:12,560 --> 01:25:15,240
Speaker 2: when we talk about texts, but I really tasted it

1690
01:25:15,280 --> 01:25:17,880
Speaker 2: in that room because all anyone on stage could talk

1691
01:25:17,880 --> 01:25:20,080
Speaker 2: about is how good it looks. And every one of

1692
01:25:20,160 --> 01:25:24,240
Speaker 2: these videos people are like clapping, They're like, wow, this

1693
01:25:24,439 --> 01:25:25,440
Speaker 2: is amazing.

1694
01:25:25,680 --> 01:25:28,200
Speaker 5: Why do you think they think it looks good?

1695
01:25:28,760 --> 01:25:30,400
Speaker 8: It looks better than an Xbox.

1696
01:25:30,960 --> 01:25:33,400
Speaker 10: Yeah, And the idea is you talk to thing in

1697
01:25:33,479 --> 01:25:35,640
Speaker 10: and now a thing came out, and that's magical.

1698
01:25:35,320 --> 01:25:37,920
Speaker 7: So why virtue of not having humans work on it?

1699
01:25:37,920 --> 01:25:41,280
Speaker 2: It's so it's better than you'd have Yeah, Okay. There

1700
01:25:41,320 --> 01:25:44,280
Speaker 2: was a moment after this where Jason like joked about

1701
01:25:44,280 --> 01:25:46,840
Speaker 2: how like I don't like obviously I don't want to

1702
01:25:46,880 --> 01:25:52,720
Speaker 2: replace actors yet yeah yeah, and another Panels was like,

1703
01:25:52,720 --> 01:25:54,640
Speaker 2: I think we're gonna have to make some you have

1704
01:25:54,680 --> 01:25:57,240
Speaker 2: to see how some decisions go as to fair use,

1705
01:25:57,280 --> 01:26:00,720
Speaker 2: because obviously this is cribbing from a bunch of fuck Scorsese,

1706
01:26:01,120 --> 01:26:04,280
Speaker 2: like it kind of looked like nine. Yeah, and Thomas

1707
01:26:04,360 --> 01:26:05,080
Speaker 2: can get and.

1708
01:26:05,280 --> 01:26:08,240
Speaker 5: Later On in twenty foury nine, and Denny Villanou in general,

1709
01:26:08,280 --> 01:26:09,960
Speaker 5: like all of his films have been like a massive

1710
01:26:10,000 --> 01:26:15,200
Speaker 5: source for for these emotion and still generations, so much

1711
01:26:15,200 --> 01:26:17,120
Speaker 5: so that like I think, like later On twenty forty

1712
01:26:17,160 --> 01:26:19,160
Speaker 5: nine is like one of the easiest films to like

1713
01:26:19,160 --> 01:26:22,640
Speaker 5: like replicate film stills almost exactly for based on like

1714
01:26:22,720 --> 01:26:25,200
Speaker 5: how like how like load bearing that film has been

1715
01:26:25,240 --> 01:26:27,600
Speaker 5: for a whole bunch of these models that could be

1716
01:26:27,640 --> 01:26:28,719
Speaker 5: due to a number of factors.

1717
01:26:28,960 --> 01:26:31,360
Speaker 2: Now I know what you're wondering, How soon until we

1718
01:26:31,400 --> 01:26:34,040
Speaker 2: can get a full ninety minute movie that looks like this?

1719
01:26:34,160 --> 01:26:34,280
Speaker 1: Oh?

1720
01:26:34,439 --> 01:26:35,759
Speaker 5: I mean I'm guessing days away.

1721
01:26:36,200 --> 01:26:38,479
Speaker 2: No, no, Jason said, probably not at least for a

1722
01:26:38,560 --> 01:26:39,240
Speaker 2: decade or so.

1723
01:26:39,479 --> 01:26:42,400
Speaker 5: Really, okay, years, that's interesting.

1724
01:26:42,680 --> 01:26:45,440
Speaker 2: I don't want to wait that long. What a worthwhile endeffort.

1725
01:26:45,080 --> 01:26:48,360
Speaker 5: Though, because he could have said shorter. That actually is interesting.

1726
01:26:48,400 --> 01:26:51,080
Speaker 8: He could have said anything those chums believed.

1727
01:26:51,320 --> 01:26:53,439
Speaker 2: I think it is like he did have to spend

1728
01:26:53,680 --> 01:26:57,479
Speaker 2: probably hundreds of hours of his precious one human life

1729
01:26:57,840 --> 01:27:01,080
Speaker 2: stitching those those turds together, and he's like, it's nowhere

1730
01:27:01,120 --> 01:27:03,280
Speaker 2: near ready. There's no way it could make it nice.

1731
01:27:03,280 --> 01:27:03,960
Speaker 5: It's giving him.

1732
01:27:06,080 --> 01:27:11,040
Speaker 12: Because I've only really seen one interesting genitive video thing.

1733
01:27:11,080 --> 01:27:15,040
Speaker 12: But it wasn't a generative video thing. It was they filmed, uh, Brian,

1734
01:27:15,120 --> 01:27:18,720
Speaker 12: you know, filmed a documentary and they created, you know,

1735
01:27:18,800 --> 01:27:22,680
Speaker 12: some backhand software so that they would be able to

1736
01:27:23,680 --> 01:27:27,720
Speaker 12: do cuts of existing footage and try to focus on

1737
01:27:27,840 --> 01:27:32,120
Speaker 12: the parts of the documentary. But I never ever see

1738
01:27:32,160 --> 01:27:35,800
Speaker 12: anything interested in like constructing narratives or it's like, you

1739
01:27:35,880 --> 01:27:39,920
Speaker 12: can't teasing other aspects of the creative process. It's only

1740
01:27:40,280 --> 01:27:42,760
Speaker 12: let's try to replace, right, Let's try to so you

1741
01:27:42,840 --> 01:27:44,000
Speaker 12: can't do narrative with it.

1742
01:27:44,080 --> 01:27:46,080
Speaker 2: And that's the thing. If I if i'd sat down

1743
01:27:46,120 --> 01:27:48,240
Speaker 2: there because I'm sitting I said this. I was sitting

1744
01:27:48,280 --> 01:27:49,880
Speaker 2: next to a guy from usc who was one of

1745
01:27:49,880 --> 01:27:51,600
Speaker 2: the only people in the room who was like similarly

1746
01:27:51,640 --> 01:27:55,080
Speaker 2: critical to me of what we were seeing on stage.

1747
01:27:55,080 --> 01:27:56,760
Speaker 2: It was like, look, if they had come down and

1748
01:27:56,760 --> 01:27:58,240
Speaker 2: been like, look, this is how we can plug a

1749
01:27:58,280 --> 01:28:00,439
Speaker 2: script in and it can create a story be bored,

1750
01:28:00,800 --> 01:28:02,799
Speaker 2: and you can like kind of see like a crude

1751
01:28:02,800 --> 01:28:04,800
Speaker 2: CGI animation of how the shots will look, and that

1752
01:28:04,840 --> 01:28:08,200
Speaker 2: can help you like plan out, like like that's legitimately useful.

1753
01:28:08,240 --> 01:28:10,759
Speaker 2: That's the thing that adds value and can cut costs

1754
01:28:10,800 --> 01:28:13,479
Speaker 2: in a meaningful way to like the production of good

1755
01:28:13,600 --> 01:28:16,920
Speaker 2: TV and movies. But that's not as sexy as like

1756
01:28:17,120 --> 01:28:19,479
Speaker 2: I'm and they were all talking. There was this this

1757
01:28:20,000 --> 01:28:24,599
Speaker 2: like very weird moment where one of the panels Leslie Shannon,

1758
01:28:24,800 --> 01:28:27,479
Speaker 2: who's head of innovation for Nokia, a company that used

1759
01:28:27,479 --> 01:28:29,479
Speaker 2: to make phones and now makes panelists who pretend to

1760
01:28:29,520 --> 01:28:30,720
Speaker 2: be entertained by awkwardata.

1761
01:28:30,760 --> 01:28:33,080
Speaker 5: They also like make cameras and.

1762
01:28:33,320 --> 01:28:35,080
Speaker 2: They make a lot of stuff. I was just shitting

1763
01:28:35,080 --> 01:28:38,400
Speaker 2: on Nokia. She's like, can we use neuroscience to see

1764
01:28:38,479 --> 01:28:41,840
Speaker 2: how people are reacting to AIA generated videos? And then

1765
01:28:41,880 --> 01:28:45,080
Speaker 2: adjust the ending to be like, you know, let's make

1766
01:28:45,120 --> 01:28:47,719
Speaker 2: this resonation of a night. That way, we're helping the creative.

1767
01:28:47,760 --> 01:28:49,719
Speaker 2: And I was like, are you out of your fucking mind?

1768
01:28:49,840 --> 01:28:53,120
Speaker 8: We attached electrodes to found people skulls.

1769
01:28:53,520 --> 01:28:56,760
Speaker 2: I would I would have supported electrodes in their skulls. Yes,

1770
01:28:57,200 --> 01:28:59,880
Speaker 2: Jesus Christ, we should do the monkey neuralink thing to

1771
01:29:00,120 --> 01:29:02,800
Speaker 2: perhaps a pair of calipers.

1772
01:29:04,560 --> 01:29:04,920
Speaker 8: Skulls.

1773
01:29:05,240 --> 01:29:07,400
Speaker 2: I am fascinated the skull shapes of that fucking.

1774
01:29:07,800 --> 01:29:10,360
Speaker 10: To say that is there's so many things that you've

1775
01:29:10,360 --> 01:29:13,040
Speaker 10: said that just they wouldn't survive at that position.

1776
01:29:13,479 --> 01:29:16,160
Speaker 2: Speaking of things that wouldn't survive a deposition the sponsors

1777
01:29:16,200 --> 01:29:30,920
Speaker 2: to this podcast. Okay, so that first panel was a

1778
01:29:30,960 --> 01:29:33,840
Speaker 2: real moment for me. I went through a couple of more,

1779
01:29:34,080 --> 01:29:36,640
Speaker 2: one of which was on like advertising and AI and

1780
01:29:36,840 --> 01:29:41,400
Speaker 2: was mostly mostly pretty boring. The third panel I went

1781
01:29:41,439 --> 01:29:46,000
Speaker 2: through though, was called AI Cinematic Spatial and XR and

1782
01:29:46,120 --> 01:29:47,680
Speaker 2: I just want to actually play you guys, you'll have

1783
01:29:47,720 --> 01:29:48,360
Speaker 2: to cluster around.

1784
01:29:48,400 --> 01:29:50,880
Speaker 10: I would actually believe that was generated with chat GPT

1785
01:29:52,000 --> 01:29:53,120
Speaker 10: GPT two point zero.

1786
01:29:53,680 --> 01:29:56,479
Speaker 5: So let's start with this one. AI will be more

1787
01:29:56,560 --> 01:29:57,840
Speaker 5: impactful than.

1788
01:29:57,720 --> 01:30:08,760
Speaker 14: The Internet, maybe it's a trick question because it is

1789
01:30:08,880 --> 01:30:11,000
Speaker 14: then that was that was.

1790
01:30:11,840 --> 01:30:14,799
Speaker 1: The Internet, although it can wrong about the Internet.

1791
01:30:15,080 --> 01:30:16,800
Speaker 6: So I'm like, oh, yeah, there you go.

1792
01:30:16,800 --> 01:30:19,440
Speaker 5: All right, what's what when you impact.

1793
01:30:20,600 --> 01:30:20,960
Speaker 9: AI?

1794
01:30:21,600 --> 01:30:26,320
Speaker 5: AI is going to resolve in astronomical job losses?

1795
01:30:28,040 --> 01:30:32,800
Speaker 2: True, bolsh, there will be an evolution of job laws.

1796
01:30:34,600 --> 01:30:37,400
Speaker 7: Next redistribution of.

1797
01:30:39,800 --> 01:30:42,559
Speaker 2: M That was the scene I wanted you to hear with.

1798
01:30:42,600 --> 01:30:44,000
Speaker 2: They're like, we don't want to say it out loud,

1799
01:30:44,040 --> 01:30:45,280
Speaker 2: and then everyone chuckles.

1800
01:30:45,360 --> 01:30:47,160
Speaker 8: These people are too fucking smug.

1801
01:30:47,479 --> 01:30:50,120
Speaker 10: Yeah, these people sound too confident and too chumming and

1802
01:30:50,160 --> 01:30:51,640
Speaker 10: too happy to say things like this.

1803
01:30:52,360 --> 01:30:54,479
Speaker 2: That's not good. I don't like these people laughing about

1804
01:30:54,520 --> 01:30:55,400
Speaker 2: people losing jobs.

1805
01:30:55,520 --> 01:30:56,880
Speaker 8: No, I shouldn't have jobs.

1806
01:30:56,960 --> 01:30:59,840
Speaker 2: That's that's a good place to start. Yeah, I don't

1807
01:30:59,920 --> 01:31:02,240
Speaker 2: like that either. And the people you're hearing from. Let

1808
01:31:02,240 --> 01:31:04,040
Speaker 2: me let me tell you who's in this fucking panel

1809
01:31:04,120 --> 01:31:07,920
Speaker 2: who was just laughing about like, well, there will be

1810
01:31:08,000 --> 01:31:14,519
Speaker 2: a uh an evolution law. Yeah. So the motherfuckers who

1811
01:31:14,520 --> 01:31:17,479
Speaker 2: are all that panel laughing about people losing their jobs.

1812
01:31:17,960 --> 01:31:24,040
Speaker 2: Ted Shillowitz literally his name is Shilowitz, futurist at Cinemersion, Inc.

1813
01:31:24,479 --> 01:31:25,080
Speaker 5: That's like a j.

1814
01:31:30,280 --> 01:31:35,160
Speaker 2: Rebecca Barkin, co founder in CEO laman o One, Aaron Luber,

1815
01:31:37,080 --> 01:31:45,759
Speaker 2: Director Partnerships at Google, IPG Media Lab, Layla Emirsadegi, Principal

1816
01:31:45,760 --> 01:31:50,559
Speaker 2: program manager at Engineering Microsoft, and Katie Henson, s VP

1817
01:31:50,720 --> 01:31:53,479
Speaker 2: post Production at Fear Studios. So those are the people

1818
01:31:53,520 --> 01:31:53,960
Speaker 2: who are.

1819
01:31:53,840 --> 01:31:57,800
Speaker 5: Are sad, all laughing and like it's like genera of

1820
01:31:57,840 --> 01:32:01,360
Speaker 5: AI is like good at like one thing creatively, it's

1821
01:32:01,439 --> 01:32:06,120
Speaker 5: good at like streamlining VFX, like workflow to the workflow

1822
01:32:06,160 --> 01:32:09,439
Speaker 5: of of how to do like it is it is

1823
01:32:09,439 --> 01:32:12,439
Speaker 5: like there's aspect. Famously, the only useful thing it's been

1824
01:32:12,520 --> 01:32:15,400
Speaker 5: used for is making people's eyes blue in Dune Part two.

1825
01:32:15,479 --> 01:32:17,200
Speaker 2: It's not one hundred billion dollars.

1826
01:32:17,920 --> 01:32:20,880
Speaker 5: And like it is applicable for like changing objects into

1827
01:32:20,920 --> 01:32:23,360
Speaker 5: other objects on screen. It can produce really like kind

1828
01:32:23,360 --> 01:32:26,160
Speaker 5: of odd like uncanny effects that could be utilized by

1829
01:32:26,200 --> 01:32:29,439
Speaker 5: a team of human artists. Really well. What it can't

1830
01:32:29,479 --> 01:32:31,960
Speaker 5: do is generate a short film that is in any

1831
01:32:31,960 --> 01:32:37,680
Speaker 5: way compelling piece well that is anyway compelling as a

1832
01:32:37,680 --> 01:32:39,800
Speaker 5: piece of art. Okay, And the fact that they're like

1833
01:32:39,880 --> 01:32:42,800
Speaker 5: laughing at how much how much have you lost?

1834
01:32:42,920 --> 01:32:44,719
Speaker 2: Enough jobs they have not.

1835
01:32:45,200 --> 01:32:49,160
Speaker 10: Or that had structures full to the beauty of the flame.

1836
01:32:49,880 --> 01:32:53,800
Speaker 2: Right, although the AI keeps keeps foreboding coming for them

1837
01:32:54,160 --> 01:32:58,000
Speaker 2: and wants somethings. I'm going to end on a happy

1838
01:32:58,040 --> 01:33:00,639
Speaker 2: note because the last panel I went to was actually

1839
01:33:00,720 --> 01:33:03,799
Speaker 2: really cool. It was AI in the Crisis of Creative Rights,

1840
01:33:04,040 --> 01:33:07,280
Speaker 2: Deep Fakes, Ethics and the Law, and it featured the

1841
01:33:07,400 --> 01:33:10,000
Speaker 2: first intelligent person that I've seen at CES this year,

1842
01:33:10,479 --> 01:33:13,720
Speaker 2: Moya McTeer, who is a folklorist and senior advisor at

1843
01:33:13,720 --> 01:33:18,679
Speaker 2: the Human Artistry Campaign. It also featured Duncan Crabtree Ireland,

1844
01:33:18,680 --> 01:33:21,920
Speaker 2: who's the national executive director and chief negotiator of sag Aftra.

1845
01:33:22,040 --> 01:33:24,799
Speaker 2: There we go, There we go, and this was no bullshit.

1846
01:33:24,840 --> 01:33:26,760
Speaker 2: It was talking about all of the different lawsuits that

1847
01:33:26,800 --> 01:33:29,280
Speaker 2: are going on right now, all of the litigation around AI,

1848
01:33:29,800 --> 01:33:32,920
Speaker 2: and like the actual strategy for litigating, and like there

1849
01:33:33,000 --> 01:33:34,760
Speaker 2: was a couple of points where like Duncan was like

1850
01:33:35,080 --> 01:33:37,519
Speaker 2: a lot is going to hinge on some very brave,

1851
01:33:37,840 --> 01:33:41,880
Speaker 2: very famous people choosing to throw down some big dollar lawsuits,

1852
01:33:42,280 --> 01:33:44,679
Speaker 2: like that's what we need right now. They did talk

1853
01:33:44,720 --> 01:33:47,559
Speaker 2: about the No Fakes Act, which has bipartisan support and

1854
01:33:47,560 --> 01:33:49,640
Speaker 2: gives some legal force to allow people to push for

1855
01:33:49,720 --> 01:33:52,720
Speaker 2: AI copies of themselves to be taken down. And they

1856
01:33:52,720 --> 01:33:55,879
Speaker 2: think there's also some bipartisan possibility to get AI labeling

1857
01:33:56,600 --> 01:33:57,680
Speaker 2: like legislation.

1858
01:33:57,920 --> 01:33:59,479
Speaker 10: The thing is, any of these things would be fucking

1859
01:33:59,479 --> 01:34:02,080
Speaker 10: fight OPA, because if what you have to remove something

1860
01:34:02,080 --> 01:34:04,240
Speaker 10: from a model, how the fuck do we do that?

1861
01:34:04,360 --> 01:34:05,120
Speaker 2: Yeah, we don't know how.

1862
01:34:05,120 --> 01:34:06,840
Speaker 5: You have to throw away the entire model, you.

1863
01:34:06,800 --> 01:34:09,080
Speaker 8: Have to retrain, like there's no way around it.

1864
01:34:09,560 --> 01:34:12,599
Speaker 2: Yeah, And there was a really good point where kind

1865
01:34:12,600 --> 01:34:14,000
Speaker 2: of at the end of this part of what I

1866
01:34:14,000 --> 01:34:16,200
Speaker 2: appreciate is again there was no bullshit. Like Moya at

1867
01:34:16,200 --> 01:34:18,360
Speaker 2: one point was like, I think it is absolutely it

1868
01:34:18,560 --> 01:34:21,640
Speaker 2: being generative. AI is absolutely a net negative for the

1869
01:34:21,720 --> 01:34:24,160
Speaker 2: artistic community. The point is, the point is not to

1870
01:34:24,200 --> 01:34:26,000
Speaker 2: get something out as quick as possible to like make

1871
01:34:26,160 --> 01:34:26,640
Speaker 2: art right.

1872
01:34:26,720 --> 01:34:29,120
Speaker 5: And this has to be like one of maybe five

1873
01:34:29,200 --> 01:34:31,400
Speaker 5: people who are doing panels at CES who's like willing

1874
01:34:31,439 --> 01:34:32,040
Speaker 5: to say that.

1875
01:34:31,960 --> 01:34:34,040
Speaker 2: Yes, and Duncan got I was like, look, you can't

1876
01:34:34,040 --> 01:34:36,479
Speaker 2: stop the technology from being invented, So the best path

1877
01:34:36,520 --> 01:34:38,760
Speaker 2: forward is to like try and channel this into a

1878
01:34:38,800 --> 01:34:41,720
Speaker 2: direction that like is at least better for artists, Like

1879
01:34:42,000 --> 01:34:44,040
Speaker 2: there were there was very little for most of the

1880
01:34:44,080 --> 01:34:46,559
Speaker 2: people on the panel, very little bullshit. There was some

1881
01:34:46,680 --> 01:34:50,000
Speaker 2: bullshit from one person on the panel, Jenny Katzman, Senior

1882
01:34:50,040 --> 01:34:54,120
Speaker 2: director of Government Affairs from Microsoft. Oh I bet that

1883
01:34:54,280 --> 01:34:56,800
Speaker 2: was fun. So after there's this whole point where like

1884
01:34:56,880 --> 01:34:58,360
Speaker 2: everyone else in the panels like, yeah, I think it's

1885
01:34:58,360 --> 01:35:02,120
Speaker 2: probably in that negative for artists the whole, and Jenny comes

1886
01:35:02,160 --> 01:35:04,719
Speaker 2: on she's like, actually, I think it's a net positive.

1887
01:35:04,960 --> 01:35:07,240
Speaker 2: And her example of this is, well, you know, think

1888
01:35:07,320 --> 01:35:09,360
Speaker 2: there's a lot of stuff that you couldn't do before that.

1889
01:35:09,439 --> 01:35:12,160
Speaker 2: Thanks to AI, you could do like d aging Harrison

1890
01:35:12,240 --> 01:35:16,360
Speaker 2: Ford for the Indiana Jones movie, something that went over

1891
01:35:16,640 --> 01:35:17,160
Speaker 2: very well.

1892
01:35:18,920 --> 01:35:21,880
Speaker 5: Everyone everyone loved and a great creative.

1893
01:35:22,640 --> 01:35:24,200
Speaker 2: This is the fucking problem with all of this.

1894
01:35:24,280 --> 01:35:26,240
Speaker 10: On top of house ship it is and how expensive

1895
01:35:26,240 --> 01:35:28,800
Speaker 10: it is, which kind of AI are we talking about?

1896
01:35:28,800 --> 01:35:29,400
Speaker 8: That dip shit.

1897
01:35:29,520 --> 01:35:34,200
Speaker 10: That's not generative AI, that's not what that fucking waste.

1898
01:35:33,760 --> 01:35:36,360
Speaker 5: And it also steals us from being able to cast

1899
01:35:36,439 --> 01:35:40,960
Speaker 5: a young River Phoenix's blat lovely thing.

1900
01:35:41,720 --> 01:35:44,680
Speaker 2: It's getting cast in more stuff. Garrett, I'm very unfair.

1901
01:35:45,400 --> 01:35:47,280
Speaker 5: Well, luckily, with the power of AI.

1902
01:35:48,080 --> 01:35:52,320
Speaker 2: Look, I can put into every newspaper sequentially starting in

1903
01:35:52,360 --> 01:35:54,800
Speaker 2: eighteen thirty four, So I've not gotten to the end

1904
01:35:54,840 --> 01:35:57,320
Speaker 2: of Phoenix. It would be a really long career.

1905
01:35:57,760 --> 01:35:58,559
Speaker 5: It would be really cool.

1906
01:35:58,640 --> 01:36:00,160
Speaker 8: Sleeping guy.

1907
01:36:00,360 --> 01:36:03,120
Speaker 2: I think he's got the bold ideas. This is gonna

1908
01:36:03,120 --> 01:36:05,200
Speaker 2: work out really well for Germany.

1909
01:36:05,400 --> 01:36:07,280
Speaker 5: It won't be really cool that instead of just doing

1910
01:36:07,320 --> 01:36:09,840
Speaker 5: Young Harrison for they just do a River Phoenix deep

1911
01:36:09,880 --> 01:36:16,880
Speaker 5: fake for you him. Look it's canonical.

1912
01:36:17,080 --> 01:36:18,639
Speaker 2: Yeah great.

1913
01:36:18,880 --> 01:36:21,280
Speaker 10: Oh, I love the movies in the future of them too.

1914
01:36:21,479 --> 01:36:23,200
Speaker 10: This is so good. This is so bad.

1915
01:36:23,320 --> 01:36:25,160
Speaker 5: James mangled, You're a hacking So.

1916
01:36:25,320 --> 01:36:28,000
Speaker 2: I gotta say it was very funny because she also

1917
01:36:28,040 --> 01:36:31,679
Speaker 2: suggests Jenny, there's we can use animals without causing harm

1918
01:36:31,680 --> 01:36:33,639
Speaker 2: thanks to AI, a thing that no one had figured

1919
01:36:33,640 --> 01:36:35,920
Speaker 2: out how to do before. Nobody had ever figured out

1920
01:36:35,920 --> 01:36:39,160
Speaker 2: how just like not hurt animals in movies that didn't

1921
01:36:39,200 --> 01:36:40,839
Speaker 2: exist before a I thank.

1922
01:36:40,720 --> 01:36:44,640
Speaker 5: God, thankfully AI will never do any harmed animals or

1923
01:36:44,680 --> 01:36:45,280
Speaker 5: the environment.

1924
01:36:46,160 --> 01:36:48,960
Speaker 12: Nobody asked the lobbyist for Microsoft, what else the company

1925
01:36:49,000 --> 01:36:52,600
Speaker 12: is doing with AI? Right, with police deployments or with

1926
01:36:53,080 --> 01:36:54,280
Speaker 12: fossil fuel companies?

1927
01:36:54,360 --> 01:36:54,719
Speaker 8: Yeah?

1928
01:36:54,880 --> 01:36:56,080
Speaker 2: Is that bad for animals?

1929
01:36:56,439 --> 01:36:56,679
Speaker 5: No?

1930
01:36:56,720 --> 01:37:02,160
Speaker 12: Actually, it's really good. They they needed it. They yearn

1931
01:37:02,280 --> 01:37:02,960
Speaker 12: for the month.

1932
01:37:03,160 --> 01:37:08,160
Speaker 2: They love datasets. Great for their habitats. She said, there's

1933
01:37:08,200 --> 01:37:11,040
Speaker 2: issues with employment, but there's lots of issues that fall

1934
01:37:11,080 --> 01:37:13,559
Speaker 2: around that, and I do think you need a balance.

1935
01:37:13,920 --> 01:37:15,600
Speaker 2: And at the end of it, the guy running the

1936
01:37:15,640 --> 01:37:19,800
Speaker 2: panel just says, Okay.

1937
01:37:18,880 --> 01:37:20,560
Speaker 7: It sounds like you guys are saying a bunch of

1938
01:37:20,680 --> 01:37:21,519
Speaker 7: woke ship.

1939
01:37:21,479 --> 01:37:25,920
Speaker 2: On this panel. All right, all right, Microsoft, Just once,

1940
01:37:26,000 --> 01:37:27,720
Speaker 2: I'd like on the panel someone to go and say,

1941
01:37:27,960 --> 01:37:32,479
Speaker 2: what the fuck do you mean? Closest to that that

1942
01:37:32,520 --> 01:37:33,360
Speaker 2: you were going to get.

1943
01:37:33,520 --> 01:37:35,240
Speaker 5: I think we do need a balance of some people

1944
01:37:35,280 --> 01:37:38,200
Speaker 5: being fired like these people, and other people keeping their

1945
01:37:38,280 --> 01:37:39,240
Speaker 5: jobs like everyone else.

1946
01:37:39,800 --> 01:37:42,880
Speaker 7: Like Moya give somebody has to lose and somebody has.

1947
01:37:42,760 --> 01:37:46,679
Speaker 10: To work exactly that's their entire Somebody has the guns,

1948
01:37:46,720 --> 01:37:47,439
Speaker 10: somebody doesn't.

1949
01:37:47,600 --> 01:37:49,639
Speaker 2: Somebody knows the way the maze works and something.

1950
01:37:49,800 --> 01:37:51,679
Speaker 7: He's gonna what, we shouldn't have guns.

1951
01:37:51,800 --> 01:37:54,000
Speaker 8: We shouldn't have a man and one of them knows

1952
01:37:54,040 --> 01:37:54,840
Speaker 8: the maze and have a gun.

1953
01:37:55,000 --> 01:37:59,000
Speaker 2: We should have a gun, mace, you're talking about the gun. Now, Look,

1954
01:37:59,520 --> 01:38:01,519
Speaker 2: we all like keeping a couple of people in a

1955
01:38:01,560 --> 01:38:05,000
Speaker 2: maze beneath our house. Yeah, there's nothing wrong with this.

1956
01:38:05,000 --> 01:38:07,120
Speaker 5: This is just the dormant next this we just we

1957
01:38:07,280 --> 01:38:11,560
Speaker 5: keep doing it.

1958
01:38:11,560 --> 01:38:14,200
Speaker 2: It's a nice maize under my house they have.

1959
01:38:14,520 --> 01:38:15,720
Speaker 8: It's nice to run some of them.

1960
01:38:18,680 --> 01:38:23,040
Speaker 10: The minotag gets them only sometimes I'm the minotta.

1961
01:38:24,479 --> 01:38:25,880
Speaker 2: Anyway, the gun maze isn't real.

1962
01:38:26,080 --> 01:38:28,920
Speaker 10: But also most of their arguments kind of mostly just

1963
01:38:28,920 --> 01:38:31,719
Speaker 10: come down to, well, you can't make an omelet without breaking,

1964
01:38:31,880 --> 01:38:33,320
Speaker 10: like you have to make people.

1965
01:38:33,360 --> 01:38:36,680
Speaker 2: You have to break the human drive to create art. Obviously,

1966
01:38:36,760 --> 01:38:42,439
Speaker 2: to make an not taste good an omelet esque food,

1967
01:38:43,120 --> 01:38:45,639
Speaker 2: it's a piss omelet, Like there's piss in the omelet,

1968
01:38:46,080 --> 01:38:47,960
Speaker 2: and we had to We had to burn down the

1969
01:38:48,000 --> 01:38:51,880
Speaker 2: susteined chapel to make the piss omelet. Computer made it, though, yeah,

1970
01:38:52,840 --> 01:38:56,639
Speaker 2: and clap for the computer. We did firebomb the louver.

1971
01:38:56,880 --> 01:39:01,599
Speaker 2: But look look at look at this rock star.

1972
01:39:03,439 --> 01:39:04,000
Speaker 11: Oh god.

1973
01:39:04,520 --> 01:39:06,840
Speaker 2: All right, well that's the episode. That's all I got, folks.

1974
01:39:06,840 --> 01:39:10,280
Speaker 2: That was my first day at CES twenty twenty five. Huzzah. Yeah,

1975
01:39:10,320 --> 01:39:11,400
Speaker 2: this is just my first day.

1976
01:39:11,439 --> 01:39:12,719
Speaker 8: Better offlines here all week.

1977
01:39:13,120 --> 01:39:15,120
Speaker 10: I'm gonna hear about stuff like this all week, and

1978
01:39:15,160 --> 01:39:17,200
Speaker 10: I think I'm gonna be fully jokeified.

1979
01:39:17,240 --> 01:39:19,439
Speaker 2: I'm gonna wake up in the clown makeup on Friday.

1980
01:39:19,479 --> 01:39:21,960
Speaker 7: I'm gonna find the funnest thing to bring back for you.

1981
01:39:22,200 --> 01:39:25,400
Speaker 10: I'm gonna find an artist to put me in full joke.

1982
01:39:25,439 --> 01:39:29,400
Speaker 2: Now, I'm gonna try to steal that AI enhanced grill. Yeah,

1983
01:39:30,560 --> 01:39:33,719
Speaker 2: grill that tests you can I just like move this around.

1984
01:39:33,720 --> 01:39:35,880
Speaker 2: I just want to test that would roll se ail,

1985
01:39:36,000 --> 01:39:37,240
Speaker 2: open the door, open the door.

1986
01:39:37,520 --> 01:39:39,920
Speaker 10: As someone who's done a lot of like grilling, done

1987
01:39:39,920 --> 01:39:42,600
Speaker 10: a lot of spoken barbecue, I don't know what a

1988
01:39:42,720 --> 01:39:43,120
Speaker 10: I would do.

1989
01:39:43,200 --> 01:39:44,599
Speaker 8: Is it gonna talk to me in the sixth.

1990
01:39:44,760 --> 01:39:47,200
Speaker 2: Wait till you are you? Are you trying to tell

1991
01:39:47,280 --> 01:39:50,639
Speaker 2: us here at Zra, yes, that you have grilled meat

1992
01:39:50,800 --> 01:39:53,720
Speaker 2: without a robot texting you about it? Because I just

1993
01:39:53,720 --> 01:39:54,280
Speaker 2: don't believe.

1994
01:39:54,360 --> 01:39:57,360
Speaker 8: I don't know how I did it, but I did it.

1995
01:39:57,600 --> 01:40:03,840
Speaker 2: You're never always way took the robots. It was impossible.

1996
01:40:04,280 --> 01:40:06,320
Speaker 8: Oh god, we're at the death of innovation.

1997
01:40:06,680 --> 01:40:09,000
Speaker 2: Yeah, at the end of a lot of things maybe

1998
01:40:09,280 --> 01:40:11,080
Speaker 2: and the end of the episode. Yeah, and the end

1999
01:40:11,080 --> 01:40:14,920
Speaker 2: of the episode. Thank god. You know, everyone else be

2000
01:40:15,040 --> 01:40:34,479
Speaker 2: the cyber truck in the oh, welcome back to It

2001
01:40:34,520 --> 01:40:37,519
Speaker 2: could happen here? A podcast about it happening here, which

2002
01:40:37,560 --> 01:40:40,839
Speaker 2: is really true in a lot of ways. Tonight, Harrison

2003
01:40:40,920 --> 01:40:45,040
Speaker 2: Davis and I are seated at the Glorious Majestical hotel

2004
01:40:45,200 --> 01:40:48,840
Speaker 2: name redacted on the Las Vegas Strip. We had a

2005
01:40:48,960 --> 01:40:53,160
Speaker 2: long day at CEES Day, listening to panels, catching up

2006
01:40:53,160 --> 01:40:56,439
Speaker 2: with the latest tech news, trying gadgets, and also at

2007
01:40:56,439 --> 01:40:59,440
Speaker 2: the same time texting our dear friends in Los Angeles

2008
01:40:59,520 --> 01:41:04,240
Speaker 2: as unprecedented fire sweep them from their homes. Literally the

2009
01:41:04,280 --> 01:41:08,480
Speaker 2: getdiest threatened. Pasadena and Santa Monica are both being evacuated

2010
01:41:08,520 --> 01:41:11,479
Speaker 2: as once. It's a real one to two punch of

2011
01:41:11,520 --> 01:41:14,720
Speaker 2: America's favorite tech show in the apocalypse today. How are

2012
01:41:14,720 --> 01:41:15,120
Speaker 2: you feeling?

2013
01:41:15,160 --> 01:41:15,320
Speaker 6: Gear?

2014
01:41:15,479 --> 01:41:17,440
Speaker 5: It's an average day in America, average.

2015
01:41:17,200 --> 01:41:24,040
Speaker 2: Day in America. Temperature's not coming down anytime soon. No, no, well,

2016
01:41:24,040 --> 01:41:26,519
Speaker 2: just take a moment to breathe with that. So you

2017
01:41:26,560 --> 01:41:28,400
Speaker 2: want to start us off with what you did this morning?

2018
01:41:28,479 --> 01:41:31,559
Speaker 2: I was panel guy yesterday. There was a man of

2019
01:41:31,600 --> 01:41:35,240
Speaker 2: action walking around and mostly trying all the free massage chairs.

2020
01:41:35,360 --> 01:41:36,519
Speaker 2: What did you see this morning?

2021
01:41:36,680 --> 01:41:39,640
Speaker 5: I saw so many AI panels, half of which I

2022
01:41:39,720 --> 01:41:41,840
Speaker 5: left halfway through because I knew they.

2023
01:41:41,720 --> 01:41:43,519
Speaker 2: Weren't gonna be useful for me, just dogshit.

2024
01:41:43,680 --> 01:41:46,559
Speaker 5: The other half I took notes on and just got sad.

2025
01:41:47,080 --> 01:41:50,080
Speaker 5: But no, today was full panel starting bright and early

2026
01:41:50,120 --> 01:41:52,400
Speaker 5: in the morning, where I walked into a panel where

2027
01:41:52,400 --> 01:41:57,120
Speaker 5: I heard augmentation and not replacement about twenty times in

2028
01:41:57,160 --> 01:41:58,719
Speaker 5: the span of like twenty minutes.

2029
01:41:58,840 --> 01:42:01,479
Speaker 2: Yeah, I keep hearing for of that too. In the

2030
01:42:01,520 --> 01:42:03,800
Speaker 2: Hollywood panels, they would be like, yeah, we want to

2031
01:42:03,800 --> 01:42:05,680
Speaker 2: develop a machine that can read the brains of our

2032
01:42:05,760 --> 01:42:08,040
Speaker 2: viewers and alter the endings of movies, you know, but

2033
01:42:08,040 --> 01:42:10,759
Speaker 2: we see this as a way of augmenting the artists work.

2034
01:42:11,000 --> 01:42:13,960
Speaker 5: Yes. And the biggest thing that I noticed across multiple

2035
01:42:13,960 --> 01:42:17,920
Speaker 5: panels today is an almost like anxiety among these tech

2036
01:42:17,960 --> 01:42:24,920
Speaker 5: executives about consumers rejecting the AI slopification of everything, and

2037
01:42:25,400 --> 01:42:27,880
Speaker 5: they're trying to find ways to like actually force people

2038
01:42:27,880 --> 01:42:30,679
Speaker 5: to start like using these products or having them like

2039
01:42:30,680 --> 01:42:33,759
Speaker 5: like it. Yeah, and I haven't really since that anxiety before.

2040
01:42:33,800 --> 01:42:36,519
Speaker 5: It's all been very, very positively.

2041
01:42:36,080 --> 01:42:37,880
Speaker 2: And I think it's a mix of Number One, the

2042
01:42:37,960 --> 01:42:41,240
Speaker 2: money still isn't there where they need it to be.

2043
01:42:41,479 --> 01:42:44,000
Speaker 2: It has not started like blooming to the extent that

2044
01:42:44,000 --> 01:42:45,880
Speaker 2: they were expecting it by now. And the other part

2045
01:42:45,920 --> 01:42:49,599
Speaker 2: is people are still not happy with this stuff. I'm

2046
01:42:49,640 --> 01:42:52,280
Speaker 2: glad you felt that too, because that almost was like

2047
01:42:52,560 --> 01:42:55,880
Speaker 2: especially after the election, Like, I don't trust my feelings

2048
01:42:55,920 --> 01:42:58,040
Speaker 2: on this that they're really scared, but I really do

2049
01:42:58,160 --> 01:42:59,920
Speaker 2: think there's a piece of that coming through it.

2050
01:43:00,080 --> 01:43:02,519
Speaker 5: No of a phrase one of the panelists used this

2051
01:43:02,680 --> 01:43:06,800
Speaker 5: morning was the AI ick, Like how do we how

2052
01:43:06,840 --> 01:43:09,000
Speaker 5: do we beat the AI ick? And if you're ever

2053
01:43:09,040 --> 01:43:11,599
Speaker 5: saying yourself, how do I stop having people feel an

2054
01:43:11,600 --> 01:43:15,320
Speaker 5: ick around me? Maybe you should really look inwards. Yeah,

2055
01:43:15,400 --> 01:43:17,479
Speaker 5: maybe the problems you not them.

2056
01:43:18,200 --> 01:43:20,200
Speaker 2: You know, who doesn't need to worry about quote unquote

2057
01:43:20,200 --> 01:43:23,479
Speaker 2: ick for their product market is people who make things

2058
01:43:23,479 --> 01:43:24,240
Speaker 2: that people like.

2059
01:43:25,280 --> 01:43:27,320
Speaker 5: So but I heard a lot about, you know, and

2060
01:43:27,320 --> 01:43:28,840
Speaker 5: trying to get people to use these products is like

2061
01:43:28,880 --> 01:43:31,519
Speaker 5: making sure artists don't feel like they're being replaced instead

2062
01:43:31,840 --> 01:43:36,519
Speaker 5: having their like art production process be augmented with AI

2063
01:43:36,600 --> 01:43:40,040
Speaker 5: and how how that can make art easier to make

2064
01:43:40,080 --> 01:43:42,759
Speaker 5: while still keeping the human at the center of AI tools.

2065
01:43:43,200 --> 01:43:44,680
Speaker 5: And this is just what they talked about for like

2066
01:43:44,680 --> 01:43:47,040
Speaker 5: a while, while reiterating that lots of the developments they

2067
01:43:47,080 --> 01:43:49,559
Speaker 5: need to see on AI they have it on the

2068
01:43:49,560 --> 01:43:52,120
Speaker 5: tech side. What they need to rely on is consumer

2069
01:43:52,160 --> 01:43:54,400
Speaker 5: acceptance to really drive the innovation. To see like what

2070
01:43:54,439 --> 01:43:56,640
Speaker 5: they can get away with, like how much will the

2071
01:43:56,680 --> 01:44:01,719
Speaker 5: consumer accept the sopification of our to entertainment and customer

2072
01:44:01,800 --> 01:44:04,400
Speaker 5: service and all these things are trying to cram AI

2073
01:44:04,520 --> 01:44:05,120
Speaker 5: into and like.

2074
01:44:05,280 --> 01:44:08,400
Speaker 2: How much worse can you make the world before people

2075
01:44:08,960 --> 01:44:11,880
Speaker 2: stand up and stop you with their fists or guns.

2076
01:44:11,960 --> 01:44:13,920
Speaker 5: And you mentioned something about like trying to like tailor

2077
01:44:13,960 --> 01:44:17,040
Speaker 5: like movie endings for specific people, and I definitely heard

2078
01:44:17,040 --> 01:44:19,479
Speaker 5: them stuff about that. There's this one guy who is

2079
01:44:19,560 --> 01:44:22,360
Speaker 5: who was like the panel's resident like content creator whos

2080
01:44:22,360 --> 01:44:27,479
Speaker 5: supposed to represent like the artist block, even though he's like, eh, yeah,

2081
01:44:27,560 --> 01:44:30,320
Speaker 5: you know, some kind of like AI friendly content creator

2082
01:44:30,320 --> 01:44:32,160
Speaker 5: though on this panel, and he talked about how like

2083
01:44:32,479 --> 01:44:34,960
Speaker 5: back in the day, you need to have friends that

2084
01:44:35,000 --> 01:44:39,000
Speaker 5: would like recommend your music, and like the Spotify algorithm

2085
01:44:39,040 --> 01:44:40,640
Speaker 5: is is too based on like an echo chamber of

2086
01:44:40,640 --> 01:44:44,200
Speaker 5: what you already like, but now with a gentic AI

2087
01:44:44,439 --> 01:44:47,080
Speaker 5: this allows trust between the consumer and the machine to

2088
01:44:47,160 --> 01:44:50,160
Speaker 5: recommend new music. And like again, like so much of

2089
01:44:50,160 --> 01:44:52,200
Speaker 5: these air products is just trying to like replace friendship.

2090
01:44:52,800 --> 01:44:55,920
Speaker 2: People. Have you friends? Have you tried people?

2091
01:44:56,160 --> 01:44:59,839
Speaker 5: How can you engage with like art and culture without friends?

2092
01:45:00,080 --> 01:45:02,200
Speaker 5: How can you like learn more about like what your

2093
01:45:02,240 --> 01:45:04,360
Speaker 5: friends are into what they like? How can you discover

2094
01:45:04,479 --> 01:45:08,240
Speaker 5: new music just like without that instead replacing that beautifully

2095
01:45:08,280 --> 01:45:09,120
Speaker 5: human process.

2096
01:45:09,400 --> 01:45:12,200
Speaker 2: Every year at cees, there are points in time where

2097
01:45:12,240 --> 01:45:14,360
Speaker 2: I get that, like, oh yeah, twenty twenty really fucked

2098
01:45:14,439 --> 01:45:17,240
Speaker 2: us up a lot, Like twenty twenty really did some

2099
01:45:17,360 --> 01:45:21,160
Speaker 2: lasting damage. Like I know it was that was happening

2100
01:45:21,320 --> 01:45:24,760
Speaker 2: with the younger generation before the iPad kid generation, but

2101
01:45:24,920 --> 01:45:28,480
Speaker 2: like that that really did a number on some folks.

2102
01:45:29,080 --> 01:45:33,400
Speaker 5: Someone from Meta right of Facebook specifically they're like metaverse division,

2103
01:45:33,400 --> 01:45:34,679
Speaker 5: which they're still trying to push.

2104
01:45:34,560 --> 01:45:36,280
Speaker 2: For by the way, Oh yeah, now, I mean they're

2105
01:45:36,280 --> 01:45:38,920
Speaker 2: still calling it meta, which honestly there's a degree which

2106
01:45:38,960 --> 01:45:42,120
Speaker 2: I almost respect it because we are not binding on

2107
01:45:42,360 --> 01:45:42,920
Speaker 2: no one is.

2108
01:45:43,320 --> 01:45:46,120
Speaker 5: But she talked about how they can like blend the

2109
01:45:46,160 --> 01:45:50,640
Speaker 5: metaverse and AI to make customized personal experiences. Say that

2110
01:45:50,720 --> 01:45:55,240
Speaker 5: you're watching an immersive live concert in mixed reality, something

2111
01:45:55,240 --> 01:45:56,880
Speaker 5: that both me and Aubert do all at the time, and.

2112
01:45:56,960 --> 01:46:02,320
Speaker 2: I mixed red hairy style mixed reality concerts. We're seeing

2113
01:46:02,360 --> 01:46:04,240
Speaker 2: the one hundred gets you.

2114
01:46:04,200 --> 01:46:06,880
Speaker 5: Know, honestly, a one hundred GEX mixed reality concert could

2115
01:46:06,880 --> 01:46:07,360
Speaker 5: go crazy.

2116
01:46:07,400 --> 01:46:09,639
Speaker 2: Here, We'll finally I'll finally get you pilled on real

2117
01:46:09,680 --> 01:46:10,720
Speaker 2: big fish.

2118
01:46:10,800 --> 01:46:15,000
Speaker 5: But Basically, as you're in this like metaverse concert, they

2119
01:46:15,000 --> 01:46:17,400
Speaker 5: can have an AI that will sense your own excitement

2120
01:46:17,800 --> 01:46:20,760
Speaker 5: and personalize the ending of the experience based on your

2121
01:46:20,760 --> 01:46:23,800
Speaker 5: favorite songs or artists. So as they're getting excited from

2122
01:46:23,800 --> 01:46:27,240
Speaker 5: like AI, Taylor Swift can like finish the song like

2123
01:46:27,360 --> 01:46:29,679
Speaker 5: for you based on like your own like musical tastes,

2124
01:46:29,720 --> 01:46:31,879
Speaker 5: based on what the AI knows about you. And it's

2125
01:46:31,880 --> 01:46:33,920
Speaker 5: about creating these customized experiences.

2126
01:46:33,920 --> 01:46:35,800
Speaker 2: It's such a you can clearly tell that none of

2127
01:46:35,840 --> 01:46:37,960
Speaker 2: these people have souls, right, It's such a mismatch of

2128
01:46:38,000 --> 01:46:41,240
Speaker 2: what people get from music because they think that like, oh,

2129
01:46:41,320 --> 01:46:43,400
Speaker 2: this is just like a if I see that like

2130
01:46:43,479 --> 01:46:46,000
Speaker 2: this specific beat line is I can just sort of

2131
01:46:46,040 --> 01:46:48,120
Speaker 2: like plug this in and like I don't know, Like

2132
01:46:48,640 --> 01:46:51,160
Speaker 2: what makes people react to musicians and artists is that

2133
01:46:51,200 --> 01:46:54,120
Speaker 2: they like make things that make them feel something like

2134
01:46:54,680 --> 01:46:57,920
Speaker 2: That's why people get like really into artists, is they

2135
01:46:58,000 --> 01:47:01,880
Speaker 2: feel seen and I identify with a piece of art

2136
01:47:02,200 --> 01:47:05,120
Speaker 2: as opposed to like, oh, oh, that guy really like

2137
01:47:05,240 --> 01:47:09,679
Speaker 2: the first opening bars to fucking Octopuses Garden, Like, let's

2138
01:47:09,880 --> 01:47:12,240
Speaker 2: let's just like really turn up the octopus a lot

2139
01:47:12,520 --> 01:47:16,040
Speaker 2: more octopuses? How many more octopuses. Can we fit in

2140
01:47:16,040 --> 01:47:17,960
Speaker 2: this fucking in this track?

2141
01:47:18,240 --> 01:47:18,280
Speaker 1: No.

2142
01:47:19,000 --> 01:47:21,120
Speaker 5: Another panel I went to later in the day was about,

2143
01:47:21,120 --> 01:47:24,519
Speaker 5: like how do you market to gen z? Very funny panel. Yeah,

2144
01:47:24,520 --> 01:47:28,000
Speaker 5: and and they're talking about how like authenticity is so important,

2145
01:47:28,080 --> 01:47:30,240
Speaker 5: like you need to partner with influencers that have like

2146
01:47:30,360 --> 01:47:33,599
Speaker 5: have like an authentic brand, and it's funny having that

2147
01:47:34,040 --> 01:47:36,599
Speaker 5: ductionapost with like these like these like AI slot panels

2148
01:47:36,600 --> 01:47:39,000
Speaker 5: were like you need like an AI Taylor Swift to

2149
01:47:39,040 --> 01:47:41,599
Speaker 5: come like boost the excitement for all these kids who

2150
01:47:41,600 --> 01:47:45,680
Speaker 5: are in their metaverse concerts. Oh boy, But no, like

2151
01:47:45,720 --> 01:47:49,240
Speaker 5: personalized content like like targeting, like ai AI generated content

2152
01:47:49,400 --> 01:47:52,640
Speaker 5: specifically for certain people, for certain users, whether that's on

2153
01:47:52,680 --> 01:47:54,960
Speaker 5: social media, whether that's on you know, the metaverse. Like

2154
01:47:54,960 --> 01:47:57,040
Speaker 5: some of these people talk about someone on the panel

2155
01:47:57,080 --> 01:47:59,720
Speaker 5: from Adobe, who's you know, Adobe's integrating a whole bunch

2156
01:47:59,720 --> 01:48:02,400
Speaker 5: of gen Ai into their like suite of products, right,

2157
01:48:02,520 --> 01:48:06,519
Speaker 5: like a Photoshop premiere after effects, right, big big company

2158
01:48:06,520 --> 01:48:08,560
Speaker 5: in the create A space. He said that, like the

2159
01:48:08,680 --> 01:48:12,000
Speaker 5: personalized content is always the most impactful, like content that

2160
01:48:12,040 --> 01:48:15,120
Speaker 5: a person feels like a genuine connection to, and that

2161
01:48:15,240 --> 01:48:17,120
Speaker 5: connection could be formed by just being like, you know,

2162
01:48:17,320 --> 01:48:20,360
Speaker 5: a compelling artist where you can recognize shared experiences of

2163
01:48:20,560 --> 01:48:24,200
Speaker 5: shared experiences of humanity. But now you don't need that

2164
01:48:24,360 --> 01:48:27,519
Speaker 5: artist part anymore. He said, they only need three parts

2165
01:48:27,520 --> 01:48:31,160
Speaker 5: to create a pipeline. You need data, you need compelling

2166
01:48:31,200 --> 01:48:34,040
Speaker 5: like journeys to take the user on, and you need

2167
01:48:34,080 --> 01:48:36,719
Speaker 5: the content itself. And the goal is to create content

2168
01:48:36,760 --> 01:48:40,400
Speaker 5: at scale that's highly personalized. He said quotes. We're good

2169
01:48:40,400 --> 01:48:43,040
Speaker 5: at the first two parts. Now we just need to

2170
01:48:43,080 --> 01:48:46,400
Speaker 5: improve the actual content side, which I don't even think

2171
01:48:46,439 --> 01:48:48,559
Speaker 5: that's true. I don't think AI is good at creating

2172
01:48:48,680 --> 01:48:50,000
Speaker 5: compelling human journeys.

2173
01:48:50,000 --> 01:48:51,840
Speaker 2: I had it. So the video I didn't play you

2174
01:48:51,880 --> 01:48:55,120
Speaker 2: guys from my terrible fucking AI generated videos was this.

2175
01:48:55,520 --> 01:48:58,000
Speaker 2: It was like a girl coming to college, we need

2176
01:48:58,040 --> 01:48:59,400
Speaker 2: a picture of her dad, and it was like a

2177
01:48:59,520 --> 01:49:03,000
Speaker 2: narration of her life with her father who like is

2178
01:49:03,120 --> 01:49:06,360
Speaker 2: dead that she misses and all that she learned from Dada,

2179
01:49:06,600 --> 01:49:08,400
Speaker 2: and it like it's a mix of like all these

2180
01:49:08,400 --> 01:49:10,120
Speaker 2: different Like there's a chunk where it looks like a

2181
01:49:10,200 --> 01:49:13,160
Speaker 2: Disney animated picture, there's a chunk where it looks like anime.

2182
01:49:13,720 --> 01:49:16,240
Speaker 2: She and her dad having these like adventures around the world.

2183
01:49:16,280 --> 01:49:17,719
Speaker 2: There's a bit of it that looks like a Marvel

2184
01:49:17,800 --> 01:49:19,960
Speaker 2: movie and he's like, we can do all of these

2185
01:49:20,000 --> 01:49:23,120
Speaker 2: different you know, animation styles and they're seamless and like,

2186
01:49:23,320 --> 01:49:25,400
Speaker 2: you know, the audience really goes on a journey with this,

2187
01:49:25,520 --> 01:49:28,120
Speaker 2: and it's it's like, but there's there was no girl

2188
01:49:28,240 --> 01:49:30,720
Speaker 2: who lost your dad. Nobody lost their dad here. This

2189
01:49:30,840 --> 01:49:33,640
Speaker 2: is you just had a computer generate text about a

2190
01:49:33,720 --> 01:49:37,640
Speaker 2: dad dying. Like there's nothing underpinning this, right, nobody has

2191
01:49:37,680 --> 01:49:39,840
Speaker 2: anything they're trying to get across, Like you just know

2192
01:49:40,000 --> 01:49:42,719
Speaker 2: in this one they look like Marvel heroes for some reason.

2193
01:49:42,960 --> 01:49:45,280
Speaker 2: In this one they look like Zulu warriors kind of

2194
01:49:45,280 --> 01:49:47,920
Speaker 2: done up in a slightly racist lion king style. Like

2195
01:49:48,160 --> 01:49:51,920
Speaker 2: what is being transmitted other than like, look at all

2196
01:49:51,960 --> 01:49:54,040
Speaker 2: of the different art styles we can rip off.

2197
01:49:54,160 --> 01:49:56,680
Speaker 5: No, they do not have a journey. But even they

2198
01:49:56,720 --> 01:49:59,519
Speaker 5: themselves admit that they still don't have the content. The

2199
01:49:59,560 --> 01:50:01,639
Speaker 5: content it's elf still isn't even there. And that's something

2200
01:50:01,680 --> 01:50:03,479
Speaker 5: like they even acknowledge. And this is like a hurdle

2201
01:50:03,520 --> 01:50:05,360
Speaker 5: to this is this is a hurdle to get over.

2202
01:50:05,640 --> 01:50:07,240
Speaker 5: What they do have is the data. And like this

2203
01:50:07,320 --> 01:50:09,400
Speaker 5: is like something that Adobe has done, because if you

2204
01:50:09,479 --> 01:50:12,200
Speaker 5: use Adobe products now some of the most used creative products.

2205
01:50:12,280 --> 01:50:14,640
Speaker 5: Adobe trains all the all of their AI systems on

2206
01:50:14,680 --> 01:50:17,439
Speaker 5: the stuff that you make using their products, which you know,

2207
01:50:17,479 --> 01:50:20,679
Speaker 5: he really just blazed past that point because that's that's

2208
01:50:20,720 --> 01:50:23,559
Speaker 5: a whole other discussion. But even they know that they

2209
01:50:23,560 --> 01:50:26,439
Speaker 5: don't have like the actual products, and this is still

2210
01:50:26,479 --> 01:50:30,080
Speaker 5: reliant on like consumer acceptance. As as they said before,

2211
01:50:30,520 --> 01:50:32,519
Speaker 5: someone from Meta, the same person on the panel that

2212
01:50:32,680 --> 01:50:35,439
Speaker 5: talked about how like a few days ago on Instagram

2213
01:50:35,479 --> 01:50:39,400
Speaker 5: they tried to announce like you'll have like AI profiles right,

2214
01:50:39,520 --> 01:50:42,680
Speaker 5: like like completely AI generated pictures profiles, like you know,

2215
01:50:42,720 --> 01:50:45,760
Speaker 5: like fake people who have their own accounts, and this

2216
01:50:45,840 --> 01:50:48,519
Speaker 5: created such a big backlash that they rolled this back

2217
01:50:48,840 --> 01:50:51,680
Speaker 5: and they simply announced this before Cees.

2218
01:50:51,400 --> 01:50:54,759
Speaker 2: One of these accounts was literally like I'm a mother

2219
01:50:54,880 --> 01:50:58,439
Speaker 2: of two, queer black woman. You know, yeah, I got

2220
01:50:58,439 --> 01:51:00,200
Speaker 2: a lot to say about the world.

2221
01:51:00,240 --> 01:51:03,559
Speaker 5: Someone call up the situationists please, And some like people

2222
01:51:03,600 --> 01:51:05,400
Speaker 5: started talking to her like we're any black people at

2223
01:51:05,439 --> 01:51:07,760
Speaker 5: all involved in like making this chat Bob.

2224
01:51:07,920 --> 01:51:10,080
Speaker 2: She's like, well no, and that's a real problem. That

2225
01:51:10,240 --> 01:51:12,120
Speaker 2: is a real problem, Okay.

2226
01:51:11,960 --> 01:51:14,720
Speaker 5: Yes, And the excuse that this person from Meta said

2227
01:51:14,760 --> 01:51:17,680
Speaker 5: is that the market just isn't ready yet. It's not

2228
01:51:17,720 --> 01:51:20,600
Speaker 5: that the actual product itself is like bad or like

2229
01:51:20,680 --> 01:51:23,719
Speaker 5: no one really wants. The market's not ready yet.

2230
01:51:23,960 --> 01:51:27,360
Speaker 2: Well, they're so used to everything that they've done so far.

2231
01:51:27,960 --> 01:51:31,519
Speaker 2: They've kept getting money right, and like it slowed down

2232
01:51:31,640 --> 01:51:34,680
Speaker 2: and they've had to do layoffs, but like nobody's just

2233
01:51:34,800 --> 01:51:39,439
Speaker 2: made them stop at any point, which, honestly, you know,

2234
01:51:39,720 --> 01:51:42,559
Speaker 2: I made a comment about healthcare executives a while back,

2235
01:51:42,680 --> 01:51:45,240
Speaker 2: needing like a fucking retirement plan paid in millimeters. So

2236
01:51:45,240 --> 01:51:48,200
Speaker 2: I'm not going to make that same comment about tech

2237
01:51:48,200 --> 01:51:51,040
Speaker 2: industry rules because you know, we all know what's in

2238
01:51:51,080 --> 01:51:55,200
Speaker 2: the news. But something has to be done to force

2239
01:51:55,280 --> 01:52:00,200
Speaker 2: these people to stop moving in this direction. And I

2240
01:52:00,240 --> 01:52:02,599
Speaker 2: don't know how to get across, and like they're already

2241
01:52:02,600 --> 01:52:05,360
Speaker 2: at this point, like they seem to really want not

2242
01:52:05,520 --> 01:52:07,519
Speaker 2: want this, and we have to find a way. They're

2243
01:52:07,560 --> 01:52:08,960
Speaker 2: just not ready. We have to find a way to

2244
01:52:08,960 --> 01:52:11,400
Speaker 2: force this on them. Ideas, I don't know how to

2245
01:52:11,479 --> 01:52:14,400
Speaker 2: get across to them in a peaceful manner. Oh oh sorry,

2246
01:52:14,800 --> 01:52:17,919
Speaker 2: people don't want this. I'm a man of peace, Garrison.

2247
01:52:18,000 --> 01:52:20,000
Speaker 2: I'm a man of peace. I'm not a plumber.

2248
01:52:23,840 --> 01:52:25,040
Speaker 5: The last thing I had to add out of this

2249
01:52:25,040 --> 01:52:27,280
Speaker 5: panel just in terms of how much this stuff is

2250
01:52:27,320 --> 01:52:29,040
Speaker 5: just actually taking over more and more of the market

2251
01:52:29,040 --> 01:52:31,599
Speaker 5: even if people don't want it. Is that the guy

2252
01:52:31,640 --> 01:52:33,880
Speaker 5: from Adobe announced it in the fourth quarter of last year.

2253
01:52:34,240 --> 01:52:36,479
Speaker 5: They were able to boost all of the Adobe's like

2254
01:52:36,520 --> 01:52:38,800
Speaker 5: you know emails. If you send like an email to Adobe, right,

2255
01:52:39,200 --> 01:52:41,000
Speaker 5: you have a problem, like you need help. But like

2256
01:52:41,160 --> 01:52:43,800
Speaker 5: everything that they do on emails is now one hundred

2257
01:52:43,840 --> 01:52:47,080
Speaker 5: percent generated by AI. And this was boosted from fifty

2258
01:52:47,120 --> 01:52:48,840
Speaker 5: percent at the start of last year. Now it's one

2259
01:52:48,880 --> 01:52:51,840
Speaker 5: hundred percent of all of their email content is now

2260
01:52:51,880 --> 01:52:54,440
Speaker 5: done by AI with some moderation.

2261
01:52:55,320 --> 01:52:58,440
Speaker 2: There comes like when the company itself is like communicating

2262
01:52:58,520 --> 01:52:59,639
Speaker 2: with customers through email.

2263
01:52:59,760 --> 01:53:01,320
Speaker 5: That's that's what it sounded like.

2264
01:53:01,400 --> 01:53:05,320
Speaker 2: Yes, they're still writing emails sometimes to each other or

2265
01:53:05,800 --> 01:53:07,120
Speaker 2: AI for that too.

2266
01:53:07,520 --> 01:53:09,920
Speaker 5: He described it as like email content, So I'm pretty

2267
01:53:09,960 --> 01:53:13,280
Speaker 5: sure it is like content then customer service stuff like marketing,

2268
01:53:13,320 --> 01:53:16,160
Speaker 5: maybe like outreach, like certain like outreach things. But yeah,

2269
01:53:17,040 --> 01:53:21,200
Speaker 5: now generated by AI with some human like moderation. But yeah,

2270
01:53:21,439 --> 01:53:23,559
Speaker 5: that is where things are moving. And that's how I

2271
01:53:23,600 --> 01:53:24,839
Speaker 5: started my morning.

2272
01:53:25,080 --> 01:53:28,000
Speaker 2: Well better than a cup of coffee. Is that sense

2273
01:53:28,040 --> 01:53:30,439
Speaker 2: of creeping dread that, Like, wow, I just saw a

2274
01:53:30,479 --> 01:53:33,960
Speaker 2: bunch of people who will probably would rather kill the

2275
01:53:34,040 --> 01:53:38,880
Speaker 2: world than be stopped from shoveling AI slop into people's mouths,

2276
01:53:38,920 --> 01:53:41,280
Speaker 2: because this is the only future they can imagine is

2277
01:53:41,280 --> 01:53:43,960
Speaker 2: one in which they work for a company that feeds

2278
01:53:43,960 --> 01:53:47,640
Speaker 2: the planet poison and kills the human concept of creativity

2279
01:53:47,960 --> 01:53:50,120
Speaker 2: so that they can buy a house in San Francisco.

2280
01:53:50,680 --> 01:53:53,559
Speaker 5: Do you know what I want to feed the concept of.

2281
01:53:54,040 --> 01:54:06,559
Speaker 2: Yeah, we'll talk about that, but here's some ants. We're back.

2282
01:54:07,160 --> 01:54:09,200
Speaker 2: What was part two of this episode? That's be buddy,

2283
01:54:09,200 --> 01:54:11,320
Speaker 2: I'm a oh, let's talk about that helicopter.

2284
01:54:11,600 --> 01:54:14,559
Speaker 5: No, yeah, I think as I was going from panel

2285
01:54:14,640 --> 01:54:18,280
Speaker 5: to panels stripling notes on AI has some very exciting

2286
01:54:18,320 --> 01:54:20,640
Speaker 5: news stories drop that we'll talk about later. What were

2287
01:54:20,680 --> 01:54:21,800
Speaker 5: you up to, Robert.

2288
01:54:21,520 --> 01:54:24,639
Speaker 2: Well, I was. I was trawling the show floor as

2289
01:54:24,680 --> 01:54:27,120
Speaker 2: I off to do at some point in a cees

2290
01:54:27,800 --> 01:54:31,559
Speaker 2: and I came across a number of majestic products. You know,

2291
01:54:31,600 --> 01:54:33,560
Speaker 2: a lot of it was AI based, and we'll talk

2292
01:54:33,640 --> 01:54:35,840
Speaker 2: some more about that here, but I ran into something

2293
01:54:35,920 --> 01:54:38,480
Speaker 2: that was, thank god, had nothing to do with AI,

2294
01:54:38,800 --> 01:54:41,360
Speaker 2: and it's a death trap. Every every one of these.

2295
01:54:41,200 --> 01:54:43,360
Speaker 5: There's like some sort yes, we find a new death.

2296
01:54:43,880 --> 01:54:45,640
Speaker 2: There's a lot of connected vehicles. There were a lot

2297
01:54:45,680 --> 01:54:48,560
Speaker 2: of evs last year. There were a ton of different

2298
01:54:48,680 --> 01:54:52,400
Speaker 2: flying taxi type options. People that were really trying to.

2299
01:54:52,560 --> 01:54:53,840
Speaker 5: You don't see it all this year.

2300
01:54:53,920 --> 01:54:56,560
Speaker 2: Nothing this year, nothing this year, because it's a terrible idea.

2301
01:54:56,680 --> 01:54:59,400
Speaker 2: It's a terrible idea. The people who are rich enough

2302
01:54:59,400 --> 01:55:01,640
Speaker 2: to pay for flo flying vehicles don't want it to

2303
01:55:01,680 --> 01:55:06,160
Speaker 2: be a taxi, and the people who can't afford their

2304
01:55:06,160 --> 01:55:09,880
Speaker 2: own flying vehicles also can't afford anyway. So this is

2305
01:55:10,240 --> 01:55:14,960
Speaker 2: instead of any of that Richter Richter ri ct O

2306
01:55:15,280 --> 01:55:19,840
Speaker 2: R which is a Chinese company. Their ads say, I'll say,

2307
01:55:20,040 --> 01:55:25,240
Speaker 2: why be normal? Are saying this the future of travel

2308
01:55:25,320 --> 01:55:29,160
Speaker 2: will not be on the ground. And the Richter is

2309
01:55:29,200 --> 01:55:33,960
Speaker 2: a hybrid. It is like a smart car style sized vehicle.

2310
01:55:33,960 --> 01:55:36,880
Speaker 2: It's like it only has two wheels, though it looks

2311
01:55:36,920 --> 01:55:39,200
Speaker 2: more like a scooter. It's more like a weird little scooter,

2312
01:55:40,640 --> 01:55:43,360
Speaker 2: but it's fully enclosed and in addition to having its

2313
01:55:43,360 --> 01:55:45,200
Speaker 2: wheels and being able to travel about on the ground,

2314
01:55:45,320 --> 01:55:49,120
Speaker 2: it has four like quadricopter style rotors. Because it is

2315
01:55:49,160 --> 01:55:52,879
Speaker 2: an aquatic flying car aquatic flying i. Saw no evidence

2316
01:55:52,920 --> 01:55:54,120
Speaker 2: that could actually go in the water.

2317
01:55:54,400 --> 01:55:55,800
Speaker 5: How high can these things go up?

2318
01:55:56,000 --> 01:55:58,000
Speaker 2: Less than two hundred meters? You know why, Garrison?

2319
01:55:58,080 --> 01:55:58,240
Speaker 5: Why?

2320
01:55:58,400 --> 01:55:59,960
Speaker 2: Why is that? Because if you try to go up

2321
01:56:00,240 --> 01:56:01,480
Speaker 2: that you need a pilot's license.

2322
01:56:01,640 --> 01:56:03,560
Speaker 5: You don't need a pilot's license.

2323
01:56:03,640 --> 01:56:04,120
Speaker 8: I have that.

2324
01:56:04,400 --> 01:56:06,040
Speaker 2: When I was interviewing them, I was like, so I

2325
01:56:06,080 --> 01:56:08,000
Speaker 2: assume there's gonna be some sort of pilot's license for

2326
01:56:08,040 --> 01:56:10,160
Speaker 2: this flying craft. And they're like, no, as long as

2327
01:56:10,200 --> 01:56:11,880
Speaker 2: you stand or two hundred meters you get, do you

2328
01:56:12,040 --> 01:56:13,120
Speaker 2: need drivers?

2329
01:56:13,160 --> 01:56:13,200
Speaker 4: Like?

2330
01:56:13,880 --> 01:56:15,680
Speaker 5: Are you gonna put a license plate on this? Or

2331
01:56:15,680 --> 01:56:16,800
Speaker 5: is there's no space for one?

2332
01:56:16,840 --> 01:56:17,120
Speaker 8: Buddy?

2333
01:56:17,440 --> 01:56:19,040
Speaker 5: Completely unregularly to be.

2334
01:56:19,040 --> 01:56:21,920
Speaker 2: Honest, and I don't say this for any problematic reason,

2335
01:56:21,960 --> 01:56:25,120
Speaker 2: but like, these folks are Chinese and did not seem

2336
01:56:25,160 --> 01:56:27,160
Speaker 2: to have a great deal of knowledge about the US words.

2337
01:56:27,520 --> 01:56:31,880
Speaker 2: Sure that said, I can't imagine China's less strict about

2338
01:56:31,960 --> 01:56:33,120
Speaker 2: personal aircraft.

2339
01:56:33,240 --> 01:56:35,520
Speaker 5: I would like to take this fucker on the I five.

2340
01:56:36,080 --> 01:56:36,840
Speaker 2: Just start.

2341
01:56:38,400 --> 01:56:41,240
Speaker 5: Start zooming. Yeah, see it up in the air, because

2342
01:56:41,320 --> 01:56:43,160
Speaker 5: you could probably do like a pretty a pretty good

2343
01:56:43,200 --> 01:56:44,880
Speaker 5: road trip on this right you can you can you

2344
01:56:44,880 --> 01:56:45,840
Speaker 5: can move about that.

2345
01:56:45,920 --> 01:56:48,720
Speaker 2: So it's very small and it's completely electric. So I

2346
01:56:48,760 --> 01:56:50,640
Speaker 2: asked him, how much time do you get in the

2347
01:56:50,680 --> 01:56:53,840
Speaker 2: air with this bad boy on battery? Maybe twenty five minutes?

2348
01:56:56,640 --> 01:57:01,920
Speaker 2: What happens after twenty minutes. I did ask this and

2349
01:57:02,040 --> 01:57:03,760
Speaker 2: I was like, this is just rough out of the sky,

2350
01:57:03,840 --> 01:57:05,840
Speaker 2: and they were like, no, we're working on like a

2351
01:57:06,200 --> 01:57:10,600
Speaker 2: like an intelligent thing that will like yeah, which is

2352
01:57:10,640 --> 01:57:14,000
Speaker 2: also very exciting, really looking forward to seeing how they

2353
01:57:14,000 --> 01:57:16,480
Speaker 2: pull that off. The videos that they have show it

2354
01:57:16,560 --> 01:57:19,680
Speaker 2: driving on the highway too. They weren't able to tell

2355
01:57:19,720 --> 01:57:22,320
Speaker 2: me what a top speed was. It has no rear

2356
01:57:22,400 --> 01:57:24,520
Speaker 2: view mirrors and no side view mirrors, but they said

2357
01:57:24,520 --> 01:57:26,720
Speaker 2: there's lots of cameras on the inside, so I'm sure

2358
01:57:26,760 --> 01:57:30,120
Speaker 2: that's fine. It's a death trap. This thing will get

2359
01:57:30,200 --> 01:57:32,920
Speaker 2: everyone who even looks at it wrong killed. They should

2360
01:57:32,960 --> 01:57:36,320
Speaker 2: be a video of the prototype. It was completely frameless.

2361
01:57:36,320 --> 01:57:38,720
Speaker 2: It was just quadr coopter blades and like a chair

2362
01:57:38,840 --> 01:57:41,160
Speaker 2: on a platform lifting a guy into the air. It

2363
01:57:41,200 --> 01:57:44,560
Speaker 2: couldn't go forward or backwards. But they're like, a year,

2364
01:57:44,680 --> 01:57:45,600
Speaker 2: we can have this figure out.

2365
01:57:45,760 --> 01:57:47,160
Speaker 5: It can't. It can't move forward.

2366
01:57:47,320 --> 01:57:49,480
Speaker 2: It only only went up in the videos I saw,

2367
01:57:51,200 --> 01:57:55,840
Speaker 2: so you can't actually travel absolutely not by the way.

2368
01:57:56,040 --> 01:57:59,160
Speaker 2: I couldn't fit in this thing like you would be

2369
01:57:59,240 --> 01:58:00,360
Speaker 2: cramped in this fucker.

2370
01:58:00,480 --> 01:58:01,960
Speaker 5: But it's good for vertical travel.

2371
01:58:02,040 --> 01:58:04,200
Speaker 2: It's great if you just need to go up to

2372
01:58:04,400 --> 01:58:09,400
Speaker 2: under two hundred meters, there's no more efficient ways.

2373
01:58:09,400 --> 01:58:12,440
Speaker 5: If you're gonna pull over by the cops, you just.

2374
01:58:11,920 --> 01:58:15,200
Speaker 2: Just go up above them. I'm in the sky. Now

2375
01:58:15,480 --> 01:58:18,280
Speaker 2: you get new shit to me for twenty five mass minutes.

2376
01:58:21,040 --> 01:58:23,320
Speaker 2: Oh god, it's like if you're just driving, you go

2377
01:58:23,360 --> 01:58:25,280
Speaker 2: up to one hundred kilometers, which made me think, so

2378
01:58:25,440 --> 01:58:28,680
Speaker 2: a second, that's like sixty the AAR for twenty minutes

2379
01:58:28,720 --> 01:58:30,560
Speaker 2: than I land. Then my battery is dead.

2380
01:58:30,920 --> 01:58:31,760
Speaker 5: Then you can't go anywhere.

2381
01:58:31,800 --> 01:58:33,160
Speaker 2: You can't go anywhere. You can get back.

2382
01:58:33,360 --> 01:58:36,160
Speaker 5: The battery issue is gonna is gonna be troubling.

2383
01:58:36,240 --> 01:58:37,560
Speaker 2: But it seems completely useless.

2384
01:58:37,600 --> 01:58:40,960
Speaker 5: But as we've heard NonStop the past two days, this

2385
01:58:41,040 --> 01:58:41,880
Speaker 5: is the worst it's gonna be.

2386
01:58:41,960 --> 01:58:44,360
Speaker 2: This is the worst it's gonna be. Only gonna get better. Things,

2387
01:58:44,440 --> 01:58:47,640
Speaker 2: only ever get better. That's that's what everyone was trying

2388
01:58:47,640 --> 01:58:50,640
Speaker 2: to insist upon to me. Here, what else did you

2389
01:58:50,640 --> 01:58:53,000
Speaker 2: see on the show floor that caught your off Garrison?

2390
01:58:53,120 --> 01:58:57,040
Speaker 2: So many magical, wonderful, marvelous things, most of which were

2391
01:58:57,120 --> 01:59:00,200
Speaker 2: just like various different AI connected smart houses. That was

2392
01:59:00,240 --> 01:59:03,040
Speaker 2: what Samsung was showing off. That was what LG was

2393
01:59:03,120 --> 01:59:06,720
Speaker 2: showing off. I believe you saw one as well, right, Yeah,

2394
01:59:06,720 --> 01:59:09,040
Speaker 2: I mean I I walked through the l G booth.

2395
01:59:09,480 --> 01:59:11,600
Speaker 5: It was kind of the same as same as last year.

2396
01:59:11,640 --> 01:59:14,400
Speaker 5: The Samsung booth was too intimidating. But I should check

2397
01:59:14,400 --> 01:59:16,959
Speaker 5: it out because last year we didn't do the Samsung

2398
01:59:17,000 --> 01:59:22,480
Speaker 5: booth because we were going to and then either either

2399
01:59:22,520 --> 01:59:24,760
Speaker 5: one of us threw up or spilled something.

2400
01:59:25,360 --> 01:59:29,240
Speaker 2: Hey, okay, okay, yes, right?

2401
01:59:29,400 --> 01:59:31,720
Speaker 5: Did I did I.

2402
01:59:31,640 --> 01:59:35,400
Speaker 2: Pour my crative into a constant into a carbonated beverage

2403
01:59:35,520 --> 01:59:40,360
Speaker 2: that spewed a geyser a blood red foam into the

2404
01:59:40,400 --> 01:59:41,520
Speaker 2: sky around to.

2405
01:59:41,560 --> 01:59:43,120
Speaker 5: The white Samsung guard.

2406
01:59:43,200 --> 01:59:46,839
Speaker 2: Did the security guard stare at me as it happened?

2407
01:59:47,200 --> 01:59:49,720
Speaker 2: Did I set the drink down as it continued to

2408
01:59:49,760 --> 01:59:52,400
Speaker 2: spew and said, I'll go get some towels and then

2409
01:59:52,480 --> 01:59:53,480
Speaker 2: leave forever.

2410
01:59:53,480 --> 01:59:57,200
Speaker 5: Wet towels left.

2411
01:59:57,240 --> 01:59:59,280
Speaker 2: We fucking bounced, So.

2412
01:59:59,160 --> 02:00:02,280
Speaker 5: We couldn't do this some booths last year. Maybe I'll

2413
02:00:02,320 --> 02:00:05,600
Speaker 5: try this year. But tell me about these smart houses.

2414
02:00:06,120 --> 02:00:08,480
Speaker 2: Well, Garrett sam Sumi has a great idea for a

2415
02:00:08,560 --> 02:00:11,520
Speaker 2: smart house. First of all, you remember that game the Sims. No,

2416
02:00:12,160 --> 02:00:15,800
Speaker 2: well they're really betting that you do, because their current

2417
02:00:15,880 --> 02:00:19,360
Speaker 2: plan is design your home with the AI powered map views. Okay, okay,

2418
02:00:19,400 --> 02:00:21,760
Speaker 2: sure you get like you feed it like a picture,

2419
02:00:21,880 --> 02:00:24,880
Speaker 2: you like, you lay out your your floor, planning your house,

2420
02:00:25,080 --> 02:00:26,680
Speaker 2: and it gives you like a three D model, and

2421
02:00:26,680 --> 02:00:29,360
Speaker 2: you can take pictures of your furniture or pictures of

2422
02:00:29,400 --> 02:00:31,960
Speaker 2: furniture that you want, and then it really places it

2423
02:00:31,960 --> 02:00:34,440
Speaker 2: around and you can place them. Now, a couple of things,

2424
02:00:34,440 --> 02:00:37,280
Speaker 2: one of them is that there's no scaling done by

2425
02:00:37,320 --> 02:00:40,200
Speaker 2: the AI, so it's up to you to figure out

2426
02:00:40,480 --> 02:00:44,160
Speaker 2: how the furniture you might want to buy measures up

2427
02:00:44,200 --> 02:00:45,919
Speaker 2: in comparison to the apartment.

2428
02:00:46,040 --> 02:00:46,280
Speaker 5: Sure.

2429
02:00:46,360 --> 02:00:49,440
Speaker 2: Sure, but it does look like the actual like map

2430
02:00:49,440 --> 02:00:51,200
Speaker 2: that they've got. I'll show you the picture that I took.

2431
02:00:52,120 --> 02:00:54,880
Speaker 2: I'll try to put it up somewhere like it looks

2432
02:00:54,960 --> 02:00:58,880
Speaker 2: like the video game the Sims. You're populating like a

2433
02:00:58,880 --> 02:01:01,560
Speaker 2: little three D CG house. And I was like, okay,

2434
02:01:01,560 --> 02:01:04,200
Speaker 2: well there's there's a use there, right. People like planning

2435
02:01:04,200 --> 02:01:06,200
Speaker 2: out like you're you're moving into a new apartment. You

2436
02:01:06,200 --> 02:01:07,920
Speaker 2: can like fill it in here and before you even

2437
02:01:07,960 --> 02:01:10,240
Speaker 2: move in, you can figure out what kind of furniture

2438
02:01:10,280 --> 02:01:12,520
Speaker 2: you need or how you're existing furnish will fit in there.

2439
02:01:13,080 --> 02:01:15,760
Speaker 2: I would never have used that. I usually picked up

2440
02:01:15,760 --> 02:01:17,960
Speaker 2: all of my furniture from the trash before I had

2441
02:01:17,960 --> 02:01:20,720
Speaker 2: a house when I moved into a new place. But

2442
02:01:20,800 --> 02:01:22,800
Speaker 2: I know people who would have used that. Sure, that

2443
02:01:22,840 --> 02:01:25,360
Speaker 2: seems useful. So I talked about security. Some one thing

2444
02:01:25,400 --> 02:01:27,200
Speaker 2: that concerned me is like the first guy I talked,

2445
02:01:27,240 --> 02:01:29,960
Speaker 2: he was like, oh, yeah, I think it's all stored locally.

2446
02:01:30,400 --> 02:01:32,200
Speaker 2: And I was like, so Samsung doesn't have any access

2447
02:01:32,240 --> 02:01:34,560
Speaker 2: to any of the data on like my house and

2448
02:01:34,600 --> 02:01:36,800
Speaker 2: it's layout. And he was like, let me, let me

2449
02:01:36,920 --> 02:01:38,880
Speaker 2: get you to one of our engineers because he can

2450
02:01:38,920 --> 02:01:41,920
Speaker 2: answer that question. And the engineer's answer was, and I'm

2451
02:01:41,960 --> 02:01:43,000
Speaker 2: paraphrasing here.

2452
02:01:43,160 --> 02:01:44,760
Speaker 5: Oh okay.

2453
02:01:44,800 --> 02:01:45,960
Speaker 2: So that made me very confident.

2454
02:01:46,040 --> 02:01:48,960
Speaker 5: That does make you feel safe about sharing your personal data?

2455
02:01:49,080 --> 02:01:51,520
Speaker 2: Right, yeah, I'm the layout of my actual house.

2456
02:01:51,560 --> 02:01:51,720
Speaker 3: Well.

2457
02:01:51,720 --> 02:01:53,520
Speaker 5: And the thing is, I really don't like that at all,

2458
02:01:53,560 --> 02:01:55,720
Speaker 5: because this is this is something that people were asking

2459
02:01:55,760 --> 02:01:59,000
Speaker 5: Facebook slash meta when they were doing like their you know,

2460
02:01:59,120 --> 02:02:01,960
Speaker 5: like metaver stuff because their headsets are recording you know,

2461
02:02:02,160 --> 02:02:06,200
Speaker 5: very very extensively, like your home layout and the whole point. Well,

2462
02:02:06,320 --> 02:02:08,600
Speaker 5: part of the point was that some of that data

2463
02:02:08,800 --> 02:02:11,320
Speaker 5: could then be used to send you targeted advertisements based

2464
02:02:11,320 --> 02:02:15,960
Speaker 5: on them seeing everything in your home. And I suspect

2465
02:02:16,080 --> 02:02:20,320
Speaker 5: that Samsung might also have some interest in targeted advertisements,

2466
02:02:20,360 --> 02:02:23,800
Speaker 5: being a tech company, but you know, I could never say.

2467
02:02:24,280 --> 02:02:26,840
Speaker 2: Yeah, and they were that wasn't really One thing they

2468
02:02:26,840 --> 02:02:29,120
Speaker 2: had is for like their retail segment, they had like

2469
02:02:29,600 --> 02:02:32,840
Speaker 2: a live video grocery store ad showing you prices of

2470
02:02:32,880 --> 02:02:35,840
Speaker 2: different produce and I think like the insinuation that didn't

2471
02:02:35,920 --> 02:02:38,240
Speaker 2: layout is like you can change prices on the fly,

2472
02:02:38,720 --> 02:02:41,040
Speaker 2: you know it, which kind of made me think about that.

2473
02:02:41,480 --> 02:02:43,360
Speaker 2: There was some talk last year of like, Okay, we

2474
02:02:43,480 --> 02:02:46,120
Speaker 2: want to be able to like face scan customers so

2475
02:02:46,160 --> 02:02:48,640
Speaker 2: we can see if they have money and increase prices

2476
02:02:48,680 --> 02:02:51,640
Speaker 2: for like products for certain people, which I'm sure they're

2477
02:02:51,640 --> 02:02:53,840
Speaker 2: going to try. They are too enticed by that idea

2478
02:02:53,960 --> 02:02:57,080
Speaker 2: not to so I caught a little bit of that,

2479
02:02:57,120 --> 02:02:59,440
Speaker 2: But they really like to the extent of how big

2480
02:02:59,480 --> 02:03:02,520
Speaker 2: And this was an interesting last year Samsung and LG

2481
02:03:02,640 --> 02:03:04,280
Speaker 2: their boots were huge and they had a lot of

2482
02:03:04,280 --> 02:03:07,960
Speaker 2: get different gadgets. Samsung's booth is big this year, forty

2483
02:03:08,000 --> 02:03:11,800
Speaker 2: percent of it was that scan your furniture, scan your

2484
02:03:11,840 --> 02:03:15,280
Speaker 2: fucking like map acts, not that much like, very little

2485
02:03:15,520 --> 02:03:17,240
Speaker 2: actual shit going on.

2486
02:03:17,360 --> 02:03:19,920
Speaker 5: The people slapped the word AI onto everything there was.

2487
02:03:19,960 --> 02:03:22,960
Speaker 2: Another big thing was all Samsung, because Samsung makes a

2488
02:03:22,960 --> 02:03:25,920
Speaker 2: ton of appliances, they make TVs, all sorts of entertainment products.

2489
02:03:25,960 --> 02:03:27,320
Speaker 2: All of them have this I figure what they called

2490
02:03:27,320 --> 02:03:30,200
Speaker 2: like Samsung tag or something that you can you can

2491
02:03:30,240 --> 02:03:31,640
Speaker 2: map it in your phone, so you can have a

2492
02:03:31,640 --> 02:03:33,960
Speaker 2: whole map of all of the devices and shit that

2493
02:03:34,000 --> 02:03:35,760
Speaker 2: you have in your phone and you can control them

2494
02:03:35,760 --> 02:03:38,160
Speaker 2: all from a single point. And right, no one, by

2495
02:03:38,200 --> 02:03:40,760
Speaker 2: the way, had any interest in answering my security questions there.

2496
02:03:40,800 --> 02:03:43,000
Speaker 2: But also if you're into that, if you want to

2497
02:03:43,080 --> 02:03:46,040
Speaker 2: have all of your appliances and entertainment things linked up

2498
02:03:46,120 --> 02:03:49,320
Speaker 2: and controlled on your phone and all of them are Samsung,

2499
02:03:49,560 --> 02:03:50,960
Speaker 2: you don't care. You don't care.

2500
02:03:51,320 --> 02:03:53,560
Speaker 5: No, if you're getting a smart home, I don't think

2501
02:03:53,600 --> 02:03:54,840
Speaker 5: you really care about that.

2502
02:03:54,880 --> 02:03:56,760
Speaker 2: But also none of it was like, yeah, I can

2503
02:03:56,800 --> 02:03:59,960
Speaker 2: control everything from my phone. You've been promising me that, literally,

2504
02:04:00,320 --> 02:04:03,720
Speaker 2: like in twenty eleven. It's decades they were promising me

2505
02:04:03,760 --> 02:04:05,440
Speaker 2: You're gonna be able to control your whole house.

2506
02:04:05,360 --> 02:04:07,680
Speaker 5: Thing feels new this year. This is the thing. Is

2507
02:04:07,720 --> 02:04:10,120
Speaker 5: like even walking through the LGBO, which usually has some

2508
02:04:10,240 --> 02:04:14,000
Speaker 5: really cool new thing this year nothing new. No, nothing new.

2509
02:04:14,240 --> 02:04:16,480
Speaker 5: They slapped the word AI on one corner of their

2510
02:04:16,560 --> 02:04:19,640
Speaker 5: television set. Right, I guess lg does have like a

2511
02:04:19,720 --> 02:04:22,320
Speaker 5: large language model in like one corner of their booth,

2512
02:04:22,360 --> 02:04:24,160
Speaker 5: but like, so does everyone else, Like that's not like,

2513
02:04:24,360 --> 02:04:25,200
Speaker 5: yeah compelling.

2514
02:04:25,440 --> 02:04:28,760
Speaker 2: There was sk which is a South South Korea company

2515
02:04:29,160 --> 02:04:33,240
Speaker 2: there booth again the massive like AI a big thing,

2516
02:04:33,280 --> 02:04:33,880
Speaker 2: but it's nothing.

2517
02:04:33,880 --> 02:04:35,400
Speaker 5: It's just a big visual.

2518
02:04:35,160 --> 02:04:37,120
Speaker 2: Display that looks cool, that looks like a bunch of

2519
02:04:37,200 --> 02:04:39,680
Speaker 2: server racks, like you're in this huge cube of servers.

2520
02:04:40,040 --> 02:04:43,920
Speaker 2: But everything does in different actual products. One of them

2521
02:04:44,000 --> 02:04:48,280
Speaker 2: was real time CCTVs that use an AI like an

2522
02:04:48,680 --> 02:04:51,840
Speaker 2: M type thing to summarize pictures. So I like walked

2523
02:04:51,840 --> 02:04:54,160
Speaker 2: through and it did pick me out as a notable person.

2524
02:04:54,560 --> 02:04:56,560
Speaker 2: So I've got like this people of interest thing where

2525
02:04:56,560 --> 02:05:00,120
Speaker 2: it's like a man holding a smartphone standing next to

2526
02:05:00,160 --> 02:05:03,240
Speaker 2: another man. But also I'm like, what does that really

2527
02:05:03,240 --> 02:05:05,680
Speaker 2: get you? Like the fact that you're summarizing up like

2528
02:05:05,720 --> 02:05:08,080
Speaker 2: these people who are like this person's kneeling and taking

2529
02:05:08,080 --> 02:05:11,800
Speaker 2: a pictures person standing because I like actually tried deliberately,

2530
02:05:11,840 --> 02:05:13,920
Speaker 2: I like reached my bag to try to be suspicious.

2531
02:05:13,920 --> 02:05:16,480
Speaker 2: I like did finger guns and it never marked me out,

2532
02:05:16,560 --> 02:05:18,720
Speaker 2: and like I didn't pull a real gun or anything,

2533
02:05:18,760 --> 02:05:21,200
Speaker 2: because I very rarely bring that to the cees for

2534
02:05:22,720 --> 02:05:25,200
Speaker 2: But I don't know, like I can see how there

2535
02:05:25,200 --> 02:05:27,160
Speaker 2: could be a utility there if you're actually able to

2536
02:05:27,520 --> 02:05:30,280
Speaker 2: say you're setting up like surveillance outside of a residential

2537
02:05:30,320 --> 02:05:33,120
Speaker 2: building and it can alert security that like something is

2538
02:05:33,120 --> 02:05:36,680
Speaker 2: happening outside. There's a potential you if it's good enough

2539
02:05:36,760 --> 02:05:39,440
Speaker 2: utility in that that they didn't display it at the show.

2540
02:05:39,720 --> 02:05:43,760
Speaker 2: It was literally just describing randos from the audience, And like,

2541
02:05:43,840 --> 02:05:45,640
Speaker 2: I just don't see how a security guy is there's

2542
02:05:45,640 --> 02:05:47,920
Speaker 2: a guy with a phone on outside of the building, Like.

2543
02:05:48,040 --> 02:05:51,640
Speaker 5: Ah, yeah, no, it's it doesn't seem very new, it

2544
02:05:51,640 --> 02:05:53,800
Speaker 5: doesn't seem very innovative. Nah.

2545
02:05:54,040 --> 02:05:56,480
Speaker 2: So again when I'm when I'm seeing here overwhelmingly for

2546
02:05:56,520 --> 02:05:59,920
Speaker 2: all to talk about, like there's no resisting it. AI's coming.

2547
02:06:00,120 --> 02:06:02,680
Speaker 2: It's going to dominate everything. This is the next big thing.

2548
02:06:03,120 --> 02:06:06,160
Speaker 2: A remarkable lack outside of what I will say, the

2549
02:06:06,200 --> 02:06:09,160
Speaker 2: one thing where there are continuously new products that are

2550
02:06:09,160 --> 02:06:13,440
Speaker 2: better every year. The smart glasses, yes, they're getting more impressives.

2551
02:06:14,760 --> 02:06:16,920
Speaker 2: I don't think I'll ever be a smart glasses guy.

2552
02:06:17,520 --> 02:06:19,760
Speaker 2: I hated glasses enough that I let them shoot me

2553
02:06:19,800 --> 02:06:22,920
Speaker 2: in the eye with lasers. Shout out to our lacek sponsors.

2554
02:06:23,400 --> 02:06:27,480
Speaker 2: But I see why people would like it, and there

2555
02:06:27,480 --> 02:06:31,040
Speaker 2: seems to be legitimately substantial utility.

2556
02:06:30,760 --> 02:06:32,960
Speaker 5: If we have high power smart glasses. Yeah, that looks

2557
02:06:33,000 --> 02:06:34,720
Speaker 5: like a regular pair of glasses. I will get a

2558
02:06:34,840 --> 02:06:36,560
Speaker 5: pair eventually, because yeah, why not.

2559
02:06:36,800 --> 02:06:39,080
Speaker 2: There was a great demo. I'm pulling over to an

2560
02:06:39,680 --> 02:06:42,800
Speaker 2: LAWK view. They had like one glass that was the

2561
02:06:42,840 --> 02:06:46,280
Speaker 2: first world smart glasses for TikTok Life. Not particularly excited

2562
02:06:46,280 --> 02:06:48,720
Speaker 2: about that. But they had another set of ar glasses

2563
02:06:49,040 --> 02:06:51,720
Speaker 2: with a twelve hour battery, where like, if it works

2564
02:06:51,720 --> 02:06:53,680
Speaker 2: as well as the demo, and that's a big if,

2565
02:06:53,720 --> 02:06:56,560
Speaker 2: but it seems to like your smart watch. So it'll

2566
02:06:56,600 --> 02:06:58,400
Speaker 2: tell you you can see in a heads up display

2567
02:06:58,440 --> 02:07:00,640
Speaker 2: as you're cycling, that was the demo. It'll both like

2568
02:07:00,680 --> 02:07:03,760
Speaker 2: give you directions like in your eyes, and it seemed

2569
02:07:03,760 --> 02:07:05,560
Speaker 2: to be like fairly well thought out, so it's not

2570
02:07:05,640 --> 02:07:09,320
Speaker 2: like overly corrupting your view. It'll show you your heart rate.

2571
02:07:09,400 --> 02:07:11,480
Speaker 2: You know, it'll show you like all that kind of stuff.

2572
02:07:12,280 --> 02:07:15,680
Speaker 2: So you get like a useful degree of control and

2573
02:07:15,720 --> 02:07:18,320
Speaker 2: assistance from that kind of thing. And that is I

2574
02:07:18,360 --> 02:07:21,280
Speaker 2: will say the last three cees is the glasses get

2575
02:07:21,320 --> 02:07:24,240
Speaker 2: a little better and a little smaller every year. Smaller, certainly,

2576
02:07:24,280 --> 02:07:27,600
Speaker 2: I would say that's a real product that's probably going

2577
02:07:27,680 --> 02:07:29,000
Speaker 2: to continue to improve.

2578
02:07:29,760 --> 02:07:33,200
Speaker 5: Do you know what else always seeks improvement, Robert No,

2579
02:07:33,720 --> 02:07:39,360
Speaker 5: The capacity for you to get personalized possibly AI powered ads. Well,

2580
02:07:39,400 --> 02:07:42,200
Speaker 5: that human is exciting form the consumer choices. Let's all

2581
02:07:42,240 --> 02:07:44,560
Speaker 5: sit down for some AI powered ads.

2582
02:07:53,040 --> 02:07:56,520
Speaker 2: Wow, I can't believe they put Jay Shetty's voice the

2583
02:07:56,600 --> 02:07:59,800
Speaker 2: d aged Harris and Ford the latest in Handed Jones movie,

2584
02:08:00,200 --> 02:08:02,360
Speaker 2: My Dick's Hard. How are you Garrison? Oh?

2585
02:08:02,400 --> 02:08:05,720
Speaker 5: I feel good because today, as we are recording this,

2586
02:08:05,800 --> 02:08:10,560
Speaker 5: it's late Tuesday night, there was a series of fascinating

2587
02:08:10,600 --> 02:08:14,640
Speaker 5: breaking news articles that happened as we were sitting or

2588
02:08:14,720 --> 02:08:17,000
Speaker 5: at least as I was sitting in on these AI panels,

2589
02:08:17,080 --> 02:08:19,920
Speaker 5: which made it hard to not just like completely interrupt

2590
02:08:20,000 --> 02:08:23,040
Speaker 5: everything and be like, yeah, hey, hey, any comment on this.

2591
02:08:23,360 --> 02:08:27,040
Speaker 2: Guys, Guys, something real happened. Shut your fucking stupid mouds

2592
02:08:27,080 --> 02:08:29,760
Speaker 2: about this AI Hollywood bullshit.

2593
02:08:30,000 --> 02:08:34,000
Speaker 5: So yeah, So a few weeks ago, if you were unaware,

2594
02:08:34,800 --> 02:08:37,320
Speaker 5: a Green Beret rented a test the cyber truck to

2595
02:08:37,320 --> 02:08:41,920
Speaker 5: feel like Batman and Halo and drove to first the

2596
02:08:42,000 --> 02:08:46,160
Speaker 5: wrong Las Vegas and then eventually Las Vegas, Nevada, parked

2597
02:08:46,240 --> 02:08:50,400
Speaker 5: outside of the Trump Hotel and Casino and then loo

2598
02:08:50,440 --> 02:08:53,360
Speaker 5: himself up. And this has been a big news story.

2599
02:08:53,400 --> 02:08:55,879
Speaker 5: It happened during the same day as a pretty horrible

2600
02:08:56,160 --> 02:08:59,280
Speaker 5: terrorist attack in New Orleans, which resulted in about fifteen

2601
02:08:59,320 --> 02:09:02,800
Speaker 5: people dead, done by a guy who was employed by Deloitte,

2602
02:09:02,840 --> 02:09:07,320
Speaker 5: a frequent frequent CES sponsor. So this this, these felt

2603
02:09:07,320 --> 02:09:10,400
Speaker 5: like a very Cees style of attacks, you know, one

2604
02:09:10,440 --> 02:09:14,320
Speaker 5: delowed guy driving into people, murdering hosh you guys. And

2605
02:09:14,360 --> 02:09:17,120
Speaker 5: then this cyber truck explosion in Vegas like a week

2606
02:09:17,160 --> 02:09:19,640
Speaker 5: before Cees, you know, very odd. And then and then

2607
02:09:19,760 --> 02:09:22,360
Speaker 5: Robert some news drops today that I would love to

2608
02:09:22,360 --> 02:09:24,800
Speaker 5: hear you announce.

2609
02:09:24,920 --> 02:09:26,840
Speaker 2: You know, Garrison. I made a comment the other night

2610
02:09:26,920 --> 02:09:30,320
Speaker 2: about how like it's pretty well documented that veterans, you know,

2611
02:09:30,920 --> 02:09:33,200
Speaker 2: not that they're more likely to carry out violence, but

2612
02:09:33,240 --> 02:09:35,040
Speaker 2: when they do, they tend to have higher body counts

2613
02:09:35,040 --> 02:09:38,720
Speaker 2: because they have more skills. It turns out I thought

2614
02:09:38,760 --> 02:09:41,320
Speaker 2: we were getting more literal bang for our buck training

2615
02:09:41,320 --> 02:09:44,200
Speaker 2: Green Berets than we are. My assumption is because my

2616
02:09:44,440 --> 02:09:47,200
Speaker 2: uncle was a Green Beret and he did some very scary,

2617
02:09:47,400 --> 02:09:51,440
Speaker 2: probably wore crime shit in Vietnam, and I assumed like thatman,

2618
02:09:51,520 --> 02:09:53,600
Speaker 2: I'll tell you one thing about my uncle, Jim, that

2619
02:09:53,800 --> 02:09:56,880
Speaker 2: man could make a bomb. That man would not need

2620
02:09:56,920 --> 02:09:59,600
Speaker 2: to ask anyone for advice if he needed to make

2621
02:09:59,640 --> 02:10:02,240
Speaker 2: a bomb. He's not with us anymore. God rest his soul.

2622
02:10:02,680 --> 02:10:06,520
Speaker 2: But it turns out this Green Beret, who you know,

2623
02:10:06,720 --> 02:10:10,760
Speaker 2: a fucking dollar store TJ Max version of the Green

2624
02:10:10,840 --> 02:10:14,000
Speaker 2: Berets is what we're working with now, asked chat Gpt

2625
02:10:14,200 --> 02:10:16,400
Speaker 2: how to build a fucking bomb, and it sounds like

2626
02:10:16,440 --> 02:10:18,840
Speaker 2: he was trying to make it triggered by Tannerit with it,

2627
02:10:18,840 --> 02:10:21,720
Speaker 2: which is a bipartite explosive compound that you use as

2628
02:10:21,760 --> 02:10:24,440
Speaker 2: like an exploding target, so it'll go boom big, but

2629
02:10:24,480 --> 02:10:26,280
Speaker 2: you have to shoot it with something like a rifle

2630
02:10:26,280 --> 02:10:29,400
Speaker 2: that's high velocity, or use like a blasting cap. Otherwise

2631
02:10:29,400 --> 02:10:32,240
Speaker 2: it's very stable and very safe, which obviously has use.

2632
02:10:32,360 --> 02:10:34,920
Speaker 2: You know, it was invented actually to set off avalanches

2633
02:10:34,960 --> 02:10:38,280
Speaker 2: and stuff anyway, because that's very available in very high power.

2634
02:10:38,320 --> 02:10:40,080
Speaker 2: He was looking to like fill his car with that

2635
02:10:40,240 --> 02:10:41,840
Speaker 2: and then shoot it with a rifle while he was

2636
02:10:41,880 --> 02:10:44,720
Speaker 2: in it, and that's what he was asking chat gpt about.

2637
02:10:45,040 --> 02:10:47,920
Speaker 2: So it's not clear to me. Actually, the actual headline

2638
02:10:47,960 --> 02:10:50,560
Speaker 2: is that like he used chat gpt to make his bomb.

2639
02:10:51,240 --> 02:10:55,280
Speaker 2: It seems and I'm not privy to what the police

2640
02:10:55,320 --> 02:10:58,080
Speaker 2: are obviously, but it seems like based on what I

2641
02:10:58,120 --> 02:11:00,720
Speaker 2: read in the article, we're not or if he actually

2642
02:11:00,800 --> 02:11:03,200
Speaker 2: used chat gpt to make a bomb. It's more that

2643
02:11:03,280 --> 02:11:06,919
Speaker 2: he was interested in making a bomb setting off tannerite

2644
02:11:06,960 --> 02:11:10,120
Speaker 2: by shooting it, but may have ultimately decided not to

2645
02:11:10,160 --> 02:11:13,040
Speaker 2: do that because he would then be alive for the explosion,

2646
02:11:13,120 --> 02:11:15,920
Speaker 2: which he didn't want to be. Also, the authorities don't

2647
02:11:15,960 --> 02:11:18,520
Speaker 2: seem to fully know how he triggered it. Yeah, so

2648
02:11:18,560 --> 02:11:21,000
Speaker 2: it's still kind of unclear to me. I guess hopefully

2649
02:11:21,040 --> 02:11:24,520
Speaker 2: we'll get more later. But he he definitely needed chat

2650
02:11:24,560 --> 02:11:27,360
Speaker 2: GPT's help to try and figure out how to make

2651
02:11:27,400 --> 02:11:27,760
Speaker 2: the bomb.

2652
02:11:28,160 --> 02:11:32,160
Speaker 5: He's certainly used chat gpt in the planning process of

2653
02:11:32,200 --> 02:11:32,839
Speaker 5: this attack.

2654
02:11:33,000 --> 02:11:34,520
Speaker 2: Yeah, fair to say that.

2655
02:11:34,840 --> 02:11:38,360
Speaker 5: And It's odd because both me and you spent a

2656
02:11:38,480 --> 02:11:41,760
Speaker 5: number of hours today actually like attending like demos from

2657
02:11:41,800 --> 02:11:44,760
Speaker 5: like these you know, speech to text, text to speech

2658
02:11:45,200 --> 02:11:48,320
Speaker 5: AI systems. We went to like two specific ones that

2659
02:11:48,360 --> 02:11:52,600
Speaker 5: they like, you know, demonstrated demonstrated the capabilities of their

2660
02:11:52,680 --> 02:11:55,960
Speaker 5: like you know, like AI assistive tech. The first one

2661
02:11:55,960 --> 02:11:59,920
Speaker 5: we went to spent twenty minutes talking about how they're

2662
02:12:00,000 --> 02:12:03,480
Speaker 5: bigest inspiration there quote unquote. North Star was the movie

2663
02:12:03,560 --> 02:12:05,960
Speaker 5: Her with Joaqui Fi.

2664
02:12:06,160 --> 02:12:09,600
Speaker 15: They had a whole slide about how that was the

2665
02:12:09,640 --> 02:12:15,280
Speaker 15: gold standard for AI human communications. The movie Her, in

2666
02:12:15,320 --> 02:12:19,360
Speaker 15: which Joaquin Phoenix falls in love with an AI chatbot

2667
02:12:19,480 --> 02:12:23,800
Speaker 15: voiced by Scarlett Johansson who hires a prostitute to have

2668
02:12:23,960 --> 02:12:28,120
Speaker 15: sex with them while she participates vocally. And then it

2669
02:12:28,160 --> 02:12:31,000
Speaker 15: turns out the AI is really kind of Polly and

2670
02:12:31,120 --> 02:12:33,760
Speaker 15: Joaquin Phoenix is not okay with that, and then maybe

2671
02:12:33,760 --> 02:12:35,760
Speaker 15: the ais all go to space. It's kind of unclear

2672
02:12:35,800 --> 02:12:37,640
Speaker 15: at the end. I don't think it was a great movie.

2673
02:12:37,680 --> 02:12:40,160
Speaker 15: A lot of people liked it. I don't see whether

2674
02:12:40,240 --> 02:12:42,240
Speaker 15: you or not you like it, why this is your

2675
02:12:42,320 --> 02:12:44,160
Speaker 15: vision of how what chatbot should work.

2676
02:12:44,480 --> 02:12:46,600
Speaker 5: The actual chatbot they had was like fine. It was.

2677
02:12:46,880 --> 02:12:49,680
Speaker 5: It was. It was actually pretty good at translation, you know,

2678
02:12:50,000 --> 02:12:51,600
Speaker 5: translitting from Spanish to English.

2679
02:12:51,680 --> 02:12:54,360
Speaker 2: It works quite well. Yeah, the demo was like solid,

2680
02:12:54,440 --> 02:12:55,760
Speaker 2: It was pretty accurate.

2681
02:12:55,800 --> 02:12:55,960
Speaker 11: You know.

2682
02:12:56,040 --> 02:12:58,080
Speaker 2: I love coming here and fucking with people. I love

2683
02:12:58,160 --> 02:13:01,560
Speaker 2: like being a dicky. They asked for a volunteer, and

2684
02:13:01,600 --> 02:13:04,280
Speaker 2: at that point we knew about the chat GPTs. I

2685
02:13:04,360 --> 02:13:07,680
Speaker 2: wanted to go up and ask, like live this robot

2686
02:13:07,760 --> 02:13:11,360
Speaker 2: to like help me make a bomb. But the guy

2687
02:13:11,440 --> 02:13:15,320
Speaker 2: who was pretty handsome and like an interesting like English

2688
02:13:15,360 --> 02:13:18,760
Speaker 2: Spanish like that you specified he was, and he didn't

2689
02:13:18,760 --> 02:13:22,280
Speaker 2: want to be mean to him. He seemed nice, handsome.

2690
02:13:22,360 --> 02:13:25,440
Speaker 2: Girl wasn't shitty like he was. There were just ten

2691
02:13:25,520 --> 02:13:27,440
Speaker 2: people in this room that was supposed to have two hundred.

2692
02:13:27,480 --> 02:13:29,360
Speaker 2: I'm sure she wasn't the one that talked about her.

2693
02:13:29,560 --> 02:13:30,560
Speaker 5: That was someone else.

2694
02:13:30,560 --> 02:13:32,360
Speaker 2: That that was someone else at his company. And like

2695
02:13:32,400 --> 02:13:34,880
Speaker 2: he just seemed like he wanted to do I didn't

2696
02:13:34,920 --> 02:13:36,280
Speaker 2: want to be a dick to it. No, no, and

2697
02:13:36,320 --> 02:13:37,680
Speaker 2: like it it wasn't hurting anything.

2698
02:13:37,720 --> 02:13:39,720
Speaker 5: It was fine. Like similarly, we went to this a

2699
02:13:40,120 --> 02:13:42,400
Speaker 5: nice jaw line, we went to this other one about

2700
02:13:42,400 --> 02:13:45,400
Speaker 5: this like actually a much more dubious concept in my mind,

2701
02:13:45,400 --> 02:13:48,000
Speaker 5: which is like this this AI assistant to help like

2702
02:13:48,320 --> 02:13:50,960
Speaker 5: elderly people, like people in like their eighties and nineties

2703
02:13:51,120 --> 02:13:53,320
Speaker 5: who don't want to be an assistant living facilities, who

2704
02:13:53,400 --> 02:13:55,320
Speaker 5: have been living on their own, but they're getting to

2705
02:13:55,320 --> 02:13:57,160
Speaker 5: the point in their life where like they need like

2706
02:13:57,240 --> 02:13:58,680
Speaker 5: some degree like in home care.

2707
02:13:58,840 --> 02:14:01,000
Speaker 2: He specified. A lot of them are people who have

2708
02:14:01,240 --> 02:14:04,160
Speaker 2: either just lost a spouse or maybe their spouse is

2709
02:14:04,200 --> 02:14:06,400
Speaker 2: aging faster and worse than them. It is no longer

2710
02:14:06,440 --> 02:14:09,400
Speaker 2: really able to be the kind of companion that they

2711
02:14:09,400 --> 02:14:10,000
Speaker 2: were before.

2712
02:14:10,480 --> 02:14:12,920
Speaker 5: Yeah, so it's like this. It's both like a conversation tool.

2713
02:14:12,960 --> 02:14:15,800
Speaker 5: It helps like memory recall. It's kind of in some

2714
02:14:15,840 --> 02:14:17,800
Speaker 5: ways has the features that like, you know, someone in

2715
02:14:17,840 --> 02:14:19,920
Speaker 5: their sixties would just use their smartphone for it to

2716
02:14:19,960 --> 02:14:21,920
Speaker 5: help keep in touch with their family. It's kind of

2717
02:14:21,960 --> 02:14:25,160
Speaker 5: simplified and more automated, so you know, ways to help

2718
02:14:25,240 --> 02:14:27,560
Speaker 5: keep in touch with like your family can prove like

2719
02:14:27,600 --> 02:14:29,640
Speaker 5: your memory, like talked about your own life.

2720
02:14:29,680 --> 02:14:32,240
Speaker 2: And the device is weird. It's about the width of

2721
02:14:32,280 --> 02:14:35,600
Speaker 2: like a bedside table maybe six to eight inches deep,

2722
02:14:35,760 --> 02:14:37,960
Speaker 2: So think about like eighteen inches long to maybe six

2723
02:14:38,000 --> 02:14:40,840
Speaker 2: inches deep something like that. Half of it is like

2724
02:14:40,880 --> 02:14:43,879
Speaker 2: a little tablet, like a seven inch tablet with a speaker.

2725
02:14:44,920 --> 02:14:48,280
Speaker 2: Half of it is something about the shape and size

2726
02:14:48,280 --> 02:14:50,680
Speaker 2: of a head on like a neck that can pivot

2727
02:14:50,760 --> 02:14:54,080
Speaker 2: and nod on the neck. There's no face, so when

2728
02:14:54,080 --> 02:14:56,640
Speaker 2: it's talking, there's like a white light in the center

2729
02:14:56,680 --> 02:14:59,600
Speaker 2: of it that kind of like pulses in time with

2730
02:14:59,800 --> 02:15:02,360
Speaker 2: the the speaking that it does. So we saw this

2731
02:15:02,400 --> 02:15:04,280
Speaker 2: picture of the device and we saw the description of

2732
02:15:04,360 --> 02:15:07,720
Speaker 2: like this is an AI companion for the elderly, and

2733
02:15:07,760 --> 02:15:09,120
Speaker 2: we were both like, number one of these people are

2734
02:15:09,120 --> 02:15:10,840
Speaker 2: gonna be monsters. This is going to be like something

2735
02:15:10,880 --> 02:15:12,760
Speaker 2: to shovel you're dying dad off with because you don't

2736
02:15:12,760 --> 02:15:14,160
Speaker 2: want to spend it. You want to spend time with

2737
02:15:14,200 --> 02:15:18,400
Speaker 2: your family, Scum, You're too busy AI generating scam music

2738
02:15:19,680 --> 02:15:22,480
Speaker 2: and trying to sell your shitty robot to Garrison and me.

2739
02:15:22,720 --> 02:15:25,480
Speaker 2: More on that tomorrow, More on that tomorrow. And so

2740
02:15:25,560 --> 02:15:28,120
Speaker 2: that's what we came in prepped to this meeting, like

2741
02:15:28,160 --> 02:15:29,320
Speaker 2: this is this idea I.

2742
02:15:29,280 --> 02:15:33,200
Speaker 5: Find pretty distasteful in general, is like replacing actual like

2743
02:15:33,240 --> 02:15:35,720
Speaker 5: you know, friends or human contact or like like in

2744
02:15:35,800 --> 02:15:39,360
Speaker 5: home care with a fucking like Alexa machine essentially, and

2745
02:15:39,400 --> 02:15:39,960
Speaker 5: to be clear.

2746
02:15:40,200 --> 02:15:43,040
Speaker 2: I still think this product might be a bad idea

2747
02:15:43,080 --> 02:15:46,520
Speaker 2: that doesn't work. But the guy behind it, who is

2748
02:15:46,560 --> 02:15:49,040
Speaker 2: the dude that we talked to, cares a lot and

2749
02:15:49,160 --> 02:15:52,520
Speaker 2: is really very clearly trying to do a good thing

2750
02:15:52,760 --> 02:15:56,600
Speaker 2: and thought through the ethics and the efficacy of what

2751
02:15:56,680 --> 02:15:59,960
Speaker 2: he was doing a lot, And I I'm not convinced

2752
02:16:00,200 --> 02:16:03,400
Speaker 2: it will actually do anything, but I like wish him

2753
02:16:03,400 --> 02:16:03,840
Speaker 2: the best.

2754
02:16:03,960 --> 02:16:06,000
Speaker 5: No, Like, it specifically is designed to not look like

2755
02:16:06,040 --> 02:16:08,360
Speaker 5: a human, so that someone's using it, you know, wouldn't

2756
02:16:08,400 --> 02:16:11,320
Speaker 5: like start to believe it's like human, Like, we don't.

2757
02:16:11,160 --> 02:16:13,360
Speaker 2: Want to trick people. We don't want them to mistake it.

2758
02:16:14,080 --> 02:16:16,640
Speaker 5: It refers to itself to like like as a robot

2759
02:16:16,760 --> 02:16:18,480
Speaker 5: as like, it refers to its own like you know,

2760
02:16:18,480 --> 02:16:22,160
Speaker 5: like motors and functionality like like pretty consistently to to

2761
02:16:22,360 --> 02:16:24,400
Speaker 5: like you know, make sure that the person who's talking

2762
02:16:24,400 --> 02:16:26,400
Speaker 5: to it gets like reminded of that. And something I

2763
02:16:26,400 --> 02:16:27,720
Speaker 5: talked about is, you know, there's been a lot of

2764
02:16:27,720 --> 02:16:32,120
Speaker 5: news stories this year about people building very unhealthy attachments

2765
02:16:32,120 --> 02:16:35,640
Speaker 5: and relationships to these kind of ai AI programs, like

2766
02:16:36,000 --> 02:16:37,760
Speaker 5: character AI. There's a story like a year and a

2767
02:16:37,800 --> 02:16:40,240
Speaker 5: half ago about like a journalist to quote unquote like

2768
02:16:40,280 --> 02:16:42,160
Speaker 5: you know, like like fell in love with some kind

2769
02:16:42,200 --> 02:16:45,840
Speaker 5: of chat thing that resulted in him killing himself. You know,

2770
02:16:46,000 --> 02:16:47,320
Speaker 5: but these kind of these systems like he.

2771
02:16:47,320 --> 02:16:51,200
Speaker 2: Was not a teenager, was no character? Was that a journalist?

2772
02:16:51,360 --> 02:16:53,760
Speaker 5: Last year there was there was a journalist people fellow

2773
02:16:53,760 --> 02:16:56,280
Speaker 5: in love with an ai chat thing. A few weeks

2774
02:16:56,280 --> 02:16:58,880
Speaker 5: ago there was the kid who you know was talking

2775
02:16:58,879 --> 02:16:59,720
Speaker 5: to this like a character.

2776
02:17:00,680 --> 02:17:03,920
Speaker 2: Also, I just need to reiterate her not a great movie.

2777
02:17:05,160 --> 02:17:06,920
Speaker 5: But but you know, there has been a lot of

2778
02:17:06,920 --> 02:17:08,920
Speaker 5: these stories of these things like going wrong or you know,

2779
02:17:09,120 --> 02:17:12,360
Speaker 5: encouraging or like not stopping you know, like these like

2780
02:17:12,440 --> 02:17:16,000
Speaker 5: intense conversations with like suicidal ideation or you know, like

2781
02:17:16,080 --> 02:17:17,720
Speaker 5: self harm, all these things.

2782
02:17:18,040 --> 02:17:20,160
Speaker 2: We brought these up kind of thinking he would flinch

2783
02:17:20,200 --> 02:17:22,360
Speaker 2: away and not want to talk about it, and he

2784
02:17:22,520 --> 02:17:24,760
Speaker 2: very much acknowledged that, like he was aware of this,

2785
02:17:24,800 --> 02:17:27,240
Speaker 2: and this is something that they were attempting to build in.

2786
02:17:27,360 --> 02:17:28,840
Speaker 5: This is this is like this is you know, built

2787
02:17:28,840 --> 02:17:30,640
Speaker 5: into it. I think this is still you know, a

2788
02:17:30,640 --> 02:17:33,400
Speaker 5: big problem with this entire industries. I'm sure everyone would

2789
02:17:33,440 --> 02:17:34,959
Speaker 5: say this is the you know obviously that we have

2790
02:17:35,040 --> 02:17:36,520
Speaker 5: we have guardrails for this, and then becomes a new

2791
02:17:36,560 --> 02:17:39,760
Speaker 5: story when those guardrails fail. Similarly, was to go back

2792
02:17:39,800 --> 02:17:43,120
Speaker 5: to the Tesla bomb. You know, they're supposed to be

2793
02:17:43,200 --> 02:17:45,720
Speaker 5: guardrails and chat GPT to make sure he doesn't tell

2794
02:17:45,720 --> 02:17:48,840
Speaker 5: you how to build a bomb and those guardrails can fail.

2795
02:17:49,160 --> 02:17:51,400
Speaker 2: He showed us one which was like he told the robot,

2796
02:17:51,560 --> 02:17:54,320
Speaker 2: I love you. What was it? L e q l

2797
02:17:54,480 --> 02:17:56,560
Speaker 2: q was the l eq e l l i q,

2798
02:17:57,080 --> 02:17:59,960
Speaker 2: I love you l eq and the robot like respond

2799
02:18:00,280 --> 02:18:02,480
Speaker 2: with a like, oh that makes like my fans are

2800
02:18:02,520 --> 02:18:04,520
Speaker 2: all spinning or something like that, where he's like, I

2801
02:18:04,560 --> 02:18:07,800
Speaker 2: wanted the responsibility that it's reminding the person talking to

2802
02:18:07,840 --> 02:18:10,520
Speaker 2: it that it's a machine, that it can't think we're

2803
02:18:10,600 --> 02:18:12,840
Speaker 2: love them back. We don't want it to be negative,

2804
02:18:12,840 --> 02:18:14,880
Speaker 2: but we like we don't want to be like feeding

2805
02:18:14,879 --> 02:18:16,280
Speaker 2: into that. And I don't know that that's the best

2806
02:18:16,280 --> 02:18:18,600
Speaker 2: way to do that, but like, at least they're thinking

2807
02:18:18,640 --> 02:18:21,039
Speaker 2: about that kind of thing. The thing that it was

2808
02:18:21,040 --> 02:18:22,520
Speaker 2: interesting to me is that he build this as the

2809
02:18:22,560 --> 02:18:26,279
Speaker 2: first proactive home AI thing, so unlike an Alexa or whatever,

2810
02:18:26,280 --> 02:18:29,119
Speaker 2: where it's just waiting for you to ask it something,

2811
02:18:29,160 --> 02:18:32,480
Speaker 2: but it does not chime in randomly to talk to you,

2812
02:18:32,720 --> 02:18:33,360
Speaker 2: or it won't like.

2813
02:18:33,360 --> 02:18:35,720
Speaker 5: Change the subject either and like continue conversation.

2814
02:18:36,000 --> 02:18:38,200
Speaker 2: This will prompt you out of the blue, be like, hey,

2815
02:18:38,240 --> 02:18:40,280
Speaker 2: how are you doing? How are you feeling today? It's

2816
02:18:40,280 --> 02:18:41,240
Speaker 2: been a way and specific.

2817
02:18:41,280 --> 02:18:42,600
Speaker 5: You want to see pictures of your family?

2818
02:18:42,640 --> 02:18:44,680
Speaker 2: You see pictures of your family. Do you want to

2819
02:18:44,680 --> 02:18:45,360
Speaker 2: call your son?

2820
02:18:45,640 --> 02:18:45,840
Speaker 5: You know?

2821
02:18:46,320 --> 02:18:47,520
Speaker 2: But do you want to play a game?

2822
02:18:47,800 --> 02:18:49,879
Speaker 5: Talk to me about that movie you saw last Talk to.

2823
02:18:49,879 --> 02:18:51,400
Speaker 2: Me about that. Hey, remind me how did you meet

2824
02:18:51,400 --> 02:18:53,320
Speaker 2: your husband? You know? Like literally, these are all the

2825
02:18:53,320 --> 02:18:56,520
Speaker 2: things that will do. And it had some side features

2826
02:18:56,560 --> 02:18:58,400
Speaker 2: like if it prompts you to start telling a story,

2827
02:18:58,440 --> 02:19:00,840
Speaker 2: it'll save that as like a memos things so that

2828
02:19:01,000 --> 02:19:03,959
Speaker 2: like you know, when your elderly mother passes or whatever,

2829
02:19:04,000 --> 02:19:07,480
Speaker 2: it saved up this like collection of stories over the years,

2830
02:19:07,600 --> 02:19:09,760
Speaker 2: and you can like show it pictures while you're telling

2831
02:19:09,800 --> 02:19:12,119
Speaker 2: it stories and it will listen and it'll have comments

2832
02:19:12,120 --> 02:19:15,000
Speaker 2: and it'll ask you further questions about so, how did

2833
02:19:15,040 --> 02:19:17,320
Speaker 2: you feel, you know after meeting them this way, Like

2834
02:19:17,400 --> 02:19:20,120
Speaker 2: that's really interesting, I didn't know that. Explain to me

2835
02:19:20,160 --> 02:19:22,480
Speaker 2: how it worked. And it will also prompt you to

2836
02:19:22,560 --> 02:19:26,280
Speaker 2: send those to your kids. And the big thing almost

2837
02:19:26,320 --> 02:19:29,520
Speaker 2: every kind of dialogue thing would prompt you to send

2838
02:19:29,520 --> 02:19:31,560
Speaker 2: a message to a friend or your kid. So a

2839
02:19:31,560 --> 02:19:33,520
Speaker 2: big part of it seemed to be this is not

2840
02:19:33,600 --> 02:19:36,560
Speaker 2: a replacement. This is a machine that we hope people

2841
02:19:36,600 --> 02:19:39,480
Speaker 2: will get comfortable with and then it can prompt them

2842
02:19:39,520 --> 02:19:42,480
Speaker 2: to try to engage with the world more. And yeah,

2843
02:19:42,600 --> 02:19:45,080
Speaker 2: loved ones, because that's our whole goal is to connect

2844
02:19:45,440 --> 02:19:46,400
Speaker 2: them to people.

2845
02:19:46,720 --> 02:19:48,400
Speaker 5: I asked him, is like, you know, part of this

2846
02:19:48,440 --> 02:19:50,360
Speaker 5: product is designed to like, you know, help solve like

2847
02:19:50,800 --> 02:19:53,520
Speaker 5: loneliness in older adults, And like, how much of this

2848
02:19:53,560 --> 02:19:55,400
Speaker 5: is really just like kind of trying to like replace

2849
02:19:55,480 --> 02:19:58,200
Speaker 5: actual human contact with this, like you know AI contact.

2850
02:19:58,440 --> 02:20:01,480
Speaker 5: Will that really help, you know, lowliness? And he talked

2851
02:20:01,520 --> 02:20:03,720
Speaker 5: about how like like I think, like he said, like

2852
02:20:03,800 --> 02:20:06,040
Speaker 5: ninety percent of the people who like use this, like

2853
02:20:06,120 --> 02:20:09,840
Speaker 5: it results in actually more more communication with their family.

2854
02:20:10,240 --> 02:20:12,920
Speaker 2: They have this in like some two thousand homes right now,

2855
02:20:13,320 --> 02:20:14,920
Speaker 2: they have like two thousand units.

2856
02:20:15,040 --> 02:20:17,280
Speaker 5: It's like a subscription model. I think it's right now

2857
02:20:17,320 --> 02:20:19,360
Speaker 5: it is like ninety nine dollars a Month's gonna be

2858
02:20:19,360 --> 02:20:21,120
Speaker 5: boost up to like one hundred and fifty with some

2859
02:20:21,240 --> 02:20:22,720
Speaker 5: like extra features in the next year.

2860
02:20:22,879 --> 02:20:24,760
Speaker 2: It's very much still under evolution. So one thing he

2861
02:20:24,840 --> 02:20:27,600
Speaker 2: pointed at is that, like, yeah, initially we had the

2862
02:20:27,640 --> 02:20:31,640
Speaker 2: ability to like connect people to other elderly folks using this,

2863
02:20:31,800 --> 02:20:33,600
Speaker 2: and so they've kind of formed their own community, had

2864
02:20:33,640 --> 02:20:35,720
Speaker 2: like a weekly Bingo game and asked us to build

2865
02:20:35,720 --> 02:20:37,960
Speaker 2: in more chats so they can message each other directly,

2866
02:20:38,000 --> 02:20:40,199
Speaker 2: and so some of them are like playing bingo directly

2867
02:20:40,240 --> 02:20:43,480
Speaker 2: now through these machines. And I'm like, well, that seems

2868
02:20:43,680 --> 02:20:44,480
Speaker 2: probably good.

2869
02:20:44,920 --> 02:20:47,680
Speaker 5: Yeah, yeah, because like I still am like fundamentally opposed

2870
02:20:47,720 --> 02:20:51,039
Speaker 5: to this premise. Yes, but it's interesting to seeing someone still.

2871
02:20:50,760 --> 02:20:54,520
Speaker 2: But age sad aging. Yeah right, that's not their fault.

2872
02:20:54,600 --> 02:20:56,960
Speaker 5: And it's interesting to see someone like approach this from

2873
02:20:56,959 --> 02:20:59,600
Speaker 5: like a you know, a very like compassionate standpoint, even

2874
02:20:59,640 --> 02:21:01,640
Speaker 5: if I find the actual kind of nature of this

2875
02:21:01,680 --> 02:21:04,520
Speaker 5: thing existing to be like deeply uncomfortable.

2876
02:21:03,920 --> 02:21:06,720
Speaker 2: Because yeah, I can't not find it off putting, but

2877
02:21:06,840 --> 02:21:10,320
Speaker 2: I I think there's a chance that it will help

2878
02:21:10,600 --> 02:21:15,760
Speaker 2: with the real problem. I certainly would prefer if it helped. Yeah,

2879
02:21:16,120 --> 02:21:17,440
Speaker 2: So I don't know. It was kind of it was

2880
02:21:17,640 --> 02:21:19,800
Speaker 2: a unique in this world of like as it was

2881
02:21:19,840 --> 02:21:22,080
Speaker 2: a unique kind of like product for me where it's

2882
02:21:22,120 --> 02:21:26,160
Speaker 2: like I don't know that this application of AI technology

2883
02:21:26,640 --> 02:21:30,280
Speaker 2: will actually do what you're hoping it will. But I

2884
02:21:30,360 --> 02:21:33,000
Speaker 2: got the vibe from that guy I got was nothing

2885
02:21:33,080 --> 02:21:33,720
Speaker 2: but good will.

2886
02:21:33,920 --> 02:21:34,080
Speaker 3: Yeah.

2887
02:21:34,760 --> 02:21:36,960
Speaker 5: Some of the other people we talked to today who

2888
02:21:36,959 --> 02:21:38,519
Speaker 5: are completely soul.

2889
02:21:38,760 --> 02:21:42,080
Speaker 2: Out of yes, yes, nothing behind their eyes, dead eyes,

2890
02:21:42,160 --> 02:21:43,840
Speaker 2: black eyes, like a dull's eye.

2891
02:21:44,040 --> 02:21:45,520
Speaker 5: Even the way this guy is talking, you could tell

2892
02:21:45,520 --> 02:21:47,600
Speaker 5: you you had like a very like empathetic voice, like

2893
02:21:48,440 --> 02:21:48,879
Speaker 5: much like.

2894
02:21:48,920 --> 02:21:50,680
Speaker 2: One of the things he did is he he would

2895
02:21:50,680 --> 02:21:52,360
Speaker 2: tell it like, I'm in some pain, and then the

2896
02:21:52,440 --> 02:21:55,200
Speaker 2: robot would cycle through to the pain scale and would

2897
02:21:55,200 --> 02:21:56,840
Speaker 2: try to because one of the things it does is

2898
02:21:56,879 --> 02:21:59,960
Speaker 2: it will take information for care and it will text actively,

2899
02:22:00,360 --> 02:22:04,040
Speaker 2: so it's not just communicating with the old person. It

2900
02:22:04,080 --> 02:22:08,040
Speaker 2: will text and message their kids, you know, and whatnot,

2901
02:22:08,760 --> 02:22:10,920
Speaker 2: prompt their kids, Hey, your mom's lonely.

2902
02:22:11,120 --> 02:22:13,360
Speaker 5: Yeah, or it'll even say if you know, someone like

2903
02:22:13,400 --> 02:22:14,840
Speaker 5: didn't take their meds today.

2904
02:22:14,760 --> 02:22:19,560
Speaker 2: And again it's kind of sad that. But also his

2905
02:22:19,720 --> 02:22:21,680
Speaker 2: part of this is he was talking a lot about

2906
02:22:21,680 --> 02:22:24,400
Speaker 2: like empathy, and I think just because of the kind

2907
02:22:24,400 --> 02:22:25,760
Speaker 2: of brain you have to have to want to do this,

2908
02:22:26,240 --> 02:22:28,760
Speaker 2: he used it in terms of like the machines empathy,

2909
02:22:28,959 --> 02:22:32,959
Speaker 2: which it doesn't have, but the whole project, it was

2910
02:22:33,000 --> 02:22:36,120
Speaker 2: impossible not to see that he was a deeply empathetic

2911
02:22:36,200 --> 02:22:39,400
Speaker 2: man who was really trying to make the world better.

2912
02:22:39,440 --> 02:22:42,120
Speaker 2: And I can't not respect that.

2913
02:22:43,680 --> 02:22:46,800
Speaker 5: Well, I think that does it for us here at Cees.

2914
02:22:47,160 --> 02:22:51,000
Speaker 2: That's right, what a packed thirteen. No worry, no empathy.

2915
02:22:51,040 --> 02:22:55,320
Speaker 2: Tomorrow takes just a real dead eyed monster. I am

2916
02:22:55,360 --> 02:22:58,400
Speaker 2: a true villain you're gonna hear from in the next episode.

2917
02:22:58,480 --> 02:23:01,240
Speaker 5: I am a stumbbag. I am the best that I'm

2918
02:23:01,240 --> 02:23:03,360
Speaker 5: gonna be. Because I'm starting this week, I can still

2919
02:23:03,400 --> 02:23:07,680
Speaker 5: feel the Cees magic. Yeah, by Friday, I am going

2920
02:23:07,760 --> 02:23:12,920
Speaker 5: to be a different person. I am going to rip

2921
02:23:13,040 --> 02:23:17,360
Speaker 5: some poor pr person two shreds, I swear, but yeah,

2922
02:23:17,360 --> 02:23:20,720
Speaker 5: tune tomorrow to hear are our takes from the Cees

2923
02:23:20,920 --> 02:23:24,600
Speaker 5: kind of side show called show Stoppers to here also

2924
02:23:25,080 --> 02:23:29,280
Speaker 5: some exclusive, brand new AI generated SKA music, So we'll

2925
02:23:29,280 --> 02:23:32,640
Speaker 5: give you that hint for tomorrow's episode. See you see

2926
02:23:32,640 --> 02:23:33,199
Speaker 5: you there.

2927
02:23:33,200 --> 02:23:36,000
Speaker 2: M h We'll see you all there. I love you all.

2928
02:23:36,240 --> 02:23:53,520
Speaker 2: Good help, oh man, welcome to it can happen here?

2929
02:23:53,640 --> 02:23:57,560
Speaker 2: A podcast that's happening here? If here is your ears.

2930
02:23:58,640 --> 02:24:01,840
Speaker 2: If you're deaf and reading this, then it's happening to

2931
02:24:01,920 --> 02:24:04,519
Speaker 2: your eyes either way, it's happening.

2932
02:24:04,640 --> 02:24:07,120
Speaker 5: Here here also being Las Vegas.

2933
02:24:07,440 --> 02:24:10,720
Speaker 2: Well, yes, also Las Vegas. That's Nevada, not the other one.

2934
02:24:10,800 --> 02:24:17,880
Speaker 2: Nevada A yeah, uh huh. Podcast number three, How the

2935
02:24:18,000 --> 02:24:19,400
Speaker 2: time does fly?

2936
02:24:19,879 --> 02:24:20,360
Speaker 5: Sure does.

2937
02:24:20,680 --> 02:24:22,640
Speaker 2: By the time you listen to this, Garrison and I

2938
02:24:22,680 --> 02:24:25,119
Speaker 2: will have just had the best meal that we're going

2939
02:24:25,120 --> 02:24:25,360
Speaker 2: to have.

2940
02:24:25,440 --> 02:24:27,840
Speaker 5: Oh my god. Yeah, it's tomorrow for.

2941
02:24:27,800 --> 02:24:30,840
Speaker 2: Us still, but we're still we're very excited about Moramoto,

2942
02:24:30,879 --> 02:24:34,160
Speaker 2: which is a fantastic Every year we have a very

2943
02:24:34,200 --> 02:24:37,640
Speaker 2: special dinner just them and me and a couple of

2944
02:24:37,680 --> 02:24:40,800
Speaker 2: friends who will remain anonymous because people get weird on

2945
02:24:40,840 --> 02:24:41,280
Speaker 2: the internet.

2946
02:24:41,320 --> 02:24:44,840
Speaker 5: Sometimes it is literally the highlight of my year. Sometimes

2947
02:24:45,400 --> 02:24:47,840
Speaker 5: it does keep me going. Actually really gives me a

2948
02:24:47,879 --> 02:24:49,680
Speaker 5: lot of power. Some of the best tacos I've ever

2949
02:24:49,680 --> 02:24:52,640
Speaker 5: had in my life. So good. Uh huh.

2950
02:24:52,680 --> 02:24:55,600
Speaker 2: Anyway, ah, we're just thinking about delicious food. Let's talk

2951
02:24:55,600 --> 02:24:57,440
Speaker 2: about the dead eyed ghoul we met. Oh wait, no,

2952
02:24:57,440 --> 02:25:00,760
Speaker 2: we yet. We met a dead eyed that I'm gonna

2953
02:25:00,760 --> 02:25:05,600
Speaker 2: spoil now. Real monster, like real, real, real evil vibes,

2954
02:25:05,720 --> 02:25:08,640
Speaker 2: Like if this guy as soon as I met him,

2955
02:25:08,680 --> 02:25:10,520
Speaker 2: shook his hand like oh if you get if this

2956
02:25:10,600 --> 02:25:13,280
Speaker 2: guy gets power, you're going to be responsible for a

2957
02:25:13,320 --> 02:25:14,520
Speaker 2: lot of death and suffering.

2958
02:25:14,680 --> 02:25:16,400
Speaker 5: I mean speaking of kind of think he will.

2959
02:25:16,480 --> 02:25:20,440
Speaker 2: He's just not that talented. He wishes, but you never

2960
02:25:20,440 --> 02:25:21,640
Speaker 2: know where these guys are gonna end up.

2961
02:25:21,680 --> 02:25:26,880
Speaker 5: Speaking of sad evil Uh, Twitter X the everything appy,

2962
02:25:27,920 --> 02:25:30,280
Speaker 5: that's what people are calling it. They gave a keynote

2963
02:25:30,280 --> 02:25:31,400
Speaker 5: which was very sad.

2964
02:25:31,959 --> 02:25:35,840
Speaker 2: The the CEO Linda Linda really yakarino about Twitter for

2965
02:25:35,879 --> 02:25:37,400
Speaker 2: a while oh so bad.

2966
02:25:38,080 --> 02:25:43,520
Speaker 5: So they started by talking about how Facebook meta has

2967
02:25:43,600 --> 02:25:47,320
Speaker 5: has copied Twitter's like fact checking policy of actually not

2968
02:25:47,480 --> 02:25:51,000
Speaker 5: having real fact checks. Yes, now maybe has actually kind

2969
02:25:51,000 --> 02:25:53,959
Speaker 5: of failed as an industry, but for you know, our

2970
02:25:54,040 --> 02:25:57,800
Speaker 5: problems perhaps with fact checking very different from these people's problems.

2971
02:25:58,240 --> 02:26:01,400
Speaker 5: And the fact now that that Facebook is walking away

2972
02:26:01,760 --> 02:26:05,119
Speaker 5: from actual, like genuine like fact checks against like disinformation

2973
02:26:05,200 --> 02:26:09,000
Speaker 5: misinformation and parting ways with like using like legacy media

2974
02:26:09,040 --> 02:26:11,640
Speaker 5: aulets to verify information because those media aulets are too

2975
02:26:11,720 --> 02:26:16,120
Speaker 5: political quote unquote, and instead is copying the current X

2976
02:26:16,240 --> 02:26:19,480
Speaker 5: model of free speech and specifically saying like there's been

2977
02:26:19,480 --> 02:26:22,359
Speaker 5: way too much censorship on gender issues.

2978
02:26:22,720 --> 02:26:26,240
Speaker 2: Now you can comment that women are a piece of property, well, I.

2979
02:26:26,160 --> 02:26:28,119
Speaker 5: Mean I think specifically this is this is like trans

2980
02:26:28,320 --> 02:26:29,400
Speaker 5: like no, no, no stuff too.

2981
02:26:29,560 --> 02:26:32,000
Speaker 2: One of the things that is specific exemption now is

2982
02:26:32,040 --> 02:26:34,039
Speaker 2: that you can now refer to women as if they

2983
02:26:34,040 --> 02:26:36,519
Speaker 2: are property on Facebook.

2984
02:26:36,959 --> 02:26:39,200
Speaker 5: This is the future of communication.

2985
02:26:38,920 --> 02:26:42,199
Speaker 2: Right, Yeah, thank god, Linda is really blazing a trail

2986
02:26:42,280 --> 02:26:43,199
Speaker 2: for women everywhere.

2987
02:26:43,360 --> 02:26:48,040
Speaker 5: Linda was very excited about that. And they yakarino about

2988
02:26:48,040 --> 02:26:51,879
Speaker 5: that for like a good ten minutes about how you

2989
02:26:51,879 --> 02:26:53,720
Speaker 5: know this is this is where we're really entering a

2990
02:26:53,760 --> 02:26:56,959
Speaker 5: new era of free speech and social media. And then

2991
02:26:57,040 --> 02:27:01,119
Speaker 5: she got asked a question about how much x Twitter,

2992
02:27:01,200 --> 02:27:04,520
Speaker 5: the everything app, will we'll take a part in Elon

2993
02:27:04,640 --> 02:27:09,600
Speaker 5: Musk's plans for the Department of Government Efficiency DOGE, and

2994
02:27:09,600 --> 02:27:12,840
Speaker 5: and this got the the first applause of the panel.

2995
02:27:12,920 --> 02:27:15,760
Speaker 5: It applause only happens two times during the DOGE section.

2996
02:27:16,040 --> 02:27:19,360
Speaker 5: Was the first, like you know, room, room starts clapping moment,

2997
02:27:19,480 --> 02:27:23,640
Speaker 5: everyone goes crazy. How how many minutes in was that? Oh?

2998
02:27:23,680 --> 02:27:26,040
Speaker 5: Maybe it was like maybe like maybe like twelve thirteen minutes?

2999
02:27:26,200 --> 02:27:28,720
Speaker 2: People really yeah, I had to had to be intentional here.

3000
02:27:28,760 --> 02:27:31,000
Speaker 2: This is not like they were just overdue for class.

3001
02:27:31,080 --> 02:27:33,920
Speaker 5: No, no, no. They talked about Vivak talked about, you know,

3002
02:27:33,959 --> 02:27:37,040
Speaker 5: elon turning to Twitter, X the everything app for like

3003
02:27:37,080 --> 02:27:40,879
Speaker 5: suggestions on which government agencies to get rid of.

3004
02:27:42,959 --> 02:27:46,160
Speaker 2: I hope we get rid of the ATF so so

3005
02:27:46,280 --> 02:27:49,760
Speaker 2: that that was machine guns mandatory? Why not at this point, right,

3006
02:27:49,840 --> 02:27:53,160
Speaker 2: it can only help, It can only help. Look, if

3007
02:27:53,160 --> 02:27:55,640
Speaker 2: we learned anything from a thing I'm not going to

3008
02:27:55,640 --> 02:28:00,000
Speaker 2: specify that happened late last year. More suppressors is always

3009
02:28:00,160 --> 02:28:04,800
Speaker 2: is handy.

3010
02:28:05,040 --> 02:28:06,920
Speaker 5: The second thing that got applause was what they talked

3011
02:28:06,959 --> 02:28:11,240
Speaker 5: about next, was about you know, everyone's everyone's turning to

3012
02:28:11,240 --> 02:28:15,360
Speaker 5: to x, Twitter everything everything else for information now and

3013
02:28:15,360 --> 02:28:17,959
Speaker 5: and and Twitter x everything app played a crucial part

3014
02:28:18,040 --> 02:28:22,120
Speaker 5: in bringing to light the Muslim rape gang story in

3015
02:28:22,200 --> 02:28:25,279
Speaker 5: the UK and how that was so important for saving

3016
02:28:25,400 --> 02:28:27,920
Speaker 5: children and we have to we have to post more,

3017
02:28:28,120 --> 02:28:30,800
Speaker 5: not less, and like this was the other thing that

3018
02:28:30,879 --> 02:28:35,120
Speaker 5: got massive applause was talking about the rape gangs.

3019
02:28:35,280 --> 02:28:37,879
Speaker 2: People love rape gangs, people love rape gangs. That that

3020
02:28:37,959 --> 02:28:40,760
Speaker 2: was a pretty good Star Trek episode. That was Doctor

3021
02:28:40,920 --> 02:28:42,440
Speaker 2: R's Planet with the rape gangs, one.

3022
02:28:42,360 --> 02:28:44,280
Speaker 5: Of one of the more black pilling things.

3023
02:28:45,000 --> 02:28:46,640
Speaker 2: It wasn't a very good Star Trek episode.

3024
02:28:46,800 --> 02:28:49,320
Speaker 5: It's also not a good track episode. I was referring

3025
02:28:49,320 --> 02:28:52,600
Speaker 5: to the panel, not the Trek episode. But that's the

3026
02:28:52,600 --> 02:28:54,600
Speaker 5: other thing that got massive applause is it's like save

3027
02:28:54,680 --> 02:28:57,720
Speaker 5: the children type rhetoric and you know, saying, you know,

3028
02:28:57,760 --> 02:29:00,280
Speaker 5: like as a mother, it's it's so important that the

3029
02:29:00,400 --> 02:29:04,240
Speaker 5: more people post about this problem. That was the two

3030
02:29:04,280 --> 02:29:07,240
Speaker 5: big applause moments. But I think in general, this this

3031
02:29:07,280 --> 02:29:10,640
Speaker 5: whole panel was trying to like, you know, demonstrate how

3032
02:29:10,720 --> 02:29:14,400
Speaker 5: symbiotic a new Trump presidency and Elon Musk's Twitter.

3033
02:29:14,240 --> 02:29:17,000
Speaker 2: This is issue a direct info line, this is a

3034
02:29:17,080 --> 02:29:18,800
Speaker 2: tap from the Trump presidency.

3035
02:29:18,840 --> 02:29:21,080
Speaker 5: Tou this is how you talk to the new government. Like,

3036
02:29:21,200 --> 02:29:23,560
Speaker 5: this is how you talk to all of these new people,

3037
02:29:23,640 --> 02:29:25,959
Speaker 5: all these new cabinet members. They're all on Twitter. They're

3038
02:29:26,000 --> 02:29:27,959
Speaker 5: all talking on Twitter. This is this is how you

3039
02:29:28,000 --> 02:29:30,320
Speaker 5: stay connected to the new government.

3040
02:29:30,480 --> 02:29:33,200
Speaker 2: It's interesting. One thing I'm curious about so that this

3041
02:29:33,280 --> 02:29:35,800
Speaker 2: is the thing that happened the last set of Nazis

3042
02:29:35,840 --> 02:29:38,039
Speaker 2: that gained power in a country in a big way,

3043
02:29:38,480 --> 02:29:42,039
Speaker 2: the German ones. There was this common attitude of like

3044
02:29:42,080 --> 02:29:45,200
Speaker 2: if only Hitler knew. Because Nazi policies didn't help the

3045
02:29:45,200 --> 02:29:47,000
Speaker 2: people that were supposed to help, They hurt a lot

3046
02:29:47,000 --> 02:29:49,440
Speaker 2: of people like they were just bad at everything, like

3047
02:29:49,520 --> 02:29:52,080
Speaker 2: fascists tend to be. And there was this attitude that like, well,

3048
02:29:52,120 --> 02:29:55,920
Speaker 2: Hitler can't know, like the fact that, like we the

3049
02:29:55,959 --> 02:29:58,200
Speaker 2: country's been handed over to gangsters who were continuing to

3050
02:29:58,280 --> 02:30:00,400
Speaker 2: hurt the people Hitler problems to help. He must not

3051
02:30:00,440 --> 02:30:02,560
Speaker 2: be aware, like if he knew, he would fix this,

3052
02:30:02,680 --> 02:30:05,200
Speaker 2: only he knew. So I'm wondering how that's going to

3053
02:30:05,400 --> 02:30:08,640
Speaker 2: play in here. As Trump's policies continue to hurt the

3054
02:30:08,680 --> 02:30:11,040
Speaker 2: people who a lot of the people who voted from,

3055
02:30:11,040 --> 02:30:12,920
Speaker 2: not the rich people who voted for him, but the

3056
02:30:12,959 --> 02:30:15,680
Speaker 2: people who like flipped between him and Biden or whatever,

3057
02:30:15,879 --> 02:30:18,480
Speaker 2: Like those folks are going to get fucked like the

3058
02:30:18,520 --> 02:30:22,000
Speaker 2: rest of us. And I kind of wonder if they're

3059
02:30:22,000 --> 02:30:24,879
Speaker 2: going to if there's going to be what win the

3060
02:30:24,920 --> 02:30:28,600
Speaker 2: blowback against X, the everything app will happen, right like

3061
02:30:29,120 --> 02:30:32,760
Speaker 2: as people are like either I'm being ignored or I'm

3062
02:30:32,800 --> 02:30:36,920
Speaker 2: being called like a retard by Elon Musk for complaining that,

3063
02:30:37,160 --> 02:30:41,560
Speaker 2: Like Elon Musk tweets it and randomly to people when

3064
02:30:41,600 --> 02:30:45,080
Speaker 2: they make very valid critiques of the shit that he's doing,

3065
02:30:45,720 --> 02:30:48,920
Speaker 2: Like that's literally what he's calling. He's saying it like everything,

3066
02:30:48,959 --> 02:30:51,480
Speaker 2: like constantly. I'm not using it as a slur, that's

3067
02:30:51,520 --> 02:30:54,280
Speaker 2: just the term he's using. If they comment that like

3068
02:30:54,360 --> 02:30:57,880
Speaker 2: their fucking medicaid got cut because Trump put doctor Oz

3069
02:30:57,959 --> 02:31:00,760
Speaker 2: in charge of it, and Elon Musk calls them like,

3070
02:31:00,959 --> 02:31:04,000
Speaker 2: you know, a slur, What does that do to you?

3071
02:31:04,080 --> 02:31:05,400
Speaker 2: Like they like, I don't even know, I don't even

3072
02:31:05,440 --> 02:31:07,520
Speaker 2: have any more intelligent than like, yeah, I wonder what

3073
02:31:07,560 --> 02:31:09,359
Speaker 2: that does to Twitter's bottom line?

3074
02:31:09,280 --> 02:31:11,840
Speaker 5: Ye, I mean yeah, I'm not sure if they care anymore.

3075
02:31:11,840 --> 02:31:14,360
Speaker 5: I mean, something else Linda talked about is how you know,

3076
02:31:14,360 --> 02:31:17,840
Speaker 5: Twitter's the only place for independent news to spread and

3077
02:31:18,280 --> 02:31:20,240
Speaker 5: as both of us have, you know, worked in the

3078
02:31:20,280 --> 02:31:24,600
Speaker 5: independent journalism, minds nothing nothing spreads on Twitter anymore.

3079
02:31:24,680 --> 02:31:26,920
Speaker 2: Now if it's news, it doesn't. The only thing that

3080
02:31:26,959 --> 02:31:29,920
Speaker 2: spreads is yeah, like the shit that makes people very

3081
02:31:29,959 --> 02:31:32,960
Speaker 2: angry but keeps them on the site, like articles, videos,

3082
02:31:32,959 --> 02:31:34,600
Speaker 2: if it takes you off site, it doesn't mean.

3083
02:31:34,680 --> 02:31:36,640
Speaker 5: Yeah, the things that go viral and get spread is

3084
02:31:36,680 --> 02:31:40,400
Speaker 5: like encouraging racial bias, yes, pogrooms essentially.

3085
02:31:40,160 --> 02:31:42,400
Speaker 2: Yeah, which is what happened last year in the UK,

3086
02:31:42,480 --> 02:31:43,880
Speaker 2: and they're sure trying to do it again.

3087
02:31:43,959 --> 02:31:45,920
Speaker 5: I mean, I think some of someone's some of what

3088
02:31:45,959 --> 02:31:47,400
Speaker 5: she's referencing is, you know, there's a lot of like

3089
02:31:47,480 --> 02:31:51,039
Speaker 5: throttling attentionally of you know people on maybe our proclivities,

3090
02:31:51,280 --> 02:31:53,720
Speaker 5: and there is a degree of boosting for more you know,

3091
02:31:53,800 --> 02:31:57,080
Speaker 5: centrist or right wing journalists. And maybe that's that's some

3092
02:31:57,160 --> 02:31:59,280
Speaker 5: of some of what they could be kind of more

3093
02:31:59,440 --> 02:32:02,200
Speaker 5: more refer to there, But you know it was it

3094
02:32:02,240 --> 02:32:05,440
Speaker 5: was a short keynote, only thirty minutes. Just the two

3095
02:32:05,480 --> 02:32:07,800
Speaker 5: things that got applause are doge.

3096
02:32:07,680 --> 02:32:09,720
Speaker 2: Well Linda doesn't know that many words, so they really

3097
02:32:09,800 --> 02:32:11,400
Speaker 2: need to keep it under thirty minutes.

3098
02:32:11,360 --> 02:32:14,440
Speaker 5: Jen and literally Muslim rape gangs is you know this

3099
02:32:14,760 --> 02:32:18,080
Speaker 5: type of like like very very gross racial fear mongering,

3100
02:32:18,440 --> 02:32:20,400
Speaker 5: and those are things that like lit up the room.

3101
02:32:20,879 --> 02:32:22,879
Speaker 2: You know, we all want there to be an after

3102
02:32:23,200 --> 02:32:27,560
Speaker 2: where there's even the minimal degree of accountability that happened

3103
02:32:27,600 --> 02:32:30,760
Speaker 2: after the Nazis. But like what I try to in

3104
02:32:30,840 --> 02:32:33,800
Speaker 2: my darker moments think is like, well that's another person

3105
02:32:33,840 --> 02:32:36,640
Speaker 2: who like really made the argument of like what needs

3106
02:32:36,680 --> 02:32:41,800
Speaker 2: to happen when this this ends, because it's just I

3107
02:32:41,879 --> 02:32:45,400
Speaker 2: want to hurt people. My business is enabling a harm.

3108
02:32:45,480 --> 02:32:48,960
Speaker 2: I want to get mobs in the street beating migrants

3109
02:32:49,440 --> 02:32:53,560
Speaker 2: like that's Linda's business. That's the business she has willfully

3110
02:32:53,600 --> 02:32:58,280
Speaker 2: attached herself to. And we should all see that it's

3111
02:32:58,360 --> 02:33:00,920
Speaker 2: very important to not stop to talking about it like

3112
02:33:00,959 --> 02:33:05,320
Speaker 2: what it is. These people are trying to cause racial violence,

3113
02:33:05,600 --> 02:33:08,640
Speaker 2: and they are trying to cause gendered violence, and they

3114
02:33:08,640 --> 02:33:13,039
Speaker 2: are trying to cause harm at scale to communities of

3115
02:33:13,120 --> 02:33:18,959
Speaker 2: people that they see financial profit in damaging. Well in

3116
02:33:19,040 --> 02:33:22,680
Speaker 2: other uplifting c yes, news, cool stuff. I love the

3117
02:33:22,680 --> 02:33:24,200
Speaker 2: Consumer Electronics Show.

3118
02:33:26,200 --> 02:33:28,320
Speaker 5: Actually, I think it might be time for an ad

3119
02:33:28,360 --> 02:33:31,440
Speaker 5: break speaking of damaging communities of people.

3120
02:33:31,600 --> 02:33:33,200
Speaker 2: That's right, there's a chance.

3121
02:33:33,560 --> 02:33:45,400
Speaker 5: Yeah, ads, Oh well we're back.

3122
02:33:45,640 --> 02:33:49,040
Speaker 2: Boy. I'm so glad that those ads told me that

3123
02:33:49,040 --> 02:33:52,800
Speaker 2: Fragaccio Blow is touring with Bono. I never thought they'd

3124
02:33:52,840 --> 02:33:56,440
Speaker 2: do it, but boy howdy, and they're singing each other's song,

3125
02:33:56,680 --> 02:34:00,400
Speaker 2: so you know, that's really exciting. It's like when Barbara Diden.

3126
02:34:01,000 --> 02:34:03,119
Speaker 2: I don't know who Barber Selene is, but that's, oh

3127
02:34:03,160 --> 02:34:06,240
Speaker 2: my god, that's cool. Robert Luckily, I do know what

3128
02:34:06,320 --> 02:34:06,960
Speaker 2: SKA is.

3129
02:34:07,040 --> 02:34:11,560
Speaker 5: I consider myself a day of culture and for for tonight,

3130
02:34:12,120 --> 02:34:15,080
Speaker 5: me and Robert attended this kind of like side event

3131
02:34:15,200 --> 02:34:19,240
Speaker 5: at CEES called Showstoppers, And as you walk around the

3132
02:34:19,280 --> 02:34:22,360
Speaker 5: CEES floor, there's a lot of frankly garbage. There's a

3133
02:34:22,360 --> 02:34:25,400
Speaker 5: lot of just like mostly mostly garbage stuff.

3134
02:34:25,160 --> 02:34:27,280
Speaker 2: That you or stuff that like you're just not interested

3135
02:34:27,320 --> 02:34:30,760
Speaker 2: in because you're literally buying like screens from a manufacturer

3136
02:34:30,760 --> 02:34:32,680
Speaker 2: in China. Like it's like that's just not the business

3137
02:34:32,680 --> 02:34:34,360
Speaker 2: you're into because some of this stuff has be meant

3138
02:34:34,360 --> 02:34:34,840
Speaker 2: for companies.

3139
02:34:35,080 --> 02:34:38,280
Speaker 5: So much floor space, Like there's like, I we walked

3140
02:34:38,280 --> 02:34:39,439
Speaker 5: to what twenty town.

3141
02:34:39,280 --> 02:34:41,240
Speaker 2: That I spent the first seven years of my life

3142
02:34:41,280 --> 02:34:43,520
Speaker 2: in is smaller than one of the room CEU.

3143
02:34:44,720 --> 02:34:47,720
Speaker 5: It's across like three hotels and a massive convention center.

3144
02:34:48,160 --> 02:34:50,400
Speaker 2: Ninety thousand people come into town for this thing.

3145
02:34:50,440 --> 02:34:53,039
Speaker 5: It could be hard to like see everything you want to. Now,

3146
02:34:53,080 --> 02:34:55,800
Speaker 5: what's cool about Showstoppers, this is the side event at

3147
02:34:55,800 --> 02:34:59,440
Speaker 5: the Bellaggio, is that basically it's a room full of

3148
02:34:59,520 --> 02:35:01,440
Speaker 5: kind of all all the coolest stuff, a whole bunch

3149
02:35:01,440 --> 02:35:04,720
Speaker 5: of stuff that has won CEES innovation awards, all packed

3150
02:35:04,760 --> 02:35:08,680
Speaker 5: into one room with food and alcohol. So oh boy,

3151
02:35:08,840 --> 02:35:11,320
Speaker 5: did I order free food and free alcohol, so many

3152
02:35:11,440 --> 02:35:15,160
Speaker 5: drinks that I then just left on tables. And it

3153
02:35:15,280 --> 02:35:17,280
Speaker 5: always pretty good food, pretty good food.

3154
02:35:17,440 --> 02:35:17,640
Speaker 2: Yeah.

3155
02:35:17,879 --> 02:35:20,600
Speaker 5: So we walked around show stoppers and there was a

3156
02:35:20,680 --> 02:35:23,800
Speaker 5: number of pretty pretty cool stuff that we saw. Yeah,

3157
02:35:23,959 --> 02:35:26,640
Speaker 5: but I think I think it's maybe time to talk

3158
02:35:26,680 --> 02:35:29,199
Speaker 5: about the saddest, the saddest man.

3159
02:35:29,200 --> 02:35:32,760
Speaker 2: The villain, the villain of the episode and of our

3160
02:35:33,080 --> 02:35:36,000
Speaker 2: this year's ces. I have trouble. Can you bring up

3161
02:35:36,040 --> 02:35:37,680
Speaker 2: their name? Because I'm gonna want to get this right.

3162
02:35:37,920 --> 02:35:40,359
Speaker 5: Oh, so we could be dangerous.

3163
02:35:40,840 --> 02:35:42,560
Speaker 2: We had neither of us had eaten and I had

3164
02:35:42,600 --> 02:35:45,119
Speaker 2: had like a hot dog eight hours ago and walked

3165
02:35:45,200 --> 02:35:48,600
Speaker 2: literally nineteen thousand steps and also done forty minutes of

3166
02:35:48,640 --> 02:35:51,160
Speaker 2: push ups in between. So I was starving. So we

3167
02:35:51,160 --> 02:35:53,760
Speaker 2: we like shovel food into our faces and we turn

3168
02:35:53,840 --> 02:35:56,840
Speaker 2: the first booth we see is called open Droid.

3169
02:35:57,600 --> 02:36:00,400
Speaker 5: Open Droid or open Droids Droids droids.

3170
02:36:00,480 --> 02:36:02,880
Speaker 2: Yes, I did. There is an s Open Droids and

3171
02:36:02,920 --> 02:36:05,640
Speaker 2: it's like kind of Star Wars. He font it is

3172
02:36:05,720 --> 02:36:08,760
Speaker 2: and I did ask them if you know they had

3173
02:36:08,800 --> 02:36:12,800
Speaker 2: any issues with Lucasfilm. Apparently not yet sue them. Lucasfilm,

3174
02:36:12,959 --> 02:36:14,160
Speaker 2: by the way, sue these kids.

3175
02:36:14,240 --> 02:36:16,360
Speaker 5: I know there's people who work for Lucasfilm who listen

3176
02:36:16,440 --> 02:36:17,519
Speaker 5: to this, crush.

3177
02:36:17,280 --> 02:36:22,000
Speaker 2: Them, burn them like Los Angeles is burning down as

3178
02:36:22,040 --> 02:36:22,480
Speaker 2: we see.

3179
02:36:22,600 --> 02:36:25,040
Speaker 5: They had a giant sign that said R two D three.

3180
02:36:25,320 --> 02:36:27,120
Speaker 2: Yeah, that's the name of the robot that they're selling.

3181
02:36:27,160 --> 02:36:29,160
Speaker 2: And the robot. The robot that they're selling is like

3182
02:36:29,240 --> 02:36:35,440
Speaker 2: a an AI enabled household helping slash like retail you know,

3183
02:36:35,879 --> 02:36:39,080
Speaker 2: like you know robot where it basically is like a

3184
02:36:39,160 --> 02:36:44,360
Speaker 2: human torso with articulated arms and pincher hands on. And

3185
02:36:44,400 --> 02:36:47,880
Speaker 2: then the base is like a little tank. Basically it's

3186
02:36:47,879 --> 02:36:49,680
Speaker 2: got like treads or wheels and it rolls.

3187
02:36:49,760 --> 02:36:50,640
Speaker 5: It is wheels, yeah, and.

3188
02:36:50,680 --> 02:36:54,080
Speaker 2: Then the torso there's like a tall maybe six foot

3189
02:36:54,120 --> 02:36:58,320
Speaker 2: tall like pillar built into this like rolling base that

3190
02:36:58,360 --> 02:37:01,000
Speaker 2: the torso slides up and down on. And this was

3191
02:37:01,040 --> 02:37:04,400
Speaker 2: their way of not making like what Musk is trying

3192
02:37:04,440 --> 02:37:06,360
Speaker 2: to do, right, a humanoid robot where you have to

3193
02:37:06,360 --> 02:37:08,959
Speaker 2: figure out like knees and balance and stuff. It's like now,

3194
02:37:09,040 --> 02:37:11,800
Speaker 2: well like Boston dynamic wheel right, wheels are cheap in

3195
02:37:11,920 --> 02:37:14,520
Speaker 2: a role it works in most situations, you know, and

3196
02:37:14,560 --> 02:37:17,080
Speaker 2: then but you still have the ability for it to

3197
02:37:17,440 --> 02:37:20,560
Speaker 2: articulate and go up higher or go down lower like

3198
02:37:20,600 --> 02:37:23,360
Speaker 2: something that can crouch, but it's much simpler. You don't

3199
02:37:23,400 --> 02:37:25,760
Speaker 2: have to deal with nearly as much. And so I

3200
02:37:25,800 --> 02:37:28,039
Speaker 2: saw that, I'm like, oh, well, that's at least somebody

3201
02:37:28,040 --> 02:37:29,879
Speaker 2: who's thinking about, like how do we make something like

3202
02:37:29,920 --> 02:37:32,680
Speaker 2: this like more affordable and less complicated, less to fuck up?

3203
02:37:33,120 --> 02:37:35,520
Speaker 2: And so I start talking with one of the co

3204
02:37:35,560 --> 02:37:39,039
Speaker 2: founders of the company, who is an Indian guy in

3205
02:37:39,080 --> 02:37:42,240
Speaker 2: his forties something around that he had like gray hair,

3206
02:37:42,280 --> 02:37:44,720
Speaker 2: he'd clearly he said he'd spent twenty years in robotics.

3207
02:37:45,440 --> 02:37:47,800
Speaker 2: Very nice guy, you know. I brought up that I

3208
02:37:47,800 --> 02:37:50,840
Speaker 2: thought the design was interesting, and he was very much specifying, like,

3209
02:37:50,840 --> 02:37:54,240
Speaker 2: here's the things we didn't do because they were too difficult,

3210
02:37:54,280 --> 02:37:57,560
Speaker 2: too inefficient, you know, this is what we're thinking of.

3211
02:37:57,680 --> 02:37:59,680
Speaker 2: This is a machine that can fold laundry. This is

3212
02:37:59,680 --> 02:38:01,920
Speaker 2: a machine that can do dishes. This is a machine.

3213
02:38:01,959 --> 02:38:04,520
Speaker 2: And he was very much specifying and the way he

3214
02:38:04,560 --> 02:38:07,800
Speaker 2: phrases like, these are undesirable tasks people don't want to do,

3215
02:38:08,280 --> 02:38:10,680
Speaker 2: and this is a robot that can handle those for

3216
02:38:10,800 --> 02:38:13,240
Speaker 2: like small businesses or for households. And we do see

3217
02:38:13,280 --> 02:38:16,600
Speaker 2: this as eventually like a you know, something like this

3218
02:38:16,760 --> 02:38:18,720
Speaker 2: we want to have in households. But he was more

3219
02:38:18,760 --> 02:38:22,680
Speaker 2: focused on small businesses and he was again, very focused

3220
02:38:22,680 --> 02:38:25,880
Speaker 2: on this is a thing that will do undesirable tasks

3221
02:38:26,640 --> 02:38:31,039
Speaker 2: for people, right, And as I started asking more questions,

3222
02:38:31,040 --> 02:38:34,400
Speaker 2: at a certain point, I got foisted off to the

3223
02:38:34,400 --> 02:38:37,080
Speaker 2: co founder of the company. Is it the co founder

3224
02:38:37,120 --> 02:38:38,840
Speaker 2: or is it just like another one of their rests.

3225
02:38:38,879 --> 02:38:41,160
Speaker 2: You know, I'm assuming co founder because I think it's

3226
02:38:41,240 --> 02:38:43,320
Speaker 2: just a couple of guys, but maybe I'm not gonna sorry.

3227
02:38:43,400 --> 02:38:45,720
Speaker 5: I got foisted over to the other of the two guys.

3228
02:38:45,720 --> 02:38:48,840
Speaker 2: There were two guys there, right, I'm not sure because

3229
02:38:48,879 --> 02:38:51,360
Speaker 2: they don't have listed any where what their role in

3230
02:38:51,400 --> 02:38:54,440
Speaker 2: the company is. I got a co founder's vibe from them.

3231
02:38:54,680 --> 02:38:57,240
Speaker 2: That's how it seemed to be to me, at least

3232
02:38:57,280 --> 02:38:59,039
Speaker 2: in terms of like the way these two were talking.

3233
02:38:59,480 --> 02:39:03,199
Speaker 2: But I don't know the scope of the open Droid's company.

3234
02:39:03,240 --> 02:39:06,039
Speaker 2: Maybe there's a lot more there, but these were the

3235
02:39:06,080 --> 02:39:07,840
Speaker 2: two guys who were there talking to us. So one

3236
02:39:07,840 --> 02:39:11,000
Speaker 2: of them is this very wonky engineer who's been at

3237
02:39:11,000 --> 02:39:13,520
Speaker 2: this a long time and was really focused on the

3238
02:39:13,600 --> 02:39:15,760
Speaker 2: nuts and bolts details and wanted to build a robot

3239
02:39:15,800 --> 02:39:18,600
Speaker 2: that could handle unpleasant tasks for human beings, right, the

3240
02:39:18,640 --> 02:39:20,440
Speaker 2: same thing we've all been wanting to see. So at

3241
02:39:20,440 --> 02:39:22,920
Speaker 2: this point, I'm like, this could work. Maybe this is

3242
02:39:22,959 --> 02:39:23,760
Speaker 2: a viable product.

3243
02:39:23,879 --> 02:39:24,039
Speaker 1: Right.

3244
02:39:24,800 --> 02:39:30,920
Speaker 2: The second guy, Jack j Jessenowski, So he is wearing

3245
02:39:31,520 --> 02:39:36,160
Speaker 2: what Garrison described as a Jordan Peterson suit because it

3246
02:39:36,280 --> 02:39:44,000
Speaker 2: is half purple face suit split down the motherfucking middle.

3247
02:39:44,080 --> 02:39:46,800
Speaker 5: With like like new age hippie like.

3248
02:39:46,520 --> 02:39:50,599
Speaker 2: Necklaces, five necklaceslaces, five necklaces.

3249
02:39:50,800 --> 02:39:54,160
Speaker 5: He had pants with like like embroidered flowers on those

3250
02:39:54,200 --> 02:39:56,440
Speaker 5: and like a nose bridge, like it looked like one

3251
02:39:56,440 --> 02:39:57,959
Speaker 5: of those things you put in your nose. That was

3252
02:39:57,959 --> 02:40:01,080
Speaker 5: one of the other things U showstoppers. There was a

3253
02:40:01,080 --> 02:40:02,039
Speaker 5: company that was doing that.

3254
02:40:02,160 --> 02:40:05,200
Speaker 2: So yeah, he had want to be Steve Jobs vibes

3255
02:40:05,200 --> 02:40:09,160
Speaker 2: from his half unbuttoned shirt and like many many spiritual

3256
02:40:09,200 --> 02:40:14,680
Speaker 2: medallions to his like Jordan Peterson's suit, and very much

3257
02:40:14,840 --> 02:40:18,560
Speaker 2: just that. Like I am the charismatic founder and what

3258
02:40:18,680 --> 02:40:20,520
Speaker 2: I bring to the table. My partner knows how to

3259
02:40:20,560 --> 02:40:26,960
Speaker 2: build robots. I'm charismatic. I'm Jack j Jessanowski, and Jack

3260
02:40:27,000 --> 02:40:30,800
Speaker 2: and I started talking and boy howdy, we had us

3261
02:40:30,800 --> 02:40:33,720
Speaker 2: a conversation and I think we're just going to play that.

3262
02:40:34,080 --> 02:40:35,560
Speaker 2: What do I need to do to set this up?

3263
02:40:35,640 --> 02:40:38,200
Speaker 5: No, I think you've set it up. We walk up

3264
02:40:38,200 --> 02:40:41,440
Speaker 5: to Jack, I start, I start recording, and we start

3265
02:40:41,480 --> 02:40:44,120
Speaker 5: talking about the robot, and then things spin in some

3266
02:40:44,440 --> 02:40:45,800
Speaker 5: pretty interesting directions.

3267
02:40:45,879 --> 02:40:52,000
Speaker 2: Yeah, all right, So what is this thing useful for?

3268
02:40:53,680 --> 02:40:57,680
Speaker 3: Well, generally capable, just like a human can reach to

3269
02:40:57,720 --> 02:41:00,440
Speaker 3: the floor and reach up high to a cupboard, go

3270
02:41:00,560 --> 02:41:01,039
Speaker 3: up and down.

3271
02:41:01,080 --> 02:41:02,080
Speaker 11: That's what we made this for.

3272
02:41:02,200 --> 02:41:05,160
Speaker 3: Obviously in a little bit of a different fashion because

3273
02:41:05,280 --> 02:41:07,879
Speaker 3: most surfaces are level.

3274
02:41:08,120 --> 02:41:09,480
Speaker 11: We don't need to reinvent the wheel.

3275
02:41:10,400 --> 02:41:15,080
Speaker 3: And the biggest market that we're going after is households.

3276
02:41:15,200 --> 02:41:18,760
Speaker 3: Domestic dishes, laundry, make the bed, clean up around the house,

3277
02:41:19,200 --> 02:41:22,560
Speaker 3: eventually cooking that's more fine tuned, you know, dishes and

3278
02:41:22,600 --> 02:41:26,120
Speaker 3: laundry is really that first task that is gonna be

3279
02:41:26,120 --> 02:41:31,520
Speaker 3: fully autonomous. Obviously from a folding standpoint and cooking standpoint,

3280
02:41:31,560 --> 02:41:35,959
Speaker 3: you can do teleoperation today, so can use cheaper labor

3281
02:41:36,000 --> 02:41:41,119
Speaker 3: internationally through a robot. But full autonomous is coming very quickly,

3282
02:41:41,200 --> 02:41:42,840
Speaker 3: like Jensen talked about recently.

3283
02:41:43,800 --> 02:41:47,000
Speaker 2: So I see there's a lot of folks in the

3284
02:41:47,080 --> 02:41:50,600
Speaker 2: robot space that are trying robots based on the human form. Right,

3285
02:41:50,840 --> 02:41:53,720
Speaker 2: you guys have not gone that route. Talk to me about.

3286
02:41:53,520 --> 02:41:55,199
Speaker 11: That droid form.

3287
02:41:55,520 --> 02:42:00,000
Speaker 3: Yes, Well, as we know, robots didn't evolve from monkey,

3288
02:42:00,520 --> 02:42:03,720
Speaker 3: and so we have an ability to reimagine them. All

3289
02:42:03,760 --> 02:42:06,800
Speaker 3: of the existing hardware we use in the world has

3290
02:42:06,840 --> 02:42:10,440
Speaker 3: wheels for a reason. It just works better. It's easier,

3291
02:42:10,480 --> 02:42:13,560
Speaker 3: there's less friction. That means there's less maintenance. That means

3292
02:42:13,600 --> 02:42:17,920
Speaker 3: there's less energy output. It's efficiency. It's also easier for

3293
02:42:18,040 --> 02:42:21,240
Speaker 3: us to manufacture that stuff at scale. So I think

3294
02:42:21,280 --> 02:42:29,240
Speaker 3: long term, do robots all have legs? Yeah, more or less.

3295
02:42:29,280 --> 02:42:31,520
Speaker 3: The home robot does turn into the like robot because

3296
02:42:31,520 --> 02:42:33,400
Speaker 3: then it can go with you in the car everything.

3297
02:42:33,760 --> 02:42:37,320
Speaker 3: But I think the early stages the wheels, because of

3298
02:42:37,360 --> 02:42:42,080
Speaker 3: their cheaperness, because of their reliability. I think that will

3299
02:42:42,120 --> 02:42:45,600
Speaker 3: be what wins early stage. That's where we started here.

3300
02:42:45,879 --> 02:42:47,560
Speaker 2: You just said, because the robot can go in the

3301
02:42:47,600 --> 02:42:49,800
Speaker 2: car with you, what do you see people wanting to

3302
02:42:49,840 --> 02:42:51,360
Speaker 2: have a robot in the car with them for?

3303
02:42:51,800 --> 02:42:54,600
Speaker 3: I think it will just become basically the same way

3304
02:42:54,640 --> 02:42:57,560
Speaker 3: if you have enough money, a lot of people afford

3305
02:42:57,720 --> 02:43:01,120
Speaker 3: like an assistant to come with them places.

3306
02:43:01,680 --> 02:43:02,920
Speaker 11: It's a lot of people.

3307
02:43:04,560 --> 02:43:08,000
Speaker 2: Because that seems like a niche market compared to household

3308
02:43:09,320 --> 02:43:12,680
Speaker 2: I think it's the barrier I think is because of

3309
02:43:12,720 --> 02:43:16,400
Speaker 2: the the cost and then the humanness, like then you

3310
02:43:16,400 --> 02:43:18,119
Speaker 2: have to care for another human.

3311
02:43:18,400 --> 02:43:21,920
Speaker 3: And whereas in this case it's kind of all positive

3312
02:43:22,000 --> 02:43:27,600
Speaker 3: sum And yeah, I guess it's wrong to try to

3313
02:43:27,640 --> 02:43:30,160
Speaker 3: say majority.

3314
02:43:29,840 --> 02:43:35,320
Speaker 11: Of people, but anyone who's you know in media.

3315
02:43:35,280 --> 02:43:38,280
Speaker 3: You know they videographer will be something you use a

3316
02:43:38,360 --> 02:43:40,840
Speaker 3: robot for to follow you around and take media and

3317
02:43:40,879 --> 02:43:43,800
Speaker 3: film for you. Every won't get tired and say go

3318
02:43:43,879 --> 02:43:46,600
Speaker 3: grab me a drink or you know, go figure that

3319
02:43:46,640 --> 02:43:48,000
Speaker 3: thing out.

3320
02:43:48,120 --> 02:43:51,480
Speaker 2: But it also can't decide, oh, that's actually not a

3321
02:43:51,480 --> 02:43:53,760
Speaker 2: good location to film from. It's not going to look

3322
02:43:53,800 --> 02:43:55,720
Speaker 2: as good. We need to get over here, or we

3323
02:43:55,760 --> 02:43:57,840
Speaker 2: need another camera on this side here, we need to

3324
02:43:57,879 --> 02:44:00,920
Speaker 2: get like different angles because we're want to edit this

3325
02:44:01,000 --> 02:44:03,920
Speaker 2: together into a thing. And as a videographer, I'm not

3326
02:44:04,000 --> 02:44:07,640
Speaker 2: just a machine. I'm a part of a collaborative creative enterprise.

3327
02:44:08,959 --> 02:44:13,920
Speaker 3: I think we're starting to see just how artistic these

3328
02:44:14,520 --> 02:44:15,400
Speaker 3: ais can be.

3329
02:44:16,040 --> 02:44:18,080
Speaker 2: What's the best example of that, you sing.

3330
02:44:19,640 --> 02:44:21,840
Speaker 3: Well, I think the most used thing is this the

3331
02:44:21,920 --> 02:44:25,680
Speaker 3: Jenai art And then you have some of the new

3332
02:44:25,720 --> 02:44:30,279
Speaker 3: video models are pretty cool and.

3333
02:44:29,120 --> 02:44:32,680
Speaker 11: They're using certain sort of zoom in shots.

3334
02:44:32,800 --> 02:44:38,400
Speaker 3: Everything I think they'll make just as good of movies

3335
02:44:38,680 --> 02:44:39,359
Speaker 3: as humans.

3336
02:44:39,440 --> 02:44:39,600
Speaker 2: Oh.

3337
02:44:39,640 --> 02:44:42,199
Speaker 11: I think the best reference in order to.

3338
02:44:42,120 --> 02:44:45,960
Speaker 3: Actually say that that's possible is music. I don't know

3339
02:44:46,000 --> 02:44:48,400
Speaker 3: if you've played with the most recent AI music. There's

3340
02:44:48,400 --> 02:44:49,760
Speaker 3: song GBT dot com.

3341
02:44:50,000 --> 02:44:52,760
Speaker 2: I've heard some things people call music that are produced

3342
02:44:52,760 --> 02:44:53,000
Speaker 2: by that.

3343
02:44:53,160 --> 02:44:55,880
Speaker 11: Yeah, we can make one live right now that.

3344
02:44:55,920 --> 02:44:59,560
Speaker 3: I don't know if you've heard like the latest models

3345
02:45:00,480 --> 02:45:02,199
Speaker 3: up pick me a genre.

3346
02:45:03,320 --> 02:45:09,320
Speaker 2: Uh Irish spirituals SCA, you can try Scott too.

3347
02:45:09,480 --> 02:45:14,200
Speaker 3: You love sk ska is like definitely probably niche stuff

3348
02:45:14,280 --> 02:45:18,760
Speaker 3: is where it's gonna have a harder time, but skka

3349
02:45:19,120 --> 02:45:19,960
Speaker 3: s ka.

3350
02:45:20,879 --> 02:45:23,720
Speaker 11: I wonder how much SKA data there is out there.

3351
02:45:24,280 --> 02:45:26,039
Speaker 2: There's a lot of SKA music out there.

3352
02:45:26,120 --> 02:45:27,320
Speaker 11: What should we make it about? Should we make it

3353
02:45:27,360 --> 02:45:28,280
Speaker 11: about iHeart radio?

3354
02:45:28,959 --> 02:45:29,240
Speaker 2: Sure?

3355
02:45:29,520 --> 02:45:30,280
Speaker 11: iHeart radio?

3356
02:45:30,480 --> 02:45:31,480
Speaker 2: And communication?

3357
02:45:31,680 --> 02:45:36,080
Speaker 3: Robert and clear Channel Communications?

3358
02:45:36,560 --> 02:45:40,240
Speaker 11: All right, it's here a SKA song. We're like, oh,

3359
02:45:40,280 --> 02:45:41,960
Speaker 11: it has to load for like thirty seconds.

3360
02:45:41,959 --> 02:45:45,400
Speaker 3: It feels weirdly like I'm upset that I have to

3361
02:45:45,400 --> 02:45:47,840
Speaker 3: wait that long for something to load online.

3362
02:45:48,160 --> 02:45:49,760
Speaker 2: Is that really how it feels to you? Huh?

3363
02:45:49,840 --> 02:45:51,640
Speaker 3: Yeah, I got what They're playing with it a lot,

3364
02:45:52,080 --> 02:45:54,320
Speaker 3: But it's funny to think about how much time and

3365
02:45:54,360 --> 02:45:57,400
Speaker 3: effort it does take to like produce a song typically

3366
02:45:58,360 --> 02:45:59,240
Speaker 3: I am twenty seven.

3367
02:46:00,879 --> 02:46:03,680
Speaker 2: That's interesting, wouldn't a guess that?

3368
02:46:05,600 --> 02:46:05,680
Speaker 5: What?

3369
02:46:05,879 --> 02:46:09,400
Speaker 2: Uh one thing that's really compelling to me is your partner.

3370
02:46:09,879 --> 02:46:11,959
Speaker 2: When I came in here was very very much talking

3371
02:46:11,959 --> 02:46:14,680
Speaker 2: about the utility of this in terms of replacing human

3372
02:46:14,720 --> 02:46:19,680
Speaker 2: beings and tasks that are generally unpleasant laundry, doing the dishes,

3373
02:46:19,800 --> 02:46:22,920
Speaker 2: cleaning up trash. You seem a lot more bullish on

3374
02:46:23,920 --> 02:46:27,320
Speaker 2: robots replacing human beings and what are generally considered to

3375
02:46:27,360 --> 02:46:31,360
Speaker 2: be enterprises people want to do with their time. Is

3376
02:46:31,400 --> 02:46:33,920
Speaker 2: that like a discrepancy that that that you guys have

3377
02:46:34,080 --> 02:46:36,240
Speaker 2: kind of talked about, or do you think it's something

3378
02:46:36,480 --> 02:46:38,679
Speaker 2: you guys are more on the same page with stuff.

3379
02:46:38,720 --> 02:46:42,360
Speaker 3: From a business endpoint, we're one hundred percent going after

3380
02:46:43,320 --> 02:46:49,600
Speaker 3: the dishes, laundry, uh nursing practice of just doing vitals,

3381
02:46:49,600 --> 02:46:55,520
Speaker 3: which is the very repetitive task that's the push. I

3382
02:46:55,560 --> 02:46:58,879
Speaker 3: was starting to just talk into the aspect of the

3383
02:46:59,000 --> 02:47:03,440
Speaker 3: legged robots and kind of imagining why a leged version

3384
02:47:03,959 --> 02:47:07,920
Speaker 3: would have better utility or be something someone wants to

3385
02:47:07,959 --> 02:47:11,640
Speaker 3: purchase scare rather than the wheeled robot and Yeah, stairs

3386
02:47:11,720 --> 02:47:14,160
Speaker 3: is definitely a big one of those.

3387
02:47:14,200 --> 02:47:17,560
Speaker 11: There are wheel types we're working on right now.

3388
02:47:17,600 --> 02:47:21,040
Speaker 3: We have ability to climb like single stairs obviously easiest,

3389
02:47:21,040 --> 02:47:23,360
Speaker 3: and that's what most people have in their home if

3390
02:47:23,400 --> 02:47:25,240
Speaker 3: they do have stairs.

3391
02:47:25,280 --> 02:47:27,160
Speaker 2: Oh, are we gonna listen to some robots?

3392
02:47:27,160 --> 02:47:30,000
Speaker 11: Scott my Heart Listeners System or.

3393
02:47:41,640 --> 02:47:59,760
Speaker 5: Scott Clear Crazy sco Is this Scot?

3394
02:48:01,280 --> 02:48:05,039
Speaker 2: It's a pretty basic melody. I mean there's horns in it,

3395
02:48:05,080 --> 02:48:06,760
Speaker 2: but I feel like it's kind of takes in a

3396
02:48:07,560 --> 02:48:09,720
Speaker 2: I think it's Ophelia is trying to do pot that

3397
02:48:09,800 --> 02:48:12,840
Speaker 2: it's just thrown some horns in on. This is a

3398
02:48:12,840 --> 02:48:32,560
Speaker 2: little closer to SCA, although it's still Yeah, it's not

3399
02:48:32,640 --> 02:48:41,760
Speaker 2: really singing, but I guess that's a matter of taste.

3400
02:48:42,720 --> 02:48:43,520
Speaker 2: What do you listen to?

3401
02:48:43,680 --> 02:48:45,039
Speaker 11: This is the worst that's gonna be.

3402
02:48:45,840 --> 02:48:46,720
Speaker 5: I hear that a lot.

3403
02:48:47,200 --> 02:48:51,240
Speaker 2: It's interesting because GPT four took fifty times as much

3404
02:48:51,280 --> 02:48:54,160
Speaker 2: power as GPT three to train, and there's a lot

3405
02:48:54,160 --> 02:48:57,760
Speaker 2: of mixed reactions on that. And we're entering into a

3406
02:48:57,800 --> 02:49:02,000
Speaker 2: period where we're very likely looking at a recession venture

3407
02:49:02,040 --> 02:49:05,039
Speaker 2: capital funding. There's a chance it's not going to be

3408
02:49:05,080 --> 02:49:07,879
Speaker 2: what it has been. Is that concern you at all that,

3409
02:49:08,000 --> 02:49:10,840
Speaker 2: like this vaunted next level for all of this stuff,

3410
02:49:10,879 --> 02:49:15,000
Speaker 2: the energy cost, the investment cost is just not going

3411
02:49:15,080 --> 02:49:18,080
Speaker 2: to be borne by a market that uh is not

3412
02:49:18,160 --> 02:49:20,959
Speaker 2: going to be as strong tomorrow as it was today,

3413
02:49:21,560 --> 02:49:23,240
Speaker 2: at least in the immediate term.

3414
02:49:23,640 --> 02:49:28,600
Speaker 3: I think even if we created no more energy as

3415
02:49:28,640 --> 02:49:32,400
Speaker 3: a human species today, the amount of advancements we create

3416
02:49:33,320 --> 02:49:38,080
Speaker 3: would from an architectural standpoint, continue to advance.

3417
02:49:38,400 --> 02:49:41,119
Speaker 11: So you have other models.

3418
02:49:43,280 --> 02:49:46,080
Speaker 3: Like I think LAMA three point three, which has matched

3419
02:49:46,160 --> 02:49:51,480
Speaker 3: four ohs capabilities and is I forget how many parameters,

3420
02:49:51,480 --> 02:49:54,520
Speaker 3: but like super like much much much smaller and was

3421
02:49:54,640 --> 02:49:56,879
Speaker 3: much scheaper to train, and like we're continuing to see

3422
02:49:56,920 --> 02:50:02,560
Speaker 3: like smaller models that are just as effective and we're

3423
02:50:02,680 --> 02:50:03,840
Speaker 3: much cheaper training runs.

3424
02:50:03,840 --> 02:50:06,480
Speaker 11: I think Deep Seek was one of the newest ones.

3425
02:50:07,120 --> 02:50:09,200
Speaker 2: I'm what I'm concerned about is I'm looking at the

3426
02:50:09,640 --> 02:50:12,280
Speaker 2: p and L right, I'm looking at open AI's PNL.

3427
02:50:12,560 --> 02:50:15,000
Speaker 2: I'm looking at the fact that they're losing five or

3428
02:50:15,000 --> 02:50:18,760
Speaker 2: six billion dollars last year and we're very good chance

3429
02:50:18,760 --> 02:50:20,360
Speaker 2: it's going to be somewhere in the neighborhood to double

3430
02:50:20,400 --> 02:50:23,480
Speaker 2: that this year. And I'm it's not that there's nothing

3431
02:50:23,520 --> 02:50:25,440
Speaker 2: impressive there it's not that I don't see like, oh,

3432
02:50:25,480 --> 02:50:29,000
Speaker 2: you can generate a song that's got like guitar and

3433
02:50:29,920 --> 02:50:32,560
Speaker 2: trumpets and vocals and stuff and you know a minute

3434
02:50:32,640 --> 02:50:34,840
Speaker 2: or so. It's not that that's not impressive, but like

3435
02:50:35,600 --> 02:50:39,400
Speaker 2: a pilier trick, isn't a trillion dollar business, And that's

3436
02:50:39,440 --> 02:50:41,360
Speaker 2: the kind of investment they're looking at. And I do

3437
02:50:41,480 --> 02:50:44,600
Speaker 2: wonder like, is it not much more reasonable to focus

3438
02:50:44,640 --> 02:50:45,760
Speaker 2: on folding laundry?

3439
02:50:46,320 --> 02:50:51,480
Speaker 3: Well, obviously, I personally am in the the boathouse of

3440
02:50:51,560 --> 02:50:56,760
Speaker 3: focusing on allowing this intelligence to flourish and doing these

3441
02:50:56,840 --> 02:51:00,440
Speaker 3: laborious tasks and getting them in the households. I do

3442
02:51:00,560 --> 02:51:04,720
Speaker 3: think from open eye standpoint, and the reason why vcs

3443
02:51:04,800 --> 02:51:08,680
Speaker 3: and private investors will value them so highly is what's

3444
02:51:08,760 --> 02:51:14,599
Speaker 3: next is white collar work. A lot of the jobs online.

3445
02:51:15,720 --> 02:51:16,959
Speaker 11: That's what they do.

3446
02:51:17,000 --> 02:51:21,280
Speaker 3: Have an internal model which is able to control the computer,

3447
02:51:22,000 --> 02:51:23,760
Speaker 3: you know, the same way you would ask an executive

3448
02:51:23,760 --> 02:51:26,720
Speaker 3: assistant to do certain things online.

3449
02:51:27,360 --> 02:51:30,959
Speaker 2: Now it's just Sdobe's handing along all of their emails

3450
02:51:31,040 --> 02:51:34,640
Speaker 2: now through AIS, which is you know, we'll see how

3451
02:51:35,000 --> 02:51:36,560
Speaker 2: well that works on the long term. There's been some

3452
02:51:36,640 --> 02:51:41,560
Speaker 2: interesting polling on like the degree to which customers and

3453
02:51:41,879 --> 02:51:46,360
Speaker 2: investors feel trust when somebody's responding to them with an AI.

3454
02:51:46,840 --> 02:51:49,600
Speaker 2: But what's interesting, I mean more here is the dichotomy

3455
02:51:49,680 --> 02:51:53,360
Speaker 2: between what I see here is a very pragmatic choice,

3456
02:51:53,360 --> 02:51:56,080
Speaker 2: which is, we're not going to try and remake a

3457
02:51:56,160 --> 02:51:59,200
Speaker 2: human being formed robot and deal with like knees and

3458
02:51:59,280 --> 02:52:01,400
Speaker 2: hips and all of that stuff. We don't need that.

3459
02:52:01,440 --> 02:52:04,160
Speaker 2: We can have it turn up and down on this

3460
02:52:04,240 --> 02:52:08,600
Speaker 2: platform and reach things the same way, melded to what

3461
02:52:08,640 --> 02:52:10,840
Speaker 2: I consider to be kind of a little more high

3462
02:52:10,840 --> 02:52:14,160
Speaker 2: in the sky. We're doing this as eventually something that

3463
02:52:14,200 --> 02:52:18,560
Speaker 2: can take creative roles and think independently and make things,

3464
02:52:19,000 --> 02:52:21,520
Speaker 2: which is it's interesting to me to see that in

3465
02:52:21,560 --> 02:52:23,680
Speaker 2: a company's DNA of what you guys are eight months

3466
02:52:23,680 --> 02:52:26,879
Speaker 2: out right now, yep. Is that what you're more interested in?

3467
02:52:27,440 --> 02:52:30,400
Speaker 11: I'd say, I tailor my pitch to the person I'm

3468
02:52:30,400 --> 02:52:32,320
Speaker 11: talking to. Uh huh.

3469
02:52:32,840 --> 02:52:36,520
Speaker 3: So some people definitely enjoy thinking about more of the

3470
02:52:36,520 --> 02:52:39,800
Speaker 3: sci fi futures that are coming, for example, the droids

3471
02:52:39,800 --> 02:52:44,840
Speaker 3: building Droid's moment. It's when you know you are decreasing

3472
02:52:44,920 --> 02:52:49,640
Speaker 3: your own manufacturing costs by using your own hardware to

3473
02:52:49,640 --> 02:52:51,879
Speaker 3: build more of that hardware and.

3474
02:52:51,959 --> 02:52:54,000
Speaker 11: Parts are just being shipped into the factory.

3475
02:52:54,480 --> 02:52:58,480
Speaker 3: Obviously, I think the first fully automated phone factory just

3476
02:52:58,520 --> 02:53:01,600
Speaker 3: came out in chinery, which is like some cool press

3477
02:53:01,600 --> 02:53:05,480
Speaker 3: in news, but the phone is separate from the actual

3478
02:53:05,520 --> 02:53:06,800
Speaker 3: manufacturing process.

3479
02:53:07,480 --> 02:53:10,920
Speaker 11: So there's that interesting component.

3480
02:53:11,480 --> 02:53:16,920
Speaker 3: The exciting part of the idea that how do we

3481
02:53:16,959 --> 02:53:21,080
Speaker 3: reach true abundance as a species of material and resources

3482
02:53:21,160 --> 02:53:27,760
Speaker 3: is well, because GDP is a calculation of capita times productivity,

3483
02:53:28,200 --> 02:53:34,280
Speaker 3: a robot really represents capita one unit of creation. And

3484
02:53:35,040 --> 02:53:38,360
Speaker 3: I'd say that's where the sci fi thinking comes into play.

3485
02:53:38,400 --> 02:53:44,560
Speaker 3: And it's not not worth going there when just dreaming

3486
02:53:44,560 --> 02:53:46,880
Speaker 3: about the future robotics and talking about it and having

3487
02:53:46,920 --> 02:53:51,640
Speaker 3: an interesting, engaging conversation, But definitely when it comes to

3488
02:53:52,000 --> 02:53:54,480
Speaker 3: what are we doing from an engineering standpoint on the

3489
02:53:54,560 --> 02:53:56,280
Speaker 3: day to day and how are we trying to approach

3490
02:53:56,320 --> 02:53:59,920
Speaker 3: the market, those conversations are not at being had.

3491
02:54:00,200 --> 02:54:03,000
Speaker 2: Okay, well, I appreciate your time and he gave me

3492
02:54:03,040 --> 02:54:04,600
Speaker 2: a lot. I'm gonna let you get to the other peak.

3493
02:54:04,680 --> 02:54:06,120
Speaker 2: Thank you, Thank you so much to meet you.

3494
02:54:06,200 --> 02:54:06,440
Speaker 1: Jack.

3495
02:54:07,600 --> 02:54:10,760
Speaker 2: Oh wow, that's super interesting. I hope you all liked

3496
02:54:10,920 --> 02:54:12,959
Speaker 2: Jack Jay as much as I didn't.

3497
02:54:13,120 --> 02:54:15,760
Speaker 5: Getting to twenty seventy years old and not knowing what

3498
02:54:15,879 --> 02:54:18,160
Speaker 5: Scott is that old. I thought he was much younger,

3499
02:54:18,560 --> 02:54:20,720
Speaker 5: like you thought he was like twenty two. Yes, but

3500
02:54:20,760 --> 02:54:23,360
Speaker 5: the fact that he didn't know what SKA was as

3501
02:54:23,400 --> 02:54:25,560
Speaker 5: a genre, he wasn't was unaware of it.

3502
02:54:25,600 --> 02:54:27,160
Speaker 2: I don't think he listens to music.

3503
02:54:26,920 --> 02:54:29,959
Speaker 5: Well, he listens to AI generated hess to AI generated music,

3504
02:54:29,959 --> 02:54:32,440
Speaker 5: he's just as good. He has the most, he has

3505
02:54:32,480 --> 02:54:36,200
Speaker 5: the most. I listened to AI generated music vines. If

3506
02:54:36,240 --> 02:54:38,280
Speaker 5: anyone I've ever seen before just.

3507
02:54:38,360 --> 02:54:41,800
Speaker 2: Very clearly does not have a soul, no, like nothing,

3508
02:54:41,959 --> 02:54:45,520
Speaker 2: nothing would leave the universe if he did, right, Like.

3509
02:54:45,480 --> 02:54:48,080
Speaker 5: It's so opposite from the first guy you talked to,

3510
02:54:48,240 --> 02:54:51,160
Speaker 5: who was it so like about? No? I want to

3511
02:54:51,160 --> 02:54:54,840
Speaker 5: how smart actual tasks that people don't enjoy. Yeah, I

3512
02:54:54,920 --> 02:54:59,119
Speaker 5: love cinematography, I love I love filmmaking. I don't First

3513
02:54:59,160 --> 02:55:02,760
Speaker 5: of all, I don't think a robot can can replace this.

3514
02:55:03,080 --> 02:55:03,200
Speaker 1: No.

3515
02:55:03,400 --> 02:55:07,240
Speaker 2: I watched five different AI generated movies yesterday, and they

3516
02:55:07,280 --> 02:55:08,119
Speaker 2: all looked like shit.

3517
02:55:08,000 --> 02:55:10,240
Speaker 5: Even like a robot handling a physical camera to make

3518
02:55:10,280 --> 02:55:13,280
Speaker 5: like to make like choices on like shot framing and composition,

3519
02:55:13,320 --> 02:55:14,160
Speaker 5: and like it's.

3520
02:55:14,040 --> 02:55:15,600
Speaker 2: One thing to be like we want we have a

3521
02:55:15,680 --> 02:55:17,760
Speaker 2: race cargoing, and so we've got this robot on a

3522
02:55:17,840 --> 02:55:19,600
Speaker 2: track so we can go seventy miles an hour and

3523
02:55:19,879 --> 02:55:21,920
Speaker 2: we're just kind of running on a street. Follow it

3524
02:55:21,920 --> 02:55:24,240
Speaker 2: because a human being can move that. That's sure. One

3525
02:55:24,280 --> 02:55:26,120
Speaker 2: thing we've left out of this up so far. So

3526
02:55:26,160 --> 02:55:29,160
Speaker 2: this this machine that I described earlier, this robot that

3527
02:55:29,200 --> 02:55:31,840
Speaker 2: goes up and down this rolling base has a floppy

3528
02:55:31,920 --> 02:55:35,280
Speaker 2: Donald Trump mask over the over its head, which first

3529
02:55:35,280 --> 02:55:37,960
Speaker 2: attracted us to this. Yeah, that's why we showed up

3530
02:55:37,959 --> 02:55:38,400
Speaker 2: there in the.

3531
02:55:38,879 --> 02:55:42,720
Speaker 5: A robot moving its arms around wearing a Donald Trump mask.

3532
02:55:42,760 --> 02:55:46,080
Speaker 5: And as Robert was interviewing this guy, the robot was

3533
02:55:46,080 --> 02:55:48,560
Speaker 5: like moving around and like trying to stimulate its washing

3534
02:55:48,640 --> 02:55:52,320
Speaker 5: dishes capability, and it knocked over the same water bottle

3535
02:55:52,360 --> 02:55:55,800
Speaker 5: about five times. It couldn't it couldn't pick it up consistently,

3536
02:55:56,160 --> 02:55:58,600
Speaker 5: So I will not trust it with my fine china.

3537
02:55:58,680 --> 02:55:59,240
Speaker 5: I'll say that.

3538
02:55:59,280 --> 02:56:00,760
Speaker 2: As soon as I got up there, I asked like,

3539
02:56:00,800 --> 02:56:02,600
Speaker 2: I couldn't take my jacket off, now can it fold?

3540
02:56:02,640 --> 02:56:04,800
Speaker 2: And he's like, well, we'd have to reprogram it. And

3541
02:56:04,879 --> 02:56:06,840
Speaker 2: it was this when I talked to the guy, I

3542
02:56:07,040 --> 02:56:08,960
Speaker 2: was like because he was like, yeah, we really see

3543
02:56:09,000 --> 02:56:13,000
Speaker 2: this as being you know, potentially good for elder care. Sure,

3544
02:56:13,040 --> 02:56:14,920
Speaker 2: and you know, we had just seen the product we

3545
02:56:14,920 --> 02:56:17,320
Speaker 2: talked about in the last episode, which, for all of

3546
02:56:17,360 --> 02:56:19,440
Speaker 2: it's I don't know that I think it'll work, was

3547
02:56:19,879 --> 02:56:22,240
Speaker 2: a lot of thought and care went into it. I

3548
02:56:22,280 --> 02:56:23,959
Speaker 2: was like, okay, so, like, what work have you done

3549
02:56:24,040 --> 02:56:26,680
Speaker 2: to build a machine that can like communicate and be

3550
02:56:26,760 --> 02:56:29,560
Speaker 2: helpful to like people who are dealing with health issues

3551
02:56:29,600 --> 02:56:31,920
Speaker 2: in their their later years. They're like, well, that's why

3552
02:56:32,040 --> 02:56:34,880
Speaker 2: it's open, right, someone else will yeahs open sources. Someone

3553
02:56:34,920 --> 02:56:36,600
Speaker 2: else can do a bit part. See, you guys are

3554
02:56:36,600 --> 02:56:38,600
Speaker 2: just you guys are just saying it can do everything

3555
02:56:38,640 --> 02:56:41,760
Speaker 2: because somebody could potentially code something for it.

3556
02:56:41,879 --> 02:56:44,000
Speaker 5: Yeah cool, there always could be code.

3557
02:56:44,080 --> 02:56:47,400
Speaker 2: Yeah, there could be code. I mean again, the other guy,

3558
02:56:48,200 --> 02:56:52,560
Speaker 2: the actual engineer, seemed very interested in the nuts and

3559
02:56:52,600 --> 02:56:58,000
Speaker 2: bolts of making an affordable, reproducible machine that could handle

3560
02:56:58,000 --> 02:57:02,520
Speaker 2: specific tasks, and Jack Jay had absolutely no interest in

3561
02:57:02,560 --> 02:57:04,840
Speaker 2: the actual machine that they were making. This is clearly,

3562
02:57:04,879 --> 02:57:07,080
Speaker 2: could not be clear. This is just a stepping stone,

3563
02:57:07,280 --> 02:57:09,480
Speaker 2: and he's kind of grossed out by it because it's

3564
02:57:09,520 --> 02:57:12,279
Speaker 2: not replacing all human art with a machine that he owns.

3565
02:57:12,160 --> 02:57:15,800
Speaker 5: He's a man completely fueled by Lex Friedman podcasts, and

3566
02:57:15,879 --> 02:57:18,440
Speaker 5: he doesn't want to actually do any real work. He

3567
02:57:18,520 --> 02:57:21,600
Speaker 5: just wants to talk about how AI is going to

3568
02:57:21,720 --> 02:57:24,879
Speaker 5: take over everything and we have to welcome it in

3569
02:57:25,280 --> 02:57:26,960
Speaker 5: and here listen to this is SKA.

3570
02:57:27,320 --> 02:57:30,480
Speaker 2: He wants to take money by owning something that does

3571
02:57:30,520 --> 02:57:34,080
Speaker 2: not provide anything and also put people out of work. Like,

3572
02:57:34,720 --> 02:57:37,720
Speaker 2: at no point did he express a desire to do

3573
02:57:37,879 --> 02:57:42,080
Speaker 2: anything other than replace something people were already doing with

3574
02:57:42,160 --> 02:57:47,280
Speaker 2: something worse that tech guys could profit from. That's all

3575
02:57:47,360 --> 02:57:49,720
Speaker 2: there is to this man. He's not a human.

3576
02:57:49,959 --> 02:57:51,040
Speaker 5: It's so anti human.

3577
02:57:51,280 --> 02:57:54,920
Speaker 2: Yeah, I cannot overemphasize the degree to which there was

3578
02:57:55,000 --> 02:57:56,680
Speaker 2: nothing behind this boy's eyes.

3579
02:57:57,760 --> 02:58:02,000
Speaker 5: Well, do you know what, there's also nothing super intelligent behind.

3580
02:58:02,240 --> 02:58:04,320
Speaker 2: That's not true. All of our ads are sponsored by

3581
02:58:04,400 --> 02:58:08,040
Speaker 2: real people, even if they're bad people, they're at least people.

3582
02:58:08,480 --> 02:58:11,920
Speaker 2: They live and they love and they hate and you know,

3583
02:58:12,040 --> 02:58:25,119
Speaker 2: maybe they have a promo code. Let's let's see, all.

3584
02:58:25,120 --> 02:58:29,560
Speaker 5: Right, So after our our lovely, our lovely Robotic Jack

3585
02:58:29,760 --> 02:58:34,280
Speaker 5: j Jessanowski SKA Adventure. Oh god.

3586
02:58:34,680 --> 02:58:38,200
Speaker 2: Also the SKA was shit, not good, not good? It

3587
02:58:38,240 --> 02:58:40,800
Speaker 2: did it just kept saying the words Scott.

3588
02:58:40,920 --> 02:58:44,280
Speaker 5: He kept saying the word Ska music and saying the

3589
02:58:44,280 --> 02:58:49,400
Speaker 5: word Robert Robert Scott, well, just doing random noises. After

3590
02:58:49,440 --> 02:58:51,080
Speaker 5: we had our fill of that, we did walk around

3591
02:58:51,160 --> 02:58:55,320
Speaker 5: the rest of Showstoppers. He was so surprised that I wasn't.

3592
02:58:55,440 --> 02:58:57,760
Speaker 2: I wasn't impressed by any of the He was like,

3593
02:58:57,760 --> 02:59:00,680
Speaker 2: you must have heard the lady, man, I hear them.

3594
02:59:00,720 --> 02:59:04,320
Speaker 2: It's not good. It's like it's like I made this

3595
02:59:04,360 --> 02:59:08,160
Speaker 2: comparison a few times. If somebody like walked in while

3596
02:59:08,160 --> 02:59:10,680
Speaker 2: I'm at a house party was like, hey, man, I

3597
02:59:10,800 --> 02:59:14,360
Speaker 2: taught my dog to masturbate to pornography with its with

3598
02:59:14,400 --> 02:59:17,800
Speaker 2: its pause. I would be like, well, I mean that's like,

3599
02:59:18,200 --> 02:59:21,080
Speaker 2: I guess impressed. I didn't think a dog could do that.

3600
02:59:21,640 --> 02:59:26,000
Speaker 2: Like I am kind of impressed, I guess, But I

3601
02:59:26,040 --> 02:59:30,240
Speaker 2: don't want this like this. This doesn't do anything for me.

3602
02:59:30,520 --> 02:59:33,280
Speaker 2: It's why you figured this out?

3603
02:59:33,400 --> 02:59:35,200
Speaker 5: What what value does this?

3604
02:59:35,600 --> 02:59:37,959
Speaker 2: How does the dog know who Farah Fauce it is?

3605
02:59:38,000 --> 02:59:41,600
Speaker 2: I have questions, sure, but it doesn't give me anything

3606
02:59:42,120 --> 02:59:47,160
Speaker 2: like no Fara Fauce it was Garrison. No, God damn it.

3607
02:59:47,320 --> 02:59:48,200
Speaker 5: What do you think I do?

3608
02:59:48,320 --> 02:59:48,440
Speaker 2: What?

3609
02:59:48,800 --> 02:59:53,959
Speaker 5: I I don't know anymore? Well, what I did is

3610
02:59:54,000 --> 02:59:57,080
Speaker 5: walk around the rest of showstoppers. I saw this one

3611
02:59:57,120 --> 02:59:59,800
Speaker 5: booth that had like a like an iPhone case with

3612
02:59:59,840 --> 03:00:02,000
Speaker 5: like a little like keyboard on the bottom that like

3613
03:00:02,040 --> 03:00:05,039
Speaker 5: plugs in, and I started messing around with it, and

3614
03:00:05,120 --> 03:00:06,840
Speaker 5: the guy that booth walked up to me and made

3615
03:00:06,879 --> 03:00:09,959
Speaker 5: fun of me because he's like, you've never you've never

3616
03:00:10,040 --> 03:00:13,320
Speaker 5: held a phone with the BlackBerry. He literally said, like,

3617
03:00:13,640 --> 03:00:15,360
Speaker 5: you've never had a BlackBerry before?

3618
03:00:15,440 --> 03:00:15,720
Speaker 3: Have you?

3619
03:00:15,920 --> 03:00:18,840
Speaker 5: Like no, Like, yeah, you're typing all wrong on there.

3620
03:00:19,200 --> 03:00:23,680
Speaker 2: There was a solid nine day news cycle when Barack Obama,

3621
03:00:23,760 --> 03:00:26,520
Speaker 2: newly the president, revealed that he had a BlackBerry.

3622
03:00:26,640 --> 03:00:30,600
Speaker 5: I remember that, which sounds like a lifetime.

3623
03:00:30,760 --> 03:00:33,560
Speaker 2: There was a company called r I M once and

3624
03:00:33,600 --> 03:00:36,640
Speaker 2: they made a tablet that was pretty good, and we

3625
03:00:36,720 --> 03:00:39,160
Speaker 2: only made a couple of rim job jokes about it,

3626
03:00:39,560 --> 03:00:41,720
Speaker 2: but it didn't do very well, and so I gave

3627
03:00:41,760 --> 03:00:44,080
Speaker 2: it to my dad and accidentally there was still a

3628
03:00:44,120 --> 03:00:46,720
Speaker 2: picture of my dick on it. Anyway, that's a story

3629
03:00:46,760 --> 03:00:49,400
Speaker 2: for another day.

3630
03:00:49,440 --> 03:00:49,760
Speaker 5: Cool.

3631
03:00:51,840 --> 03:00:53,640
Speaker 2: These are the kind of things you get recording at

3632
03:00:53,640 --> 03:00:56,039
Speaker 2: eleven fifty six pm and we.

3633
03:00:56,480 --> 03:00:59,880
Speaker 5: Caught Tuesday night. Gott to get to bed, but no,

3634
03:01:00,160 --> 03:01:01,480
Speaker 5: keep making fun of me. For not knowing how to

3635
03:01:01,560 --> 03:01:03,359
Speaker 5: use a smartphone keyboard.

3636
03:01:03,520 --> 03:01:04,280
Speaker 2: He did the right thing.

3637
03:01:04,280 --> 03:01:05,720
Speaker 5: I don't need to use that because I have a

3638
03:01:05,800 --> 03:01:08,440
Speaker 5: keyboard on my phone built in already. It's much faster.

3639
03:01:08,680 --> 03:01:12,120
Speaker 5: So anyway, we stopped it at this company that makes

3640
03:01:12,400 --> 03:01:16,279
Speaker 5: well now just makes the software to use in conjunction

3641
03:01:16,360 --> 03:01:20,119
Speaker 5: with the augmented reality glasses and any like high powered laptop.

3642
03:01:20,240 --> 03:01:22,600
Speaker 5: Specifically the laptops that have like built in like you know,

3643
03:01:22,680 --> 03:01:25,840
Speaker 5: like co pilots because they require like higher processing power,

3644
03:01:26,000 --> 03:01:28,840
Speaker 5: they have an NPU or something like that, like a

3645
03:01:28,959 --> 03:01:31,920
Speaker 5: like an ampuro processing unit is what they're calling like

3646
03:01:32,000 --> 03:01:36,600
Speaker 5: the AI dedicated GPU thing. Effectively, it allows you to

3647
03:01:36,600 --> 03:01:40,640
Speaker 5: hook up these glasses and run you know, possibly infinite

3648
03:01:40,920 --> 03:01:43,959
Speaker 5: amount of monitors using AR. And we talked about this

3649
03:01:44,000 --> 03:01:47,080
Speaker 5: company last year because we saw them at show stoppers.

3650
03:01:47,080 --> 03:01:48,760
Speaker 2: You put on the glasses and it's like you've got

3651
03:01:48,879 --> 03:01:51,560
Speaker 2: six monitors or whatever that are all full size.

3652
03:01:51,360 --> 03:01:55,080
Speaker 5: And it's actually really easy to use. It works very well, seamlessly,

3653
03:01:55,120 --> 03:01:58,120
Speaker 5: it's nice, it's it's good quality, easy to use, you

3654
03:01:58,160 --> 03:01:59,240
Speaker 5: can move the monitors around.

3655
03:01:59,320 --> 03:02:00,800
Speaker 2: It's an excellent an excellent game.

3656
03:02:00,879 --> 03:02:02,560
Speaker 5: We talked to them last year, and the main thing

3657
03:02:02,600 --> 03:02:05,000
Speaker 5: that was holding this, like holding us back on it

3658
03:02:05,040 --> 03:02:07,199
Speaker 5: is that you needed to use their own proprietary lap.

3659
03:02:07,200 --> 03:02:09,160
Speaker 5: It was their own laptop, and it wasn't a great one.

3660
03:02:09,240 --> 03:02:11,400
Speaker 5: It was just like a Linux laptop didn't. It didn't

3661
03:02:11,440 --> 03:02:13,400
Speaker 5: have everything I like I want out of my own

3662
03:02:13,440 --> 03:02:14,160
Speaker 5: personal laptop.

3663
03:02:14,320 --> 03:02:15,760
Speaker 2: And we were still impressed with it.

3664
03:02:15,800 --> 03:02:17,600
Speaker 5: Then it was still it was still good. Yeah, and

3665
03:02:17,640 --> 03:02:19,600
Speaker 5: now you can just use any high power laptop with

3666
03:02:19,640 --> 03:02:23,080
Speaker 5: it essentially. So it's lovely to see that improved. We

3667
03:02:23,120 --> 03:02:28,160
Speaker 5: saw this lovely like like very small foldable projector. Oh yeah,

3668
03:02:28,200 --> 03:02:28,840
Speaker 5: that was cool.

3669
03:02:28,879 --> 03:02:30,800
Speaker 2: What's the company? That company name, because we should be

3670
03:02:30,840 --> 03:02:32,840
Speaker 2: we should be giving out the names of these Yes.

3671
03:02:32,760 --> 03:02:35,760
Speaker 5: The ar glasses and the software system is called spacetop

3672
03:02:36,080 --> 03:02:40,320
Speaker 5: very good by a company called Sightful. It works works great.

3673
03:02:40,480 --> 03:02:43,960
Speaker 5: But yeah, this this little folding folding projector currently has

3674
03:02:43,959 --> 03:02:47,840
Speaker 5: a Kickstarter. The company is called Aura Zen yeah or

3675
03:02:47,920 --> 03:02:53,160
Speaker 5: a Zen specifically, it was the zip trifled projector right now.

3676
03:02:53,160 --> 03:02:55,960
Speaker 5: It's it's a seven to twenty p very small foldable projector.

3677
03:02:56,360 --> 03:02:58,000
Speaker 5: It has a whole it has like like an auto

3678
03:02:58,040 --> 03:03:00,840
Speaker 5: focusing auto keystone. There were could get it up to

3679
03:03:00,840 --> 03:03:04,080
Speaker 5: ten VP, but they're running a kickstarter right now to

3680
03:03:04,200 --> 03:03:07,800
Speaker 5: ship in about three months. Super good quality stuff.

3681
03:03:07,840 --> 03:03:09,840
Speaker 2: If you're a gadget Ritina, Like it felt like a

3682
03:03:10,080 --> 03:03:13,200
Speaker 2: quality piece of electronics in my hands, Like the way

3683
03:03:13,240 --> 03:03:17,200
Speaker 2: it like snapped when it closed, just felt good. I'm

3684
03:03:17,280 --> 03:03:20,240
Speaker 2: I think I'm gonna buy one like it's it's exactly

3685
03:03:20,280 --> 03:03:22,360
Speaker 2: what I want for traveling, which is the ability to

3686
03:03:22,440 --> 03:03:24,360
Speaker 2: it goes up to like eighty inches of screen and

3687
03:03:24,400 --> 03:03:27,000
Speaker 2: like very good resolution, the ability to just have that

3688
03:03:27,600 --> 03:03:30,400
Speaker 2: plugged in to a battery or the wall and my

3689
03:03:30,480 --> 03:03:33,680
Speaker 2: laptop and like wherever I happen to be, I've got

3690
03:03:33,720 --> 03:03:35,600
Speaker 2: a movie screen that I don't have to worry about

3691
03:03:35,600 --> 03:03:37,560
Speaker 2: the fucking hooking up a TV to my laptop or

3692
03:03:37,600 --> 03:03:37,920
Speaker 2: some shit.

3693
03:03:38,080 --> 03:03:40,400
Speaker 5: It doesn't need Wi Fi to work. It just can

3694
03:03:40,480 --> 03:03:42,400
Speaker 5: cast from your phone.

3695
03:03:42,320 --> 03:03:46,280
Speaker 2: A U r Z and ZIP trifle projector or zen

3696
03:03:46,400 --> 03:03:48,240
Speaker 2: yep yep. I think they're selling them for two fifty

3697
03:03:48,320 --> 03:03:51,360
Speaker 2: right now. That's for the for the Kickstarter, for the Kickstarter.

3698
03:03:51,400 --> 03:03:53,400
Speaker 2: It will go up a little when it's a product.

3699
03:03:53,400 --> 03:03:56,160
Speaker 2: But we saw it. It works. They had a lot

3700
03:03:56,200 --> 03:03:59,400
Speaker 2: of they had tracking and stuff so it like automatically

3701
03:03:59,440 --> 03:04:00,320
Speaker 2: would focus.

3702
03:04:00,440 --> 03:04:04,000
Speaker 5: Auto focuses and at like it scales correctly, forwards, projecting

3703
03:04:04,640 --> 03:04:07,320
Speaker 5: it automatically like adjusts like the tilt of it so

3704
03:04:07,440 --> 03:04:08,000
Speaker 5: that it you.

3705
03:04:07,920 --> 03:04:10,200
Speaker 2: Know, yeah, obviously this isn't the full review because we

3706
03:04:10,200 --> 03:04:12,520
Speaker 2: don't own one, but everything we could tell by looking

3707
03:04:12,520 --> 03:04:13,400
Speaker 2: at it in the moment.

3708
03:04:13,360 --> 03:04:15,120
Speaker 5: We tried it out, I hooked up my phone to it.

3709
03:04:15,160 --> 03:04:17,280
Speaker 5: As I went to my phone screen, I realized I

3710
03:04:17,360 --> 03:04:22,600
Speaker 5: have a slightly I would say, artful flute image of

3711
03:04:22,640 --> 03:04:26,080
Speaker 5: an angel. So I quickly swiped away from we shouldn't

3712
03:04:26,080 --> 03:04:28,720
Speaker 5: show your dick to your damn my home screen of

3713
03:04:28,879 --> 03:04:31,440
Speaker 5: my phone. You know, things can always be worse.

3714
03:04:31,480 --> 03:04:35,039
Speaker 2: Things could always be worse. But I think where will

3715
03:04:35,160 --> 03:04:37,800
Speaker 2: end is? And this actually is not entirely in order,

3716
03:04:37,840 --> 03:04:40,480
Speaker 2: because this is the next after we had that conversation

3717
03:04:40,560 --> 03:04:45,039
Speaker 2: with our friend Jack Jay, which just left me thinking about, like,

3718
03:04:45,600 --> 03:04:48,120
Speaker 2: some people aren't really people, right, That's what I The

3719
03:04:48,120 --> 03:04:48,720
Speaker 2: sole thing.

3720
03:04:48,640 --> 03:04:51,400
Speaker 5: Is is a sham. It's all. It's all for rooms.

3721
03:04:51,560 --> 03:04:52,439
Speaker 5: It's soulless.

3722
03:04:52,760 --> 03:04:54,800
Speaker 2: We immediately walk over and we just kind of like

3723
03:04:54,879 --> 03:04:58,360
Speaker 2: randomly turn a corner and there's like a human shin

3724
03:04:58,959 --> 03:05:03,760
Speaker 2: like tibia amphibia basically with like a carbon fiber you know,

3725
03:05:04,120 --> 03:05:06,680
Speaker 2: frame around it. That's roughly the shape of like a

3726
03:05:06,879 --> 03:05:12,160
Speaker 2: person's lower leg, lower leg, and it's called bio leg.

3727
03:05:12,240 --> 03:05:16,520
Speaker 2: It's a powered microprocessor knee made in Japan, where it

3728
03:05:16,600 --> 03:05:20,200
Speaker 2: is a prosthetic, but unlike most prosthetics, it is powered

3729
03:05:20,280 --> 03:05:22,640
Speaker 2: and has a muscle built into it, so like when

3730
03:05:22,680 --> 03:05:25,120
Speaker 2: you lift up your prosthetic, it doesn't hang it, it doesn't lock.

3731
03:05:25,480 --> 03:05:27,920
Speaker 2: It actually has a degree of motion and it feels

3732
03:05:28,000 --> 03:05:30,080
Speaker 2: like what lifts the rest of the leg what you're

3733
03:05:30,160 --> 03:05:34,160
Speaker 2: remaining muscles like. It measures based on like it can

3734
03:05:34,200 --> 03:05:37,360
Speaker 2: like take measurements from them and it can act intelligently

3735
03:05:38,040 --> 03:05:41,200
Speaker 2: based on that. And I know that it works because

3736
03:05:41,200 --> 03:05:43,600
Speaker 2: the inventor was there and he was a man who

3737
03:05:43,640 --> 03:05:46,440
Speaker 2: was missing his leg below the knee and had built

3738
03:05:46,440 --> 03:05:47,600
Speaker 2: this for himself.

3739
03:05:48,240 --> 03:05:49,680
Speaker 5: He's spent like ten years working on this.

3740
03:05:49,840 --> 03:05:53,000
Speaker 2: Yeah, eight years, he's said, eight years. And that's like

3741
03:05:53,120 --> 03:05:56,400
Speaker 2: really the thing that is like so both like addictive

3742
03:05:56,520 --> 03:05:59,160
Speaker 2: and also like this like very tonal whiples you get

3743
03:05:59,160 --> 03:06:01,320
Speaker 2: at ces as you will go from like this dead

3744
03:06:01,400 --> 03:06:04,160
Speaker 2: eyed con man trying to scam the world so he

3745
03:06:04,200 --> 03:06:06,360
Speaker 2: can do god knows what kinds of other harms with

3746
03:06:06,480 --> 03:06:10,400
Speaker 2: absolutely nothing, nothing inside him at all, And then I

3747
03:06:10,520 --> 03:06:13,360
Speaker 2: lost my leg and I built a better prosthetic to

3748
03:06:13,440 --> 03:06:17,360
Speaker 2: help the entire world. And that's like thirty seconds between

3749
03:06:17,400 --> 03:06:19,360
Speaker 2: those two experiences.

3750
03:06:18,760 --> 03:06:21,560
Speaker 5: And like that's like that's like the dark magic of Cees.

3751
03:06:21,760 --> 03:06:25,000
Speaker 5: And like I don't, like I'm not like anti tech,

3752
03:06:25,160 --> 03:06:28,160
Speaker 5: like I think there. I think technology can really improve

3753
03:06:28,200 --> 03:06:31,280
Speaker 5: people's lives if used well. And sometimes I get kind

3754
03:06:31,280 --> 03:06:34,440
Speaker 5: of black pilled walking around Cees. But then we'll stumble

3755
03:06:34,480 --> 03:06:37,800
Speaker 5: across this, like you know someone who like literally lost

3756
03:06:37,800 --> 03:06:41,240
Speaker 5: a leg and made themselves their own better leg.

3757
03:06:41,360 --> 03:06:43,520
Speaker 2: Eight years figuring out how to do this.

3758
03:06:43,680 --> 03:06:46,440
Speaker 5: Yeah, is winning award. It's like for it award winning

3759
03:06:46,560 --> 03:06:47,720
Speaker 5: like tech innovations.

3760
03:06:47,760 --> 03:06:50,200
Speaker 2: It's changing your as a person who has lost your

3761
03:06:50,200 --> 03:06:52,560
Speaker 2: lower like changing being able to like have a normal

3762
03:06:52,640 --> 03:06:57,320
Speaker 2: gait and balance again, like massive potential to improve people's

3763
03:06:57,400 --> 03:06:58,640
Speaker 2: lives as a result of this.

3764
03:06:59,520 --> 03:07:03,760
Speaker 5: Yeah, just steps away from Ai ska at Ai and

3765
03:07:04,080 --> 03:07:09,560
Speaker 5: Donald trump Man the laund the profile. The company is

3766
03:07:09,600 --> 03:07:14,000
Speaker 5: again Bionic IM and it's the bioleg biolags the product. Yeah,

3767
03:07:14,000 --> 03:07:16,039
Speaker 5: the biolague is the product by Bionic Im.

3768
03:07:16,200 --> 03:07:17,760
Speaker 2: I'm going to try to check it out more.

3769
03:07:17,640 --> 03:07:20,200
Speaker 5: Tomorrow at Eureka Park, which at this point, you know

3770
03:07:20,320 --> 03:07:24,080
Speaker 5: that'll be like maybe future episodes come next week. But

3771
03:07:24,280 --> 03:07:28,720
Speaker 5: I guess this closes our actual Like Coridge, Let's go

3772
03:07:28,840 --> 03:07:32,440
Speaker 5: get fucked up and eat Japanese food. Oh I'm down. Yeah,

3773
03:07:32,480 --> 03:07:32,920
Speaker 5: I'm down.

3774
03:07:33,040 --> 03:07:33,640
Speaker 2: Let's do it.

3775
03:07:35,720 --> 03:07:35,959
Speaker 5: Hey.

3776
03:07:36,040 --> 03:07:39,119
Speaker 2: We'll be back Monday, with more episodes every week from

3777
03:07:39,160 --> 03:07:40,840
Speaker 2: now until the heat death of the universe.

3778
03:07:41,520 --> 03:07:44,000
Speaker 9: It Could Happen Here is a production of cool Zone Media.

3779
03:07:44,200 --> 03:07:47,200
Speaker 9: For more podcasts from cool Zone Media, visit our website

3780
03:07:47,320 --> 03:07:49,879
Speaker 9: cool Zonemedia dot com, or check us out on the

3781
03:07:49,920 --> 03:07:53,960
Speaker 9: iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts.

3782
03:07:54,280 --> 03:07:56,240
Speaker 1: You can now find sources for It Could Happen Here,

3783
03:07:56,240 --> 03:07:59,240
Speaker 1: listed directly in episode descriptions. Thanks for listening.