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Speaker 1: Hello the Internet, and welcome to season two ninety five,

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Speaker 1: Episode three of DAILY'SAI Ice Day production of iHeartRadio.

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Speaker 2: This is a.

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Speaker 1: Podcast where we take a deep dive into America's shared consciousness.

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Speaker 1: And it is Thursday, July thirteenth, twenty twenty three. My

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Speaker 1: name is Jack O'Brien aka Oh can't you see Ronda

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Speaker 1: santis Man. No one likes you even in the least

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Speaker 1: he he he. What's that you say, Ronda santis Man?

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Speaker 1: Your general vibe is off putting, in bleak he he

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Speaker 1: he A Rono, No, that is courtesy of Pat on

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Speaker 1: the discord, just Pat. Pat says kind of low effort

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Speaker 1: on my part, Not low effort enough for me, Pat,

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Speaker 1: I love it, Thank you, sir, he he he. I

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Speaker 1: went with a Michael Jackson direction on that one. You know, Okay, Well,

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Speaker 1: I'm thrilled to be joined by very special guest co host,

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Speaker 1: the producer behind shows like Fake Doctor's Real Friends, Welcome

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Speaker 1: to our show. A brilliant writer who you can read

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Speaker 1: at Vulture, the Avy Club team book page, the advocate.

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Speaker 1: You've heard her on Pop Culture Happy. It's the brilliant,

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Speaker 1: the talented Joe Almoney.

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Speaker 2: You guys have to add filmmaker to the list.

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Speaker 1: Now filmmaking.

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Speaker 2: Oh yeah, she's making her filmmaking bag now. Hell, all

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Speaker 2: the film are just pouring out.

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Speaker 1: The filmmaking.

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Speaker 2: Oh my gosh. I'm executive producing a short, which is

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Speaker 2: a wild thing to say. People ask me what I'm doing.

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Speaker 2: I'm like, I'm still not sure yet. It's a lot

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Speaker 2: of email campaigns, a lot of yes we can get

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Speaker 2: that after grants. It's basically money and paperwork, which is

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Speaker 2: a very interesting and news side of filmmaking for me.

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Speaker 2: But I'm excited because my friend's from college. He was

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Speaker 2: my cinematographer. He named David Chari. He went to a

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Speaker 2: FI he's been making movies. He wanted to challenge himself

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Speaker 2: to make something he's never done before. So he's doing

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Speaker 2: a one take film. So it's it is one shot,

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Speaker 2: We're just going straight through. It is nineteen whoa yeah, yeah, yeah,

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Speaker 2: it's a really long shot.

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Speaker 1: A bit of a Russian arc situation.

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Speaker 3: Russian Yeah.

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Speaker 4: Chat with you.

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Speaker 1: Thank you.

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Speaker 3: First.

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Speaker 1: My second film, deep cut film reference to a movie

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Speaker 1: I haven't seen.

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Speaker 4: Yeah, Off, Mike, you were talking about the red violin.

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Speaker 1: Bit of red violin pitched a red violin type style

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Speaker 1: film that follows a Dare T shirt off mic. So, yeah, my.

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Speaker 2: Next film, I've already decided we're just going to add

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Speaker 2: it to the roster there. It is perfect. Yeah, so

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Speaker 2: we're excited. It's gonna be. Uh it's a mystery thriller satire.

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Speaker 2: So it's it's funny, it's kind of dark and twisty.

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Speaker 2: It's very surprising if you want to learn more about it,

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Speaker 2: following you over on Instagram. So that's yes, this what's

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Speaker 2: been happening in my world. And I'm excited to be

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Speaker 2: back here with you.

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Speaker 1: Well, we're excited to have you, and we're also excited

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Speaker 1: to be joined in our third seat by another stalwart

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Speaker 1: of the film scene, very talented writer, stand up comedian,

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Speaker 1: podcast host of The Bechdel Cast. She also has to

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Speaker 1: have a master's degree in film film. The most anagrammable

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Speaker 1: name in the English language, it's Caitlin de Roda.

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Speaker 3: Glad to be it's.

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Speaker 1: Great to have you.

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Speaker 2: Now.

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Speaker 1: You are wearing a T shirt that says night paint.

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Speaker 2: It's just paint night, paint night or night paint, but

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Speaker 2: paint night.

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Speaker 4: So do either of you know what paint night is?

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Speaker 2: I know, bite your shirt which is black with a

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Speaker 2: beautiful splash of color right over the pea. I assumed

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Speaker 2: it's either for couples and it's very romantic and or

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Speaker 2: friend groups and there's some drinking and you paint an

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Speaker 2: to nighttime. How close am.

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Speaker 4: I mailed it? Yes, so I'm riding this shirt. I

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Speaker 4: have this shirt because I was formerly a paint Night

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Speaker 4: instructor for the first few months I lived in LA

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Speaker 4: That's right, not to brag or anything.

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Speaker 1: But.

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Speaker 4: I would show up. I'd set up the event and

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Speaker 4: then people would show up, often either on a date

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Speaker 4: or it was a lot of friend groups. It was

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Speaker 4: a lot of like middle aged women who were there

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Speaker 4: to get drunk with their friends. Things like that. And

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Speaker 4: you would usually paint a tree, okay most always a tree,

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Speaker 4: and I would kind of guide you through how to

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Speaker 4: make this painting because I'm among so many other talents

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Speaker 4: and a painter.

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Speaker 3: Wow.

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Speaker 4: But make no mistake, I'm not a good painter. You

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Speaker 4: do not have to be a very good to be

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Speaker 4: a Paint Night instructor. You just kind of it's sort

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Speaker 4: of it's not quite paint by numbers because you're like

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Speaker 4: it's acrylic paints and you're like blending you're mixing colors. Yeah, yeah,

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Speaker 4: those are two different things. Little known fact. But yeah,

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Speaker 4: so I would be like, here's how you paint this tree,

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Speaker 4: and then they would paint it, and then they would

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Speaker 4: get drunk and I'd have to like sell my soul

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Speaker 4: to be like, please buy a raffle ticket to another

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Speaker 4: paint night event, and then I would keep the money.

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Speaker 4: It was it was, you know, and then oh, here's

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Speaker 4: a fun story. Also, when I was first living in

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Speaker 4: LA I was very broke, and people would usually order

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Speaker 4: food because these would take place at generally like restaurants.

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Speaker 4: There would be like a separate room and then people

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Speaker 4: would order food and then they would leave a lot

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Speaker 4: of it behind. So I would just take their leftovers.

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Speaker 2: Oh yeah, yeah, poor survival one oh one for food.

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Speaker 2: Never pass it out.

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Speaker 4: Never pass it up.

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Speaker 2: I used to work at an AMC and at the

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Speaker 2: end of the night, I guess were the hot dogs went?

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Speaker 2: Oh straight home with me? Baby? Yeah.

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Speaker 4: I Like there would be like a half eaten piece

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Speaker 4: of pizza that I would eat. I would even cut.

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Speaker 1: This was pretty covid.

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Speaker 4: I threw caution to the wind. Okay, I love it.

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Speaker 1: I I ate my weight. Like when I was a waiter,

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Speaker 1: I ate my weight and leftover fries, like which fries

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Speaker 1: don't even keep that well, but they had a little

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Speaker 1: little sauce that brought them back to life. Just hurriedly

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Speaker 1: shoveling cold fries into my mouth over a trash can, Like, no,

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Speaker 1: I'm throwing us away? What are you talking about?

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Speaker 3: Yeah? Truly?

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Speaker 1: All right, Well, Caitlin, we're going to get to know

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Speaker 1: you a little bit better, including I can't wait to

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Speaker 1: hear your thoughts on the new Wonka trailer, which appears

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Speaker 1: to be Paddington with with with Timothy Challamy as a

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Speaker 1: funny little guy instead of Paddington like in there. But first,

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Speaker 1: a few of the things we're talking about. Tucker Carlson.

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Speaker 1: I'm kind of proud of that. If Tucker Carlson vlogs

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Speaker 1: in the Woods and nobody watches, does Tucker Carlson actually vlog?

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Speaker 1: Because it turns out we had raised the question. Everybody

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Speaker 1: was like, oh, he's on Twitter now, and like his

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Speaker 1: first video was seen by the equivalent of like the

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Speaker 1: entire US population. We're fucked. And then like the other day,

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Speaker 1: I think on this show, I was like, wait, did

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Speaker 1: he never dropped a second episode huh, and I was like, yeah, no,

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Speaker 1: Like Fox News sent a season to sist and I

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Speaker 1: think he just like never got around. He's had eight

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Speaker 1: episodes since then, and just nobody's really watching them or

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Speaker 1: it's it's not making much of a mark. So we're

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Speaker 1: gonna look at Tucky's numbers and just the question of,

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Speaker 1: like how much of the their whole bullshit is just

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Speaker 1: you know, media about their media, and like them just

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Speaker 1: make themselves seem more important and popular than they actually are.

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Speaker 1: We will catch up on We're in an interesting place

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Speaker 1: with regards to AI and the popular imagination, and AI

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Speaker 1: is the villain in the upcoming Mission Impossible movie, So

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Speaker 1: we're just going to talk about that and some of

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Speaker 1: the claims we're seeing in headlines and how they hold

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Speaker 1: up to the truth. All of that plenty more. But first, Caitlin,

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Speaker 1: I would love to ask you, what is something from

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Speaker 1: your search history that's revealing about who you are?

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Speaker 4: I googled first movie to show a toilet flushing?

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Speaker 2: Yes, which one was it?

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Speaker 4: Well? It is the movie Psycho by mister Alfred Hitchcock.

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Speaker 2: It would be.

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Speaker 1: I guess the Haze Code or something.

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Speaker 4: Honestly, I feel like it maybe it would have been

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Speaker 4: or like just the idea of toilets or you know,

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Speaker 4: like pooping would be considered too improper by the Code.

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Speaker 4: But by the sixties, which I think the movie came

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Speaker 4: out in nineteen sixty, you know the Code, people were

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Speaker 4: taking some liberties.

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Speaker 1: People are taking some nasty shit, and we're just ready

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Speaker 1: to see it confronted in their entertainment. Yeah, that movie

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Speaker 1: was like the Old Boy of its day, Like every

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Speaker 1: like they were like, yeah, we're gonna like throw this

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Speaker 1: twist in where like the star dies halfway through, and

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Speaker 1: also we're gonna show you a fucking toilet people, Like

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Speaker 1: I do wonder if people were scandalized by that. That

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Speaker 1: was That was a movie when it came out where

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Speaker 1: everyone was like people are like throw going up in

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Speaker 1: theaters and just running out of the running out of

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Speaker 1: the theater because they're so scared of it like that.

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Speaker 3: It was a big, big rage. So yeah, I.

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Speaker 1: Wonder if it was the toilet that got it.

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Speaker 4: It was that absolutely disgusted people. Well, I googled it

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Speaker 4: because I I go to a trivia night. Sometimes I'm

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Speaker 4: not just a paint night person. I also do trivia.

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Speaker 1: I don't host all the nights.

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Speaker 4: Yeah, every night of the week, there's a night for

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Speaker 4: me and I will go and play like pub trivia,

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Speaker 4: and there's this one that I frequent and they will

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Speaker 4: post like one question ahead of time that you can

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Speaker 4: look up and like. So it's basically like a freebee

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Speaker 4: but so you have to like follow the account and

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Speaker 4: then you get this like freebe question. And the question

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Speaker 4: I think was what nineteen sixty horror movie was the

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Speaker 4: first movie to show a toilet flushing on screens. So

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Speaker 4: I was like, oh, I have to know this.

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Speaker 1: And then I knew it and you googled it. So

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Speaker 1: you googled the answer while you were doing the thing.

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Speaker 1: You're a cheater at trivia.

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Speaker 4: I well, I wish, but I didn't even end up

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Speaker 4: going to the trivia that night. Oh OK, So I

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Speaker 4: was like, oh, no, I know the answer and I

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Speaker 4: can stay home.

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Speaker 2: That's perfect. I would you guys know that it was

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Speaker 2: the Hays Code. They considered toilets vulgar in sixties. They

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Speaker 2: started to loose in the code and they were concerned

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Speaker 2: because there's toilet paper in it, and they were like

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Speaker 2: contents in a toilet disgusting. We don't know about that,

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Speaker 2: but they were like, listen, you can't see any actual

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Speaker 2: body material in the toilet, so relax, and they did,

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Speaker 2: and then it got out there, and I think that's hilarious.

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Speaker 2: We were scared of toilets for a while in our cinema.

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Speaker 2: H what would children have done? Well?

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Speaker 1: For some reason, so I was remembering the like swirling

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Speaker 1: shot of like where the camera like swirls in on

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Speaker 1: her eye like as she's dying in the shower, and

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Speaker 1: then the camera like kind of swirls around the swirling

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Speaker 1: water in the and I assumed that it was like

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Speaker 1: a match to that. It just like went to a

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Speaker 1: toilet and showed like a toilet swirling, but that that's

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Speaker 1: not what happened.

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Speaker 2: Yeah, I'm trying to just.

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Speaker 1: Cut to somebody flashing a toilet, like finishing up their business.

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Speaker 4: Yeah. Norman Bates is like, yes, I have to take

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Speaker 4: a break from Psycho Poopy.

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Speaker 1: Yeah, well amazing. What is what's something that you think

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Speaker 1: is overrated?

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Speaker 4: I think that Wes Anderson is overrated?

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Speaker 2: What the job for saying what exactly about Les Anderson

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Speaker 2: is over.

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Speaker 4: I think, look, I he just tells the same quirky

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Speaker 4: little story over and over again. Granted I have not

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Speaker 4: seen Asteroid City yet, so maybe this one will just

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Speaker 4: blow me away and I will be excited again. To

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Speaker 4: be clear, there was a time period where I did

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Speaker 4: like his work, but I don't know. Maybe I think

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Speaker 4: it's just like too repetitive. I don't know, And but like, yeah,

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Speaker 4: I get it, like he's an not a tour so

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Speaker 4: he's going to have a distinct style that he kind

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Speaker 4: of just keeps recycling over and over. But like, I

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Speaker 4: don't know, I just like find his quirky little quirkiness

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Speaker 4: a bit tiresome these days.

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Speaker 3: Yea, what era?

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Speaker 2: I would say, Well, the.

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Speaker 4: Peak for me was fantastic mister Fox.

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Speaker 2: Hell yeah, that's.

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Speaker 4: Probably my favorite of his and leading up to but

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Speaker 4: then like after that, I felt like there was just a.

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Speaker 2: Dec rise Kingdom.

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Speaker 4: I think that one was like okay, fine, and then

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Speaker 4: and then again another controversial take here, but I thought

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Speaker 4: that Isle of Dogs was speaking of toilets.

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Speaker 2: Yeah, oh gosh, I think that's totally fair. I okay,

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Speaker 2: here's my experience watching almost every Wes Anderson film. Just like, gosh,

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Speaker 2: that's really pretty Ohen Wilson, He's always delightful. Costume should

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Speaker 2: get credit. Wow, the fits are fitting that movie was

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Speaker 2: that I'll never watch it again and don't revisit Wes

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Speaker 2: Anderson films. They're like very beautiful. I feel like they

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Speaker 2: have just en a story to avoid being museum pieces.

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Speaker 3: You know what I mean.

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Speaker 2: You've seen a movie in the museum.

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Speaker 1: Yeah.

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Speaker 4: Right, But it's like, tell me the plot of any

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Speaker 4: Wes Anderson movie. You can't like, I can't do it, Like,

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Speaker 4: I don't like, they just don't stick with me. I

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Speaker 4: feel like it's just like you see a frame and

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Speaker 4: you're like, wow, look at all that headroom. That's nice,

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Speaker 4: and look at this misan sen woo, and then you're like,

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Speaker 4: but what was the plot? And also why did I

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Speaker 4: watch it?

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Speaker 2: Yeah?

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Speaker 1: I feel like he is going to be the least

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Speaker 1: likely filmmaker too for you to be like, oh, he

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Speaker 1: really surprised me on this one. This is this is

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Speaker 1: not I did not see this one coming. I think

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Speaker 1: his like French Dispatch, was his attempt to do something different,

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Speaker 1: and I think it's like it's it's definitely my least

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Speaker 1: favorite of his movies.

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Speaker 4: I skimmed it. I couldn't be bothered.

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Speaker 1: Yeah, but it's it also seems like I don't see

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Speaker 1: that many people like writing for that one usually, like

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Speaker 1: I feel like his movies are some people really like it,

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Speaker 1: some people really hate it, and you know, it's it's

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Speaker 1: kind of random, Like I really like Grand Budapest Hotel,

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Speaker 1: but I don't like a lot of the ones around that,

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Speaker 1: and that's kind of the only later era one that

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Speaker 1: I really enjoy. But a lot of people like really

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Speaker 1: love The Life Aquatic. I've never really connected with that

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Speaker 1: one either, But yeah, French Dispatch felt like people were like, huh,

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Speaker 1: we're we're not going with you on this one. There

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Speaker 1: are some really good performances in it. Yeah, I don't know.

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Speaker 1: I like part of me wants to see the Asteroid

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Speaker 1: City one because I don't know, it just looks like

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Speaker 1: he's instead of trying to do something different, he's like,

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Speaker 1: I'm going to do the same so hard.

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Speaker 4: Oh you didn't like it when I tried something new, Well, fine,

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Speaker 4: I'll do the thing again.

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Speaker 1: Yeah, how about if Wes Anderson tried to make a

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Speaker 1: Wes Anderson movie. He like got jealous of all the

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Speaker 1: people being like on social media being like what if

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Speaker 1: your day was a Wes Anderson movie? And it's like a.

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Speaker 2: Wild to me that he was upset about that. I

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Speaker 2: was really trying to ut that yeah, oh well, yeah

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Speaker 2: he came out. He was like, I would never look

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Speaker 2: at those, and people were like, it's like people are

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Speaker 2: just homoging. You felt like that. I think that the

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Speaker 2: highest form of flattery is like the entire Internet got

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Speaker 2: together and was like, I think your aesthetic is so

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Speaker 2: utterly charming. I'm gonna place myself inside of it. It

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Speaker 2: was I really liked to meet I thought it was cute.

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Speaker 2: Disappointing court your fan base, please, I think it's This

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Speaker 2: is the best a parasocial relationship can be, is just

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Speaker 2: to say, hey, guys, I see you and I liked

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Speaker 2: what you did, and thank you and then just move on.

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Speaker 3: Yeah.

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Speaker 2: I don't know.

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Speaker 1: I wonder if it's also like you, it would just

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Speaker 1: be like he's worried. It would like fuck him up

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Speaker 1: a little bit to see everybody's like version of him,

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Speaker 1: the way that like seeing someone do an impression of

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Speaker 1: you can be a little unnerving for the first time.

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Speaker 2: You know that is fair that okay?

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Speaker 1: Maybe, or maybe it's just an asshole. I couldn't possibly

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Speaker 1: imagine a universe where that's true.

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Speaker 2: That's from Wes Anderson it's his quote directly. So okay,

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Speaker 2: So Wes Anderson said about the memes, I'm very good

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Speaker 2: at protecting myself from seeing all that stuff. If somebody

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Speaker 2: sends me something like that, I'll immediately erase it and say,

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Speaker 2: please sorry, do not send me things of people doing me,

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Speaker 2: because I do not want to look at it thinking.

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Speaker 4: Is that what I do?

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Speaker 2: Is that? What I mean? I don't want to see

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Speaker 2: too much of someone else thinking about what I try

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Speaker 2: to be, because God knows I could then start doing it.

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Speaker 2: So to your point, Jack, this is a form of

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Speaker 2: self protection. And now I dismissed the love of his fans.

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Speaker 1: Yeah, okay, all right. Also he does betray in that

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Speaker 1: quote like a little bit of like a thing I

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Speaker 1: always suspect about anybody who got famous before, like a

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Speaker 1: certain point like that, they just like don't know how

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Speaker 1: technology works. Is like, if someone sends me something, I

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Speaker 1: erase it. What you mean, you erase it the link

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Speaker 1: that they sent to you?

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Speaker 4: They delete it from Internet dot org.

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Speaker 1: Oh man, what Caitlin, what's something you think is underrated?

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Speaker 4: All right? Continuing on the on the movie train two, Yeah,

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Speaker 4: mission impossible. Four people are not out here riding for

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Speaker 4: Mission Impossible four specifically the way that I think they should.

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Speaker 4: People are like, oh wow, Rogue Nation, Oh wow, the

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Speaker 4: number six one. Oh, I really love number three. No,

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Speaker 4: there's no love ever for Mission Impossible. For Ghost protocol.

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Speaker 1: Oh, I think that's the best one.

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Speaker 4: I think so too. It's so good, I know, But no,

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Speaker 4: we are alone in this world, I think Jack.

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Speaker 1: I thought I thought that one was the one where

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Speaker 1: everyone was like, okay this, Like I thought that one

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Speaker 1: kind of picked it up at least in like the

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Speaker 1: critical consensus. Like three, two was not good. It was stinky.

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Speaker 4: It was another toilet movie hair commercial.

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Speaker 1: It was a hair like he just it was just

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Speaker 1: glamour shots of Tom Cruise and then Tom Cruise doing

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Speaker 1: impossible things, but not in the way that we like

359
00:20:12,880 --> 00:20:17,639
Speaker 1: jumping a motorcycle be not off of something, just making

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00:20:17,680 --> 00:20:22,440
Speaker 1: it jump somehow. But and then three was like kind

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Speaker 1: of but like four seemed every everybody seemed on board

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Speaker 1: with it, and like brad Bird so huge, right.

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Speaker 2: Four so fucking fire. I just get up and I

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Speaker 2: couldn't remember. I don't if we just say numbers, I'm like,

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00:20:35,040 --> 00:20:35,800
Speaker 2: I've seen so many.

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Speaker 1: Sorry, Joel, Mission Impossible, Ghost Protocols of the program again.

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Speaker 2: Not plot, but I remember places. You remember Tom Cruise

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Speaker 2: hanging off the side of a building, just scaling.

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Speaker 1: One of the tops building in the world.

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Speaker 2: The window and then his gloves are failing, and then

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Speaker 2: a sandstorm is coming while they're trying to do switch

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Speaker 2: through inside the building. Fucking fantastic. But I was just

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Speaker 2: on this list in fuck Tom Cruise. But man makes

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Speaker 2: a good movie. I'm sorry, It's really fuck good. And

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Speaker 2: it's like the intensity is so great. We have Paula Patton,

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Speaker 2: a black woman in here, just being the hot lady

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00:21:12,400 --> 00:21:15,440
Speaker 2: that's walking around in gorgeous dresses. We love to see it,

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00:21:15,600 --> 00:21:19,920
Speaker 2: like yeah, Ghost and Ghost Protocol just title alone peak.

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Speaker 2: But then you also get if you're a person who

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Speaker 2: really loves the relationships in Mission Impossible, there's like a

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Speaker 2: lot of heart coming through with he gets back with

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Speaker 2: his lady. I think spoiler alert at the end, he's like,

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Speaker 2: oh God, I see my girl and I love her.

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Speaker 2: This is also the movie where the final villain is

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Speaker 2: just raining cars on people and Tom's got one. Yeah,

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Speaker 2: freaking rocks, dude.

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Speaker 1: One of those car lift things that I've always like

388
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Speaker 1: seen passing by, like on a highway and been like

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Speaker 1: that can't be like the best way of historic cars?

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Speaker 4: How does this work?

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Speaker 1: Yeah?

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Speaker 4: I feel like that's is that not the first one?

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Speaker 4: Where like Simon Peg is pretty heavily involved both as

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Speaker 4: a cast member, and I think I'm the like kind

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Speaker 4: of writing team.

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Speaker 1: Yeah, I think that's yeah, Yeah, It's just it's good.

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Speaker 4: But people are like, some what is the sixth one called?

398
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Speaker 4: I keep trying to conjure faut I think fallout?

399
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Speaker 3: Fallout?

400
00:22:23,040 --> 00:22:26,320
Speaker 4: People are like, oh, remember the fight in the bathroom

401
00:22:26,480 --> 00:22:29,520
Speaker 4: when they're punching each other. I'm like, what about the

402
00:22:29,560 --> 00:22:30,760
Speaker 4: fourth movie? Though?

403
00:22:31,160 --> 00:22:37,000
Speaker 2: Ye arm slash guncock is an iconic moment and mission

404
00:22:37,000 --> 00:22:41,240
Speaker 2: impossible history and sexuality of this country. We all really

405
00:22:41,320 --> 00:22:43,560
Speaker 2: changed and evolved? Was that two seconds?

406
00:22:44,000 --> 00:22:46,800
Speaker 1: Henry Call? Is that who we're talking about?

407
00:22:46,840 --> 00:22:51,040
Speaker 2: He had a mustache in his arms like they were guns.

408
00:22:51,160 --> 00:22:54,240
Speaker 2: It was time to be alive. We were two years

409
00:22:54,280 --> 00:22:56,680
Speaker 2: from COVID, We were innocent, We didn't know, we didn't

410
00:22:56,680 --> 00:22:59,720
Speaker 2: not know. But yeah, as a whole, I don't know

411
00:22:59,720 --> 00:23:01,360
Speaker 2: if it beats out am I four for.

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Speaker 4: Me my best?

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00:23:04,160 --> 00:23:07,359
Speaker 1: I wonder if there's some intentional like kind of diminishing

414
00:23:07,359 --> 00:23:10,040
Speaker 1: of ghost protocol because everything since then I think has

415
00:23:10,080 --> 00:23:13,520
Speaker 1: been Christopher Macquarie and Tom Cruise, and they like partner

416
00:23:13,560 --> 00:23:16,480
Speaker 1: on everything, so maybe they're you know, they just don't

417
00:23:16,520 --> 00:23:19,040
Speaker 1: want to talk about it and like emphasize it as much.

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Speaker 1: But I do feel like, like people who are into movies,

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Speaker 1: that is their favorite mission impossible movie. So if you

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Speaker 1: if you have somehow missed it, if you've fallen into

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Speaker 1: the Cruise Macquarie Gray out of ghost protocol, you know that,

422
00:23:35,760 --> 00:23:38,600
Speaker 1: definitely check it out. It's a it's a blast it

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00:23:38,960 --> 00:23:41,160
Speaker 1: all right. Have you seen the new one came out?

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Speaker 1: I believe last night?

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Speaker 4: Oh did it?

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Speaker 1: Oh?

427
00:23:44,240 --> 00:23:47,480
Speaker 4: Like as an early release? No, I have an AMC

428
00:23:48,240 --> 00:23:53,200
Speaker 4: A List stubs reservation. We come to this place for magic,

429
00:23:53,320 --> 00:23:53,760
Speaker 4: of course.

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00:23:54,600 --> 00:24:01,080
Speaker 1: Yeah, my reservation pants and high heels and through puddles.

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00:24:01,320 --> 00:24:04,080
Speaker 4: I am seeing it on Saturday morning.

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Speaker 2: Nice.

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Speaker 1: I think I need to get that my last trip

434
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Speaker 1: to the movies with my kids to see Across the

435
00:24:10,880 --> 00:24:13,320
Speaker 1: Spider Verse, which, by the way, I do want to

436
00:24:13,359 --> 00:24:16,720
Speaker 1: get your opinion on that. Calen Gosh, you are the

437
00:24:16,760 --> 00:24:20,480
Speaker 1: person who made me watch Into the Spider Verse with

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00:24:20,480 --> 00:24:22,720
Speaker 1: my son when he was You didn't make me, but

439
00:24:22,840 --> 00:24:23,359
Speaker 1: you told me.

440
00:24:23,680 --> 00:24:24,439
Speaker 4: I held you.

441
00:24:24,880 --> 00:24:27,320
Speaker 1: You got me so excited about that movie. I was like,

442
00:24:27,359 --> 00:24:30,040
Speaker 1: we're going to watch this even though you're three, and

443
00:24:30,480 --> 00:24:33,080
Speaker 1: he loved it and he loved this one. But the

444
00:24:33,160 --> 00:24:36,640
Speaker 1: line at the concessions, they finally wore me down where

445
00:24:36,680 --> 00:24:40,560
Speaker 1: the line was like a Disney World line, like it

446
00:24:40,640 --> 00:24:44,080
Speaker 1: was just it was not moving. It was thirty minutes

447
00:24:44,400 --> 00:24:46,360
Speaker 1: of waiting to get the concessions.

448
00:24:46,840 --> 00:24:50,120
Speaker 2: Oh yeah, so you get yourself a little stubs car.

449
00:24:50,200 --> 00:24:52,720
Speaker 2: You could just pre order that. Yeah, and why do

450
00:24:52,760 --> 00:24:55,640
Speaker 2: you walk up your radio? Although I do recommend not

451
00:24:55,680 --> 00:24:59,199
Speaker 2: pre ordering your popcorn if you go first thing in

452
00:24:59,200 --> 00:25:01,040
Speaker 2: the morning. Listen, here's if you go on first Thing

453
00:25:01,040 --> 00:25:03,000
Speaker 2: in the morning, like Kaitlin's going Saturday morning, you can

454
00:25:03,040 --> 00:25:04,960
Speaker 2: preorder your popcorn because they pop their popcorn fresh in

455
00:25:05,000 --> 00:25:06,960
Speaker 2: the morning and it's totally fine and it'll be tasty.

456
00:25:07,200 --> 00:25:10,120
Speaker 2: If you're going to a movie after three o'clock, I'll

457
00:25:10,160 --> 00:25:13,040
Speaker 2: get your popcorn in the AMC goal line, because it's

458
00:25:13,119 --> 00:25:15,240
Speaker 2: popcorn that they popped like four hours ago and it's

459
00:25:15,240 --> 00:25:16,800
Speaker 2: all crumbly and it's just not good.

460
00:25:17,520 --> 00:25:19,480
Speaker 1: All right, let's take a quick break and we'll come back.

461
00:25:19,800 --> 00:25:22,800
Speaker 1: I have two different films that I want to ask

462
00:25:22,920 --> 00:25:37,400
Speaker 1: Kaylin about we'll be right back, and we're back. And Kaylin,

463
00:25:38,359 --> 00:25:46,080
Speaker 1: you are famous famously a huge proponent of Paddington, Paddington two,

464
00:25:47,160 --> 00:25:51,919
Speaker 1: and Paddington three, the Willy Wonka origin story, which seems

465
00:25:51,920 --> 00:25:54,560
Speaker 1: to be, well, we've just seen a trailer for so

466
00:25:54,600 --> 00:25:56,879
Speaker 1: we took a little break. We stopped down during the

467
00:25:56,880 --> 00:25:58,080
Speaker 1: break to for.

468
00:25:58,080 --> 00:25:59,880
Speaker 3: Everybody to watch the wank.

469
00:26:01,160 --> 00:26:04,960
Speaker 1: Is that what it's called? Wonka Wonka? Just Wonka cut

470
00:26:05,000 --> 00:26:09,800
Speaker 1: the Willie and Chocolate Factory, just Wonka. It's cleaner, that

471
00:26:09,960 --> 00:26:15,119
Speaker 1: has a lot of like the Paddingtonian like charms, and

472
00:26:15,280 --> 00:26:16,840
Speaker 1: like it feels like it takes place in the same

473
00:26:16,920 --> 00:26:20,119
Speaker 1: universe as Paddington and certain certain parts.

474
00:26:19,920 --> 00:26:22,440
Speaker 4: I mean, from what I can tell, they're recycling sets

475
00:26:22,720 --> 00:26:26,480
Speaker 4: from yeah, Paddington movies. They're recycling the cast of the

476
00:26:26,520 --> 00:26:30,439
Speaker 4: Paddington movies. And obviously it's the same director and screenwriter

477
00:26:30,680 --> 00:26:34,280
Speaker 4: as I mean, Paul King directed both Paddington one and

478
00:26:34,320 --> 00:26:37,439
Speaker 4: two except for not three, which I'm a little nervous about.

479
00:26:38,320 --> 00:26:38,959
Speaker 1: But uh.

480
00:26:39,040 --> 00:26:41,520
Speaker 4: And then Simon Farnaby is that his name?

481
00:26:42,359 --> 00:26:46,920
Speaker 1: I hope it is Simon Farnaby the fuck out of here,

482
00:26:47,640 --> 00:26:51,080
Speaker 1: so British get me.

483
00:26:51,880 --> 00:26:54,840
Speaker 4: He co wrote the screenplay for Paddington two. He's also

484
00:26:54,880 --> 00:26:59,520
Speaker 4: the guy who plays the guard in both Paddington movies.

485
00:27:00,160 --> 00:27:00,800
Speaker 4: He has a.

486
00:27:00,680 --> 00:27:03,400
Speaker 1: Little bit part does he have a mustache and kind

487
00:27:03,400 --> 00:27:04,160
Speaker 1: of a round face.

488
00:27:04,600 --> 00:27:07,399
Speaker 4: No mustache, but he's got I was actually called it

489
00:27:07,440 --> 00:27:10,520
Speaker 4: more of a square face, okay. And he's the guy

490
00:27:10,720 --> 00:27:15,320
Speaker 4: who the joke is that he's attracted to men who

491
00:27:15,400 --> 00:27:19,880
Speaker 4: are dressed as women. So the implications of that and

492
00:27:20,240 --> 00:27:24,280
Speaker 4: it being framed as a joke is a little questionable.

493
00:27:24,400 --> 00:27:28,480
Speaker 4: But he that's like his gimmick in both movies.

494
00:27:29,160 --> 00:27:34,200
Speaker 1: Wow, yeah, I mean so okay, this guy that's about

495
00:27:34,200 --> 00:27:36,960
Speaker 1: what I'd expect to Simon Farnabye to look like it's

496
00:27:36,960 --> 00:27:41,000
Speaker 1: got wild hair. But yeah, it just seems like and

497
00:27:41,640 --> 00:27:45,200
Speaker 1: there's some like mildly problematic like I don't know, white

498
00:27:45,240 --> 00:27:50,720
Speaker 1: saviorism stuff happening here. And somebody who like at one

499
00:27:50,720 --> 00:27:52,919
Speaker 1: point steps out of a car that seems to be

500
00:27:52,920 --> 00:27:56,520
Speaker 1: completely full of shit, which I'm not sure what or

501
00:27:56,520 --> 00:27:57,520
Speaker 1: maybe that's chocolate.

502
00:27:57,560 --> 00:27:59,320
Speaker 2: Chocolate, that's chocolate.

503
00:28:02,640 --> 00:28:05,200
Speaker 4: You know the movie about a man who makes chocolate.

504
00:28:05,840 --> 00:28:10,880
Speaker 4: There's a car full of shit corn. Also, I think

505
00:28:11,000 --> 00:28:13,560
Speaker 4: Keith and Michael Key is in a fat suit at

506
00:28:13,560 --> 00:28:19,160
Speaker 4: one point, like killing him. I'm not sure about this movie.

507
00:28:19,200 --> 00:28:25,320
Speaker 4: I also have so much Timothy Charalamaie that's the word fatigue. Yeah,

508
00:28:25,440 --> 00:28:26,440
Speaker 4: I'm very fatigued.

509
00:28:27,720 --> 00:28:30,080
Speaker 3: Yeah mmm mm hmmm.

510
00:28:30,760 --> 00:28:36,080
Speaker 2: So the young lad is talented. I quite liked his

511
00:28:36,560 --> 00:28:43,920
Speaker 2: performance in Dune. He was doing the thing. Yeah. Here

512
00:28:44,680 --> 00:28:48,960
Speaker 2: it seems I can't even necessarily say miscasts. It just doesn't.

513
00:28:49,000 --> 00:28:52,440
Speaker 2: Nothing seems to quite be coming together in a way

514
00:28:52,480 --> 00:28:56,760
Speaker 2: that gives you the warm guey's that a Paddington gives you.

515
00:28:56,800 --> 00:29:00,440
Speaker 2: Perhapsicus Paddington is an animated bear and precocious and able

516
00:29:00,480 --> 00:29:07,240
Speaker 2: beyond all measure. And that's a boy. He's a very

517
00:29:07,280 --> 00:29:10,240
Speaker 2: famous guy who's face for all fail. It just lacks

518
00:29:10,360 --> 00:29:15,160
Speaker 2: the whole suspension of disbelief required to kind of sink

519
00:29:15,160 --> 00:29:17,520
Speaker 2: into like, oh it's a cozy British movie. And it

520
00:29:17,600 --> 00:29:20,160
Speaker 2: was funny because as the credits wereroll and they're like

521
00:29:20,520 --> 00:29:22,880
Speaker 2: from the team that brought you Paddington as producers of

522
00:29:22,920 --> 00:29:25,240
Speaker 2: Harry Potter, I said, of course, of course they did this.

523
00:29:25,360 --> 00:29:29,160
Speaker 2: Look at these aerial shots, so CG spaces completely unnecessarily.

524
00:29:29,480 --> 00:29:33,320
Speaker 2: Britain is no shortage of adorable little shops you could

525
00:29:33,400 --> 00:29:37,320
Speaker 2: fill matt and or imitate why are we here in

526
00:29:37,360 --> 00:29:41,680
Speaker 2: this like cement circle space. I didn't understand any of

527
00:29:41,680 --> 00:29:45,360
Speaker 2: the choices made. I wasn't getting the tone or story

528
00:29:45,400 --> 00:29:47,200
Speaker 2: that I think one would hope for. And I also

529
00:29:47,240 --> 00:29:49,520
Speaker 2: think oopah oompus are so problematic and you can't just

530
00:29:49,600 --> 00:29:52,120
Speaker 2: keep doing this with rolled all properties and being like,

531
00:29:52,680 --> 00:29:55,080
Speaker 2: so we know racism was involved, but we just ran

532
00:29:55,120 --> 00:29:57,560
Speaker 2: in the other direction. So it's totally fine, we are

533
00:29:57,600 --> 00:30:00,800
Speaker 2: not addressing it. But also we're not giving up these properties.

534
00:30:00,840 --> 00:30:04,840
Speaker 2: So here, Yeah, formerly racist thing right in your face.

535
00:30:05,200 --> 00:30:07,640
Speaker 2: Enjoys keep now though it's charming, it's not. It's very annoying.

536
00:30:07,680 --> 00:30:09,640
Speaker 4: Now Hugh Grant plays that would ask.

537
00:30:09,560 --> 00:30:12,880
Speaker 1: The question what if Grant was orange?

538
00:30:13,240 --> 00:30:15,760
Speaker 2: Yeah, I will say I got the smallest chuckle out

539
00:30:15,800 --> 00:30:18,680
Speaker 2: of once we start, we can't stop dancing, and Hugrant's

540
00:30:18,720 --> 00:30:20,360
Speaker 2: ass is just in the air. I was like, yeah,

541
00:30:21,200 --> 00:30:24,880
Speaker 2: I bet this movie gets me at points because they

542
00:30:24,920 --> 00:30:28,200
Speaker 2: know how to trigger someone into crying and or like

543
00:30:28,560 --> 00:30:31,719
Speaker 2: being like, oh, but I doubt it's cohesive as an

544
00:30:31,880 --> 00:30:34,640
Speaker 2: entire film. Is where I'm asking off that trailer.

545
00:30:34,760 --> 00:30:38,320
Speaker 1: We will see. Willy Wonka is a creep in the

546
00:30:38,360 --> 00:30:44,040
Speaker 1: movies that succeed like he's so I'm not sure what

547
00:30:44,080 --> 00:30:46,800
Speaker 1: they're doing, what they're going to do with that part

548
00:30:47,040 --> 00:30:49,040
Speaker 1: like that. That is kind of what's fun about him

549
00:30:49,120 --> 00:30:51,880
Speaker 1: is there's like an edge of danger there and this

550
00:30:52,040 --> 00:30:56,080
Speaker 1: just seems to be like he's the story of an amazing,

551
00:30:56,760 --> 00:31:02,080
Speaker 1: magical kid who's good at everything and yeah, has a

552
00:31:02,080 --> 00:31:05,800
Speaker 1: lot of a lot of whimsy and gumption. Anyways, and

553
00:31:06,360 --> 00:31:10,120
Speaker 1: then across the Spider verse, just a full triumph.

554
00:31:10,400 --> 00:31:14,520
Speaker 4: Oh, best movie I've ever seen, maybe people.

555
00:31:14,760 --> 00:31:17,760
Speaker 1: Yeah, I loved it. Okay, all right, speaking of it

556
00:31:18,080 --> 00:31:22,240
Speaker 1: the best movie we've ever seen, that's yeah, I mean

557
00:31:22,840 --> 00:31:27,760
Speaker 1: it's it's great. Yeah, not great. Tucker Carlson's Man of

558
00:31:27,800 --> 00:31:32,320
Speaker 1: the Woods era seems to be so I don't know,

559
00:31:32,840 --> 00:31:36,680
Speaker 1: his show dropped. There was like the announcement that everybody

560
00:31:36,720 --> 00:31:39,000
Speaker 1: made a big deal about got one hundred and thirty

561
00:31:39,000 --> 00:31:43,440
Speaker 1: seven million tweet views. That's not people who viewed the video,

562
00:31:43,720 --> 00:31:47,960
Speaker 1: that's people who saw the tweet episode one one hundred

563
00:31:48,000 --> 00:31:52,880
Speaker 1: and twenty million. That's like a big portion of the

564
00:31:53,000 --> 00:31:57,160
Speaker 1: US population. And then it kind of disappeared off my radar.

565
00:31:57,520 --> 00:32:00,800
Speaker 1: I did not know what was happening. But yeah, so

566
00:32:01,200 --> 00:32:03,560
Speaker 1: there's like this new thing making the rounds because he's

567
00:32:03,600 --> 00:32:08,040
Speaker 1: interviewing accused rapist Andrew Taate for what appears to be

568
00:32:08,080 --> 00:32:11,160
Speaker 1: two and a half hours. Oh seems to be too

569
00:32:11,200 --> 00:32:15,360
Speaker 1: many hours for that interview to take place. But the

570
00:32:15,360 --> 00:32:18,680
Speaker 1: most surprising thing for me was that it is episode

571
00:32:19,000 --> 00:32:24,360
Speaker 1: eight of his show. We've they've kept making them and

572
00:32:24,480 --> 00:32:27,400
Speaker 1: just like nobody like really noticed or gave a shit.

573
00:32:27,760 --> 00:32:30,560
Speaker 1: They seemed there seemed to be a promise with like

574
00:32:30,600 --> 00:32:34,360
Speaker 1: the announcement in the first episode, like Elon and Carlson together,

575
00:32:34,480 --> 00:32:39,640
Speaker 1: now you know you're in trouble friends, And it's just

576
00:32:39,960 --> 00:32:43,840
Speaker 1: it doesn't seem to have taken And now I think

577
00:32:43,880 --> 00:32:46,400
Speaker 1: that's where the Andrew Tait thing's coming from, presumably like

578
00:32:46,480 --> 00:32:51,160
Speaker 1: the liver King is coming to the show soon, because

579
00:32:51,200 --> 00:32:55,560
Speaker 1: it's just like, well, this person's popular on the internet, right,

580
00:32:56,080 --> 00:32:57,200
Speaker 1: we'll go with them.

581
00:32:57,880 --> 00:32:59,960
Speaker 4: Anything for ratings.

582
00:32:59,480 --> 00:33:03,680
Speaker 1: Anything for anything for them, sweet internet ratings. But yeah,

583
00:33:03,760 --> 00:33:07,360
Speaker 1: the numbers go from one hundred and twenty million tweet

584
00:33:07,440 --> 00:33:11,720
Speaker 1: views on the first to sixty million to uh there

585
00:33:11,800 --> 00:33:14,360
Speaker 1: was Trump got indicted, So bounce back up to one

586
00:33:14,400 --> 00:33:17,920
Speaker 1: hundred and four but down to thirty two million, then

587
00:33:18,040 --> 00:33:21,640
Speaker 1: seventeen fifteen eight point six million tweet views on the

588
00:33:21,720 --> 00:33:25,400
Speaker 1: last one, which, yeah, I think in the world of

589
00:33:25,440 --> 00:33:28,520
Speaker 1: trend forecasting, that's known as a death spiral. But you

590
00:33:28,560 --> 00:33:31,720
Speaker 1: can actually see the spiral shape because there's a trailing

591
00:33:31,760 --> 00:33:37,560
Speaker 1: tale of panicked diarrhea. Yeah, that's the official I'm just

592
00:33:37,600 --> 00:33:42,000
Speaker 1: reading that off and it is chocolate, right, Yeah, But

593
00:33:42,080 --> 00:33:43,800
Speaker 1: I don't know. I'm sure there's gonna be an uptick

594
00:33:43,960 --> 00:33:47,760
Speaker 1: with Andrew Tait interview, but I don't know.

595
00:33:48,360 --> 00:33:52,120
Speaker 2: I wonder if he's a victim of like atomic habits.

596
00:33:52,320 --> 00:33:54,880
Speaker 2: You know, he's no longer on his like main network, right,

597
00:33:54,920 --> 00:33:58,440
Speaker 2: so people who are routinely watching Fox and like, okay,

598
00:33:58,480 --> 00:34:01,520
Speaker 2: so there we are, we go, it can easily find him,

599
00:34:01,840 --> 00:34:03,560
Speaker 2: Like now you have to kind of go searching for him.

600
00:34:03,600 --> 00:34:09,120
Speaker 2: And then Twitter is imploding like rapidly. And so because

601
00:34:09,160 --> 00:34:12,279
Speaker 2: I don't think his base, at least from what I

602
00:34:12,320 --> 00:34:15,799
Speaker 2: know has been, is discouraged or off of watching him,

603
00:34:15,800 --> 00:34:19,120
Speaker 2: I doubt it's his actual popularity and so much of

604
00:34:19,239 --> 00:34:21,839
Speaker 2: shifting I wanted to be his popularity. I just think

605
00:34:21,880 --> 00:34:24,120
Speaker 2: it's probably more likely that all of the shifting of

606
00:34:24,160 --> 00:34:26,840
Speaker 2: platforms and the ever changing social media landscape as it

607
00:34:26,880 --> 00:34:31,320
Speaker 2: exists currently might be interfering overall with people just naturally

608
00:34:31,360 --> 00:34:32,399
Speaker 2: being able to find him.

609
00:34:33,280 --> 00:34:33,680
Speaker 1: Yeah.

610
00:34:33,880 --> 00:34:36,720
Speaker 4: Also, these numbers are tweet views, right, so it doesn't

611
00:34:36,760 --> 00:34:41,440
Speaker 4: even mean that the person watched the episode, right.

612
00:34:41,440 --> 00:34:45,000
Speaker 1: So we actually found those numbers for like people who

613
00:34:45,080 --> 00:34:47,560
Speaker 1: actually watched the episode. And first of all, these numbers

614
00:34:47,600 --> 00:34:50,040
Speaker 1: are wildly inflated because in order to count as a

615
00:34:50,120 --> 00:34:52,759
Speaker 1: video view, you just have to have watched it for

616
00:34:52,800 --> 00:34:57,759
Speaker 1: two seconds. But they're like all a fraction of the

617
00:34:57,880 --> 00:35:01,360
Speaker 1: numbers were that you would see the tweet views, Like

618
00:35:01,440 --> 00:35:03,920
Speaker 1: his episode one was not viewed by one hundred and

619
00:35:04,000 --> 00:35:07,520
Speaker 1: twenty million people, the actual episode was viewed twenty six

620
00:35:07,520 --> 00:35:12,920
Speaker 1: point seven million times for at least two seconds. And

621
00:35:13,040 --> 00:35:15,719
Speaker 1: his latest one was not eight point six million, it

622
00:35:15,760 --> 00:35:19,320
Speaker 1: was three point eight million, which again that's two seconds

623
00:35:19,320 --> 00:35:23,560
Speaker 1: of viewing. That's about as much as his like TV

624
00:35:23,640 --> 00:35:26,640
Speaker 1: show ratings, where like people actually have to watch like

625
00:35:26,760 --> 00:35:28,960
Speaker 1: a chunk of the episode in order for it to count.

626
00:35:29,280 --> 00:35:32,919
Speaker 1: Like and this is like, you know, three point eight

627
00:35:33,200 --> 00:35:38,080
Speaker 1: million people glancing past it, watching it for two seconds

628
00:35:38,120 --> 00:35:41,719
Speaker 1: accidentally and then being like no thanks, So and the

629
00:35:41,840 --> 00:35:44,759
Speaker 1: trend is just very has to be a little discouraging.

630
00:35:45,040 --> 00:35:48,600
Speaker 1: So I'm assuming there's going to be a lot of

631
00:35:48,600 --> 00:35:51,280
Speaker 1: tweets from Elon Musk being like these numbers are actually great,

632
00:35:51,520 --> 00:35:53,919
Speaker 1: and you know he's going to be pushing these really

633
00:35:53,960 --> 00:35:57,480
Speaker 1: hard to get the numbers back up because this drastically

634
00:35:57,600 --> 00:36:02,040
Speaker 1: undercuts his narrative the Twitter is like the future of media,

635
00:36:02,120 --> 00:36:04,440
Speaker 1: and that he's doing a good job, I think is

636
00:36:04,480 --> 00:36:09,080
Speaker 1: actually the main part of his narrative. But I also

637
00:36:09,239 --> 00:36:12,880
Speaker 1: I also just think that there's there there's certainly a

638
00:36:12,920 --> 00:36:16,320
Speaker 1: sizable portion, like too large a portion of the population

639
00:36:16,520 --> 00:36:20,600
Speaker 1: that watches Tucker Carlson and thinks he makes sense and

640
00:36:20,840 --> 00:36:26,840
Speaker 1: shit like that. But I think they have in the

641
00:36:26,880 --> 00:36:30,279
Speaker 1: mainstream media and like our media echo chamber have like

642
00:36:30,520 --> 00:36:35,239
Speaker 1: over made us over estimate how big that is. And

643
00:36:35,320 --> 00:36:38,719
Speaker 1: like they're like the far right movement, Like I think

644
00:36:38,760 --> 00:36:42,400
Speaker 1: they're finding out like in a bunch of ways, across

645
00:36:42,440 --> 00:36:44,400
Speaker 1: a bunch of different stories, like we're finding out the

646
00:36:44,840 --> 00:36:49,200
Speaker 1: book bands are like only like there's like a handful

647
00:36:49,239 --> 00:36:52,839
Speaker 1: of people who made the book bands happen, the like

648
00:36:53,000 --> 00:36:56,080
Speaker 1: Mothers for Liberty is like a loose assortment of people

649
00:36:56,120 --> 00:36:58,799
Speaker 1: like from across the country who are like they're not

650
00:36:58,840 --> 00:37:02,120
Speaker 1: even from the school boards or the school districts that

651
00:37:02,400 --> 00:37:07,000
Speaker 1: they are terrorizing, and like the midterms, where I think

652
00:37:07,120 --> 00:37:08,879
Speaker 1: a lot of people were like, oh, this will be

653
00:37:09,400 --> 00:37:10,960
Speaker 1: a wake up call. There should be a wake up

654
00:37:11,000 --> 00:37:15,600
Speaker 1: call people don't like when the Republican Party goes in

655
00:37:15,640 --> 00:37:19,160
Speaker 1: this like far right direction, or at least not enough

656
00:37:19,200 --> 00:37:23,000
Speaker 1: people like it. So I don't know, it just feels

657
00:37:23,040 --> 00:37:29,279
Speaker 1: like we've been seeing stories repeatedly that's like people aren't

658
00:37:29,440 --> 00:37:33,680
Speaker 1: like this crazy. You know, like the the like super

659
00:37:33,719 --> 00:37:37,680
Speaker 1: far right is not as big as they appear to

660
00:37:37,719 --> 00:37:41,120
Speaker 1: be when in the mainstream media, and like when you're

661
00:37:41,280 --> 00:37:44,080
Speaker 1: when you're seeing the news stories that like they generate.

662
00:37:44,280 --> 00:37:48,440
Speaker 1: There's like a lot of the Westboro Baptist Church effect

663
00:37:48,520 --> 00:37:52,920
Speaker 1: happening where that organization, which actually was just a family

664
00:37:52,960 --> 00:37:56,360
Speaker 1: out in Kansas, was able to like dominate mainstream media

665
00:37:56,719 --> 00:37:58,200
Speaker 1: coverage for like a decade.

666
00:37:58,520 --> 00:37:58,759
Speaker 3: You know.

667
00:37:59,080 --> 00:38:03,440
Speaker 1: It's like the by being outrageous and infuriating, you were

668
00:38:03,480 --> 00:38:08,120
Speaker 1: able to make yourself seem like you're one of the biggest,

669
00:38:08,160 --> 00:38:13,319
Speaker 1: most important, like cultural movements instead of just being you know,

670
00:38:13,920 --> 00:38:16,440
Speaker 1: kind of making a lot of people uncomfortable.

671
00:38:17,239 --> 00:38:20,040
Speaker 2: Yeah, there's a whole digital masking as well. If you

672
00:38:20,080 --> 00:38:23,560
Speaker 2: think about, you know, the way Facebook drove us from

673
00:38:24,200 --> 00:38:27,600
Speaker 2: written work to video with false numbers. A lot of

674
00:38:27,600 --> 00:38:30,920
Speaker 2: people are claiming they're seeing similar trends on Twitter, and

675
00:38:31,040 --> 00:38:34,239
Speaker 2: that as soon as they started showing stats under tweets,

676
00:38:34,600 --> 00:38:37,680
Speaker 2: people were like, these numbers seem off as compared to like,

677
00:38:38,680 --> 00:38:41,319
Speaker 2: you know, people have independent trackers that like get all

678
00:38:41,360 --> 00:38:44,200
Speaker 2: of this information from their Twitter handles, and the numbers

679
00:38:44,200 --> 00:38:47,560
Speaker 2: are not correlating. And so it wouldn't surprise me if

680
00:38:47,600 --> 00:38:52,440
Speaker 2: even the generosity of those numbers Undertucker's show were you know,

681
00:38:52,760 --> 00:38:54,320
Speaker 2: being doctored from within.

682
00:38:54,840 --> 00:38:56,080
Speaker 3: Yeah, oh for sure.

683
00:38:56,920 --> 00:39:00,960
Speaker 1: And like, I don't know how they could have foreseen

684
00:39:01,080 --> 00:39:04,400
Speaker 1: that a pivot to video would not work, you know,

685
00:39:04,920 --> 00:39:09,279
Speaker 1: decades after that like basically broke the rest of the media.

686
00:39:09,440 --> 00:39:11,920
Speaker 1: But that seems to be one of his main strategies.

687
00:39:11,960 --> 00:39:15,200
Speaker 1: It's almost like Elon Musk's not that smart, almost like

688
00:39:15,360 --> 00:39:18,719
Speaker 1: Sean Musk is not as smart as we've all been

689
00:39:18,840 --> 00:39:21,760
Speaker 1: pretending he has. All right, let's take a quick break

690
00:39:21,920 --> 00:39:24,040
Speaker 1: and we'll come back and talk about.

691
00:39:23,800 --> 00:39:36,160
Speaker 3: AI and we're back.

692
00:39:37,080 --> 00:39:41,279
Speaker 1: And so the latest Mission Impossible dropped last night or

693
00:39:41,280 --> 00:39:45,360
Speaker 1: two nights ago. It's getting great reviews. It does indeed

694
00:39:45,440 --> 00:39:49,400
Speaker 1: seem to be about Ethan Hunt battling a self aware

695
00:39:49,480 --> 00:39:54,359
Speaker 1: AI program known as the entity which I don't know

696
00:39:54,520 --> 00:39:59,759
Speaker 1: this like he might as well from my perspective, like

697
00:39:59,800 --> 00:40:04,840
Speaker 1: that is as dramatically impactful as like he's battling a

698
00:40:04,880 --> 00:40:09,520
Speaker 1: wizard this time. It's just like okay, well that doesn't

699
00:40:09,560 --> 00:40:13,279
Speaker 1: really make sense to me or like does it's not

700
00:40:13,320 --> 00:40:15,719
Speaker 1: like really a thing, and that does. When people are

701
00:40:15,760 --> 00:40:17,880
Speaker 1: critical of the movie, they seem to say, like all

702
00:40:17,920 --> 00:40:20,160
Speaker 1: the stuff about AI kind of drags a little bit,

703
00:40:20,600 --> 00:40:26,720
Speaker 1: but and visually it is depicted as basically an evil

704
00:40:26,760 --> 00:40:31,320
Speaker 1: screensaver from nineteen ninety nine, like it's just the black

705
00:40:31,400 --> 00:40:36,640
Speaker 1: circle with like white dots of light. Three hundred million

706
00:40:36,640 --> 00:40:40,359
Speaker 1: dollar movie, by the way, but it can so. The

707
00:40:40,400 --> 00:40:44,400
Speaker 1: main claim that the movie makes about this AI and

708
00:40:44,440 --> 00:40:48,040
Speaker 1: I guess AI in general is that the program can

709
00:40:48,120 --> 00:40:53,319
Speaker 1: also see the future, like very specifically through its predictive technology.

710
00:40:53,640 --> 00:40:57,200
Speaker 1: And that's like based on what we're seeing in news

711
00:40:57,200 --> 00:41:02,360
Speaker 1: headlines like the you know, deep learning teaching computers to

712
00:41:02,440 --> 00:41:05,040
Speaker 1: predict the future is like a headline. I feel like

713
00:41:05,040 --> 00:41:08,920
Speaker 1: I've seen a hundred times spooky Artificial intelligence can accurately

714
00:41:08,920 --> 00:41:11,960
Speaker 1: predict the future, and it's about to be asked more questions,

715
00:41:12,520 --> 00:41:15,879
Speaker 1: a I can now predict crime before it happens as well.

716
00:41:16,000 --> 00:41:17,480
Speaker 1: We'll get into in a moment.

717
00:41:17,880 --> 00:41:18,680
Speaker 3: And you mean like.

718
00:41:18,640 --> 00:41:20,960
Speaker 4: That movie Minority Report.

719
00:41:21,040 --> 00:41:25,319
Speaker 1: Also, yeah, Tom Cruise Cruise, Yeah, he's he. I feel

720
00:41:25,360 --> 00:41:29,719
Speaker 1: like his sense of reality is probably more blurred by

721
00:41:29,719 --> 00:41:32,720
Speaker 1: the movies he's in than any other movie star.

722
00:41:33,560 --> 00:41:37,360
Speaker 2: Well, yeah, because he has an entire organization blurring his reality. Yeah,

723
00:41:37,440 --> 00:41:39,840
Speaker 2: before he hits set and then you're in that magical

724
00:41:39,880 --> 00:41:43,680
Speaker 2: landscape where he's made himself king. Yeah. I mean that

725
00:41:43,880 --> 00:41:46,239
Speaker 2: that guy's not living on planet earthly, he doesn't need to.

726
00:41:46,960 --> 00:41:47,520
Speaker 3: Yeah.

727
00:41:48,360 --> 00:41:50,960
Speaker 1: This movie actually was like written, it was supposed to

728
00:41:51,000 --> 00:41:54,520
Speaker 1: come out like years ago, but the production famously shut

729
00:41:54,560 --> 00:41:58,880
Speaker 1: down due to the coronavirus outbreak, which, according to reports

730
00:41:58,920 --> 00:42:02,400
Speaker 1: in February twenty twenty, was putting a real damper on

731
00:42:02,560 --> 00:42:06,720
Speaker 1: offshore location shoots. Wow, he gave us the Tom Cruise

732
00:42:07,320 --> 00:42:09,600
Speaker 1: thing where it was clear that he thought he was

733
00:42:09,920 --> 00:42:13,720
Speaker 1: the last barrier between like the end of the world

734
00:42:13,960 --> 00:42:18,160
Speaker 1: and you know, coronavirus just like killing everyone where he

735
00:42:18,239 --> 00:42:20,480
Speaker 1: was like shouting at everyone. But first of all, I

736
00:42:20,560 --> 00:42:24,279
Speaker 1: just want to say to AI, speaking of I'd like

737
00:42:24,280 --> 00:42:26,600
Speaker 1: to issue a big where were you on that one?

738
00:42:26,640 --> 00:42:30,400
Speaker 1: Dipshit to the AI on predicting the global pandemic, Like

739
00:42:30,920 --> 00:42:33,239
Speaker 1: we couldn't we couldn't see that one coming. A lot

740
00:42:33,320 --> 00:42:36,480
Speaker 1: of people saw it coming, people just mere people saw

741
00:42:36,520 --> 00:42:40,680
Speaker 1: it coming. But yeah, the whole predicting the future thing

742
00:42:41,040 --> 00:42:46,200
Speaker 1: seems to be vastly overstated. Like there's a so the

743
00:42:46,400 --> 00:42:50,720
Speaker 1: MI T researchers who had created a computer that could supposedly.

744
00:42:50,960 --> 00:42:56,319
Speaker 4: Mission impossible technology Mission Impossible Technology am I technology.

745
00:42:56,920 --> 00:43:02,960
Speaker 1: Yes, yeah, they are loosely associated. But it was basically

746
00:43:03,000 --> 00:43:06,440
Speaker 1: like they were predicting. They would show the computer a

747
00:43:06,480 --> 00:43:11,839
Speaker 1: picture and then the computer would predict, like what would

748
00:43:11,920 --> 00:43:15,160
Speaker 1: happen for the next one point five seconds in that picture?

749
00:43:16,040 --> 00:43:19,200
Speaker 1: And like that it was like someone walking across a

750
00:43:19,239 --> 00:43:21,480
Speaker 1: golf course and they were like, that thing's about to

751
00:43:21,520 --> 00:43:23,680
Speaker 1: keep walking across that golf course and they were like,

752
00:43:23,760 --> 00:43:29,560
Speaker 1: holy shit, you guys. Or like one was a wave crashing,

753
00:43:30,200 --> 00:43:33,799
Speaker 1: they were like, I think it's gonna keep crashing. Call

754
00:43:33,920 --> 00:43:38,239
Speaker 1: me crazy, and they were like, fucking a, do you

755
00:43:38,400 --> 00:43:44,400
Speaker 1: think it's brilliant? Thing's magical and yeah, and then like

756
00:43:44,920 --> 00:43:48,759
Speaker 1: MIT Mission Possible Technology also developed an algorithm that could

757
00:43:48,760 --> 00:43:51,719
Speaker 1: predict how people will greet each other and got a

758
00:43:51,719 --> 00:43:54,400
Speaker 1: lot of cool headlines for that it was trained on

759
00:43:54,440 --> 00:43:58,560
Speaker 1: YouTube videos and reruns of the Office and Desperate Housewives amazing,

760
00:43:59,040 --> 00:44:02,680
Speaker 1: and it was only right just over forty three percent

761
00:44:02,719 --> 00:44:03,200
Speaker 1: of the time.

762
00:44:03,719 --> 00:44:05,360
Speaker 2: We never greet each other like we do on Just

763
00:44:05,440 --> 00:44:07,800
Speaker 2: for Housewives. I wish we did, but like, I can't

764
00:44:07,840 --> 00:44:09,600
Speaker 2: just walk up to my enemy and slap them across

765
00:44:09,600 --> 00:44:09,959
Speaker 2: the face.

766
00:44:10,000 --> 00:44:13,680
Speaker 1: It's not reality, I know, and that sucks. And hopefully

767
00:44:13,680 --> 00:44:17,520
Speaker 1: we'll get to that world soon. That's my future that

768
00:44:17,560 --> 00:44:21,120
Speaker 1: I'm hopeful for. But so one second away from the greeting,

769
00:44:21,160 --> 00:44:23,239
Speaker 1: they could only predict it forty three percent at the time.

770
00:44:23,560 --> 00:44:27,359
Speaker 1: So this is what I'm always wondering, Like, what how

771
00:44:27,360 --> 00:44:30,920
Speaker 1: does that compare to humans? Humans making the same prediction

772
00:44:31,280 --> 00:44:34,759
Speaker 1: were right seventy one percent of the time. Oh so

773
00:44:35,960 --> 00:44:39,200
Speaker 1: much better than this AI that everyone did. You guys

774
00:44:39,239 --> 00:44:42,080
Speaker 1: ever see the sixty minutes where they like this sixty

775
00:44:42,120 --> 00:44:44,359
Speaker 1: minutes about AI. I think it came out like back

776
00:44:44,400 --> 00:44:49,560
Speaker 1: in March or April, and like Scott, I think it's

777
00:44:49,600 --> 00:44:52,440
Speaker 1: Scott Pelly, like one of the old journalists on sixty

778
00:44:52,480 --> 00:44:56,239
Speaker 1: Minutes is just like okay, like write a speech for

779
00:44:56,360 --> 00:44:59,440
Speaker 1: me about this, and then he seems to be blown

780
00:44:59,480 --> 00:45:03,239
Speaker 1: away by the amount of text it produces like he's

781
00:45:03,320 --> 00:45:07,400
Speaker 1: like that just rolled a whole speech in four seconds,

782
00:45:07,480 --> 00:45:11,040
Speaker 1: Like he's it's like he's never seen a computer before.

783
00:45:13,200 --> 00:45:17,360
Speaker 1: But I do think we're at a weird place where

784
00:45:17,400 --> 00:45:21,200
Speaker 1: people will you can just be like magic AI is

785
00:45:21,280 --> 00:45:25,360
Speaker 1: bad guy in movie, and everyone's like, yep, that makes sense,

786
00:45:25,560 --> 00:45:28,359
Speaker 1: because like we we should be afraid of AI, but

787
00:45:28,400 --> 00:45:31,960
Speaker 1: people don't really know why necessarily we should be, and

788
00:45:32,040 --> 00:45:35,600
Speaker 1: so we just you know, like it's the same thing

789
00:45:35,640 --> 00:45:39,319
Speaker 1: that happens with all our conspiracy theories, and you know,

790
00:45:39,480 --> 00:45:42,160
Speaker 1: we should be scared about the pandemic, we should be

791
00:45:42,360 --> 00:45:45,880
Speaker 1: scared about the government's response to the pandemic, but people

792
00:45:46,040 --> 00:45:49,799
Speaker 1: create like vast conspiracy theories just to make sure they're

793
00:45:49,840 --> 00:45:54,000
Speaker 1: like scared about it for nonsensical reasons. It's like we

794
00:45:54,360 --> 00:45:57,640
Speaker 1: need that like cognitive dissonance transference happening, and I feel

795
00:45:57,680 --> 00:45:59,799
Speaker 1: like we're starting to see that with AI, where they're

796
00:45:59,800 --> 00:46:01,960
Speaker 1: like AI is going to be magic and tell us

797
00:46:02,040 --> 00:46:04,040
Speaker 1: everything that could happen to us for the rest of

798
00:46:04,080 --> 00:46:10,440
Speaker 1: our lives. And that's that's not the scary thing about AI, right.

799
00:46:10,640 --> 00:46:14,480
Speaker 4: The scary thing is like Skynet, and that's the thing,

800
00:46:14,520 --> 00:46:19,000
Speaker 4: like movies have been having AI as the villain for

801
00:46:19,040 --> 00:46:23,440
Speaker 4: like decades now, and I feel like I don't know

802
00:46:23,480 --> 00:46:26,880
Speaker 4: what my point is gonna be. But remember Terminator one.

803
00:46:27,440 --> 00:46:30,560
Speaker 4: I remember the Matrix from nineteen ninety nine, over twenty

804
00:46:30,640 --> 00:46:34,320
Speaker 4: years ago. Like, I just think it's funny that AI,

805
00:46:34,520 --> 00:46:38,080
Speaker 4: I guess, hasn't evolved very much in movies.

806
00:46:39,960 --> 00:46:41,560
Speaker 1: Yeah, I don't know.

807
00:46:42,560 --> 00:46:44,480
Speaker 4: No, I mean for real, like we've been knowing though,

808
00:46:44,520 --> 00:46:45,240
Speaker 4: is what I'm saying.

809
00:46:45,760 --> 00:46:50,319
Speaker 2: Yeah, and I think it's interesting. You know, movies, by

810
00:46:50,360 --> 00:46:53,919
Speaker 2: their very nature have to heighten and dramatize in order

811
00:46:54,000 --> 00:46:57,479
Speaker 2: to effectively bring home their matches, particularly if we're looking

812
00:46:57,520 --> 00:47:02,759
Speaker 2: at genre films, and the AI has always been like,

813
00:47:03,320 --> 00:47:06,000
Speaker 2: you know, turns into a cop and now it's hunting you,

814
00:47:06,320 --> 00:47:11,240
Speaker 2: or you know, it takes away your freedom of choice,

815
00:47:11,560 --> 00:47:16,319
Speaker 2: you know, if we look at the Matrix, Yes, what's

816
00:47:16,320 --> 00:47:20,680
Speaker 2: the other one that report about minority report Like they're

817
00:47:21,560 --> 00:47:23,080
Speaker 2: that crime.

818
00:47:23,120 --> 00:47:25,240
Speaker 1: And it's said in the future. It's like a sci

819
00:47:25,239 --> 00:47:30,000
Speaker 1: fi movie where sorry, mission impossible is supposed to be,

820
00:47:30,480 --> 00:47:33,200
Speaker 1: like even though they can take masks off that make

821
00:47:33,280 --> 00:47:36,840
Speaker 1: them look exactly like the other person, like you wouldn't

822
00:47:37,000 --> 00:47:41,960
Speaker 1: see Tom Cruise fighting robots necessarily, or like no, you

823
00:47:42,000 --> 00:47:46,759
Speaker 1: know that that feels like it's a step into this

824
00:47:46,960 --> 00:47:48,560
Speaker 1: is ship that can actually happen.

825
00:47:49,200 --> 00:47:53,080
Speaker 2: Yeah, yeah, and I appreciate the grounding. I think we

826
00:47:53,400 --> 00:47:56,880
Speaker 2: got there because it's so much more tactile in this moment.

827
00:47:57,040 --> 00:48:00,759
Speaker 2: I think between if you think about your grocery store

828
00:48:00,800 --> 00:48:04,400
Speaker 2: workers who have been concerned about self checkout for like

829
00:48:04,480 --> 00:48:07,080
Speaker 2: over a decade, a lot of those stories are unionized

830
00:48:07,120 --> 00:48:10,960
Speaker 2: and have actively been like, hey, you're taking jobs from

831
00:48:11,080 --> 00:48:14,560
Speaker 2: like checkout counter people, some of whom are elderly, and

832
00:48:14,600 --> 00:48:16,440
Speaker 2: this is like the last place they can work and

833
00:48:16,480 --> 00:48:21,040
Speaker 2: make money consistently. To the writers and actors who may

834
00:48:21,280 --> 00:48:23,960
Speaker 2: be on strike by the time this comes out, and

835
00:48:24,080 --> 00:48:29,359
Speaker 2: there feared that not only could AI bes to take

836
00:48:29,400 --> 00:48:32,880
Speaker 2: their job, but I mean for performers, can my face

837
00:48:33,000 --> 00:48:37,080
Speaker 2: be doing things long, long, long after I'm dead? And

838
00:48:37,320 --> 00:48:41,760
Speaker 2: what will that look like? I think, as we're sort

839
00:48:41,760 --> 00:48:46,520
Speaker 2: of confronting these things. AI is a really freaking dope tool.

840
00:48:46,760 --> 00:48:50,080
Speaker 2: I'm so much more concerned about these executives who are like,

841
00:48:50,440 --> 00:48:52,560
Speaker 2: oh great, now I don't have to pay people. Did

842
00:48:52,560 --> 00:48:54,239
Speaker 2: you guys see the I on nine article that was

843
00:48:54,239 --> 00:48:56,479
Speaker 2: written by AI. They came out a couple days ago.

844
00:48:56,760 --> 00:48:58,680
Speaker 2: They have Internet in a tangle.

845
00:48:59,160 --> 00:49:04,560
Speaker 1: Yeah, this is like I've ever since I've been you know,

846
00:49:05,160 --> 00:49:08,880
Speaker 1: working on the Internet, creating trying to create like unique

847
00:49:09,160 --> 00:49:13,040
Speaker 1: interesting content. There's also been this push to just flood

848
00:49:13,040 --> 00:49:17,239
Speaker 1: the zone with shit. To quote Steve Bannon, like we

849
00:49:17,239 --> 00:49:20,080
Speaker 1: we used to like cracked zoned by a company that

850
00:49:20,239 --> 00:49:25,359
Speaker 1: was like also just destroying Google's search results with just

851
00:49:25,440 --> 00:49:29,520
Speaker 1: like fake articles like answers to questions and stuff. And

852
00:49:29,520 --> 00:49:32,000
Speaker 1: they were like trying to get us to like do that,

853
00:49:32,239 --> 00:49:34,760
Speaker 1: just like there was like scale, how do we scale?

854
00:49:35,040 --> 00:49:38,520
Speaker 1: And like that's been you know that that intersection of

855
00:49:38,640 --> 00:49:43,520
Speaker 1: tech and media has always been scary because it's very

856
00:49:43,560 --> 00:49:47,400
Speaker 1: easy for them to just create ninety nine million articles

857
00:49:47,560 --> 00:49:52,760
Speaker 1: using technology now that like yeah, pass like a little

858
00:49:52,760 --> 00:49:55,000
Speaker 1: bit like you have to like read a paragraph to

859
00:49:55,040 --> 00:49:57,840
Speaker 1: be like wait this this wasn't written by a human,

860
00:49:58,000 --> 00:49:58,279
Speaker 1: was it.

861
00:49:58,360 --> 00:50:02,040
Speaker 2: Yeah. So the article was like ranking the Star Wars

862
00:50:02,080 --> 00:50:06,479
Speaker 2: movies in like chronological order, I think, yeah, And they

863
00:50:06,520 --> 00:50:09,799
Speaker 2: got the dates wrong on many of them. Sometimes they

864
00:50:09,800 --> 00:50:13,280
Speaker 2: weren't even listed in chronological order. The way they were dated.

865
00:50:13,719 --> 00:50:17,839
Speaker 2: The information in individual movies was in correct, it was

866
00:50:17,920 --> 00:50:22,520
Speaker 2: just error filled, Like no logical saying editor would have

867
00:50:22,600 --> 00:50:26,200
Speaker 2: been like and publish. And what's crazy is I don't

868
00:50:26,239 --> 00:50:29,200
Speaker 2: know his entire union that's actively been like, hey, we

869
00:50:29,360 --> 00:50:31,400
Speaker 2: do not want this. It does not make sense to

870
00:50:31,440 --> 00:50:35,160
Speaker 2: ask an AI program that cannot consistently write fact checked

871
00:50:35,280 --> 00:50:39,480
Speaker 2: articles to be publishing these and then asking somebody an

872
00:50:39,600 --> 00:50:43,759
Speaker 2: editor to like go through and essentially rewrite this incorrect

873
00:50:43,800 --> 00:50:46,840
Speaker 2: work that isn't benefiting anyone, because who needs this list?

874
00:50:47,560 --> 00:50:50,279
Speaker 2: We have twenty seven thousand of this very specific Well,

875
00:50:50,360 --> 00:50:53,719
Speaker 2: this is dumb and it's wild that, you know. I'm

876
00:50:53,719 --> 00:50:58,480
Speaker 2: glad the Internet roasted the editor in chief pretty intently.

877
00:50:58,480 --> 00:51:00,760
Speaker 2: I'm really hoping that brings them back to the table

878
00:51:00,840 --> 00:51:03,760
Speaker 2: and there can be working discussions about how this company

879
00:51:03,800 --> 00:51:06,239
Speaker 2: decides to use AI and the future. But it's that

880
00:51:06,320 --> 00:51:09,040
Speaker 2: kind of stupid shit that's like really draining, I think

881
00:51:09,080 --> 00:51:12,520
Speaker 2: on workers across job titles. It's just like, what are

882
00:51:12,560 --> 00:51:14,919
Speaker 2: we what are you trying? You just want to force

883
00:51:14,920 --> 00:51:17,480
Speaker 2: people out of work, and that's not a logical thing

884
00:51:17,560 --> 00:51:21,240
Speaker 2: for you to do to yourself. You mean the product money.

885
00:51:21,120 --> 00:51:24,400
Speaker 5: Yeah, to your point, the product sucks, like the product,

886
00:51:24,520 --> 00:51:26,879
Speaker 5: like the AI as bad as it's bad at its

887
00:51:26,920 --> 00:51:29,880
Speaker 5: fucking job. Like with regards to like the self checkout

888
00:51:29,880 --> 00:51:34,000
Speaker 5: thing that the AI like can't stop people from stealing stuff,

889
00:51:34,080 --> 00:51:36,239
Speaker 5: and so the companies are like losing massive amounts of

890
00:51:36,280 --> 00:51:39,640
Speaker 5: money on theft, and then in order to combat that,

891
00:51:39,680 --> 00:51:42,880
Speaker 5: they're just like making claims that like everyone's shoplifting when

892
00:51:42,920 --> 00:51:46,200
Speaker 5: it's just like no, like you just put a like

893
00:51:47,400 --> 00:51:51,520
Speaker 5: inadequate program like as the thing in charge of like

894
00:51:51,560 --> 00:51:55,080
Speaker 5: making sure people can't steal stuff or like that they

895
00:51:55,120 --> 00:51:57,640
Speaker 5: don't want to steal stuff, and it's just not going

896
00:51:57,680 --> 00:51:59,640
Speaker 5: to it's not gonna work.

897
00:52:00,160 --> 00:52:02,799
Speaker 2: But they made it way too easy because when I

898
00:52:02,840 --> 00:52:04,640
Speaker 2: was in college, we would for sure bring up like

899
00:52:04,719 --> 00:52:08,160
Speaker 2: a full roast chicken as like some grapes. Man, we're good.

900
00:52:09,680 --> 00:52:12,520
Speaker 2: Stop no, uh, if there's a personally, this is the

901
00:52:12,560 --> 00:52:14,040
Speaker 2: possible way you could get like that used to be

902
00:52:14,080 --> 00:52:16,640
Speaker 2: somebody's job to scan and bag your stuff. There's no

903
00:52:16,719 --> 00:52:18,960
Speaker 2: possible way to get around. So that's I'm saying, though

904
00:52:19,080 --> 00:52:21,680
Speaker 2: none of these things are thought out. They just see

905
00:52:21,680 --> 00:52:24,800
Speaker 2: a way to potentially preserve money without looking at the

906
00:52:24,920 --> 00:52:25,880
Speaker 2: long term costs.

907
00:52:26,000 --> 00:52:28,600
Speaker 4: And then they still have to hire someone to monitor

908
00:52:28,760 --> 00:52:33,719
Speaker 4: the self checkout areas. So it's well, how much money

909
00:52:33,719 --> 00:52:36,920
Speaker 4: are you actually saving, mister grocery.

910
00:52:37,040 --> 00:52:40,560
Speaker 1: Yeah, big grocery, which are now owned by just like

911
00:52:40,600 --> 00:52:43,719
Speaker 1: two companies in all of America. But yeah, like so

912
00:52:44,080 --> 00:52:47,920
Speaker 1: they've also been using it in crime, like predictive crime

913
00:52:48,000 --> 00:52:51,920
Speaker 1: fighting technology. But of course, first of all, the data

914
00:52:51,960 --> 00:52:56,480
Speaker 1: that it's being fed is from police data, so it's

915
00:52:56,760 --> 00:53:00,840
Speaker 1: you know, using the it's not predicting crime. It's predicting

916
00:53:00,960 --> 00:53:05,200
Speaker 1: like where police are spending their time is essentially which

917
00:53:05,280 --> 00:53:08,879
Speaker 1: is in low income and you know, non white neighborhoods,

918
00:53:09,080 --> 00:53:14,680
Speaker 1: And it's just complete bullshit. There's one great anecdote where

919
00:53:15,080 --> 00:53:19,440
Speaker 1: Chicago's predictive policing software, created by the Illinois Institute of Technology,

920
00:53:19,440 --> 00:53:21,960
Speaker 1: compiled a list of people most likely to be involved

921
00:53:21,960 --> 00:53:25,840
Speaker 1: in a violent crime and they're, oh, no ah, AI baby,

922
00:53:26,320 --> 00:53:29,319
Speaker 1: check out this because it was generated by AI, you

923
00:53:29,360 --> 00:53:32,839
Speaker 1: can trust this. And then an investigation later found that

924
00:53:32,880 --> 00:53:36,000
Speaker 1: the software is a list of future criminals included every

925
00:53:36,080 --> 00:53:41,800
Speaker 1: single person arrested or fingerprinted in Chicago since twenty thirteen. Whoa,

926
00:53:42,120 --> 00:53:45,200
Speaker 1: So it was just like copying off of another like

927
00:53:45,560 --> 00:53:46,240
Speaker 1: data sells.

928
00:53:46,320 --> 00:53:49,240
Speaker 2: Ay I can do is it can gather and redistribute

929
00:53:49,320 --> 00:53:55,359
Speaker 2: information using keywords that the person using inputs, which means

930
00:53:55,400 --> 00:53:58,839
Speaker 2: it's logically going to be flawed because it's not understanding

931
00:53:58,880 --> 00:54:01,120
Speaker 2: the words. It's just re organizing them in a way

932
00:54:01,160 --> 00:54:03,600
Speaker 2: that it thinks make sense to you. It's not a

933
00:54:03,680 --> 00:54:08,040
Speaker 2: thinking machine. Yeah, the technology is not there yet and

934
00:54:08,080 --> 00:54:11,120
Speaker 2: people are way too eager to jump into it and

935
00:54:11,360 --> 00:54:14,239
Speaker 2: in a way that would almost assuredly collapse society. Which

936
00:54:14,239 --> 00:54:15,680
Speaker 2: is why it's insane to me that we're still having

937
00:54:15,719 --> 00:54:18,560
Speaker 2: to like explain this to people to be like they're like,

938
00:54:19,440 --> 00:54:21,760
Speaker 2: did you see the article from was it the Hollywood

939
00:54:21,800 --> 00:54:25,319
Speaker 2: Reporter that came out about the executives and how they're

940
00:54:25,440 --> 00:54:26,680
Speaker 2: choosing to fight the unions.

941
00:54:27,000 --> 00:54:28,279
Speaker 1: Yeah, by just waiting them out.

942
00:54:29,040 --> 00:54:33,319
Speaker 2: Yeah, essentially, And the key part that's resonated with me

943
00:54:33,480 --> 00:54:35,880
Speaker 2: was like, this is a necessary evil.

944
00:54:36,480 --> 00:54:36,719
Speaker 3: Yeah.

945
00:54:36,800 --> 00:54:39,239
Speaker 2: It was the verbatim words from this quote story. So

946
00:54:39,280 --> 00:54:42,040
Speaker 2: we're pretty sure this is a scare tactic to get

947
00:54:42,080 --> 00:54:46,000
Speaker 2: sad to not strike and them saying, hey, we'll wade out.

948
00:54:46,000 --> 00:54:48,319
Speaker 2: The writers will wait out the actors as well. But

949
00:54:48,840 --> 00:54:51,839
Speaker 2: I think what's really happened is they fired up the

950
00:54:51,840 --> 00:54:55,719
Speaker 2: base of unions to be like, excuse me, a necessary

951
00:54:55,840 --> 00:55:00,200
Speaker 2: evil is not destroying the beautiful industry and community that

952
00:55:00,200 --> 00:55:03,800
Speaker 2: people have tried to create here for literally over a century.

953
00:55:04,280 --> 00:55:07,319
Speaker 2: It's nonsensical. I just I can't understand the end game

954
00:55:07,840 --> 00:55:10,759
Speaker 2: other than I guess forty people will be a little

955
00:55:10,800 --> 00:55:11,520
Speaker 2: bit wealthier.

956
00:55:12,080 --> 00:55:14,239
Speaker 1: Yeah, that's all it is. Yeah, I don't think it's

957
00:55:14,280 --> 00:55:16,960
Speaker 1: going to cause society to collapse. It'll just accelerate the

958
00:55:16,960 --> 00:55:21,160
Speaker 1: thing that's already happening, which is just like grotesque inequality

959
00:55:21,320 --> 00:55:24,760
Speaker 1: like gets worse and like they're able to hire fewer

960
00:55:24,800 --> 00:55:29,400
Speaker 1: people to do jobs. And also the other thing that

961
00:55:29,400 --> 00:55:31,840
Speaker 1: that seems to be happening is like the quality of

962
00:55:31,920 --> 00:55:36,560
Speaker 1: everything is getting worse, and like that's going to keep happening,

963
00:55:36,719 --> 00:55:39,239
Speaker 1: you know, like TV shows, movies or But.

964
00:55:39,160 --> 00:55:43,120
Speaker 2: To my point, isn't that like if we can track

965
00:55:43,280 --> 00:55:45,799
Speaker 2: revolutions based off the price of bread, right, Like, so

966
00:55:45,840 --> 00:55:48,160
Speaker 2: when people can no longer afford bread, that's when they

967
00:55:48,200 --> 00:55:50,520
Speaker 2: riot in the streets and take out leaders and get

968
00:55:50,600 --> 00:55:54,640
Speaker 2: very serious about their actions because everybody's base staple for survival.

969
00:55:55,200 --> 00:55:59,959
Speaker 2: Then we have a fairly large The entertainment community is huge.

970
00:56:00,040 --> 00:56:01,920
Speaker 2: I know some people think this is like a very

971
00:56:01,960 --> 00:56:05,080
Speaker 2: small or elite space, but it's ginormous, and most of

972
00:56:05,080 --> 00:56:07,960
Speaker 2: the people working in it are like working class people.

973
00:56:08,360 --> 00:56:11,760
Speaker 2: And so if you eliminate everyone who I mean, because essentially,

974
00:56:11,840 --> 00:56:16,200
Speaker 2: what when AI gets good enough, when it's actually usable,

975
00:56:16,640 --> 00:56:21,040
Speaker 2: it will eliminate everybody who is of average talent. Right.

976
00:56:21,840 --> 00:56:24,719
Speaker 2: What you do is you write pretty cute Hallmark stories

977
00:56:25,000 --> 00:56:28,080
Speaker 2: and you're pretty good at that, and they're successful enough

978
00:56:28,120 --> 00:56:30,840
Speaker 2: to get commercials into air frequently. Think you can probably

979
00:56:30,840 --> 00:56:32,200
Speaker 2: buy a house, and you can feed your kids and

980
00:56:32,200 --> 00:56:33,600
Speaker 2: send them to college, and you can have a very

981
00:56:33,600 --> 00:56:36,960
Speaker 2: comfortable life. If AI can do bad job, then you're

982
00:56:37,000 --> 00:56:40,160
Speaker 2: wiping out hundreds of thousands of jobs, and that is

983
00:56:40,320 --> 00:56:43,120
Speaker 2: really really bad news for like Los Angeles, the county.

984
00:56:44,040 --> 00:56:46,400
Speaker 2: I just to me, I know, it's like a small

985
00:56:46,480 --> 00:56:49,960
Speaker 2: first step, but to me, like the dominoes seem large

986
00:56:49,960 --> 00:56:54,759
Speaker 2: and looming absolutely like probable, like if things are not

987
00:56:54,800 --> 00:56:58,719
Speaker 2: stopped immediately. And I really think that that's the takeaway

988
00:56:58,800 --> 00:57:01,879
Speaker 2: from the WGH CAN and I really do hope SAG

989
00:57:01,960 --> 00:57:04,759
Speaker 2: joints because it's it's not just going to stop at

990
00:57:04,760 --> 00:57:06,759
Speaker 2: the entertainment industry. It's going to continue to brawl out

991
00:57:06,760 --> 00:57:10,319
Speaker 2: to it. Like we are one American workforce, yeah, and

992
00:57:10,320 --> 00:57:12,920
Speaker 2: we're all about to be impacted by this ship. It's crazy.

993
00:57:14,360 --> 00:57:17,200
Speaker 1: But yeah, I think if people had a better sense

994
00:57:17,200 --> 00:57:19,800
Speaker 1: also of how shitty AI is at its job. It

995
00:57:19,840 --> 00:57:24,000
Speaker 1: would it would help, you know, help the cause of

996
00:57:24,120 --> 00:57:26,920
Speaker 1: being like we don't want you to do a like

997
00:57:27,400 --> 00:57:30,560
Speaker 1: take a program that is like better than you know

998
00:57:30,640 --> 00:57:33,280
Speaker 1: what we could have gotten a year ago when if

999
00:57:33,360 --> 00:57:36,360
Speaker 1: we were like Microsoft word write me a screenplay, Like yeah,

1000
00:57:36,400 --> 00:57:38,840
Speaker 1: that's not good. It's not gonna be good, but like

1001
00:57:39,080 --> 00:57:43,640
Speaker 1: it's still not good. And like, you know, all all

1002
00:57:43,680 --> 00:57:46,880
Speaker 1: of anything that you're applying AI to still has all

1003
00:57:46,960 --> 00:57:51,760
Speaker 1: the problems that your previous systems had because it's using

1004
00:57:51,800 --> 00:57:55,800
Speaker 1: those previous systems as input. So just the the fact

1005
00:57:55,840 --> 00:57:59,680
Speaker 1: that like news articles about these things fail to focus

1006
00:57:59,680 --> 00:58:02,720
Speaker 1: on like the main point of the study and instead

1007
00:58:02,760 --> 00:58:06,360
Speaker 1: are just like it's predicting the future. And then like

1008
00:58:06,440 --> 00:58:10,720
Speaker 1: that leads into our you know, our mission impossible films,

1009
00:58:11,000 --> 00:58:13,360
Speaker 1: our number, like our modern day constitution?

1010
00:58:14,000 --> 00:58:14,880
Speaker 3: What what's that?

1011
00:58:15,240 --> 00:58:20,840
Speaker 1: What's going to happen? But anyways, well, Caitlin, such a

1012
00:58:20,840 --> 00:58:22,760
Speaker 1: pleasure having you on the podcast.

1013
00:58:23,200 --> 00:58:25,720
Speaker 4: The pleasure is online. Thank you so much.

1014
00:58:26,160 --> 00:58:29,600
Speaker 1: Where can people find you? Follow you all that good stuff?

1015
00:58:30,440 --> 00:58:34,880
Speaker 4: You can follow me on the social media platforms of

1016
00:58:35,040 --> 00:58:40,000
Speaker 4: lost track? What's the one that's the Instagram? One threads yeah,

1017
00:58:40,120 --> 00:58:42,920
Speaker 4: I guess I'm on that. Have I posted anything? No,

1018
00:58:43,320 --> 00:58:45,360
Speaker 4: but you can follow me there if you want and

1019
00:58:45,440 --> 00:58:48,440
Speaker 4: the other places. Also listen to my podcast, the Bechdel

1020
00:58:48,480 --> 00:58:52,560
Speaker 4: Cast that I co host with Jamie loftis Speaking of AI.

1021
00:58:53,160 --> 00:58:57,760
Speaker 4: The episode we released today is on the Disney Channel

1022
00:58:57,800 --> 00:59:03,280
Speaker 4: original movie Classic mart House. So you're welcome everybody.

1023
00:59:03,960 --> 00:59:07,600
Speaker 2: Yes, what a win for us. I can't wait you listen.

1024
00:59:08,920 --> 00:59:13,400
Speaker 4: And if you live in or near La, I've got

1025
00:59:13,400 --> 00:59:18,920
Speaker 4: some stand up comedy shows coming hit. I'll if you

1026
00:59:18,920 --> 00:59:22,520
Speaker 4: go to my website Kaitlin Durante dot com, the information

1027
00:59:22,720 --> 00:59:26,920
Speaker 4: will be there. But I have one July twenty eighth.

1028
00:59:27,000 --> 00:59:29,440
Speaker 4: It is at eleven pm, but that shouldn't stop you

1029
00:59:29,480 --> 00:59:32,880
Speaker 4: from going. That's a Friday night. And then I have

1030
00:59:33,360 --> 00:59:35,720
Speaker 4: That's Comedy Night in America.

1031
00:59:35,760 --> 00:59:36,600
Speaker 3: And so I've.

1032
00:59:36,440 --> 00:59:39,640
Speaker 4: Got paint nights, I've got trivia nights, and I've got

1033
00:59:39,800 --> 00:59:44,440
Speaker 4: comedy Night. I have another comedy night on August second.

1034
00:59:44,800 --> 00:59:48,600
Speaker 4: That one is a more reasonable hour of eight o'clock

1035
00:59:49,120 --> 00:59:49,560
Speaker 4: at night.

1036
00:59:49,880 --> 00:59:54,160
Speaker 1: There you go, so check that. We'll go check all

1037
00:59:54,360 --> 00:59:57,040
Speaker 1: of those places. Is there a work of media that

1038
00:59:57,080 --> 00:59:57,920
Speaker 1: you've been enjoying?

1039
00:59:58,240 --> 01:00:02,080
Speaker 4: Yes? These days I use plug a movie. This one

1040
01:00:02,120 --> 01:00:05,520
Speaker 4: came out about a month ago, but it's still in

1041
01:00:05,600 --> 01:00:07,560
Speaker 4: a lot of theaters, I think, so I would recommend

1042
01:00:07,600 --> 01:00:10,840
Speaker 4: people watch The Blackening. I thought it was super guys

1043
01:00:11,320 --> 01:00:12,960
Speaker 4: and had a blast.

1044
01:00:13,880 --> 01:00:18,160
Speaker 1: Nice Joelle, thank you so much for guest hosting on

1045
01:00:18,280 --> 01:00:21,360
Speaker 1: the show. Where can people find you and follow you?

1046
01:00:21,960 --> 01:00:27,080
Speaker 2: Yeah? Okay, so guys go to dinner dashfilm dot com.

1047
01:00:27,400 --> 01:00:30,080
Speaker 2: It's dash like a hyphen. It's as simple. If you

1048
01:00:30,120 --> 01:00:33,160
Speaker 2: need help finding it, it's on every app I'm on,

1049
01:00:33,240 --> 01:00:35,400
Speaker 2: which is all of them. Just go to the link

1050
01:00:35,440 --> 01:00:39,920
Speaker 2: in my bio and it'll pop up. Yeah, what's my handle?

1051
01:00:40,200 --> 01:00:41,760
Speaker 2: It's Joel. Want to find me all over the internet?

1052
01:00:41,760 --> 01:00:43,760
Speaker 2: Actual money gets j O E l O E m

1053
01:00:43,800 --> 01:00:46,640
Speaker 2: O N I Q you guys promoting things. It's a challenge.

1054
01:00:46,640 --> 01:00:51,480
Speaker 2: I'm gonna get better at it. A piece of media

1055
01:00:51,640 --> 01:00:57,200
Speaker 2: I have been enjoying. It is a total cop out

1056
01:00:57,200 --> 01:01:00,000
Speaker 2: because everyone already knows it shocks no one but have y'allo.

1057
01:01:00,240 --> 01:01:02,800
Speaker 2: The Bear it's great. I honestly, when people are like

1058
01:01:02,840 --> 01:01:04,360
Speaker 2: I haven't gotten into it yet, I'm like, I know,

1059
01:01:04,960 --> 01:01:07,360
Speaker 2: there's a mass number of things you could be watching.

1060
01:01:07,480 --> 01:01:09,880
Speaker 2: I know it's called The Bear, and it looks like

1061
01:01:09,920 --> 01:01:12,960
Speaker 2: it might be dramatic and depressing. I promise you it's not.

1062
01:01:13,280 --> 01:01:16,960
Speaker 2: It's so fiery and entertaining and funny, and the food

1063
01:01:17,040 --> 01:01:19,760
Speaker 2: looks so good and it makes your heart feel good

1064
01:01:19,840 --> 01:01:21,480
Speaker 2: and sometimes makes you a little bit sad, but it's

1065
01:01:21,520 --> 01:01:24,120
Speaker 2: totally worth it because the journey is so well written

1066
01:01:24,160 --> 01:01:27,320
Speaker 2: and performed. I congrats to Io with every who's so

1067
01:01:27,400 --> 01:01:31,000
Speaker 2: many nominations this year. Watching her career, it's so inspirational.

1068
01:01:31,360 --> 01:01:33,560
Speaker 2: Watch the Bear folks. Give it all the love it deserves.

1069
01:01:33,600 --> 01:01:37,080
Speaker 2: It's it's good for your soul. It's really good. What

1070
01:01:37,200 --> 01:01:38,120
Speaker 2: about you, jack.

1071
01:01:38,080 --> 01:01:40,600
Speaker 1: I'm taking that one very slowly. I'm still in the

1072
01:01:40,600 --> 01:01:42,720
Speaker 1: first season, but every time I watched it, I'm like, man,

1073
01:01:42,920 --> 01:01:43,720
Speaker 1: I really like that.

1074
01:01:43,800 --> 01:01:46,520
Speaker 2: Sure, there's no problem going slow. It's it will be

1075
01:01:46,560 --> 01:01:48,800
Speaker 2: there for you and it will remain great.

1076
01:01:48,840 --> 01:01:52,320
Speaker 1: My wife, like when I was like out of town

1077
01:01:52,360 --> 01:01:55,840
Speaker 1: one time, like left me behind on like episode five,

1078
01:01:56,040 --> 01:01:58,000
Speaker 1: like she just like binge the rest of.

1079
01:01:57,960 --> 01:01:58,920
Speaker 4: It, and so.

1080
01:02:00,520 --> 01:02:03,200
Speaker 1: Like I'm just squeezing it in and I can't do

1081
01:02:03,280 --> 01:02:06,640
Speaker 1: it when we're doing our TV watch and so.

1082
01:02:07,840 --> 01:02:09,640
Speaker 2: Watch it, Jackie, be like we could have watched this

1083
01:02:09,720 --> 01:02:11,360
Speaker 2: together this time.

1084
01:02:13,960 --> 01:02:16,680
Speaker 4: Did you intentionally say my wife like borat or was

1085
01:02:16,720 --> 01:02:17,760
Speaker 4: that unintentional?

1086
01:02:18,720 --> 01:02:22,040
Speaker 1: I didn't say, just a little bit, just.

1087
01:02:25,680 --> 01:02:26,280
Speaker 2: You're like my.

1088
01:02:26,280 --> 01:02:30,600
Speaker 1: Wife, my wife. That's just how I said, my way.

1089
01:02:30,840 --> 01:02:33,080
Speaker 1: You can find me on Twitter at Jack Underscore, O'Brien.

1090
01:02:33,400 --> 01:02:37,600
Speaker 1: You can find me on threads at Jack Underscore. Oh underscore, Brian,

1091
01:02:37,960 --> 01:02:40,880
Speaker 1: I'm on threads and so many underscores in the night.

1092
01:02:41,480 --> 01:02:46,520
Speaker 1: And a couple of tweets. I've been enjoying Sydney Battle

1093
01:02:46,680 --> 01:02:49,360
Speaker 1: tweeted if you texted me and I didn't respond, can

1094
01:02:49,400 --> 01:02:50,880
Speaker 1: you text me again to bring.

1095
01:02:50,720 --> 01:02:51,560
Speaker 3: It back to the top.

1096
01:02:51,760 --> 01:02:58,440
Speaker 1: I'm too scared to scroll down there, which yeah, uh.

1097
01:02:58,520 --> 01:03:01,000
Speaker 1: And then PJ Haven's tweeted is water and season right

1098
01:03:01,040 --> 01:03:04,720
Speaker 1: now it's hitting so hard, Oh my god? Which yeah,

1099
01:03:04,760 --> 01:03:08,960
Speaker 1: Water's good right now. It's good, delicious peak water. You

1100
01:03:08,960 --> 01:03:11,440
Speaker 1: can find us on Twitter at daily Zeitgeist. We're at

1101
01:03:11,440 --> 01:03:13,960
Speaker 1: the Daily Zeitgeist on Instagram. We have a Facebook fan

1102
01:03:14,040 --> 01:03:16,360
Speaker 1: page and a website daily zeitguist dot com, where we

1103
01:03:16,360 --> 01:03:19,960
Speaker 1: post our episodes and our footnotes put no link off

1104
01:03:20,040 --> 01:03:22,960
Speaker 1: to the information that we talked about in today's episode,

1105
01:03:23,360 --> 01:03:26,440
Speaker 1: as well as a song that we think you might enjoy.

1106
01:03:27,120 --> 01:03:29,560
Speaker 1: Miles out today, so we don't have his song Recks.

1107
01:03:29,600 --> 01:03:32,280
Speaker 1: He's on the road back tomorrow. But we have the

1108
01:03:32,320 --> 01:03:36,080
Speaker 1: special tree of getting a song recommendation from super producer Justin.

1109
01:03:36,360 --> 01:03:39,560
Speaker 1: Super producer Justin. Is there a song that you think

1110
01:03:39,600 --> 01:03:40,400
Speaker 1: people might enjoy?

1111
01:03:40,720 --> 01:03:40,800
Speaker 5: Uh?

1112
01:03:40,880 --> 01:03:41,480
Speaker 3: Yeah, I do.

1113
01:03:41,880 --> 01:03:45,720
Speaker 6: Considering we have two film people in the building today

1114
01:03:45,760 --> 01:03:48,400
Speaker 6: with us, I wanted to suggest a song that has

1115
01:03:48,600 --> 01:03:51,480
Speaker 6: like a very strong visual component when it comes to

1116
01:03:51,880 --> 01:03:54,440
Speaker 6: the music video, so I'll be linking off to that

1117
01:03:54,560 --> 01:03:57,800
Speaker 6: in the footnotes. This is a song called Smoke Break

1118
01:03:57,960 --> 01:04:02,240
Speaker 6: Dance by Mick Jenkins featuring Jid or Ji D. This

1119
01:04:02,400 --> 01:04:06,280
Speaker 6: track is super super good. It's very jazzy like boombab

1120
01:04:06,400 --> 01:04:10,040
Speaker 6: type of hip hop with you know, obviously incredible lyricism

1121
01:04:10,120 --> 01:04:16,080
Speaker 6: from Mick. But the video is has so many powerful visuals.

1122
01:04:16,120 --> 01:04:18,080
Speaker 6: It's like a feast for the eyes. Like every couple

1123
01:04:18,080 --> 01:04:21,800
Speaker 6: of seconds there's something beautiful happening with the art and

1124
01:04:21,880 --> 01:04:24,640
Speaker 6: playing off the lyrics. It's a really cool interplay of

1125
01:04:25,000 --> 01:04:27,840
Speaker 6: words and visuals. So you can go ahead and check

1126
01:04:27,880 --> 01:04:30,800
Speaker 6: this song out in the footnotes, that's Mick Jenkins Smoke

1127
01:04:30,840 --> 01:04:32,440
Speaker 6: Break Dance featuring Jid.

1128
01:04:32,800 --> 01:04:34,720
Speaker 1: All right, Well, The Daily zeit guys is a production

1129
01:04:34,760 --> 01:04:37,760
Speaker 1: of iHeartRadio. For more podcasts from iHeart Radio, the iHeartRadio,

1130
01:04:37,800 --> 01:04:40,400
Speaker 1: ap Apple Podcast, or wherever you listen to your favorite shows.

1131
01:04:40,560 --> 01:04:42,840
Speaker 1: That is gonna do it for us this morning. We

1132
01:04:42,920 --> 01:04:45,040
Speaker 1: are back this afternoon to tell you what is trending

1133
01:04:45,240 --> 01:04:47,680
Speaker 1: and we'll talk to you all then. Bye Hike,