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Speaker 1: So I plugged in my earbuds, which are substandard but

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Speaker 1: might be the best we can do.

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Speaker 2: This is a substandard podcast.

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Speaker 3: Yeah, fun true, Well, allow me to lightly Vicky.

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Speaker 2: How do you pronounce your last name? Because we have

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Speaker 2: a bet going? I think it's pronounced shabow VICKI shabow.

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Speaker 1: And what's the other alternative?

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Speaker 2: I don't know. I'm all, I'm really an Adam Sandler.

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Speaker 1: It's shabo, like pretty much answer to anything.

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Speaker 4: So it's fine, Oh shabo, Hello the Internet, and welcome

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Speaker 4: to season four ten, episode four.

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Speaker 2: Now this episode five isn't it week? Episode four of Guys.

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Speaker 2: It's a production of iHeartRadio as a podcast where you

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Speaker 2: take a deepdab into American chair consciousness. And it is Friday,

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Speaker 2: October seventeenth, twenty twenty five. I just ate a kiwied

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Speaker 2: and Kiwi and I got a little Kiwi seed in

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Speaker 2: my throat. Oh, so that's what's going on with me?

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Speaker 2: Aren't we doing well? Eating?

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Speaker 3: Kiwis ten good, ten seven GOLDI and Kiwi. It's national

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Speaker 3: Oh straight Edge Day for all the people that are

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Speaker 3: straight edge.

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Speaker 2: You know what I mean? If you're not down, never were?

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Speaker 2: I know I have homies that are straight edge to

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Speaker 2: this day. It's also a Black Poetry Day.

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Speaker 3: It's National Mammography Day, National Mulligan Day, National pasta Day.

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Speaker 2: Oh, straight edge doesn't smoke either, or do they smoke? No?

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Speaker 3: No, sorry, this new cigarette whimsical no alcohol, no drugs, alcohol, tobacco,

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Speaker 3: drugs no. Yeah, keeping all keep all that's out. It's

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Speaker 3: the straight edge lifestyle. Almost Church of Latter Day Saints.

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Speaker 3: They're like edging on Church of Latter Day Saints. But

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Speaker 3: I bet they have Do they have caffeine?

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Speaker 2: You know, depends depends on where you're at. Well, my

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Speaker 2: body is a temple, so that all sounds good to me.

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Speaker 2: My name is Jack O'Brian aka whoa Jason? Watch out Jack,

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Speaker 2: he's stan Lee's son. Whoa Jason? He's a skateboarder. That

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Speaker 2: one Curtzy Christy, I'm Gucci Mane and uh and Miles

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Speaker 2: fucking with me and tell me that Jason Lee was

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Speaker 2: stan Lee's son.

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Speaker 3: Yeah, that was actually that was an early missed like

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Speaker 3: early internet misinformation, I remember, but then it was my name. Yeah,

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Speaker 3: when my name is Earl came out, he got cleared

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Speaker 3: up real quick because there's nobody was terrifying.

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Speaker 2: Jason Lee Let's Stan Lee's. He was in maul Rets

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Speaker 2: with with his dad, So he was a comic book

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Speaker 2: guy in Malrets. Wasn't he exactly brody? There it is.

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Speaker 2: I'm thrilled to be joined as always by my co host,

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Speaker 2: mister Miles Grad.

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Speaker 3: Yes, it's Miles Great, the showgun with no gun Akay,

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Speaker 3: the Lord of Lancasham, North, Hollywood's very own thank you

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Speaker 3: so much for having me.

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Speaker 2: You're welcome. I gotta say it. Eight years now, Jack,

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Speaker 2: eight years now years and you thanked me every day.

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Speaker 2: And you know what, I don't often thank you for joint,

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Speaker 2: for being here, for having me as you about this,

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Speaker 2: how I talk about this every time I said, you know,

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Speaker 2: I've never noticed, never says things back, just says yeah,

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Speaker 2: you're welcome. You like, yeah, why are you? Why are

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Speaker 2: you thanking me? But no, yes, eight years I think

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Speaker 2: almost eight glorious years of marriage. Miles were thrilled to

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Speaker 2: be joined in our third seat by the Senior Fellow

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Speaker 2: for Gender Equity, Paid Leave and Care Policy and the

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Speaker 2: director and founder of the Entertainment Initiative at the Better

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Speaker 2: Life Lab. She's drinking water out of a comically large

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Speaker 2: beer Stein please welcome to the show, Vicky Saber.

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Speaker 3: You guys, Oh well, we try, We try in a

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Speaker 3: world where it seems there's a lot of darkness.

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Speaker 2: You know, how could you not have fun when talking

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Speaker 2: about what's going on? What I said, right right, it's

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Speaker 2: I'm very curious for you. You're in DC and the

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Speaker 2: work you're doing is basically diametrically opposed to all the

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Speaker 2: values that are coming out of this current administration. What

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Speaker 2: I'm stretched out talking about it.

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Speaker 3: And then I and I have friends, like my partner,

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Speaker 3: she's from DC, so a lot of people in her

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Speaker 3: orbit and families are federal workers. And there's all varying

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Speaker 3: forms of existential dread of getting rift. Everyone is saying

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Speaker 3: that for shorthand, and I'm like rift and like reduction

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Speaker 3: in force. And I was like, yeah, what's what's just

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Speaker 3: what's it like?

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Speaker 2: What's it like? Right? Yeah, No, that's what they say.

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Speaker 1: Yeah, what's it like? Well, I was walking down the

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Speaker 1: street yesterday and there were like four National Guard walking

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Speaker 1: towards me with rifles.

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Speaker 2: That's fun.

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Speaker 1: That's kind of what it's like right now. Yeah, it's

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Speaker 1: a little I would say two things. I think the

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Speaker 1: communities of people are coming together in ways that are

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Speaker 1: really exciting, Like neighbors are showing up for each other. Yeah,

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Speaker 1: there's like people and fatigues with weapons all over the place,

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Speaker 1: and people are scared and restaurants are kind of empty. Yeah,

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Speaker 1: and yeah, a lot of people don't have jobs or

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Speaker 1: don't know if they will have jobs.

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Speaker 2: Do you feel like it's worth it because you can

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Speaker 2: wear your watch outside again, like the administration keeps talking

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Speaker 2: about you can wear hyper expensive watches outside in DC.

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Speaker 1: Yeah, that doesn't really make up for it for me.

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Speaker 2: I can I work three ye rolexes now because I

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Speaker 2: can afford them? Of course. Right, you might get arrested

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Speaker 2: by the National Guard or as we call it, zip

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Speaker 2: zap zopped by the guard. Getting fired. Rift is so weird.

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Speaker 2: It's like we're just man. I mean, I'm reducing the

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Speaker 2: workforce and ruining people's lives. No.

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Speaker 3: I have a friend who works at HHS and they

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Speaker 3: are just like, every day you just wonder if you

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Speaker 3: get the rift alert And I'm like, at first, I

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Speaker 3: was like, am I not? I just wasn't savvy.

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Speaker 2: On the term. But yeah, that the acronym for reduction

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Speaker 2: enforce no foundation. Yeah, all right, Vicky we're going to

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Speaker 2: get to know you a little bit better in a moment. First,

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Speaker 2: a couple of things we're talking about. We're going to

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Speaker 2: talk about the world, the tech economy. I guess chat

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Speaker 2: GPT just announced that they're partnering with Walmart to make

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Speaker 2: capitalism even more frictionless and their product worse. We're breaking

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Speaker 2: this product that people are in the process of adopting

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Speaker 2: in record time. So we're going to talk about that.

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Speaker 2: We're going to talk about a new study that went

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Speaker 2: viral on Wall Street about how circular, aka incestuous, the

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Speaker 2: flow chart of money is on the AI side, it's

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Speaker 2: like a handful of companies just all slashing money back

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Speaker 2: and forth between each other, and that could be a

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Speaker 2: bad thing because as they note the statistics coming from there,

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Speaker 2: like we're going to do a blind item here and

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Speaker 2: say we're one of the plays, their statistics are very

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Speaker 2: opaque and chat GPT like they forget they're doing a

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Speaker 2: blind on the next sentence. So we'll talk about that

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Speaker 2: and just generally like there's a lot of articles also

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Speaker 2: coming out about how America is like behind the curve

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Speaker 2: behind China on the on technology robotics, and they they're

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Speaker 2: written with a tenor of fear, and I just want

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Speaker 2: to say I feel fine about this. So all of

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Speaker 2: that plenty more. But first, VICKI, we do like to

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Speaker 2: ask our guests, what is something from your search history

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Speaker 2: that's revealing about who you are?

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Speaker 1: Well? I love Broadway and I love musicals, and they

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Speaker 1: are like the thing that gives me joy in this

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Speaker 1: moment of you know a lot. So I have windows

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Speaker 1: open trying to figure out what I should go see

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Speaker 1: when I'm next in New York.

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Speaker 2: Any early contenders.

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Speaker 1: Definitely want to see Chess. Definitely want to see Ragtime.

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Speaker 1: Another fun fact about me, I go to Adult Broadway

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Speaker 1: camp every year. Uh so spend spend a week in

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Speaker 1: New York and sing and dance and uh yeah, meet

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Speaker 1: other cool people from across the country who, like may

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Speaker 1: or may not. Most of them are talented. We're all

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Speaker 1: like varying levels. I would never want to do this professionally,

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Speaker 1: but it's super fun.

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Speaker 2: What's your vocal range?

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Speaker 3: Well, just hit us with hit us with a little riff.

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Speaker 3: Sorry if you don't if you know I'm singing. We

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Speaker 3: just heard Laura Trump sing I Won't back down.

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Speaker 2: By Tom Petty. So cool.

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Speaker 1: Well, this past summer, I sang finished the Fight from Stuffs,

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Speaker 1: which was like very on point, just about the fight

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Speaker 1: for women's equality. Uh so I'm not going to do

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Speaker 1: that for you.

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Speaker 2: You mean to put you on this spot. I could,

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Speaker 2: I couldn't. I could lot with Laura Trump offering to

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Speaker 2: she could be the halftime performer if they asked me.

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Speaker 2: I don't know, I'm just an idea. I mean you

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Speaker 2: think about how mad the left would be if you

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Speaker 2: asked me to perform at the super Bowl halftime show

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Speaker 2: help my fledgling career. Yeah, start doing that too, guys.

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Speaker 2: Think about how mad the left would be if you

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Speaker 2: let me make the next Pixar movie. Yeah, right, right right? Yeah, Vicky,

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Speaker 2: are you I'm going to go out on a limb?

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Speaker 2: You must be a theater kid.

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Speaker 1: I was a theater kid.

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Speaker 2: Yeah yeah, because I'm like adult.

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Speaker 3: Broadway camp sounds like an extension of theater kid who's like,

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Speaker 3: I'm not I can't give up the stake.

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Speaker 1: So this, this camp I go to is called Broadway Weekends,

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Speaker 1: and it's basically like all theater kids that have grown

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Speaker 1: up and become you know, insurance adjusters and pediatricians and

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Speaker 1: obstetricians and lawyers and policy nerds like me, And yeah,

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Speaker 1: it's really like a lot of fun.

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Speaker 2: That's just that's probably pretty good networking too, where it's

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Speaker 2: like it's like meet a lot of interesting people at.

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Speaker 1: I meet a lot of people. Yeah, I've really I've

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Speaker 1: really enjoyed it. Lots of like it's good to see

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Speaker 1: that there is I don't know, a shared bond of Yeah, sure,

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Speaker 1: you know, grown up theater kids all across the country,

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Speaker 1: like some this year there was like a newly retired

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Speaker 1: pharmacist from Ohio and a drag king also from Ohio

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Speaker 1: and owns a Hamburger restaurant, and like preschool teachers from

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Speaker 1: New Jersey and other teachers from Sacramento. So yeah, super

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Speaker 1: super fun.

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Speaker 2: Nice. Nice. What is something you think is underrated?

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Speaker 1: I think, on point for our conversation, entertainment that helps

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Speaker 1: people see themselves reflected on screen is very underrated.

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Speaker 2: Yeah. I want to talk to you about this because

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Speaker 2: I totally disagree. I We were talking before we started

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Speaker 2: recording about this new study that you shared with us

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Speaker 2: that your organization put together that found that consumers like

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Speaker 2: to see the things they deal with on a daily

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Speaker 2: basis reflected on screen in the entertainment that they consume

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Speaker 2: things like stubbing your toe or you know, having billship pay,

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Speaker 2: having jobs. I've never noticed that, like we have jobs,

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Speaker 2: but like the friend don't, or they like have like

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Speaker 2: little five minute interludes. But for the most part, they

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Speaker 2: just live in expensive apartments and don't really work or

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Speaker 2: ever have to worry about anything ever, and like exist

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Speaker 2: in a world free of economic gravity, the economic forces

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Speaker 2: of gravity. But yeah, what did your study find?

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Speaker 1: Yeah, thank you so much for having me on to

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Speaker 1: talk about this also. So our study was conducted by

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Speaker 1: the firm market Cast, which works for lots of studios

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Speaker 1: and production companies, and they surveyed for US thirteen hundred

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Speaker 1: more than thirteen hundred people who are streaming subscribers or

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Speaker 1: in households that are streaming subscribers who watch five hours

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Speaker 1: or more of programming a week, so people who are

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Speaker 1: watching and consuming media, And what they found is overwhelmingly

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Speaker 1: ninety two percent of viewers say that seeing stories around

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Speaker 1: work and family on screen is important to them, including

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Speaker 1: nearly half who said it's very important to them, and

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Speaker 1: that people are seventy nine percent of viewers are better

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Speaker 1: able to connect with characters that have work, family, and

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Speaker 1: care circumstances that are like their own, right and so

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Speaker 1: and we found, Yeah, people want to see relate to

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Speaker 1: job concerns and financial concerns and caregiving concerns, and they

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Speaker 1: want to see characters on screen navigating these things and

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Speaker 1: finding solutions and dealing with conflict and feeling supported because

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Speaker 1: so many of us don't feel supported.

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Speaker 2: In those things.

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Speaker 3: Yeah, And like I think for me, watching TV in

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Speaker 3: the nineties and when this was like the height of

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Speaker 3: this kind of thing where it's like I don't know,

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Speaker 3: these characters live here and you're watching them do the

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Speaker 3: thing definitely contributed to my financial illiteracy and just the

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Speaker 3: idea that it's like yeah, and like you can just

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Speaker 3: kind of like have it like that, I think eventually,

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Speaker 3: because that's and I'm assuming that's all there is. That's

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Speaker 3: it's not a positive effect. It probably has on like

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Speaker 3: younger people or just people generally trying to make sense

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Speaker 3: of the world when their entertainment is like obscured like this.

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Speaker 1: Yeah, I mean to the point that Jack made earlier,

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Speaker 1: like people on on screen like often don't have jobs,

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Speaker 1: or if they do have jobs, it's sort of like

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Speaker 1: they go there, but you never see it. Yeah, women

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Speaker 1: in particular on screen are usually either shown at home

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Speaker 1: or at work.

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Speaker 2: Rather is about a blogger who lives like the richest

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Speaker 2: human being on the face of the universe.

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Speaker 1: Like right, without without a particular statement of income. Though,

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Speaker 1: I will say that the reboot and Just Like That,

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Speaker 1: which just finished this final season, they actually I thought

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Speaker 1: did a decent job with some of the characters kind

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Speaker 1: of talking about both their their work lives and their

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Speaker 1: home lives, and for parents on that show, the struggle

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Speaker 1: between the two and so obviously that's like a rarefied,

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Speaker 1: very wealthy environment.

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Speaker 2: Yeah, and we're not going to claim that reboot for

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Speaker 2: the cause here. I think that's bad. That would be

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Speaker 2: bad for the cause. Well, there were.

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Speaker 1: Things I liked about it. There are things I actually

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Speaker 1: really liked about it, but but yeah, like I I

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Speaker 1: wanted to know more about the servers and the restaurants

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Speaker 1: that the Friends went to and the domestic workers that

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Speaker 1: we're caring for them, and you know, our research kind

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Speaker 1: of shows that would have made the story and really

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Speaker 1: all stories more interesting in some ways.

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Speaker 3: Yeah, because more people are servers in this country than

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Speaker 3: are dynastic media figures who you know, live in these

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Speaker 3: impossible brownstones, but it looks great, and I get that

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Speaker 3: it starts off as escapism for people. It's like, well,

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Speaker 3: I'm trying to get away from the toil I have,

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Speaker 3: like toiling every day, and I watch this other sort

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Speaker 3: of texture of life. But I think now, especially since

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Speaker 3: the like the pandemic lockdowns, people have just become so

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Speaker 3: much more and more aware of like how people are

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Speaker 3: sort of portraying their lives or what values are trying

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Speaker 3: to sort of you know, disseminate out there like with

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Speaker 3: certain characters or lifestyle.

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Speaker 1: So yeah, I think the thing that's really interesting to

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Speaker 1: me and our research also a couple of different things.

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Speaker 1: But to your point, you know, I think people might

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Speaker 1: go to television or film for escapism, but what they

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Speaker 1: want to find are the characters that are relatable and

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Speaker 1: sort of sticky to them. And what makes a character

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Speaker 1: relatable or sticky or like somebody that you would want

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Speaker 1: to have coffee with or get to know, is when

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Speaker 1: that person's life somehow responds to or reflects your own.

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Speaker 1: And so, you know, our research showed that viewers want

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Speaker 1: is overwhelmingly want to see work, family, and care stories

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Speaker 1: embedded into shows. About science and technology into apocalyptic you know, horror,

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Speaker 1: into shows about other things. And I think that that's

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Speaker 1: really positive, you know, for for creatives and executives to know,

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Speaker 1: and for audiences really to say, like, this is what

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Speaker 1: we want to see when we're turning on our screens

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Speaker 1: or booting up our streaming service.

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Speaker 3: Because it feels like it's probably also suffering from like

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Speaker 3: executives probably being pitched this kind of stuff from writers

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Speaker 3: who are like, this is kind of like real and

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Speaker 3: they're like, nah, no, one.

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Speaker 2: Never met with that. Yeah.

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Speaker 3: Yeah, it's like, well, you have nine nannies, right, yeah, yeah,

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Speaker 3: but they're but hey, they're paid well and my kids

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Speaker 3: love them, so I don't know about this other stuff

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Speaker 3: you're trying to bitch me.

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Speaker 2: It's like, well, this is kind of real life for

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Speaker 2: a lot of people. So when's this poor guy get

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Speaker 2: rescued by his millionaire uncle. That's what I'm wondering. Yeah, yeah,

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Speaker 2: we've all got them, right. I mean that's how I

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Speaker 2: was able to work as an assistant for seven years. Yeah,

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Speaker 2: my millionaire parents. Yeah yeah. I feel like that that

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Speaker 2: is the that has to be the secret backstory to

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Speaker 2: every character on most TV shows. It's just like, oh, yeah,

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Speaker 2: they come from dynastic wealth. Yeah they don't mention that

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Speaker 2: because people who come from dynastic wealth never do. But yeah,

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Speaker 2: what is something that you think is overrated?

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Speaker 1: I definitely sort of male superheroes who solve problems all

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Speaker 1: by themselves and don't need anybody else.

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Speaker 3: Yeah, I just need I just need to be alone

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Speaker 3: for a little bit.

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Speaker 2: I'm going to go.

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Speaker 1: Solve this problem. And you know, I got here on

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Speaker 1: my own, and I'm going to fix the problem on

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Speaker 1: my own.

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Speaker 2: And do you think that's Do you think that's helped

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Speaker 2: or harmed American culture?

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Speaker 1: Totally harmed American culture?

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Speaker 2: Questions?

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Speaker 1: Yeah, no, I think like one of the big problems

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Speaker 1: with American culture and why it is that we have

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Speaker 1: such a hard time investing in policies that would help

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Speaker 1: most people, things like paid family and medical leave, which

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Speaker 1: I've worked on for the last sixteen years trying to

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Speaker 1: get the federal government and states to pass laws. Why

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Speaker 1: we have a hard time with childcare and funding home

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Speaker 1: and community based care and other services for older and

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Speaker 1: disabled people. Why we have a hard time recognizing that

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Speaker 1: we need like supports and protections in workplaces and different

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Speaker 1: cultural norms and society is that we have this idea

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Speaker 1: that everybody should just pull themselves up by their bootstraps,

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Speaker 1: and you know, kind of like if you work hard

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Speaker 1: and play by the rules, you're going to get ahead,

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Speaker 1: and it's up to each person and their own like

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Speaker 1: ingenuity to figure everything out. But that that turns out

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Speaker 1: not to be the case for many reasons.

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Speaker 2: Yeah, so you don't think Batman made the right move

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Speaker 2: investing in bat shaped fighter jets, spending spending that money

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Speaker 2: like donating to better mental health care.

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Speaker 5: It takes a village, but you know, yeah right, yeah,

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Speaker 5: that would have health care, you know, will give Arkham

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Speaker 5: an upgrade, maybe get a little more progressive than making

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Speaker 5: every sending everybody to a spooky prison if they if

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Speaker 5: they're suspected of having mental health problems.

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Speaker 1: Yeah, I think we could do a lot better.

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Speaker 2: What do you think interesting now? I'm just saying, like, yeah,

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Speaker 2: he just keeping his identity a secret, doing everything all

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Speaker 2: by himself, with like one elderly butler and an arms dealer.

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Speaker 2: I don't know. I think it's an interesting choice.

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Speaker 3: Your neighbor probably too, right, Yeah, yeah, yeah, I think

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Speaker 3: most people are like some guys. I'm like, man, if

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Speaker 3: I could just shoot laser beams out of my eyes,

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Speaker 3: I'd be able to have a relationship with my brother again.

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Speaker 2: Yeah. Also, yeah, we also talk about it. Another version

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Speaker 2: of this myth making, which is when people succeed in

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Speaker 2: America and write biographies about themselves, they cut all the

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Speaker 2: helpers out. It's always a hero's journey narrative that starts

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Speaker 2: in a garage. For some reason, you got to start

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Speaker 2: in a garage. Yeah, and then you know, the people

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Speaker 2: who actually help them along the way are like recast

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Speaker 2: to be people who are like, you'll never make it stupid,

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Speaker 2: Your idea of a personal computer is dumb.

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Speaker 1: Yeah, the hero's journey is like a very strong narrative.

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Speaker 2: America loves that shit. Yeah, me too.

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Speaker 1: And yet yeah, I mean, I think it's so much

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Speaker 1: more interesting. I think it's more interesting to surround that

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Speaker 1: heroes journey with the supports and the barriers that have

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Speaker 1: gotten in their way that are not just about them,

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Speaker 1: but are sort of more reflective of all of us.

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Speaker 1: And I think that people see themselves in the stories

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Speaker 1: of others in ways that can be really powerful.

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Speaker 2: It's just not how the world works. The world doesn't

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Speaker 2: work like that at all. It's a fun story that

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Speaker 2: people like to tell themselves when they're children, and then

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Speaker 2: you get out of that and realize you need people

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Speaker 2: and by asking for help and helping other people, that's

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Speaker 2: the only way to like happiness and fulfillment. Yeah, well,

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Speaker 2: all of our stories just like go back to that

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Speaker 2: central idea.

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Speaker 1: Right. What's interesting in this research that we did is

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Speaker 1: that actually, people overwhelmingly want to see characters being helped,

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Speaker 1: being supported by friends and family and coworkers and community.

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Speaker 1: They want to see people looking for and finding solutions.

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Speaker 1: They want to see people navigating and coming out of

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Speaker 1: conflict because those are much more realistic and relatable to

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Speaker 1: most people's lives.

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Speaker 3: That's right, And it's because it's relatable. And I think

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Speaker 3: the other thing too, is like there's just such a

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Speaker 3: culture of like this concept of being like invulnerability for

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Speaker 3: American people that's fed to us through media, like, well,

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Speaker 3: you're not vulnerable, You're strong, you can just you you.

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Speaker 2: Laser beam everybody with your eyes. You're fine.

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Speaker 3: And I think because of that we many people, I

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Speaker 3: think most people who understand the need for real, like

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Speaker 3: human centered progressive policy changes understand that we are all

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Speaker 3: very vulnerable and you and many people like it's a

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Speaker 3: really entrenched I think in conservative thinking. That's like they

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Speaker 3: don't want to acknowledge that we are vulnerable, that things

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Speaker 3: can happen and we do need help sometimes, and it's

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Speaker 3: always said you like, not me, not me, right until

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Speaker 3: it is.

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Speaker 1: What's super interesting actually in our research is conservative, more

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Speaker 1: conservative viewers and more progressive viewers both want these stories.

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Speaker 1: They're both interested in seeing people navigating conflict and finding

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Speaker 1: support in their communities. They're both interested in people looking forward,

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Speaker 1: finding solutions, and so sometimes they think there's perceptions of

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Speaker 1: you know, sort of what the other side and amusing

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Speaker 1: air quotes kind of wants, and that turns out maybe

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Speaker 1: not to be true, but because we believe it, we

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Speaker 1: perpetuate it, and storytellers perpetuate it. And the other thing. Actually,

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Speaker 1: there's really interesting new research among men and young men

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Speaker 1: in particular about how held back they feel by this

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Speaker 1: idea that they're supposed to be, that they're supposed to

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Speaker 1: be in vulnerable, that they're supposed to be self sufficient,

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Speaker 1: that they're supposed to be providers, that they're supposed to

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Speaker 1: be strong, when what they and like many men in

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Speaker 1: surveys and multiple research projects really want is to be

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Speaker 1: able to be vulnerable and to talk about their feelings

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Speaker 1: and to be able to provide care without stigma and

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Speaker 1: to feel a little bit less of that like protector

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Speaker 1: and provider pressure that I think is creating like a

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Speaker 1: lot of stress and toxicity and mental health problems. Yeah,

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Speaker 1: you know, and for women it's these other messages that

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Speaker 1: are are being sent. And you know, one of the

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Speaker 1: things we think a lot about here is like how

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Speaker 1: do you break down gender stereotypes and gender expectations and

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Speaker 1: ways that let people just be human?

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Speaker 3: Right, And so you're not spending tens of thousands of

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Speaker 3: dollars at an alpha male in vulnerability camp where.

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Speaker 2: Yeah that recent episode, yeah where I think yesterday's episode

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Speaker 2: where we broke down a man ritual where instead of

435
00:23:44,960 --> 00:23:47,879
Speaker 2: like they did end up holding each other and like

436
00:23:48,000 --> 00:23:51,000
Speaker 2: kind of had a break doing a little snuggle, but

437
00:23:51,040 --> 00:23:54,480
Speaker 2: they had to like do this man ritual where he

438
00:23:54,640 --> 00:23:57,840
Speaker 2: was like you push through my chest and I like

439
00:23:58,000 --> 00:24:03,080
Speaker 2: push and then I say move forward and you scream

440
00:24:03,280 --> 00:24:04,439
Speaker 2: I'm a man over and.

441
00:24:06,359 --> 00:24:08,879
Speaker 3: Wow, I mean we just watching him were like, man, like,

442
00:24:09,000 --> 00:24:12,280
Speaker 3: this is what happens when you. Luckily, I was raised

443
00:24:12,280 --> 00:24:15,000
Speaker 3: in a home where you know, my father was very emotional,

444
00:24:15,080 --> 00:24:19,119
Speaker 3: like it was emote and that was okay. My grandfather,

445
00:24:19,359 --> 00:24:22,119
Speaker 3: his father, my grandfather was a like he was a

446
00:24:22,160 --> 00:24:23,800
Speaker 3: famous crier in my family.

447
00:24:23,960 --> 00:24:24,560
Speaker 2: Wow, And that.

448
00:24:24,600 --> 00:24:26,040
Speaker 3: Was and that was because he was always he would

449
00:24:26,040 --> 00:24:28,040
Speaker 3: always cry because he was overcome with joy all the

450
00:24:28,040 --> 00:24:29,720
Speaker 3: time and it was very beautiful to see and that

451
00:24:29,800 --> 00:24:33,280
Speaker 3: was very normal. But I also recognized too, most a

452
00:24:33,280 --> 00:24:36,000
Speaker 3: lot of other households are just not You don't have

453
00:24:36,040 --> 00:24:38,119
Speaker 3: that example A lot of times like stop crying, be

454
00:24:38,280 --> 00:24:40,000
Speaker 3: strong whatever I'm crying.

455
00:24:40,240 --> 00:24:43,920
Speaker 6: Yeah, yeah, stop whining and right right right, But then

456
00:24:43,920 --> 00:24:45,800
Speaker 6: also realizing to just like, no, you have to be

457
00:24:45,960 --> 00:24:48,320
Speaker 6: like you have to accept that these are all parts

458
00:24:48,359 --> 00:24:51,280
Speaker 6: of who we are, and like to push that down

459
00:24:51,440 --> 00:24:52,360
Speaker 6: is just going to lead you.

460
00:24:52,320 --> 00:24:54,720
Speaker 3: To spending tens of thousands of dollars out an alpha

461
00:24:54,760 --> 00:24:56,440
Speaker 3: male camp or almost screams at you and then you

462
00:24:56,480 --> 00:24:57,280
Speaker 3: get a hug at the end.

463
00:24:57,600 --> 00:25:00,520
Speaker 1: But also if your father or grandfather were asked by

464
00:25:00,560 --> 00:25:03,439
Speaker 1: somebody whether they were criers and they didn't think that

465
00:25:03,480 --> 00:25:05,080
Speaker 1: it was going to be acceptable for them to say

466
00:25:05,080 --> 00:25:07,320
Speaker 1: that they were, would they still fess up to it

467
00:25:07,400 --> 00:25:09,160
Speaker 1: or would they pretend that that wasn't the case.

468
00:25:09,560 --> 00:25:10,280
Speaker 2: No, they would.

469
00:25:10,440 --> 00:25:14,720
Speaker 3: Luckily, they're very like self assured and who they are.

470
00:25:14,840 --> 00:25:18,399
Speaker 3: I think, yeah, I think, yeah, yeah, yeah, they were

471
00:25:18,480 --> 00:25:18,960
Speaker 3: very much.

472
00:25:19,040 --> 00:25:21,720
Speaker 2: On the other side, I've never cried, never even felt

473
00:25:21,760 --> 00:25:26,600
Speaker 2: like it. Oh yeah, well don't over here. So that's

474
00:25:26,680 --> 00:25:29,639
Speaker 2: I mean, those are just the two sides of either.

475
00:25:30,520 --> 00:25:32,520
Speaker 1: I guess should we try to make you cry right now?

476
00:25:32,880 --> 00:25:36,720
Speaker 2: Yeah? You should.

477
00:25:36,760 --> 00:25:40,600
Speaker 3: Just immediately, just the mere asking asking him, Jack, do

478
00:25:40,640 --> 00:25:41,320
Speaker 3: you need to cry?

479
00:25:41,480 --> 00:25:47,119
Speaker 2: No? No, no, all right, let's take a quick break.

480
00:25:47,280 --> 00:25:49,560
Speaker 2: We'll come back. We'll talk about the tech economy and

481
00:25:49,800 --> 00:26:02,560
Speaker 2: other fun stuff. Will be right back, and we're back.

482
00:26:02,880 --> 00:26:09,119
Speaker 2: We're back, and all right. AI is all the people

483
00:26:09,800 --> 00:26:13,560
Speaker 2: the forces of capital are talking about these days. You

484
00:26:13,640 --> 00:26:17,520
Speaker 2: got the good, you got the bad. On the good side, exciting,

485
00:26:17,720 --> 00:26:22,520
Speaker 2: exciting new announcement. Chat GPT is partnering with Walmart and

486
00:26:22,600 --> 00:26:26,520
Speaker 2: they've announced a long shit that will allow people to

487
00:26:26,720 --> 00:26:30,640
Speaker 2: buy shit from the company through conversations with AI and

488
00:26:31,080 --> 00:26:36,680
Speaker 2: even more exciting, in no way creepier, Chat GPT will

489
00:26:36,920 --> 00:26:41,400
Speaker 2: help customers quote anticipate their needs before they do, kind

490
00:26:41,400 --> 00:26:44,600
Speaker 2: of like Minority Report. But the end goal is selling

491
00:26:44,640 --> 00:26:46,240
Speaker 2: you a bunch of shit you don't need.

492
00:26:47,000 --> 00:26:50,239
Speaker 3: That's I didn't realize the ant I knew that their

493
00:26:50,240 --> 00:26:52,640
Speaker 3: whole thing was like a lot of other AI companies

494
00:26:52,640 --> 00:26:54,800
Speaker 3: have like browser extensions, so you can still be within

495
00:26:54,800 --> 00:26:57,040
Speaker 3: a browser and do things like by doing this, you're.

496
00:26:56,920 --> 00:27:01,600
Speaker 2: Like, we're keeping you inside the chat GPT infrastructure to

497
00:27:01,680 --> 00:27:06,320
Speaker 2: do all these things. And now how many errant purchases

498
00:27:06,440 --> 00:27:06,800
Speaker 2: because I.

499
00:27:06,760 --> 00:27:11,520
Speaker 3: Remember, like like in the beginning of Prime, they're like

500
00:27:11,560 --> 00:27:15,119
Speaker 3: this three year old boy seventeen thousand dollars worth of

501
00:27:15,440 --> 00:27:17,760
Speaker 3: matchsticks or whatever, and they're like, I don't know, my

502
00:27:17,880 --> 00:27:21,439
Speaker 3: kids just clicked ad to Kart five hundred times with AI,

503
00:27:21,640 --> 00:27:23,600
Speaker 3: Like are you just gonna be like, oh, man, I

504
00:27:23,600 --> 00:27:25,919
Speaker 3: wish I had a better pair of slippers, like just ordered.

505
00:27:25,920 --> 00:27:30,199
Speaker 2: They yeah, yeah, you do check your front porch, and

506
00:27:30,200 --> 00:27:33,880
Speaker 2: it's like the slippers are gonna suck. Because they have

507
00:27:34,000 --> 00:27:38,159
Speaker 2: tried to integrate with these sorts of platforms before, and

508
00:27:38,280 --> 00:27:41,680
Speaker 2: when they did, CHET GPT sometimes recommended sketchy third party

509
00:27:41,760 --> 00:27:44,760
Speaker 2: vendors or non existent vendors, which I don't even know

510
00:27:44,800 --> 00:27:48,160
Speaker 2: what that could possibly me. They're probably saying, like you

511
00:27:48,160 --> 00:27:52,080
Speaker 2: you want to go to Wonka's Hut for that, right well,

512
00:27:52,119 --> 00:27:55,879
Speaker 2: because they always like they make up fake sources, so

513
00:27:55,920 --> 00:27:58,199
Speaker 2: why not make up fake stores that can get you

514
00:27:58,280 --> 00:28:01,240
Speaker 2: the thing that you're looking for. I didn't know Walmart

515
00:28:01,280 --> 00:28:02,200
Speaker 2: was all in on AI.

516
00:28:02,320 --> 00:28:04,960
Speaker 3: I thought they were fine with just like becoming hoovering

517
00:28:05,040 --> 00:28:08,160
Speaker 3: up all of the transactions that exist in the country

518
00:28:08,320 --> 00:28:10,480
Speaker 3: at the expense of like smaller shops.

519
00:28:10,560 --> 00:28:12,720
Speaker 2: But now I guess they're only on AI. Know, yeah,

520
00:28:12,760 --> 00:28:15,720
Speaker 2: they I think they like one their chief executive like

521
00:28:16,400 --> 00:28:20,360
Speaker 2: warned of the quote existential threat of AI to literally

522
00:28:20,440 --> 00:28:23,120
Speaker 2: every job a long time ago. And by a long

523
00:28:23,160 --> 00:28:25,920
Speaker 2: time ago, I mean two weeks ago. And now he's

524
00:28:25,960 --> 00:28:29,479
Speaker 2: made good on that warning. He's like, I told you, guys, sorry,

525
00:28:29,720 --> 00:28:33,000
Speaker 2: I gave you two weeks notice. That's why I said.

526
00:28:33,040 --> 00:28:35,960
Speaker 2: It's because it was I was. I was sort of

527
00:28:36,000 --> 00:28:39,920
Speaker 2: telegraphing this move about embracing chat GPT okay good today.

528
00:28:39,960 --> 00:28:42,040
Speaker 2: But it is part of a trend that we've talked

529
00:28:42,040 --> 00:28:44,400
Speaker 2: about before on the show of like removing all friction

530
00:28:44,520 --> 00:28:48,520
Speaker 2: from consumption so you don't notice the purchases, you don't

531
00:28:48,600 --> 00:28:50,800
Speaker 2: have to deal with anybody, you don't have to deal

532
00:28:50,840 --> 00:28:57,200
Speaker 2: with any difficulty when it comes to spending money, accumulating shit,

533
00:28:57,400 --> 00:29:00,400
Speaker 2: and now that inflation is so out of control, like

534
00:29:01,000 --> 00:29:05,440
Speaker 2: being bankrupted by consumption. And also I do just think

535
00:29:05,480 --> 00:29:09,320
Speaker 2: this is probably not great for Chatgy, Like this is

536
00:29:09,360 --> 00:29:13,040
Speaker 2: a pretty quick move to slopification of a product that

537
00:29:13,120 --> 00:29:16,400
Speaker 2: I feel like, you know, according to chat GPT numbers

538
00:29:16,440 --> 00:29:21,160
Speaker 2: that like they're telling everybody is like extremely popular, but

539
00:29:21,360 --> 00:29:24,600
Speaker 2: I don't feel like it's at the plate like Facebook

540
00:29:24,640 --> 00:29:28,680
Speaker 2: and Google got like crazy market share before they started

541
00:29:28,880 --> 00:29:31,760
Speaker 2: breaking their product with that, you know, mm hmm.

542
00:29:32,040 --> 00:29:36,520
Speaker 3: Yeah, Well they're moving fast. They're moving very very fast.

543
00:29:36,560 --> 00:29:38,280
Speaker 3: I mean, even like those numbers. I was reading another

544
00:29:38,320 --> 00:29:42,240
Speaker 3: article about how like how dubious those numbers are, because

545
00:29:42,760 --> 00:29:44,920
Speaker 3: that's the next thing I want to talk about, is

546
00:29:45,200 --> 00:29:49,000
Speaker 3: that because like I just like personally, like I hear

547
00:29:49,080 --> 00:29:51,400
Speaker 3: some people being like, yeah, I use it sometimes for

548
00:29:51,480 --> 00:29:54,000
Speaker 3: like you know, writing emails or something, but I don't.

549
00:29:54,400 --> 00:29:57,440
Speaker 2: It's not like everywhere, and people aren't like using it

550
00:29:57,840 --> 00:30:02,240
Speaker 2: constantly like every but like according to their numbers, it is.

551
00:30:02,800 --> 00:30:07,520
Speaker 2: But there are some There's a recent article and chart

552
00:30:07,680 --> 00:30:09,840
Speaker 2: that went viral on Wall Street that just tracked like

553
00:30:09,920 --> 00:30:13,760
Speaker 2: how in money is being invested by like in the

554
00:30:13,840 --> 00:30:18,040
Speaker 2: AI sector, and it's just like these seven blocks and

555
00:30:18,040 --> 00:30:20,760
Speaker 2: they're all just like investing like billions of dollars back

556
00:30:20,760 --> 00:30:24,720
Speaker 2: and forth between each other, which could lead to a

557
00:30:24,720 --> 00:30:28,040
Speaker 2: domino effect if it turned out that, like one of

558
00:30:28,080 --> 00:30:31,280
Speaker 2: these companies was full of shit. For instance, right, do

559
00:30:31,360 --> 00:30:31,680
Speaker 2: we not.

560
00:30:31,720 --> 00:30:34,480
Speaker 1: Learn anything from like the tech bubble bursting?

561
00:30:35,160 --> 00:30:35,400
Speaker 2: Right? No?

562
00:30:35,800 --> 00:30:38,160
Speaker 3: I mean the one benefit with like the dot com

563
00:30:38,200 --> 00:30:40,880
Speaker 3: thing is like at least we were laying infrastructure for

564
00:30:40,960 --> 00:30:44,000
Speaker 3: the Internet, like there was like fiber optic cable and

565
00:30:44,080 --> 00:30:47,000
Speaker 3: like now I mean like the more sort of charitable

566
00:30:47,040 --> 00:30:50,360
Speaker 3: descriptions I read about like these data processing centers, Like, well,

567
00:30:51,120 --> 00:30:54,320
Speaker 3: maybe we can use the data processing centers after the

568
00:30:54,400 --> 00:30:59,520
Speaker 3: bubble pops for what. I don't It's still not entirely

569
00:30:59,520 --> 00:31:02,440
Speaker 3: clear to me, like what that is. But yeah, yeah,

570
00:31:02,520 --> 00:31:03,720
Speaker 3: here we are, they said.

571
00:31:03,840 --> 00:31:06,560
Speaker 2: You know, it could be like the aerospace industry and

572
00:31:06,600 --> 00:31:10,400
Speaker 2: the twenties through the fifties, where like Boeing funded its customers,

573
00:31:10,440 --> 00:31:13,240
Speaker 2: it funded suppliers, it owned its suppliers, like it was

574
00:31:13,280 --> 00:31:16,720
Speaker 2: all very like financially incestuous, but like it just needed

575
00:31:16,720 --> 00:31:18,719
Speaker 2: to be because like they were the only companies with

576
00:31:18,760 --> 00:31:23,600
Speaker 2: that technology. But it could also be very risky if,

577
00:31:23,880 --> 00:31:28,400
Speaker 2: for instance, there isn't as much demand as they claim

578
00:31:28,520 --> 00:31:33,160
Speaker 2: there is. And that's something that like the reason they

579
00:31:33,200 --> 00:31:36,400
Speaker 2: did this study is they were like, there's one major

580
00:31:36,600 --> 00:31:42,480
Speaker 2: player who is very opaque. The interviewer said, you wrote

581
00:31:42,480 --> 00:31:47,080
Speaker 2: increasingly You wrote quote increasingly complex transactions make it challenging

582
00:31:47,160 --> 00:31:50,640
Speaker 2: to evaluate how demand for AI is developing. Could you

583
00:31:50,680 --> 00:31:53,920
Speaker 2: explain why these transactions make it challenging? And then the

584
00:31:54,000 --> 00:31:56,360
Speaker 2: person who did the studies said, I think the opacity

585
00:31:56,720 --> 00:32:00,000
Speaker 2: we have published work for from some of my colleagues

586
00:32:00,160 --> 00:32:02,680
Speaker 2: that suggests a lot of the numbers from a certain

587
00:32:02,760 --> 00:32:08,680
Speaker 2: player are maybe somewhat aspirational. We just don't see that clarity,

588
00:32:09,000 --> 00:32:11,320
Speaker 2: which is kind of the reason why we wrote this note.

589
00:32:11,640 --> 00:32:15,320
Speaker 2: And the commitments seem somewhat binding. So some of open ais,

590
00:32:15,400 --> 00:32:18,880
Speaker 2: I mean, uh so, some of open AI's commitments with

591
00:32:19,040 --> 00:32:21,880
Speaker 2: other providers, chip makers, et cetera. They appear to be

592
00:32:21,960 --> 00:32:25,360
Speaker 2: somewhat binding, but we don't see their financial statements.

593
00:32:26,360 --> 00:32:30,480
Speaker 3: So freaks me out, just like the institutional investment in

594
00:32:30,720 --> 00:32:34,200
Speaker 3: AI too, Like people's pensions are being like tied to

595
00:32:34,280 --> 00:32:37,920
Speaker 3: this stuff. That's the really horrific part is that this

596
00:32:38,120 --> 00:32:41,719
Speaker 3: is all just this huge gamble on a thing that

597
00:32:41,760 --> 00:32:45,040
Speaker 3: they keep insisting people want to use and it's just

598
00:32:45,120 --> 00:32:50,000
Speaker 3: not there. But it's the the it's the pensions and

599
00:32:50,080 --> 00:32:52,600
Speaker 3: those kinds of things that really that's really where the

600
00:32:52,680 --> 00:32:54,160
Speaker 3: damage is going to be done. I mean, like I've

601
00:32:54,160 --> 00:32:57,280
Speaker 3: sure in there like Fantasy World. It's like were artificial

602
00:32:57,320 --> 00:33:00,560
Speaker 3: general intelligence has achieved people better watch. It's like, no,

603
00:33:00,680 --> 00:33:03,520
Speaker 3: if the bubble pops, what about people's retirements? What about

604
00:33:03,520 --> 00:33:07,200
Speaker 3: these investments? What about companies who are overly invested in it,

605
00:33:07,480 --> 00:33:10,520
Speaker 3: who now have to lay off thousands of employees because

606
00:33:10,520 --> 00:33:11,000
Speaker 3: they took a.

607
00:33:11,040 --> 00:33:15,240
Speaker 2: Huge hit from the from the AI bubble popping. So yeah,

608
00:33:15,360 --> 00:33:20,240
Speaker 2: I like I think that There's this writer Daniel Bezner,

609
00:33:20,560 --> 00:33:23,680
Speaker 2: who is one of the hosts of the American Prestige

610
00:33:23,720 --> 00:33:29,120
Speaker 2: podcast and what's that he was on before many years ago. Yeah, yeah,

611
00:33:29,160 --> 00:33:33,040
Speaker 2: and he was saying that he thinks that this because

612
00:33:33,320 --> 00:33:36,200
Speaker 2: like we've been talking about this idea for like since

613
00:33:36,240 --> 00:33:39,360
Speaker 2: the pandemic, that it just feels like the economy in

614
00:33:39,440 --> 00:33:42,320
Speaker 2: quotes like the Wall Street thing that people treat as

615
00:33:42,360 --> 00:33:45,800
Speaker 2: the economy has just like left the earth. Like when

616
00:33:45,840 --> 00:33:49,000
Speaker 2: there were like you know, people were no longer working

617
00:33:49,040 --> 00:33:52,520
Speaker 2: and stuff, the Wall Street was just like doing fine

618
00:33:52,680 --> 00:33:55,000
Speaker 2: and just like coasting by, and it was like, oh,

619
00:33:55,080 --> 00:33:59,600
Speaker 2: they've insulated themselves from everybody else. It's just this like

620
00:34:00,200 --> 00:34:04,360
Speaker 2: sort of money inflation machine that they have at a

621
00:34:04,440 --> 00:34:07,000
Speaker 2: high level that they're able to just like kind of

622
00:34:07,120 --> 00:34:11,040
Speaker 2: keep inflated with like hype and excitement and like whatever

623
00:34:11,080 --> 00:34:14,719
Speaker 2: news stories they're reading. He is at least suggesting that

624
00:34:14,920 --> 00:34:17,520
Speaker 2: it could work the other way. He said, AI is

625
00:34:17,520 --> 00:34:20,040
Speaker 2: obviously a bubble, but to my mind, a very different

626
00:34:20,080 --> 00:34:23,280
Speaker 2: one than two thousand and eight or nineteen twenty nine crashes.

627
00:34:23,560 --> 00:34:26,600
Speaker 2: AI investment has been largely limited to the very wealthy

628
00:34:26,600 --> 00:34:30,280
Speaker 2: and enormous corporations and relates primarily to stock market inflation.

629
00:34:30,880 --> 00:34:33,960
Speaker 2: Itself indicative of how the nature of capitalism has changed

630
00:34:34,000 --> 00:34:37,000
Speaker 2: since two thousand and eight, when the government essentially provided

631
00:34:37,000 --> 00:34:39,719
Speaker 2: the capitalist class with free money. So basically, I think

632
00:34:39,719 --> 00:34:43,400
Speaker 2: it's possible the bubble could pop without the widespread social

633
00:34:43,440 --> 00:34:47,480
Speaker 2: effects of eight and nineteen twenty nine, which basically undercut

634
00:34:47,520 --> 00:34:52,480
Speaker 2: assets people owned houses, for instance, or relied on. Because

635
00:34:52,520 --> 00:34:55,440
Speaker 2: capitalism is essentially a game only the wealthy play while

636
00:34:55,480 --> 00:34:59,120
Speaker 2: the rest of us look on. I'm sure like in

637
00:34:59,680 --> 00:35:05,719
Speaker 2: my experience, anytime the market is facing trouble, they use

638
00:35:05,760 --> 00:35:08,560
Speaker 2: that as an excuse to cut jobs, like it's gonna

639
00:35:09,040 --> 00:35:13,799
Speaker 2: it will affect people. But to that point of like,

640
00:35:13,920 --> 00:35:19,440
Speaker 2: it's not real estate value, it's stock market value, which, yeah,

641
00:35:19,480 --> 00:35:24,560
Speaker 2: pensions and people's retirement is obviously going to hurt people,

642
00:35:24,640 --> 00:35:29,759
Speaker 2: but it's not it's less connected to reality than it

643
00:35:29,840 --> 00:35:32,960
Speaker 2: has been in the past, I think is one possibility.

644
00:35:33,520 --> 00:35:36,040
Speaker 1: I think that's pretty interesting. I mean, part of the problem,

645
00:35:36,120 --> 00:35:38,239
Speaker 1: right is there's so much income inequality and so much

646
00:35:38,280 --> 00:35:41,440
Speaker 1: wealth inequality. And to the point that you made like

647
00:35:41,480 --> 00:35:45,600
Speaker 1: there's only a certain segment of people Americans who are

648
00:35:45,640 --> 00:35:47,920
Speaker 1: invested in the stock market at this point, or even

649
00:35:47,960 --> 00:35:51,320
Speaker 1: who have pensions, right, or retirement savings that are invested

650
00:35:51,360 --> 00:35:53,799
Speaker 1: in the market. But the trickle down effect right in

651
00:35:53,880 --> 00:35:58,000
Speaker 1: terms of jobs and wages, yeah, which are already hurting,

652
00:35:58,560 --> 00:36:00,759
Speaker 1: is yeah, is pretty It's scary.

653
00:36:00,920 --> 00:36:05,080
Speaker 2: Right, We can't like completely keep their shit from raining

654
00:36:05,120 --> 00:36:07,600
Speaker 2: down on us. But like it does feel like a

655
00:36:07,640 --> 00:36:10,720
Speaker 2: little bit less connected maybe than it was in the past,

656
00:36:10,719 --> 00:36:14,120
Speaker 2: because it feels a little less real. But yeah, I

657
00:36:14,160 --> 00:36:16,520
Speaker 2: mean the investments that are happening that are out of

658
00:36:16,560 --> 00:36:19,399
Speaker 2: people's control, I think that's the part that, Yeah, it's

659
00:36:19,400 --> 00:36:20,280
Speaker 2: really hard to avoid.

660
00:36:20,320 --> 00:36:24,040
Speaker 3: It's one thing for someone an individual, you know, investor,

661
00:36:24,080 --> 00:36:25,880
Speaker 3: to be like, well, I'm going to buy some stock

662
00:36:25,920 --> 00:36:26,120
Speaker 3: in this.

663
00:36:26,200 --> 00:36:29,160
Speaker 2: But when you're like, wait, what's our pension fund putting

664
00:36:29,200 --> 00:36:31,960
Speaker 2: the money into right now? Or the company that employs me,

665
00:36:32,080 --> 00:36:35,640
Speaker 2: what are they invested in right Oh, they're they're propped

666
00:36:35,680 --> 00:36:37,919
Speaker 2: up by stock market value. I'm the stock market's about

667
00:36:37,960 --> 00:36:41,440
Speaker 2: to take a shit. Yeah, And if the shit does

668
00:36:41,440 --> 00:36:43,920
Speaker 2: start ring, I feel like this is where people just

669
00:36:43,920 --> 00:36:44,800
Speaker 2: start eating them.

670
00:36:44,920 --> 00:36:48,800
Speaker 3: But we'll see I'll eat my AI, my Nvidia chips.

671
00:36:50,000 --> 00:36:52,080
Speaker 2: That's right, they go great, and they'll be worth as

672
00:36:52,160 --> 00:36:54,680
Speaker 2: much as a chip at that point. There is another

673
00:36:55,080 --> 00:36:59,120
Speaker 2: type of story I've been seeing where people are very

674
00:36:59,160 --> 00:37:03,080
Speaker 2: worried about Chinese robots that I think in a way.

675
00:37:02,920 --> 00:37:05,359
Speaker 3: About a sci fi way, not in a sci fi

676
00:37:06,160 --> 00:37:07,759
Speaker 3: our money kind of way.

677
00:37:08,200 --> 00:37:12,480
Speaker 2: Yeah, it's another our money story. Another executive from this

678
00:37:12,560 --> 00:37:16,080
Speaker 2: time from Ford went over to China and looked at

679
00:37:16,080 --> 00:37:21,719
Speaker 2: their factories and was like, oh, no quote, it's the

680
00:37:21,760 --> 00:37:26,080
Speaker 2: most humbling thing I've ever seen, said Ford's chief executive

681
00:37:26,280 --> 00:37:29,600
Speaker 2: about his recent trip to China. After visiting a string

682
00:37:29,600 --> 00:37:33,480
Speaker 2: of factories, Jim Farley was left, of course, Jim Farley

683
00:37:33,960 --> 00:37:37,319
Speaker 2: was left astonished by the technical innovations being packed into

684
00:37:37,440 --> 00:37:40,360
Speaker 2: Chinese cars from self driving software in to facial recognition.

685
00:37:40,760 --> 00:37:43,319
Speaker 2: Their cost and the quality of their vehicles is far

686
00:37:43,360 --> 00:37:45,719
Speaker 2: superior to what I see in the West. And this

687
00:37:45,800 --> 00:37:50,359
Speaker 2: is somebody from Ford. And then another an Australian billionaire, said,

688
00:37:50,400 --> 00:37:52,839
Speaker 2: I can take you to factories in China now where

689
00:37:52,880 --> 00:37:56,280
Speaker 2: you'll basically be alongside a big conveyor and the machines

690
00:37:56,320 --> 00:37:58,760
Speaker 2: come out on the floor and begin to assemble parts.

691
00:37:59,360 --> 00:38:02,359
Speaker 2: And your walking alongside this conveyor, and after about eight

692
00:38:02,400 --> 00:38:05,399
Speaker 2: hundred and nine hundred meters a truck drives out. There

693
00:38:05,400 --> 00:38:09,080
Speaker 2: are no people. Everything is robotic. And of course the

694
00:38:09,160 --> 00:38:11,640
Speaker 2: article makes sure to mention I think this was in

695
00:38:11,680 --> 00:38:16,440
Speaker 2: the Mirror or the Telegraph, UK Telegraph, so they they

696
00:38:16,480 --> 00:38:19,160
Speaker 2: make sure to mention that like and they could do

697
00:38:19,200 --> 00:38:22,120
Speaker 2: this to make war machines and attack us, so you

698
00:38:22,160 --> 00:38:25,759
Speaker 2: should be scared. But assuming they don't, which by the way,

699
00:38:25,760 --> 00:38:28,879
Speaker 2: that seems to be traditionally more of our thing, more

700
00:38:28,880 --> 00:38:30,880
Speaker 2: of a US thing, of like, once you have a

701
00:38:30,920 --> 00:38:36,279
Speaker 2: tech getting overleveraged on, you just invade everybody. This does

702
00:38:36,440 --> 00:38:38,960
Speaker 2: this does seem like a problem for the capitalist class

703
00:38:39,000 --> 00:38:42,640
Speaker 2: that could be good for everyone else in the sense

704
00:38:42,719 --> 00:38:46,799
Speaker 2: that like this is just America. Is yet another way

705
00:38:46,840 --> 00:38:52,239
Speaker 2: America is falling behind, and like it's because they just

706
00:38:52,600 --> 00:38:57,600
Speaker 2: keep letting people like oligarchs decide what to do. Like

707
00:38:57,600 --> 00:38:59,880
Speaker 2: they're just like, yeah, that will figure it out. Just

708
00:39:00,080 --> 00:39:02,879
Speaker 2: keep giving them more and more money, keep giving the

709
00:39:03,239 --> 00:39:05,960
Speaker 2: people at the tops of companies more and more money.

710
00:39:06,320 --> 00:39:11,000
Speaker 2: And like in China that is not like this shift

711
00:39:11,200 --> 00:39:15,239
Speaker 2: to robotics. They talk about how it's a reaction to

712
00:39:15,719 --> 00:39:20,120
Speaker 2: like we've talked before about them China having a demographics

713
00:39:20,160 --> 00:39:23,040
Speaker 2: problem because of the one child policy that they have

714
00:39:23,120 --> 00:39:25,960
Speaker 2: for so long. They're about to hit this point where

715
00:39:26,440 --> 00:39:29,080
Speaker 2: a massive part of their population all retires at once,

716
00:39:29,320 --> 00:39:33,560
Speaker 2: and then the part of their population that was, you know,

717
00:39:33,920 --> 00:39:36,719
Speaker 2: the children of the one child policy will be in

718
00:39:36,800 --> 00:39:39,560
Speaker 2: the workforce. And that's like there's this thing called the

719
00:39:39,560 --> 00:39:43,400
Speaker 2: dependency ratio where it's basically like the more people you

720
00:39:43,480 --> 00:39:46,200
Speaker 2: have who are working age compared to the people who

721
00:39:46,200 --> 00:39:49,440
Speaker 2: are either retired or too young to work, it like

722
00:39:49,960 --> 00:39:53,120
Speaker 2: so goes your economy. It's why the US economy was

723
00:39:53,160 --> 00:39:55,719
Speaker 2: like booming so much when the Baby Boom generation was

724
00:39:55,760 --> 00:39:59,279
Speaker 2: like moving through the years of like eighteen to sixty five,

725
00:39:59,560 --> 00:40:02,480
Speaker 2: and why the economy is like slowing as they hit

726
00:40:02,960 --> 00:40:07,480
Speaker 2: retirement age. And China has like an extra extreme version

727
00:40:07,560 --> 00:40:11,880
Speaker 2: of that happening, and they've just taken money as a

728
00:40:11,920 --> 00:40:15,959
Speaker 2: government and like made the centralized decision to be like, well,

729
00:40:16,239 --> 00:40:19,000
Speaker 2: this is how we solve this massive problem that we

730
00:40:19,120 --> 00:40:22,759
Speaker 2: have facing it. And it's just like so impossible to

731
00:40:22,840 --> 00:40:26,960
Speaker 2: imagine America doing something like that, you know, like doing

732
00:40:27,000 --> 00:40:30,920
Speaker 2: something with like long term vision, you know.

733
00:40:31,280 --> 00:40:34,080
Speaker 1: Yes, Instead we just talk about how I certain people

734
00:40:34,120 --> 00:40:35,960
Speaker 1: are talking about how we need to have more babies

735
00:40:36,280 --> 00:40:39,480
Speaker 1: here right exactly, But we don't have policies that support

736
00:40:39,520 --> 00:40:41,480
Speaker 1: people to have babies here.

737
00:40:41,960 --> 00:40:44,200
Speaker 2: No, no, no, no, no, because I would mess with

738
00:40:44,239 --> 00:40:47,160
Speaker 2: the millionaire and billionaires and like, yeah.

739
00:40:46,920 --> 00:40:50,759
Speaker 1: The idea that like for free enterprise and deregulation. But yeah,

740
00:40:50,840 --> 00:40:55,040
Speaker 1: like no paid family leave, no child care, insufficient childcare,

741
00:40:55,480 --> 00:40:57,600
Speaker 1: no clue. Like apparently we don't really care about clean

742
00:40:57,640 --> 00:41:01,520
Speaker 1: water or air anymore either. Guns, you know, guns aren't

743
00:41:01,520 --> 00:41:02,440
Speaker 1: a problem. People.

744
00:41:02,640 --> 00:41:06,040
Speaker 2: Well, fewer old people if we say, if our if

745
00:41:06,040 --> 00:41:08,360
Speaker 2: our water is really fucked up, fewer old people, because

746
00:41:08,360 --> 00:41:12,759
Speaker 2: you start lowering that. Uh sure, we're kind of doing

747
00:41:12,840 --> 00:41:16,520
Speaker 2: some work on our end. We're making true decisions.

748
00:41:16,520 --> 00:41:18,560
Speaker 3: Vicky, Like with the work that you do, what's like

749
00:41:18,800 --> 00:41:21,240
Speaker 3: what do you I think. I'm always thinking like, what's

750
00:41:21,280 --> 00:41:23,640
Speaker 3: just like the most efficient thing that we can be

751
00:41:23,680 --> 00:41:27,120
Speaker 3: doing policy wise that'll have like the most robust effect

752
00:41:27,200 --> 00:41:29,920
Speaker 3: across the board for people. And I'm always like I

753
00:41:30,000 --> 00:41:31,920
Speaker 3: always think of like how other nations, like when you

754
00:41:32,000 --> 00:41:37,360
Speaker 3: read about like parental policies and other countries like huh right, yeah.

755
00:41:37,160 --> 00:41:39,120
Speaker 1: So the you know, the United States is the only

756
00:41:39,719 --> 00:41:42,280
Speaker 1: one of like six countries in the world that doesn't

757
00:41:42,320 --> 00:41:44,800
Speaker 1: have paid maternity leave, or one of a handful of

758
00:41:44,840 --> 00:41:47,759
Speaker 1: high wealth countries that doesn't have paid parental leave or

759
00:41:47,760 --> 00:41:49,960
Speaker 1: family leave, or one of two high wealth countries that

760
00:41:50,000 --> 00:41:53,439
Speaker 1: doesn't even guarantee paid sick leave. Right, So providing these

761
00:41:53,520 --> 00:41:57,279
Speaker 1: like basic supports that other people have would be a

762
00:41:57,440 --> 00:42:01,320
Speaker 1: very strong way to start valuing workers in families and

763
00:42:01,719 --> 00:42:05,760
Speaker 1: fostering people being able to care for themselves and other people, right,

764
00:42:05,960 --> 00:42:10,399
Speaker 1: raising wages, guaranteeing health care, and again like ensuring that

765
00:42:10,440 --> 00:42:13,480
Speaker 1: people can care for the children that they have, whether

766
00:42:13,520 --> 00:42:16,839
Speaker 1: that's raising wages so you know, an adult can stay home,

767
00:42:17,000 --> 00:42:21,799
Speaker 1: or whether that's insteaentive is fully funding high quality childcare

768
00:42:21,960 --> 00:42:25,680
Speaker 1: with high wages for care workers so that people want

769
00:42:25,719 --> 00:42:27,160
Speaker 1: to want to do that job.

770
00:42:27,200 --> 00:42:30,800
Speaker 3: When you like meet people or I'm sure you've you

771
00:42:30,880 --> 00:42:34,040
Speaker 3: encountered people who have a completely like antithetical worldview in

772
00:42:34,120 --> 00:42:35,719
Speaker 3: terms of like, well, we don't need to invest in that.

773
00:42:35,880 --> 00:42:39,360
Speaker 3: Has anyone ever articulated something that even remotely sounded humane

774
00:42:39,480 --> 00:42:43,480
Speaker 3: as to why it's not needed or it's veiled in

775
00:42:43,520 --> 00:42:45,640
Speaker 3: some kind of language that isn't sort of centering how

776
00:42:45,680 --> 00:42:46,640
Speaker 3: inhumane that?

777
00:42:46,960 --> 00:42:49,560
Speaker 1: Yeah, it's interesting. I mean, yes, I've talked to lots

778
00:42:49,560 --> 00:42:52,600
Speaker 1: of people who don't believe that there's a role for

779
00:42:52,640 --> 00:42:55,960
Speaker 1: government and solving these problems. I think what's been really interesting,

780
00:42:56,000 --> 00:42:58,120
Speaker 1: and there's been an evolution over the sixteen or so

781
00:42:58,239 --> 00:43:01,160
Speaker 1: years that have been working on a set of policies

782
00:43:01,239 --> 00:43:05,600
Speaker 1: around work and family and caregiving. I think there's there's

783
00:43:06,160 --> 00:43:07,719
Speaker 1: and this used to give me hope, and now it

784
00:43:07,760 --> 00:43:09,799
Speaker 1: doesn't so much give me hope anymore because of where

785
00:43:09,840 --> 00:43:12,279
Speaker 1: we are. There's an agreement that we need to do

786
00:43:12,360 --> 00:43:17,120
Speaker 1: better for families, families with children, families who are Sandwich generation,

787
00:43:17,560 --> 00:43:19,520
Speaker 1: of which there are more than eleven million who are

788
00:43:19,520 --> 00:43:22,839
Speaker 1: caring for children, in caring for older adults or people

789
00:43:22,840 --> 00:43:26,400
Speaker 1: with disabilities. But the difference really comes in and whether

790
00:43:26,440 --> 00:43:29,600
Speaker 1: this is like a private pull yourself up by your bootstraps,

791
00:43:29,640 --> 00:43:32,560
Speaker 1: figure it out. George W. Bush a thousand points of

792
00:43:32,640 --> 00:43:38,520
Speaker 1: light kind of conversation, not w the real the first Bush, Sorry,

793
00:43:38,680 --> 00:43:41,799
Speaker 1: but anyway, yeah, HW. Or whether there's a role for

794
00:43:41,960 --> 00:43:47,280
Speaker 1: public policy and community systems that are funded with public dollars,

795
00:43:47,560 --> 00:43:49,920
Speaker 1: and that's really where it comes down to, where the

796
00:43:49,960 --> 00:43:52,640
Speaker 1: difference comes down to. I think there's also a big

797
00:43:52,640 --> 00:43:55,920
Speaker 1: difference about the role of women in caring for children

798
00:43:56,000 --> 00:43:58,800
Speaker 1: and left ones, and whether women belong as full, inequal

799
00:43:58,840 --> 00:44:02,839
Speaker 1: participants in the economy. And that seems to be the

800
00:44:02,840 --> 00:44:05,400
Speaker 1: dividing line. So I think we you know, there's a

801
00:44:05,400 --> 00:44:09,319
Speaker 1: lot of agreement around we need to do more. And

802
00:44:09,360 --> 00:44:12,760
Speaker 1: I will say also, like among people when you survey voters,

803
00:44:13,040 --> 00:44:16,200
Speaker 1: when you survey actually even these TV viewers that we surveyed,

804
00:44:16,560 --> 00:44:21,360
Speaker 1: there's like widespread agreement across party lines that national public

805
00:44:21,400 --> 00:44:24,879
Speaker 1: policies around paid family and medical leave, childcare, elder care

806
00:44:25,280 --> 00:44:28,920
Speaker 1: would be beneficial in terms of a more secure, more

807
00:44:28,920 --> 00:44:33,600
Speaker 1: secure families, more secure economy. People overwhelmingly across party lines

808
00:44:34,040 --> 00:44:36,960
Speaker 1: believe that we should have national paid leave, national childcare,

809
00:44:37,600 --> 00:44:41,240
Speaker 1: national elder care policies. But where the breakdown really happens

810
00:44:41,280 --> 00:44:44,680
Speaker 1: is with politicians, right, and that is you know, really

811
00:44:44,680 --> 00:44:47,160
Speaker 1: empathetical to representative government. But that is true of so

812
00:44:47,200 --> 00:44:49,320
Speaker 1: many issues where the public agrees.

813
00:44:49,600 --> 00:44:52,279
Speaker 3: And yet oh we've seen it, we saw it, We

814
00:44:52,360 --> 00:44:54,000
Speaker 3: saw it really in four K.

815
00:44:54,040 --> 00:44:54,719
Speaker 2: This last year.

816
00:44:54,760 --> 00:44:57,640
Speaker 3: I feel yeah, just yea, it seems like this, this

817
00:44:57,719 --> 00:44:59,440
Speaker 3: might be kind of where people's heads are at.

818
00:44:59,520 --> 00:45:02,880
Speaker 2: No, we're not talking about we have the proof that

819
00:45:03,080 --> 00:45:07,880
Speaker 2: letting companies and billionaires like make the decisions as opposed

820
00:45:07,920 --> 00:45:11,240
Speaker 2: to government doesn't work. Like we the fact that nobody

821
00:45:11,320 --> 00:45:15,080
Speaker 2: just like makes that point it like concerted that that

822
00:45:15,320 --> 00:45:20,759
Speaker 2: isn't just the Democrats entire position, like here, here's what

823
00:45:20,880 --> 00:45:24,360
Speaker 2: we've been doing does not work. Your quality of life

824
00:45:24,360 --> 00:45:31,080
Speaker 2: has gotten worse. Our entire our entire system is essentially

825
00:45:31,200 --> 00:45:34,680
Speaker 2: like based on trickle down in economics, and like the

826
00:45:34,719 --> 00:45:37,680
Speaker 2: idea that if we just give billionaires enough money, they'll

827
00:45:37,680 --> 00:45:40,640
Speaker 2: start investing the money back into the rest of us,

828
00:45:40,680 --> 00:45:43,560
Speaker 2: and it just like over and over and over and

829
00:45:43,600 --> 00:45:47,239
Speaker 2: over has been shown not to work. And people know that.

830
00:45:47,480 --> 00:45:51,080
Speaker 2: It's just these like institutions in between that are like,

831
00:45:51,760 --> 00:45:55,279
Speaker 2: I don't know why that's zaram guys so popular. He

832
00:45:55,360 --> 00:46:00,000
Speaker 2: seems weird to us or he seems like he's very talented,

833
00:46:00,360 --> 00:46:03,480
Speaker 2: and that's why it couldn't be his messaging that is

834
00:46:03,760 --> 00:46:04,680
Speaker 2: resonating with people.

835
00:46:04,760 --> 00:46:06,680
Speaker 1: Yeah, well, you know, when these things go to voters,

836
00:46:06,880 --> 00:46:08,440
Speaker 1: they get approved overwhelmingly.

837
00:46:08,600 --> 00:46:09,360
Speaker 2: Right. Yeah.

838
00:46:09,440 --> 00:46:12,000
Speaker 1: Colorado pass to paid family and medical leave program in

839
00:46:12,000 --> 00:46:17,520
Speaker 1: twenty twenty, the same election Trump Biden election. Counties that

840
00:46:17,560 --> 00:46:20,600
Speaker 1: went for Trump voted for the paid leave proposal. We've

841
00:46:20,640 --> 00:46:23,400
Speaker 1: seen the minimum wage and paid sick time be approved

842
00:46:23,400 --> 00:46:26,279
Speaker 1: in places like Alaska and Missouri. We've seen paid sick

843
00:46:26,320 --> 00:46:30,480
Speaker 1: time be approved in Nebraska. Yeah, not controversial for people,

844
00:46:31,040 --> 00:46:33,799
Speaker 1: real people who are working day to day and need

845
00:46:33,800 --> 00:46:36,040
Speaker 1: to care for themselves in their families. And the way

846
00:46:36,040 --> 00:46:37,799
Speaker 1: that it's polarized in legislatures.

847
00:46:37,960 --> 00:46:40,480
Speaker 3: I think that's what's so interesting too, is like certain

848
00:46:40,520 --> 00:46:43,640
Speaker 3: things are just not partisan, Like your ability to support

849
00:46:43,680 --> 00:46:46,719
Speaker 3: your family. I mean, obviously like policies affect that, but

850
00:46:46,880 --> 00:46:51,239
Speaker 3: you're to yearn for that as a human being, right,

851
00:46:51,400 --> 00:46:53,480
Speaker 3: doesn't fall along a political ideology.

852
00:46:53,520 --> 00:46:55,799
Speaker 1: And I think there's and that's where I think like

853
00:46:55,960 --> 00:46:58,719
Speaker 1: television and film storytelling can come in and start to

854
00:46:58,800 --> 00:47:01,960
Speaker 1: change how people think about these things. So, you know,

855
00:47:02,000 --> 00:47:06,400
Speaker 1: in our survey, something like seventy nine percent of viewers

856
00:47:06,440 --> 00:47:11,480
Speaker 1: said that they related to job, job instability, job concerns.

857
00:47:11,520 --> 00:47:15,640
Speaker 1: I think seventy four percent related to money concerns. This

858
00:47:15,760 --> 00:47:19,080
Speaker 1: isn't Yeah, these aren't parties in there's some divides by income,

859
00:47:19,120 --> 00:47:21,640
Speaker 1: but really it's everybody, no matter where you sit, you're

860
00:47:21,680 --> 00:47:23,640
Speaker 1: worried about these things because we live in this like

861
00:47:23,719 --> 00:47:26,479
Speaker 1: incredibly precarious and unstable time.

862
00:47:26,880 --> 00:47:29,520
Speaker 2: Yeah, hold that thought. Actually, we're gonna take a quick

863
00:47:29,520 --> 00:47:31,239
Speaker 2: break and then I'm going to come back and talk

864
00:47:31,280 --> 00:47:33,799
Speaker 2: about a movie that I think really gets it right

865
00:47:34,360 --> 00:47:38,080
Speaker 2: in terms of just like being meeting people where they're at.

866
00:47:38,200 --> 00:47:42,399
Speaker 2: It's a little film called tron Ares, And I think

867
00:47:42,480 --> 00:47:44,279
Speaker 2: you're going to like what you see here. We'll be

868
00:47:44,360 --> 00:47:58,640
Speaker 2: right back and we're back. We're back. And so I

869
00:47:58,640 --> 00:48:03,360
Speaker 2: don't know, Vicky, I have you've seen tron Aris so

870
00:48:04,480 --> 00:48:09,840
Speaker 2: either we so it in a way based on the

871
00:48:09,920 --> 00:48:14,600
Speaker 2: analysis we've been reading. It in a way addresses how

872
00:48:14,719 --> 00:48:19,440
Speaker 2: people are feeling in this precarious situation where jobs may

873
00:48:19,440 --> 00:48:26,719
Speaker 2: be threatened by AI by introducing a character who is

874
00:48:26,880 --> 00:48:31,880
Speaker 2: AI bit of a messiah figure, is only there because

875
00:48:31,880 --> 00:48:35,520
Speaker 2: he wants to help and like breaks free and then

876
00:48:35,680 --> 00:48:37,960
Speaker 2: evil tech people want to take him down because he

877
00:48:38,080 --> 00:48:40,760
Speaker 2: too good played by Jared Leto. I should I should

878
00:48:40,800 --> 00:48:45,120
Speaker 2: add so. I feel like, but for some reason this

879
00:48:45,200 --> 00:48:47,279
Speaker 2: did not connect with people. I don't know, like what

880
00:48:48,160 --> 00:48:52,319
Speaker 2: it uh? It bombed at the box office for for

881
00:48:52,360 --> 00:48:58,560
Speaker 2: some reason, even though it's this abstract political cartoon that's

882
00:48:58,600 --> 00:49:01,200
Speaker 2: trying to send the message that like what if AI

883
00:49:01,440 --> 00:49:03,279
Speaker 2: was like actually pretty cool and.

884
00:49:03,280 --> 00:49:06,359
Speaker 3: It should have been like they should have Tron. I'm

885
00:49:06,360 --> 00:49:09,760
Speaker 3: just gonna call the character Tron. How Yeah, Tron should

886
00:49:09,760 --> 00:49:12,960
Speaker 3: have been like, uh riffed, you know, should have been

887
00:49:12,960 --> 00:49:16,360
Speaker 3: a federal federal worker who got riffed or something that

888
00:49:16,400 --> 00:49:19,520
Speaker 3: would have been way more relatable than being like a.

889
00:49:19,520 --> 00:49:24,520
Speaker 2: Perfect super AI soldier who is also benevolent. Wow, what

890
00:49:25,000 --> 00:49:25,480
Speaker 2: is this? I mean?

891
00:49:25,520 --> 00:49:28,440
Speaker 3: I think again, it's I think because when we were

892
00:49:28,440 --> 00:49:31,759
Speaker 3: talking about like, you know, Jared Leto's own ties to

893
00:49:31,800 --> 00:49:34,680
Speaker 3: having his own AI company, where you're like, guys, this

894
00:49:34,760 --> 00:49:37,960
Speaker 3: is so transparent, like what is the point of this

895
00:49:38,120 --> 00:49:42,000
Speaker 3: right now? I think that's also again, it shows how

896
00:49:42,000 --> 00:49:45,279
Speaker 3: people like Jared Leto think people, how they are influenced. Well,

897
00:49:45,280 --> 00:49:47,719
Speaker 3: it's like, well, if in the movie I say AI good,

898
00:49:47,800 --> 00:49:50,120
Speaker 3: then they will believe that even though they are under

899
00:49:50,160 --> 00:49:54,880
Speaker 3: immense pressure existentially financially, in many different ways, the message.

900
00:49:54,440 --> 00:49:57,680
Speaker 2: Will get through. I'm pretty sure it's like my Pixar

901
00:49:57,800 --> 00:49:59,640
Speaker 2: idea that I did mention earlier that I want to

902
00:49:59,640 --> 00:50:02,040
Speaker 2: make a pick our movie about soup and it has

903
00:50:02,120 --> 00:50:05,880
Speaker 2: nothing to do with my soup company and the fact

904
00:50:05,960 --> 00:50:09,640
Speaker 2: that Tomato bisk soup is the hero of my movie

905
00:50:09,640 --> 00:50:10,440
Speaker 2: called Soups.

906
00:50:10,520 --> 00:50:14,120
Speaker 3: Say the character, it's called Jack's Tomato bisc soup. Yes,

907
00:50:14,280 --> 00:50:17,600
Speaker 3: tomato character. Yeah, yeah, yeah it's and yes, the font

908
00:50:17,680 --> 00:50:21,480
Speaker 3: is very similar to Campbell's. But that's being settled in

909
00:50:21,520 --> 00:50:22,120
Speaker 3: a lawsuit.

910
00:50:22,320 --> 00:50:25,680
Speaker 2: Yeah, but yeah, I don't know, like that, that has

911
00:50:25,719 --> 00:50:27,799
Speaker 2: nothing to do with why I want to make Soups

912
00:50:28,200 --> 00:50:32,920
Speaker 2: an exciting adventure about learning to except that the soup

913
00:50:33,280 --> 00:50:35,759
Speaker 2: that you have inside of you is actually going to

914
00:50:36,160 --> 00:50:40,120
Speaker 2: make you live forever. But yeah, so this is I

915
00:50:40,120 --> 00:50:43,960
Speaker 2: don't know. This just seems like such a weird place

916
00:50:44,239 --> 00:50:47,240
Speaker 2: for a Hollywood to be where they're making a sequel

917
00:50:47,280 --> 00:50:49,480
Speaker 2: to a movie that when they last made a sequel

918
00:50:49,520 --> 00:50:51,239
Speaker 2: to it, everyone's like, oh, I guess nobody wants a

919
00:50:51,239 --> 00:50:54,399
Speaker 2: sequel to that. They make a sequel to the one

920
00:50:54,440 --> 00:50:57,200
Speaker 2: that didn't do well with Jared Leto who the last

921
00:50:57,200 --> 00:50:59,640
Speaker 2: time they made a future film with Jared Letto, people

922
00:50:59,640 --> 00:51:01,600
Speaker 2: are They were like, oh, I guess nobody wants to

923
00:51:02,040 --> 00:51:05,879
Speaker 2: have a future film starring Jared Letto. Morebius they did

924
00:51:05,920 --> 00:51:08,600
Speaker 2: all the mistakes, Yeah, They're like, but what if we

925
00:51:08,680 --> 00:51:11,640
Speaker 2: made all these mistakes together and at the same time,

926
00:51:12,080 --> 00:51:15,160
Speaker 2: maybe maybe that would work. It might just pull it off,

927
00:51:15,280 --> 00:51:16,040
Speaker 2: might just pull it off.

928
00:51:16,080 --> 00:51:19,280
Speaker 3: I mean like this feels like this sort of intersects

929
00:51:19,360 --> 00:51:23,080
Speaker 3: with your research too, in that like these are the

930
00:51:23,200 --> 00:51:26,960
Speaker 3: value clearly from the filmmaker standpoints or the studios standpoint.

931
00:51:27,040 --> 00:51:30,120
Speaker 3: This is like a message that's needed without understanding what

932
00:51:30,200 --> 00:51:31,200
Speaker 3: people are looking.

933
00:51:31,360 --> 00:51:34,359
Speaker 1: Yeah. Well, and also again like this idea that you

934
00:51:34,400 --> 00:51:38,040
Speaker 1: want to see like these like muscular white men on screen,

935
00:51:38,080 --> 00:51:40,360
Speaker 1: when really what audiences say they want to see and

936
00:51:40,400 --> 00:51:45,040
Speaker 1: what research tells us make more profitable engaging films are

937
00:51:45,400 --> 00:51:49,399
Speaker 1: diverse characters from a diversity of backgrounds, right, characters not

938
00:51:49,480 --> 00:51:51,920
Speaker 1: just of your own race or ethnicity, but of different

939
00:51:52,000 --> 00:51:55,800
Speaker 1: races and ethnicities, including among white viewers in our survey,

940
00:51:56,280 --> 00:51:59,200
Speaker 1: people of different income levels, and people who are yeah

941
00:51:59,280 --> 00:52:02,960
Speaker 1: relatable and not and like explaining why it is that

942
00:52:03,000 --> 00:52:05,279
Speaker 1: they believe what they believe and having some point and

943
00:52:05,360 --> 00:52:08,400
Speaker 1: counterpoint to it and not sort of asserting things.

944
00:52:08,760 --> 00:52:11,799
Speaker 3: Wait, hold on, what's not relatable about these aries? A

945
00:52:11,920 --> 00:52:16,360
Speaker 3: master control program?

946
00:52:16,200 --> 00:52:17,960
Speaker 1: I'm in the wrong circles or.

947
00:52:17,920 --> 00:52:24,799
Speaker 2: Something like The Hollywood Reporter released an article. Thato was like, well,

948
00:52:25,160 --> 00:52:28,120
Speaker 2: that's a wrap. There will never be another major motion

949
00:52:28,200 --> 00:52:31,920
Speaker 2: picture with Jared Letto as the main character. But then

950
00:52:31,960 --> 00:52:33,399
Speaker 2: they had this to say.

951
00:52:33,239 --> 00:52:34,759
Speaker 1: But do we think that's true? I don't know.

952
00:52:35,360 --> 00:52:36,920
Speaker 2: I mean, that's that's what I thought. I thought that

953
00:52:36,920 --> 00:52:39,719
Speaker 2: people were saying that. After morevious they're like, guys, I

954
00:52:39,719 --> 00:52:42,920
Speaker 2: think it's a wrap on this. But yeah, uh they

955
00:52:43,000 --> 00:52:45,920
Speaker 2: had this paragraph in a world where Michael Fassbender, you

956
00:52:45,960 --> 00:52:48,839
Speaker 2: and McGregor and Benedict Cumberbatch are having a hard time

957
00:52:48,880 --> 00:52:51,400
Speaker 2: getting lead roles, would do you even go to a

958
00:52:51,400 --> 00:52:54,360
Speaker 2: person who can't open a movie and who has question

959
00:52:54,440 --> 00:52:57,719
Speaker 2: marks about around him as a person? Those from one

960
00:52:57,840 --> 00:53:01,560
Speaker 2: top talent manager partner who may or may not represent

961
00:53:01,600 --> 00:53:10,120
Speaker 2: Michael Fasbender really quick someone from wm H. Of course,

962
00:53:10,160 --> 00:53:13,040
Speaker 2: Fasbender also has some question marks around him as a person,

963
00:53:13,440 --> 00:53:17,399
Speaker 2: stemming from a domestic violence charge in two thousand and nine.

964
00:53:18,080 --> 00:53:21,480
Speaker 2: But yeah, I love that they They're like, Wow, what

965
00:53:21,600 --> 00:53:24,840
Speaker 2: about these other white guys that all kind of seem

966
00:53:24,920 --> 00:53:28,479
Speaker 2: similar to one another. You could be totally hiring them,

967
00:53:29,280 --> 00:53:31,960
Speaker 2: And then they couldn't even come up with the example

968
00:53:32,000 --> 00:53:35,920
Speaker 2: of someone who doesn't have problematic shit in their past, right, right.

969
00:53:36,160 --> 00:53:39,120
Speaker 1: Well, then, also Kamala Avila Samon, who used to be

970
00:53:39,120 --> 00:53:41,720
Speaker 1: at Lionsgate and now has her own production company, cast

971
00:53:41,719 --> 00:53:45,160
Speaker 1: Cast Productions. She's just really smart on this and talks

972
00:53:45,200 --> 00:53:48,440
Speaker 1: a lot about the high standards that are put in

973
00:53:48,480 --> 00:53:53,720
Speaker 1: place for films that are led by filmmakers of color,

974
00:53:53,880 --> 00:53:56,840
Speaker 1: by women, filmmakers that are on topics, you know, that

975
00:53:56,880 --> 00:53:58,960
Speaker 1: are a little bit outside the mainstream. And you know,

976
00:53:59,000 --> 00:54:00,680
Speaker 1: there's one of those, and if it doesn't do well,

977
00:54:00,680 --> 00:54:01,520
Speaker 1: it's like, oh, that's it.

978
00:54:02,160 --> 00:54:04,280
Speaker 2: Do this again. And if it does do well, like Sinners,

979
00:54:04,480 --> 00:54:08,560
Speaker 2: they're still like, well, we don't pay for one moving

980
00:54:08,560 --> 00:54:12,760
Speaker 2: the gold It did even better the next week. Uh, well,

981
00:54:12,800 --> 00:54:15,200
Speaker 2: we'll see if they make money on it forty five

982
00:54:15,280 --> 00:54:17,640
Speaker 2: years from now when the rights revert to Ryan Coogler.

983
00:54:18,239 --> 00:54:20,680
Speaker 1: Right, And so it's like, yeah, treated like an anomaly,

984
00:54:20,880 --> 00:54:25,799
Speaker 1: whereas these other other movies that are mainstream get made

985
00:54:25,840 --> 00:54:27,200
Speaker 1: over and over and over again, and some of them

986
00:54:27,239 --> 00:54:28,839
Speaker 1: do well and some of them don't, but we keep

987
00:54:28,920 --> 00:54:32,200
Speaker 1: making them because somebody believes that this is what audiences want.

988
00:54:32,080 --> 00:54:34,200
Speaker 2: To see, right Yeah. I mean that was the thing.

989
00:54:34,239 --> 00:54:36,960
Speaker 3: Like even with Tron I was like, you know, there

990
00:54:37,360 --> 00:54:39,040
Speaker 3: there are women of color like in this one, like

991
00:54:39,080 --> 00:54:41,120
Speaker 3: Greta Lee is like one of the leaders, Jody Turner

992
00:54:41,160 --> 00:54:43,560
Speaker 3: Smith and like hassanman Naj And I'm like, are you

993
00:54:43,640 --> 00:54:43,920
Speaker 3: just going to.

994
00:54:43,960 --> 00:54:46,840
Speaker 2: Do the thing where they're like they went woke? Yeah? Yeah,

995
00:54:46,840 --> 00:54:50,360
Speaker 2: and we woke. And because it was an allegory for

996
00:54:50,760 --> 00:54:54,560
Speaker 2: chat GPT being the savior of humanity.

997
00:54:54,280 --> 00:54:57,040
Speaker 3: Being led by a big creep right now who has

998
00:54:57,080 --> 00:54:59,000
Speaker 3: a terrible track record opening movies.

999
00:54:59,080 --> 00:55:01,120
Speaker 2: Yeah, it's not bad at all. It's the fact that

1000
00:55:01,120 --> 00:55:02,840
Speaker 2: Greta Lee was one of the leads. So that was

1001
00:55:02,840 --> 00:55:05,240
Speaker 2: like my one when we were talking about the movie. Initially,

1002
00:55:05,239 --> 00:55:07,480
Speaker 2: I was like, God, I hope they don't just drag

1003
00:55:07,560 --> 00:55:10,400
Speaker 2: Greta Lee for this movie being bad. Oh you know

1004
00:55:10,520 --> 00:55:13,440
Speaker 2: behind the scenes, that's exactly what's happening. I mean it

1005
00:55:13,480 --> 00:55:18,200
Speaker 2: must because how is Jared Lettle still working like after Morbius? Seriously?

1006
00:55:18,280 --> 00:55:20,560
Speaker 3: Like that was such that was like one of the

1007
00:55:20,640 --> 00:55:22,759
Speaker 3: that was like the cutthroat island of our times.

1008
00:55:23,080 --> 00:55:27,040
Speaker 2: That's what they should call getting fired from the Trump administration,

1009
00:55:27,120 --> 00:55:30,560
Speaker 2: getting morbed, got morbed. Yeah, I don't know. We'll try.

1010
00:55:30,600 --> 00:55:32,839
Speaker 2: I will workshop that all right. Well, Vicki, it's been

1011
00:55:32,920 --> 00:55:35,239
Speaker 2: such a pleasure having you on the dailies like Ice. Yeah,

1012
00:55:35,239 --> 00:55:38,080
Speaker 2: where can people find you? Follow you, hear you all

1013
00:55:38,080 --> 00:55:38,760
Speaker 2: that good stuff?

1014
00:55:39,239 --> 00:55:42,359
Speaker 1: You can follow a New America a Better Life Lab

1015
00:55:42,480 --> 00:55:44,960
Speaker 1: on Instagram. I'm on Twitter. I don't use it that

1016
00:55:45,040 --> 00:55:46,959
Speaker 1: offen anymore at view shavo.

1017
00:55:46,960 --> 00:55:49,399
Speaker 2: Oh what happened? Oh I don't know that was a.

1018
00:55:49,520 --> 00:55:53,879
Speaker 1: Thing that happened in Twitter. Also on Blue Sky, but yeah,

1019
00:55:53,880 --> 00:55:55,279
Speaker 1: I don't know. I don't spend as much time on

1020
00:55:55,320 --> 00:55:58,439
Speaker 1: either of those as I once did so, but yeah,

1021
00:55:58,719 --> 00:56:01,200
Speaker 1: look us up with all of our entertainment research is

1022
00:56:01,239 --> 00:56:04,799
Speaker 1: at New America dot org slash entertainment. The Better Life

1023
00:56:04,840 --> 00:56:07,359
Speaker 1: Lab is a New America dot org. You can find

1024
00:56:07,440 --> 00:56:11,279
Speaker 1: us on that site. But check us out, and yeah, look,

1025
00:56:11,360 --> 00:56:12,400
Speaker 1: thank you so much for having me.

1026
00:56:12,440 --> 00:56:14,560
Speaker 2: This is super fun, awesome having you. Is there a

1027
00:56:14,560 --> 00:56:16,480
Speaker 2: work of media that you've been enjoying? That is a

1028
00:56:16,560 --> 00:56:20,279
Speaker 2: question I have for you. What's a popular show or

1029
00:56:20,360 --> 00:56:21,520
Speaker 2: movie that's doing well?

1030
00:56:22,280 --> 00:56:24,279
Speaker 1: I talk about this show all the time, and I should.

1031
00:56:24,320 --> 00:56:26,560
Speaker 1: Maybe there's several of them, and several of them that

1032
00:56:26,600 --> 00:56:29,640
Speaker 1: were Emmy nominated. But the show that I've been loving

1033
00:56:29,719 --> 00:56:34,560
Speaker 1: is high potential on ABC in part because it shows

1034
00:56:34,760 --> 00:56:39,720
Speaker 1: a woman, kay Lin Eelson's character Morgan, managing work, managing family,

1035
00:56:39,840 --> 00:56:42,760
Speaker 1: like being an involved parent, having a co parent who's

1036
00:56:43,600 --> 00:56:46,440
Speaker 1: a guy, a man, a dad who is like fully confident.

1037
00:56:46,880 --> 00:56:49,440
Speaker 1: She brings her family life to work with her in

1038
00:56:49,520 --> 00:56:51,400
Speaker 1: her mind, and she brings her work home like so

1039
00:56:51,480 --> 00:56:54,319
Speaker 1: many of us do. So I've been enjoying that show.

1040
00:56:54,320 --> 00:56:56,760
Speaker 1: But also the Pit I think is doing a great job.

1041
00:56:57,320 --> 00:56:59,960
Speaker 1: Have been actually loving Severance and kind of the working

1042
00:57:00,000 --> 00:57:02,960
Speaker 1: family themes and Severance as well as the anti corporate

1043
00:57:03,160 --> 00:57:06,480
Speaker 1: them sort of workplace themes in Severance, the Bear I

1044
00:57:06,480 --> 00:57:09,360
Speaker 1: think has like lots of cool family relationships in a

1045
00:57:09,400 --> 00:57:11,640
Speaker 1: workplace where you get to know the characters and their

1046
00:57:11,640 --> 00:57:13,520
Speaker 1: family situation so loose.

1047
00:57:13,239 --> 00:57:18,600
Speaker 2: Shown after that Christmas episode that's true too much. Abbot elementary.

1048
00:57:18,640 --> 00:57:20,320
Speaker 2: What do we think of ABD elementary? I feel like

1049
00:57:20,360 --> 00:57:22,520
Speaker 2: there's from yeah.

1050
00:57:22,160 --> 00:57:24,800
Speaker 1: Yeah, like abbit a lot. There's there's another example of

1051
00:57:24,840 --> 00:57:28,000
Speaker 1: like a show that you know has makes the the

1052
00:57:28,040 --> 00:57:30,200
Speaker 1: point that like working parents need to take time off

1053
00:57:30,200 --> 00:57:31,640
Speaker 1: of work to come to school meetings.

1054
00:57:31,880 --> 00:57:32,440
Speaker 2: Yeah.

1055
00:57:32,520 --> 00:57:34,280
Speaker 1: I would say for all of these, I'd love to

1056
00:57:34,280 --> 00:57:36,920
Speaker 1: see more solutions being put in place and more sort

1057
00:57:36,960 --> 00:57:40,760
Speaker 1: of characters questioning why things are so hard and suggesting solutions.

1058
00:57:41,440 --> 00:57:44,840
Speaker 1: And I think that helps audiences then also see they

1059
00:57:44,840 --> 00:57:48,240
Speaker 1: can ask for solutions and band with others, yeah.

1060
00:57:48,080 --> 00:57:49,400
Speaker 2: To make it. Yeah.

1061
00:57:49,520 --> 00:57:51,520
Speaker 3: Yeah, because like you watched and you're like, yeah, it

1062
00:57:51,640 --> 00:57:54,240
Speaker 3: is bad. It's kind of a than like, yeah, it

1063
00:57:54,320 --> 00:57:56,040
Speaker 3: is bad, and we can do this.

1064
00:57:56,320 --> 00:57:58,480
Speaker 1: Yes, exactly, like we can do something.

1065
00:57:58,960 --> 00:57:59,200
Speaker 2: Yeah.

1066
00:57:59,200 --> 00:58:01,000
Speaker 1: There's a great sne on The Morning Show from the

1067
00:58:01,040 --> 00:58:03,440
Speaker 1: last season where one of the characters like asks a

1068
00:58:03,440 --> 00:58:05,240
Speaker 1: board member what he's going to do about equal pay

1069
00:58:05,280 --> 00:58:07,800
Speaker 1: for the rest of the people. She had gotten a

1070
00:58:07,840 --> 00:58:10,680
Speaker 1: settlement to make up for a wage gap, but she

1071
00:58:11,200 --> 00:58:14,200
Speaker 1: shows shift, which our research shows the audiences want to see,

1072
00:58:14,400 --> 00:58:15,520
Speaker 1: saying what are you going to do for all of

1073
00:58:15,520 --> 00:58:17,280
Speaker 1: the other people here who can't hire lawyers? And I

1074
00:58:17,360 --> 00:58:18,439
Speaker 1: kind of love that as well.

1075
00:58:18,680 --> 00:58:21,920
Speaker 2: Talking about the like lack of solution, we've positive the

1076
00:58:21,920 --> 00:58:25,200
Speaker 2: theory that the reason people like like post apocalyptic zombie

1077
00:58:25,240 --> 00:58:28,840
Speaker 2: movies is because it allows them to experience community and

1078
00:58:29,080 --> 00:58:33,560
Speaker 2: walkable cities for the first time. That's how they.

1079
00:58:35,480 --> 00:58:36,720
Speaker 1: I think we can do better than that.

1080
00:58:36,840 --> 00:58:39,280
Speaker 2: What if like there weren't just like cars flow, Well,

1081
00:58:39,320 --> 00:58:40,120
Speaker 2: that's what it just shows.

1082
00:58:40,120 --> 00:58:42,640
Speaker 3: It's like it's easier to think that part up than

1083
00:58:42,720 --> 00:58:44,960
Speaker 3: like a utopia where it's like, yeah, and we've figured

1084
00:58:45,000 --> 00:58:49,640
Speaker 3: out income inequality and you know, just generally like societal

1085
00:58:49,680 --> 00:58:52,200
Speaker 3: ills have been solved through wealthy distribution.

1086
00:58:53,040 --> 00:58:55,440
Speaker 1: Yeah, it's been really fun to talk to writers who

1087
00:58:55,480 --> 00:58:58,560
Speaker 1: are interested in all these topics and hopefully our data

1088
00:58:58,600 --> 00:59:02,160
Speaker 1: helps them sell them their shows and ideas.

1089
00:59:02,240 --> 00:59:06,000
Speaker 2: Yeah, right for sure. Yes, Miles, where can people find

1090
00:59:06,040 --> 00:59:08,680
Speaker 2: you as their working media you've been enjoying? Yeah, find

1091
00:59:08,720 --> 00:59:10,720
Speaker 2: me everywhere at Miles of Gray.

1092
00:59:10,920 --> 00:59:13,080
Speaker 3: You can find me talking about ninety day fiance on

1093
00:59:13,160 --> 00:59:16,000
Speaker 3: four to twenty de fiance with Sophia Alexandra.

1094
00:59:16,400 --> 00:59:17,760
Speaker 2: A couple posts I like.

1095
00:59:17,800 --> 00:59:20,680
Speaker 3: First one is at Rob Delaney dot beascout on Socialist said,

1096
00:59:20,760 --> 00:59:23,520
Speaker 3: just saw tron Ari's calling it now Jared Fogel is

1097
00:59:23,520 --> 00:59:24,720
Speaker 3: gonna win another oscar.

1098
00:59:26,320 --> 00:59:27,600
Speaker 2: Another one from Paul F.

1099
00:59:27,640 --> 00:59:30,400
Speaker 3: Tompkins at PF Tompkins dot be scout on social post said,

1100
00:59:30,640 --> 00:59:33,600
Speaker 3: I know everyone is sick of hearing this, but Dorothy

1101
00:59:33,840 --> 00:59:37,120
Speaker 3: is a mom's friend's name, not a mom's name.

1102
00:59:37,200 --> 00:59:41,120
Speaker 2: If your mom is named Dorothy, she's not really your mom.

1103
00:59:42,400 --> 00:59:44,560
Speaker 2: And I just like how assertive he was being about

1104
00:59:44,560 --> 00:59:49,480
Speaker 2: that facts. Yeah, you actually can mom's friend is named Dorothy,

1105
00:59:49,600 --> 00:59:53,560
Speaker 2: not your mom. You can find me on Twitter at

1106
00:59:53,600 --> 00:59:57,760
Speaker 2: jack Underscore, Brian on Blue Sky at jack Obe the

1107
00:59:57,880 --> 01:00:02,919
Speaker 2: number one working media enjoying trash Jones on Twitter tweeted, hey,

1108
01:00:03,520 --> 01:00:05,640
Speaker 2: hate when somebody is starting to piss me off a

1109
01:00:05,680 --> 01:00:08,640
Speaker 2: little but not enough to say anything. You're on thick

1110
01:00:08,680 --> 01:00:13,400
Speaker 2: ice with me, buddy. I feel like that it really

1111
01:00:13,400 --> 01:00:17,760
Speaker 2: resonates with me. Okay, nobody's ever been shots fired nicker

1112
01:00:17,840 --> 01:00:21,560
Speaker 2: ice been with me? Yeah, exactly. You can find us

1113
01:00:21,640 --> 01:00:23,600
Speaker 2: on Twitter and Blue Sky at daily Zeikes where a

1114
01:00:23,680 --> 01:00:26,320
Speaker 2: d daily zekeist. On Instagram, you can go to the

1115
01:00:26,360 --> 01:00:28,960
Speaker 2: description of this episode wherever you're listening to it, and

1116
01:00:29,000 --> 01:00:31,120
Speaker 2: there at the bottom you will find the footnote no,

1117
01:00:31,280 --> 01:00:33,600
Speaker 2: which is where we link off to the information that

1118
01:00:33,680 --> 01:00:36,600
Speaker 2: we talked about in today's episode. We also link off

1119
01:00:36,600 --> 01:00:38,560
Speaker 2: to a song that we think you might enjoy. Miles,

1120
01:00:38,640 --> 01:00:41,240
Speaker 2: is there a song that you think the people might mean? Yes,

1121
01:00:41,920 --> 01:00:44,080
Speaker 2: I think with d'angelo's passing.

1122
01:00:44,120 --> 01:00:45,520
Speaker 3: I've just been listening to a lot of R and

1123
01:00:45,520 --> 01:00:49,080
Speaker 3: B and also just stumbling upon some other artists I

1124
01:00:49,160 --> 01:00:51,840
Speaker 3: wasn't as familiar with. This is a new artist I

1125
01:00:51,960 --> 01:00:57,240
Speaker 3: wasn't familiar, name familiar with, named Gabriel Jacobi. The track

1126
01:00:57,320 --> 01:00:59,840
Speaker 3: is called The One, and it's got.

1127
01:01:00,120 --> 01:01:01,160
Speaker 2: He's had, like I don't.

1128
01:01:01,640 --> 01:01:03,880
Speaker 3: He's doing so many things vocally, like there's a lot

1129
01:01:03,920 --> 01:01:05,240
Speaker 3: of false sell it falsetto.

1130
01:01:05,320 --> 01:01:06,120
Speaker 2: It's like whispery.

1131
01:01:06,160 --> 01:01:08,760
Speaker 3: It kind of feels like Prince vibes sort of vocally,

1132
01:01:09,760 --> 01:01:12,120
Speaker 3: but also who else am I trying to say? It's

1133
01:01:12,120 --> 01:01:13,880
Speaker 3: like it's you're gonna enjoy it. If you like R

1134
01:01:13,920 --> 01:01:16,040
Speaker 3: and B, you're gonna like this. And it's it's a

1135
01:01:16,080 --> 01:01:17,720
Speaker 3: little bit more faster paced. It's not sort of like

1136
01:01:17,760 --> 01:01:19,280
Speaker 3: a slow R and B ballot. It's just like a

1137
01:01:19,360 --> 01:01:20,040
Speaker 3: nice track.

1138
01:01:20,200 --> 01:01:23,440
Speaker 2: It's called The One and it's by Gabriel Jacobi and

1139
01:01:23,520 --> 01:01:25,720
Speaker 2: we will link off to it in the foot Nope.

1140
01:01:26,120 --> 01:01:28,120
Speaker 2: Daily Zite Guys is a production of iHeart Radio. For

1141
01:01:28,160 --> 01:01:31,840
Speaker 2: more podcasts from iHeart Radio, visit the iHeartRadio app, Apple podcast,

1142
01:01:32,160 --> 01:01:34,240
Speaker 2: or wherever you listen to your favorite shows. That's gonna

1143
01:01:34,280 --> 01:01:38,320
Speaker 2: do it for us. This morning for this whole week. Actually,

1144
01:01:38,680 --> 01:01:43,760
Speaker 2: we're back tomorrow with the greatest hits of the moments

1145
01:01:43,760 --> 01:01:47,520
Speaker 2: from this week's episodes, and back on Monday morning to

1146
01:01:47,600 --> 01:01:50,000
Speaker 2: tell you what was trending over the weekend and what's

1147
01:01:50,040 --> 01:01:51,880
Speaker 2: trending on Monday morning, and we will talk to you

1148
01:01:51,920 --> 01:01:54,680
Speaker 2: all then, by the Daily Zeite.

1149
01:01:54,680 --> 01:01:58,000
Speaker 1: Guys is executive produced by Catherine Long, co produced by

1150
01:01:58,040 --> 01:01:58,920
Speaker 1: Bay Wag

1151
01:01:59,360 --> 01:02:03,240
Speaker 2: Co produced by Victor Wright, co written by j M mcnapp,

1152
01:02:03,760 --> 01:02:06,240
Speaker 2: edited and engineered by Justin Conner.