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Speaker 1: Hello the Internet, and welcome to Season to fifty, episode

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Speaker 1: three of Days I Guys, a a production of I

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Speaker 1: Heart Radio. This is a podcast where we take a

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Speaker 1: deep dive into America's share consciousness. And it is Wednesday,

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Speaker 1: August seventeen, twenty two. You heard right, August. It is

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Speaker 1: August seventeen yep, which of course means are you ready.

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Speaker 1: I'm ready. I'm in my three point. Here we go,

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Speaker 1: defensive slide, deny, here we go. Ballalla blah blah blah

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Speaker 1: blah blah. Black Cat appreciation, Dave, you got a black cat?

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Speaker 1: I know I do. Shout out my cat Burgee. That's

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Speaker 1: also National Massachusetts Day shot off the Commonwealth, and also

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Speaker 1: National I Love my Feet Day, brought to you by

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Speaker 1: WI for Real, brought to you by I'm sure it

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Speaker 1: could be, but we all need we all need a

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Speaker 1: little self love. In the feet department, they say it's

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Speaker 1: about footcare. It's not about like taking a gander at

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Speaker 1: your little fetcs. It's about, hey, your feet fit all right?

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Speaker 1: You got foot paint, you're walking okay into your feet

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Speaker 1: fit all right? I mean your shoes. I'm sorry you're shoes.

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Speaker 1: Your shoes because I have wider feet, and I realized

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Speaker 1: I've been wearing the most like improperly narrow shoes for

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Speaker 1: most of my life, and now I'm like starting to

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Speaker 1: be like, oh yeah, I'm kind I'm starting to feel

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Speaker 1: that an so switched to birkin Stocks. There you go,

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Speaker 1: the person. I just got my first pair, but you

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Speaker 1: got Burke's h I don't have it yet. I need

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Speaker 1: to go. I'm gonna cross over nothing. Give them miles

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Speaker 1: really live for Yeah, they're nice, or they leather. You

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Speaker 1: got the felt ones. Plastics, they're like the plastic rubber.

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Speaker 1: Oh shit, okay, waterproof. I didn't look when I was

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Speaker 1: in my phase where I was like, you know, having

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Speaker 1: my walls pleasure with booting posters and was wearing like

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Speaker 1: crust color and can I My whole school was all

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Speaker 1: into seventies fashion, like in the nineties bergen stock, so

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Speaker 1: I swore I would never wear burgen stocks. But I've

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Speaker 1: gone back on that and they are comfortable. And my

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Speaker 1: wife even allows me to have my toes out around

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Speaker 1: the house, which is that is the one part of

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Speaker 1: my body that she is openly like, it's ugly. We

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Speaker 1: should get a redo on that. Not good down here,

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Speaker 1: like not so much here here, but like down here

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Speaker 1: in this region here. Well, my name is Jack O'Brien

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Speaker 1: a k zake. Guys, Jack and Miles in action, passing

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Speaker 1: through my ma and all the while I'm drinking do uh.

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Speaker 1: That's Chrisie of Bohemian Rap City in the end, the end,

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Speaker 1: the n Yeah, shout out to Bohemian Rap City. That

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Speaker 1: was that. He gave me that one a long time

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Speaker 1: ago and I had somehow missed it. So I appreciate you,

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Speaker 1: and I'm thrilled to be joined as always by my

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Speaker 1: co host Mr Miles, Mr Miles Gray, a k a.

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Speaker 1: The Lord of the lank or sham Headeo NoHo himself.

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Speaker 1: Oh and also if you're in the valley, the San

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Speaker 1: Fernando Valley, I'm gonna be doing a live podcast with

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Speaker 1: eight one eights and Heartbreaks the local valley legends. Check

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Speaker 1: my Twitter or the eight one Eights and Heartbreak Twitter

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Speaker 1: socials to get tickets where I will be discussing my

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Speaker 1: favorite thing, which is the San Fernando Valley. So but

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Speaker 1: that's limited to people in l A, so please check

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Speaker 1: it out. That's like a valley focused podcast. Oh yeah,

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Speaker 1: all valley kids, they all went to North Hollywood. They're

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Speaker 1: all from North Hollywood, like, and I saw Molly Lambert

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Speaker 1: on it also North North Hollywood legend Molly Lambert. And

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Speaker 1: when I saw I was like, yo, I want to

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Speaker 1: be on this podcast, And like I was d m

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Speaker 1: ing them, I'm like, hey, I would like to be

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Speaker 1: a guest or like, oh okay, normally people don't do this,

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Speaker 1: but yeah, but you know what, I see eight one

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Speaker 1: eights and my heart flutters. There you go. Well, we

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Speaker 1: are thrilled to be joined in our third seat by

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Speaker 1: acclaimed science journalist, author, comedy writer, poet whose writing has

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Speaker 1: appeared in The New York Times, The Atlantic, The Guardian,

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Speaker 1: The Washington Post, The New Republic, Slate, and Salon. Her

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Speaker 1: first book, The End of Bias A Beginning, was a

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Speaker 1: finalist for All Sorts of Awards, appeared on All Sorts

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Speaker 1: of Best Book of the Year LISTE It's Jessica Nordon. Hello,

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Speaker 1: thank you so much all to be back. Yeah, welcome, welcome.

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Speaker 1: How are things? What's new? How art thou? A? Things

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Speaker 1: are good? I I am hoping they're just until about

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Speaker 1: five minutes ago there was a small child screaming outside

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Speaker 1: my window. So I'm really hoping that we have an

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Speaker 1: uninterrupted conversation. But yeah, things are good. I'm in Minneapolis.

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Speaker 1: It's like we're in this like narrow window of like

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Speaker 1: two weeks when the weather is beautiful and everyone in

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Speaker 1: the city like goes outside and like runs around maniacally

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Speaker 1: because become horrible soon. Um So I'm I'm doing well.

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Speaker 1: How are you guys doing good? Wait? What's what's the

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Speaker 1: window for a good weather in Minneapolis? Like, because the

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Speaker 1: way you say that, I'm like, oh, this is clear,

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Speaker 1: Like what I mean realistically when when at what point

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Speaker 1: are we seeing what kind of average temperatures? I mean,

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Speaker 1: I feel like it stays cold, you know, realistically, it

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Speaker 1: stays cold until like late April, and then it starts

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Speaker 1: to get cold again in like October. So it's it's

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Speaker 1: kind of a narrow, yeah, slice of of joy that

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Speaker 1: we see and hold on to enjoy it while it lasts. Meanwhile,

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Speaker 1: we can't escape mid nineties heat in California, but hey,

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Speaker 1: you know that's a part for the course. Out here,

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Speaker 1: it's hot. We're trying to air out our house because

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Speaker 1: we got you know, we're kind of getting over COVID,

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Speaker 1: trying to get all the germs blown out. But it is.

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Speaker 1: It is just static and hot. Are you guys both

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Speaker 1: in southern California? Yeah, Los Angeles where actually, if I

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Speaker 1: tilt my thing to the side, Miles is right here,

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Speaker 1: right next to him, built at throughout the kind of

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Speaker 1: keep thee o man as long as you don't so.

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Speaker 1: But so you've been lifelong Minneapolis or um, I've been

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Speaker 1: kind of bouncing around. I grew up well, I was

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Speaker 1: born in l A. Actually I was delivered by the

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Speaker 1: same doctor who delivered Chaz Bonozing. Congratulations. And my my

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Speaker 1: pediatrician was I don't know if you remember the Hollywood

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Speaker 1: Madam Heidi Flice. Oh, we just podcast about Heidi Flice

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Speaker 1: and talked about how Heidi Flice's father delivered Leonardo DiCaprio.

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Speaker 1: And was you know the pediatrician to the whole like

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Speaker 1: kind of counterculture cool Silver Lake scene. You were, Yes,

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Speaker 1: that included you. He was my pediatrician apparently, Yes, I

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Speaker 1: didn't know Leonardo leon and I shared a pediatrician. Maybe

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Speaker 1: I'll give him a call absolution, Yeah, I love yeah,

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Speaker 1: just with the most tenuous connection like Lilo Lil Liah.

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Speaker 1: It's the best game. Actually, who has the most tenuous

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Speaker 1: connection to the least significant celebrity. Absolutely, and it's I

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Speaker 1: remember I did that like once with when I met

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Speaker 1: Cindy Crawford, because I when I worked at like this

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Speaker 1: other job, I went to her house to shoot a

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Speaker 1: video and I was like, the thing is her son?

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Speaker 1: I like when I worked at a laser Tag place

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Speaker 1: I did. I was the party host twice for this

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Speaker 1: birthday and I met her and I remember as a

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Speaker 1: teenage like I met Cindy Crawford as an adult. I

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Speaker 1: went to your house and I'm like, this is gonna

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Speaker 1: sound really weird, but I went to her house for

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Speaker 1: another reason. You didn't go to her house just to

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Speaker 1: say this, yeah, just knocking on the It's me from

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Speaker 1: Ultra Zone. I told her. I said, hey, I I

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Speaker 1: this sounds weird, but I actually was like the party

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Speaker 1: guy for your kids laser Tag birthday. She was like,

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Speaker 1: oh my god. She grabbed her photo album and pulled

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Speaker 1: out the photos from the party and like my arm

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Speaker 1: wasn't a shot, and I was like, that was me.

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Speaker 1: And again that was like one of the most my

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Speaker 1: I've never felt more like entitled or whatever, just to

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Speaker 1: be like you see it was a loose thread and

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Speaker 1: she fucking ganked on it, and the whole photo album

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Speaker 1: came out. Wasn't that tenuous that I'm I'm like the

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Speaker 1: RMB in and it is cool. I would have preferred

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Speaker 1: if she pulled it out and there was just like

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Speaker 1: a wall of pictures of you, just like just like

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Speaker 1: I've been waiting for you, waiting for you could come

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Speaker 1: come back. All right, well, Jessica, we're going to get

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Speaker 1: to know you a little bit better in a moment. First,

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Speaker 1: we're gonna tell our listeners. I wanted to say the

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Speaker 1: comedy writers thing you wrote for a Prairie Home companion

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Speaker 1: I did. It was like one of my first jobs. Yes, yes,

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Speaker 1: look at you. I have Wikipedia have access to that.

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Speaker 1: But that's very cool. What was that like to work on?

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Speaker 1: It was really interesting. I mean I remember, like I

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Speaker 1: was just I was in my early twenties. I was

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Speaker 1: kind of desperately looking for a job that would pay

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Speaker 1: me for writing, and I was living in Minneapolis, and um,

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Speaker 1: it was kind of like trolling different websites and Minnesota

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Speaker 1: Public Radio had this writing job that they advertised and

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Speaker 1: with like benefits, and I was like, that sounds fantastic.

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Speaker 1: And only later did I find out that it was

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Speaker 1: for Prairie and Companions. I was like, huh, well, I

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Speaker 1: haven't really listened to the show, but I like writing comedy.

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Speaker 1: I'll give it a shot. And yeah, I ended up

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Speaker 1: working there for a few years. It was It was wild.

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Speaker 1: I mean, I I learned a lot. It was you know,

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Speaker 1: kind of in hindsight. There were some strange things that

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Speaker 1: went down that I wasn't as in tune to at

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Speaker 1: the time as I am now, but I mean it

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Speaker 1: was it was a great education in working quickly and

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Speaker 1: working hard and kind of like not being precious with

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Speaker 1: material because there was like so much material generated. We

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Speaker 1: had a few writers working on the show and then

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Speaker 1: Garrison was like writing all the time, and we would

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Speaker 1: just create so much material, so much material, and you know,

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Speaker 1: it would get thrown out. And so I feel like

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Speaker 1: it kind of taught me something that was useful for

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Speaker 1: my writing career later, which is like, don't get that

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Speaker 1: attached to stuff, just like exactly just like produced, produced, produce,

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Speaker 1: and eventually something good will emerge, but don't get yeah,

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Speaker 1: don't get too precious. So that that was really actually

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Speaker 1: very useful early kind of early career, very insight. Did

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Speaker 1: the movie a Prairie Home companion. Did you watch that movie?

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Speaker 1: Didn't get it right? I was an extra in that movie. Right,

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Speaker 1: That's pretty cool, which is my tenuous connection there is. Yeah. Yeah,

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Speaker 1: that was very weird because it was like this physical

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Speaker 1: recreation of something that only existed in our heads, like

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Speaker 1: all of the sets with the different the different sketches

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Speaker 1: and yeah, so it was kind of it was sort

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Speaker 1: of trippy in an existential way. But yeah, cool, that's funny.

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Speaker 1: I I interned at Mirror Max in two thousand two,

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Speaker 1: and yeah, like you said, like nothing over at the time,

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Speaker 1: but in retrospect, it's like, man, that places vibes were

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Speaker 1: all all fucked up. There were there was some weird

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Speaker 1: ship going on. Anyways, all right, we are going to

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Speaker 1: get to know you a little bit better in a moment. First,

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Speaker 1: a few of the things we are talking about later on,

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Speaker 1: we're gonna talk Tuckers back on the air and he's

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Speaker 1: he's ringing the bell. He wants he wants blood, it

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Speaker 1: would appear, so so we're gonna talk about blood. I mean,

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Speaker 1: he's like, I hate they blood. The implication there will

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Speaker 1: be blood question mark is his version of things. They

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Speaker 1: might be blood. Uh, we're gonna talk about unpaid internships.

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Speaker 1: Speaking of internships, the reason journalism is failing, I think

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Speaker 1: is I don't think we need the question mark. Yeah,

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Speaker 1: that's in general. Paid internships in any industry is the

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Speaker 1: reason why a lot of homogeneous thinking or just kind

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Speaker 1: of like one note mentality is happening. I feel like, yeah,

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Speaker 1: we're going to talk about the Economist. They released a

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Speaker 1: few months back there guide to the top ten best

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Speaker 1: and worst places to live in the world, And this

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Speaker 1: is from the Economist. What is the Economist Intelligence Unit?

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Speaker 1: So they're like, this is we we like to think

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Speaker 1: of ourselves as a publication, as having a CI a

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Speaker 1: part of our outlet. So this this comes from them

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Speaker 1: plenty more. But before we get to any of that, Jessica,

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Speaker 1: we like to ask our guests, what is something from

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Speaker 1: your search history? Oh, search history right now? Um gosh.

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Speaker 1: I have been looking into something called empathic discipline, which

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Speaker 1: is an an approach to creating stronger teachers, student trust

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Speaker 1: and relationships, um with the goal of decreasing racial disparities

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Speaker 1: in school discipline like suspensions, expulsions, detentions, things like that.

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Speaker 1: And there's some really interesting research done by an amazing

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Speaker 1: psychologist named Jason okana Fua who finds that and the

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Speaker 1: research found that when you train teachers to use empathic

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Speaker 1: approaches to students, when you train teachers to really focus

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Speaker 1: on building trusting relationships and avoiding labeling students and really

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Speaker 1: thinking about how important the teacher student relationship is to

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Speaker 1: helping students success, what happens is suspensions drop, and specifically

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Speaker 1: racial disparities in dispensions in suspensions decrease as well. So

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Speaker 1: it's like this really interesting approach to decreasing biaus that

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Speaker 1: doesn't actually target bias. So it's it's like kind of

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Speaker 1: this Ninja or maybe more like Judo. It's like this

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Speaker 1: this move of getting at the consequences of bias without

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Speaker 1: actually trying to tackle the bias itself. So I've been

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Speaker 1: I've been researching that just focusing on building empathy, like

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Speaker 1: with teachers having empathy with their students across the board,

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Speaker 1: and the idea that like just by having a like

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Speaker 1: an actual relationship with student will probably be like, oh, yeah,

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Speaker 1: I respect you because I trust to you. So if

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Speaker 1: you're telling me that, I'm less likely to act out

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Speaker 1: versus me being like, oh, here comes this fucking asshole

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Speaker 1: who just wants to say I'm a bad influence all

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Speaker 1: the time. That's exactly it. So what this researcher found

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Speaker 1: was that a lot of like bias interventions think of

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Speaker 1: it as this one way street, which is like the

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Speaker 1: teacher express and bias towards the student. But what Jason

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Speaker 1: o'kana fua found, Dr okana Fua, is that it's actually

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Speaker 1: like an interactive experience. So exactly as you point out, Miles, like,

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Speaker 1: the teacher is maybe having certain stereotypes about students, and

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Speaker 1: the student senses that and they are primed to act

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Speaker 1: out because they don't trust the teacher and they're expecting

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Speaker 1: to be treated in a particular way, and so that

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Speaker 1: might even create them to act out more and then

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Speaker 1: fulfill the teacher's stereotype, which just creates this like endless

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Speaker 1: and then the teachers like and I was right about them,

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Speaker 1: and then you nailed it. Yeah, So this approach is

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Speaker 1: really designed to interrupt that bad cycle and help teachers

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Speaker 1: really understand how important it is for the students to

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Speaker 1: feel respected and for the teacher to respect the students

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Speaker 1: and to create that bond. So that exactly like when

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Speaker 1: a problem arises the student like, as you point out,

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Speaker 1: is like, oh, okay, maybe this teacher's right, maybe I

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Speaker 1: do need to kind of shift things around. And I

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Speaker 1: know that they try. I know that they respect me,

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Speaker 1: and so it creates the foundation for a relationship that

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Speaker 1: then causes students to feel more respected and behave better right,

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Speaker 1: And it seems like it's just something really good teachers naturally,

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Speaker 1: do you know, like I think of like teachers I've had.

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Speaker 1: I've had teachers who have like written me off, and

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Speaker 1: I'm like, and I hated being in their classroom because

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Speaker 1: they're going to be like the second to have one

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Speaker 1: teacher who like if anybody laughed at any time, she's like, Miles,

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Speaker 1: what are you doing or something because she thought I

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Speaker 1: was distracting the class and like, dude, I don't even

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Speaker 1: sit on that side of the classroom. What the are

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Speaker 1: you talking about? And it all I was. It was

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Speaker 1: always very tense. And then I had other teachers who

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Speaker 1: knew I was I could be disruptive, but like would

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Speaker 1: come at me into like, hey, look, I know you

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Speaker 1: got you get excited, you want to talk to your friends,

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Speaker 1: but like just help me out and just kind of

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Speaker 1: like cool. It when you do it, and that approach

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Speaker 1: all like, I was always more respectful with those teachers

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Speaker 1: who are like, hey, I get it, you know, like

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Speaker 1: I get you, but like please shut up. And I

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Speaker 1: was like, okay, cool, versus like here he goes the

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Speaker 1: loud kid again, throwing up gang signs, ruining our class picture,

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Speaker 1: and I'm like, get back in your thunderbird already at least. Yeah,

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Speaker 1: I mean, one thing that was super interesting to me

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Speaker 1: about this research was that when he when this, when

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Speaker 1: the psychologist recruited teachers for the study, he didn't actually

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Speaker 1: tell them that they were doing that. He was like

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Speaker 1: doing an intervention with them. What he told them was

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Speaker 1: he was they were being recruited to kind of reflect

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Speaker 1: and review best practices in teaching might and review. And

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Speaker 1: so they were given this material that talked about how

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Speaker 1: important trusting relationships are and how important it is to

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Speaker 1: not label a student or assume that that the teacher

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Speaker 1: knows why a student is behaving that way, and to

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Speaker 1: instead look for maybe situational reasons that a student is

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Speaker 1: behaving that way rather than fix it to like some

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Speaker 1: kind of essential aspect of that student. And so they

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Speaker 1: were yeah, so exactly, they were kind of they were

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Speaker 1: told that they were just reviewing best practices and and

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Speaker 1: then they reviewed things like how how students felt when

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Speaker 1: they felt respected and how that helped them learn. And yeah,

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Speaker 1: I mean the results were pretty spectacular in terms of

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Speaker 1: actually like changing the dynamic. It's wild how much we

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Speaker 1: ask of our teachers too already, right like, because on

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Speaker 1: top of you know, the many teachers who listen to

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Speaker 1: this show, again, thank you so much. I'll continue to

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Speaker 1: retweet your wish lists, but the added sort of task

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Speaker 1: of like, yeah, look, some kids are not supported at

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Speaker 1: home and are going to come in here. And then

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Speaker 1: also we also need you to have these skills to

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Speaker 1: be able to make sure that they can navigate life

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Speaker 1: because they're not going to be through this like chaotic

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Speaker 1: sequence of like not trusting people in authority positions and

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Speaker 1: what that turns into an adulthood. Just just WoT how

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Speaker 1: much we ask. That's why very a thank a teacher

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Speaker 1: when you see them, because that's why I'm so grateful

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Speaker 1: because I think of the teachers who actually tried to

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Speaker 1: understand me rather than just kind of be like this

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Speaker 1: guy's a disruptive asshole. It was like, thank you, and

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Speaker 1: that helped me be more interested in like school at

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Speaker 1: the end of the day rather than like feeling like

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Speaker 1: I was going to a place where we're just going

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Speaker 1: to be like, you're bad. That's so dope though, that

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Speaker 1: that you've that the study found that the training like

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Speaker 1: it is changeable. It's not a fixed problem. It's it's

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Speaker 1: something where training teachers to you know, just focus on

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Speaker 1: this this part of their relationships with students, like makes

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Speaker 1: makes an impact. Is that is that something that's like

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Speaker 1: generalizable to healthcare and you know other places is at

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Speaker 1: the empathic discipline kind of being being adopted law enforcement.

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Speaker 1: I don't know, it's a really it's a really interesting question.

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Speaker 1: I mean, I think what that like if if you

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Speaker 1: zoom out a little bit, I think what this approach

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Speaker 1: shows is that if you if you want to address

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Speaker 1: people's biases, one thing that can work that we see

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Speaker 1: in this education research is enhancing their other values, like

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Speaker 1: boosting their commitment to values like trusting relationships and respect

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Speaker 1: and mutual understanding and avoiding labeling. And so if you

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Speaker 1: kind of boost and like amplify those values, then that

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Speaker 1: can override these automatic responses that that we might have

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Speaker 1: toward one another. So it kind of like, yeah, it's

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Speaker 1: almost it's like it's like, I don't know, like a vitamin,

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Speaker 1: like a kind of steroid, like you you give your

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Speaker 1: other values steroids, and then it kind of like over

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Speaker 1: this metaphor is falling apart. Sorry, Okay, we're a pro steroids.

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Speaker 1: We're always were both we're both on cycles right now.

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Speaker 1: So you do a hit a balco straight in an

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Speaker 1: empathy zone, everyone is completely juice. Yeah, yeah, so but no,

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Speaker 1: it's true bigger. My heart was enlarged. Also, heart only

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Speaker 1: grew three eyes that day. By Yeah, I'm glad that you.

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Speaker 1: I'm glad you mentioned policing, because yes, there there is

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Speaker 1: also really interesting work done in policing that I that

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Speaker 1: I talked about in my book. Actually that looks at

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Speaker 1: like how you change police behavior basically, like how you

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Speaker 1: actually get police to behave in respectful, kind humane ways

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Speaker 1: rather than just avoid like breaking the law and avoid

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Speaker 1: just like criminal behavior and that. Yeah, and I think

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Speaker 1: you know, in that case, it's a lot about creating incentives,

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Speaker 1: like we talked about. You know, I was just at

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Speaker 1: a meeting actually here in Minneapolis talking about improving police

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Speaker 1: behavior and accountability, and everyone focuses primarily on accountability and

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Speaker 1: correct punishment for you know, for failure, we need more guardrails,

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Speaker 1: more guardrails kind of thing, man exactly. And that's like,

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Speaker 1: really it's totally important. I mean, it creates like a

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Speaker 1: floor below which you cannot go in terms of bad behavior.

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Speaker 1: But I think we miss something when we talk about this,

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Speaker 1: because culture is not just what you punish, but it's

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Speaker 1: also what you reward. And I think if we if

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Speaker 1: we just talk about punishment without talking about what we're

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Speaker 1: actually trying to reward and incentivize and like the kind

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Speaker 1: of behavior we actually want, then we only kind of

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Speaker 1: have half the half the story. Yeah, And even in

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Speaker 1: like that guardrail metaphor, it's like if I think of

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Speaker 1: a cop as a bowler, like do you want the

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Speaker 1: guys like, yeah, put the bumpers up because I'm sloppy

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Speaker 1: as shit, or someone who's like there's a there's someone

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Speaker 1: in need. I know how to like solve it. Let

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Speaker 1: me just bowl of strike here. I don't need guardrails

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Speaker 1: because I'm focused on a different version, which is like

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Speaker 1: I'm out of control. How do you contain that? Mhm?

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Speaker 1: I want that guy who said, who do you think

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Speaker 1: you are. I am that's metaphor. But yeah, Jack, to

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Speaker 1: your question about values, I mean like if you can

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Speaker 1: generalize that the education work to policing, I mean I

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Speaker 1: think that you can create incentive structures. In fact, there's

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Speaker 1: a there's a small group in l A the Community

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Speaker 1: Safety Partnership that um there was just an independent evaluation

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Speaker 1: of this particular police program which was designed around changing incentive.

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Speaker 1: So officers were rewarded for building relationships instead of making arrests,

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Speaker 1: and so the value was in the relationship. That's what

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Speaker 1: they were actually being rewarded for. And the analysis that

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Speaker 1: came out of this of that program found that it

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Speaker 1: did change police behavior actually, and it also declused violent crime.

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Speaker 1: That's very cool. Shout out to l A is that

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Speaker 1: that was an l A p D. It was believe it, Yes,

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Speaker 1: it was l A p D. It was a small

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Speaker 1: group that's currently headed by a mod of ting Gardis,

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Speaker 1: Deputy Chief of Modit ting Gartis, And it was like

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Speaker 1: a small pilot program that started in in bots In

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Speaker 1: I want to say, two thousand eleven with like forty

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Speaker 1: officers that were tasked with creating relationships and were rewarded

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Speaker 1: on the basis of whether they created relationships with the community.

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Speaker 1: That's very cool. All right, let's take a quick break.

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Speaker 1: We'll be right back with your underrated and obrated. And

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Speaker 1: we're back and Jessica, we like to ask our guests,

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Speaker 1: what is something do you think is underrated? Telepathy? Mm

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Speaker 1: hm epathy. I think if we we just learned that

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Speaker 1: Trump can telepathically declassify documents, I think we are all

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Speaker 1: using telepathy far too little. Yeah, and I would like

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Speaker 1: to start using it to pay my car insurance and

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Speaker 1: resolve long running disputes with the cable company. Well, first

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Speaker 1: step is you got to start an account at ruth Social,

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Speaker 1: just like he did. And he's like, by virtue of

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Speaker 1: this tweet I hear about like I was like, oh

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Speaker 1: my god, he's really doing the fucking Michael Scott, I

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Speaker 1: declare bankruptcy a bit from the office, but with declassifying

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Speaker 1: top secret documents. Okay, I'm not up on this story.

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Speaker 1: What what do you do? He thought, because of the

426
00:25:19,720 --> 00:25:22,680
Speaker 1: whole talk about declassifying things, there was a hold, I'll

427
00:25:22,680 --> 00:25:24,720
Speaker 1: pull it up. He had this whole tweet that was like,

428
00:25:25,359 --> 00:25:28,800
Speaker 1: by virtue of this truth Social, it was like very

429
00:25:28,880 --> 00:25:35,040
Speaker 1: heavy handed, right, so yeah, just just the declaration that

430
00:25:35,200 --> 00:25:41,439
Speaker 1: is no longer classified. Yeah, hold on, Like with the

431
00:25:41,480 --> 00:25:49,399
Speaker 1: classification process is notoriously and like iconically very convoluted and

432
00:25:49,960 --> 00:25:52,760
Speaker 1: needs to go through so many layers of bureaucracy. The

433
00:25:52,800 --> 00:25:57,399
Speaker 1: fact that he thinks he could just declassify something over

434
00:25:58,160 --> 00:26:01,840
Speaker 1: true Social is proves to me that he's not even

435
00:26:01,920 --> 00:26:05,040
Speaker 1: trying anymore. I feel like, well, yeah, I mean I

436
00:26:05,080 --> 00:26:07,320
Speaker 1: think he's trying to do anything he can write, because

437
00:26:07,320 --> 00:26:09,560
Speaker 1: there's like multiple narratives ones like oh no, maybe he

438
00:26:09,880 --> 00:26:13,120
Speaker 1: you know, very benignly just took these documents that we're

439
00:26:13,119 --> 00:26:17,159
Speaker 1: gonna skiff like in a secured area. Or he's like, well,

440
00:26:17,200 --> 00:26:19,600
Speaker 1: if that's not the case, then I'm able to do

441
00:26:19,640 --> 00:26:22,960
Speaker 1: classify anything anyway, just like this truth Social said, because

442
00:26:22,960 --> 00:26:24,640
Speaker 1: you remember he was trying to do ship like by

443
00:26:24,760 --> 00:26:28,160
Speaker 1: Twitter decree too, and like even then they're like, that's

444
00:26:28,200 --> 00:26:30,760
Speaker 1: not how this works. And even if your president that

445
00:26:30,840 --> 00:26:34,440
Speaker 1: doesn't do that, So, you know, old habits die hard.

446
00:26:34,480 --> 00:26:39,120
Speaker 1: I guess what is something you think is overrated? Overrated?

447
00:26:39,440 --> 00:26:45,720
Speaker 1: I would say box fans. This is a long running

448
00:26:45,760 --> 00:26:48,320
Speaker 1: dispute in my house. My husband is not happy unless

449
00:26:48,359 --> 00:26:51,880
Speaker 1: there are air molecules at very high velocity moving over

450
00:26:51,920 --> 00:26:54,399
Speaker 1: his skin at all times. We have like fans going

451
00:26:54,440 --> 00:26:57,000
Speaker 1: in every room and it dries me insane because I

452
00:26:57,040 --> 00:27:00,040
Speaker 1: like still on moving air. It makes me sound a

453
00:27:00,040 --> 00:27:02,440
Speaker 1: little strange. Wait is he just hot? Is that why?

454
00:27:02,520 --> 00:27:08,679
Speaker 1: Like he's just like yeah, wait, so do you have

455
00:27:08,720 --> 00:27:10,520
Speaker 1: a lot of Bob I like how you caught up

456
00:27:10,600 --> 00:27:14,560
Speaker 1: box fans? It's not like air conditioner running that full blast,

457
00:27:14,600 --> 00:27:17,200
Speaker 1: having a ton of fans going like box fans, because

458
00:27:17,200 --> 00:27:20,560
Speaker 1: he just like in a room with like four box fans,

459
00:27:20,600 --> 00:27:22,240
Speaker 1: like set up in a square that he's in the

460
00:27:22,240 --> 00:27:25,879
Speaker 1: middle of, and he's like, yes, peace, I know peace.

461
00:27:26,359 --> 00:27:29,760
Speaker 1: Oh wow, okay, well dedication to the bid. Yes, respect

462
00:27:29,800 --> 00:27:33,240
Speaker 1: on a lot of box fans. Yeah, yeah, I run hot.

463
00:27:33,400 --> 00:27:35,639
Speaker 1: I I like a box fan because that's the cheapest

464
00:27:35,640 --> 00:27:38,440
Speaker 1: way to keep mosquitoes away from you without having to

465
00:27:38,640 --> 00:27:41,840
Speaker 1: because the mosquito problem is terrible in l A if

466
00:27:41,840 --> 00:27:44,679
Speaker 1: you're outside. Sadly, we used to not have that issue,

467
00:27:44,680 --> 00:27:47,680
Speaker 1: but box fans are especially for people like me who

468
00:27:47,720 --> 00:27:51,080
Speaker 1: like just get bit up all the time. I can't

469
00:27:51,119 --> 00:27:53,679
Speaker 1: recommend him enough. Oh, maybe I need to rethink my

470
00:27:53,760 --> 00:27:56,800
Speaker 1: box fan relationship. No, no, of course, I just like

471
00:27:56,880 --> 00:27:59,080
Speaker 1: to like your air. Look, I hope your husband's listening,

472
00:27:59,160 --> 00:28:02,399
Speaker 1: and yeah, may be, let's dial back the air speed

473
00:28:02,440 --> 00:28:06,359
Speaker 1: a little bit. Okay, please for everybody's sake. But just

474
00:28:06,520 --> 00:28:08,800
Speaker 1: living in a wind tunnel, you guys have to talk that.

475
00:28:08,880 --> 00:28:12,359
Speaker 1: You're like, hello, can you hear me? Let you turn

476
00:28:12,480 --> 00:28:15,359
Speaker 1: off one of your fans. Sorry, it's too hot in here.

477
00:28:15,359 --> 00:28:19,280
Speaker 1: I can't do anything right now. Well, I mean, actually,

478
00:28:19,320 --> 00:28:21,280
Speaker 1: it connects. I haven't thought about this. It connects to

479
00:28:21,320 --> 00:28:25,400
Speaker 1: bias because, um, like, offices are kept at a temperature

480
00:28:25,440 --> 00:28:29,040
Speaker 1: that is comfortable for men. Men are a little bit

481
00:28:29,080 --> 00:28:31,520
Speaker 1: warmer than women, and so yeah, I think women women

482
00:28:31,560 --> 00:28:35,159
Speaker 1: of the world are are a little bit cold. Unfortunately.

483
00:28:35,440 --> 00:28:39,360
Speaker 1: I'm just I'm like, I run hot like typically like

484
00:28:40,080 --> 00:28:42,520
Speaker 1: like my wife, she's like, do please get away from me,

485
00:28:42,720 --> 00:28:46,000
Speaker 1: like like your body, your body's emanating so much heat.

486
00:28:46,080 --> 00:28:48,160
Speaker 1: Like I'm like, I don't know what's going on. Man, Okay,

487
00:28:48,160 --> 00:28:49,320
Speaker 1: I've been like this is how I was a kid.

488
00:28:49,960 --> 00:28:53,320
Speaker 1: Um So, I tend to sweat, but I like the

489
00:28:53,360 --> 00:28:56,320
Speaker 1: air over my skin. I'm currently sweating through my shirt

490
00:28:56,440 --> 00:28:59,440
Speaker 1: right now. I saw you giving yourself the little shirt

491
00:28:59,520 --> 00:29:04,600
Speaker 1: fans and little shirt fans are those are underrated turning

492
00:29:04,600 --> 00:29:09,600
Speaker 1: your shirt into a little thing. That's also the sign

493
00:29:09,640 --> 00:29:12,080
Speaker 1: that like it's bad, you know what I mean, Like

494
00:29:12,080 --> 00:29:13,520
Speaker 1: when you get to that point you're like shirt fan,

495
00:29:13,560 --> 00:29:18,080
Speaker 1: shirt fan shirt. I mean, that's just that's an everyday

496
00:29:18,120 --> 00:29:24,200
Speaker 1: thing for me. All right, Miles. So Tucker, Tucker came back.

497
00:29:24,240 --> 00:29:27,640
Speaker 1: He took a week off kind of unannounced, but I

498
00:29:27,720 --> 00:29:33,040
Speaker 1: had some great guest hosts and then he's back, and

499
00:29:33,440 --> 00:29:36,160
Speaker 1: we we we were speculating that maybe it was like

500
00:29:36,200 --> 00:29:38,920
Speaker 1: Alex Jones thing that had him shook and that's why

501
00:29:38,960 --> 00:29:41,520
Speaker 1: he was he was m I A, we don't really know,

502
00:29:41,760 --> 00:29:44,400
Speaker 1: but he was doing his homework and he's ready to

503
00:29:45,320 --> 00:29:52,360
Speaker 1: go to civil war question. I don't know. Um, Like

504
00:29:52,400 --> 00:29:55,400
Speaker 1: we said last time, when the news came out about

505
00:29:55,440 --> 00:29:59,080
Speaker 1: Alex jones cell phone data becoming like getting into the

506
00:29:59,120 --> 00:30:02,480
Speaker 1: hands of uh Sandy Hook lawyers and then potentially the

507
00:30:02,560 --> 00:30:06,280
Speaker 1: January six Committee, they're saying Tucker was quote scared shitless

508
00:30:06,360 --> 00:30:08,479
Speaker 1: because him and Alex Jones talk all the time. Now,

509
00:30:08,480 --> 00:30:11,520
Speaker 1: who knows what's in there, but that was one theory.

510
00:30:11,840 --> 00:30:14,880
Speaker 1: But he was gone, came back and yeah, like we

511
00:30:14,880 --> 00:30:17,160
Speaker 1: were saying, we're like, I'm not sure where the right

512
00:30:17,360 --> 00:30:21,240
Speaker 1: is going with this documents ship. First it's like, well,

513
00:30:21,280 --> 00:30:24,440
Speaker 1: what is the definition of a nuke or like a

514
00:30:24,600 --> 00:30:28,000
Speaker 1: document or classified? And like when that wasn't going well,

515
00:30:28,000 --> 00:30:30,080
Speaker 1: it's like, well, then Espionage Act needs to go. And

516
00:30:30,120 --> 00:30:32,400
Speaker 1: then it was like the FBI set him up. And

517
00:30:32,480 --> 00:30:35,680
Speaker 1: now Tucker Carlson is just not being very ambiguous. He's

518
00:30:35,720 --> 00:30:39,280
Speaker 1: kind of like saying, there's an odd feeling in the air,

519
00:30:39,480 --> 00:30:42,240
Speaker 1: isn't there, And it would be wise if that were

520
00:30:42,320 --> 00:30:47,240
Speaker 1: brought down. It's of something unprecedented and something awful. You

521
00:30:47,280 --> 00:30:51,520
Speaker 1: could feel it. Even Donald Trump feels it. Maybe for

522
00:30:51,600 --> 00:30:55,520
Speaker 1: the first time in his life. Donald Trump seems sincerely

523
00:30:55,640 --> 00:30:58,280
Speaker 1: interested in lowering the temperature, not just for his own sake,

524
00:30:58,360 --> 00:31:01,160
Speaker 1: but for the country. He said that he's never said

525
00:31:01,200 --> 00:31:03,880
Speaker 1: anything like that. Maybe he doesn't mean it, but what

526
00:31:03,960 --> 00:31:06,600
Speaker 1: has he ever said that this looks you're a little

527
00:31:06,640 --> 00:31:09,880
Speaker 1: He said that you were talking about this isn't good. Yeah,

528
00:31:10,040 --> 00:31:12,880
Speaker 1: he's right, it's not good, and not just for him,

529
00:31:13,120 --> 00:31:16,760
Speaker 1: for all of us. This could get very bad, very past,

530
00:31:17,600 --> 00:31:20,760
Speaker 1: he said, very past. Um. But he seems a little shook,

531
00:31:21,120 --> 00:31:24,239
Speaker 1: like this is my first time watching these. He seems

532
00:31:24,400 --> 00:31:28,920
Speaker 1: upset as opposed to like usually he has that like Faue.

533
00:31:30,160 --> 00:31:35,200
Speaker 1: I don't know, you know, like like it's very composed outrage.

534
00:31:35,360 --> 00:31:39,000
Speaker 1: Yeah that it's like no man, but like here he's

535
00:31:39,040 --> 00:31:43,400
Speaker 1: like mispronouncing words. He like seems he he looks like

536
00:31:43,440 --> 00:31:46,360
Speaker 1: a little boy more than usual, Like he always looks

537
00:31:46,360 --> 00:31:50,680
Speaker 1: a little bit like a puffy little boy, but here

538
00:31:50,160 --> 00:31:55,040
Speaker 1: he I don't know what what what happened? I mean, look,

539
00:31:56,040 --> 00:31:59,000
Speaker 1: it sounds like again rather than like a specific theory

540
00:31:59,080 --> 00:32:02,240
Speaker 1: trying to explain away why Trump had any of these documents,

541
00:32:02,280 --> 00:32:05,400
Speaker 1: it's now more about being like democrats are trying to

542
00:32:05,520 --> 00:32:09,880
Speaker 1: kill you with justice essentially, what it's distilling down to,

543
00:32:09,960 --> 00:32:12,280
Speaker 1: he said earlier in the show, Joe Biden declared war

544
00:32:12,400 --> 00:32:16,480
Speaker 1: on all of us. And so what's weird is what

545
00:32:16,560 --> 00:32:20,959
Speaker 1: I keep seeing is the punditry and the media takes

546
00:32:21,080 --> 00:32:24,040
Speaker 1: are in one direction, like they're clearly trying to be like, yo,

547
00:32:24,240 --> 00:32:26,680
Speaker 1: we gotta get our heads in the game. Donald Trump

548
00:32:26,720 --> 00:32:29,320
Speaker 1: is in trouble and we need more chaos to maybe

549
00:32:29,360 --> 00:32:32,960
Speaker 1: fight this back, because this seems like, you know, like

550
00:32:33,000 --> 00:32:34,920
Speaker 1: I was saying, we were talking about yesterday like January

551
00:32:35,000 --> 00:32:37,840
Speaker 1: six all over again. But there's like a disconnect between

552
00:32:38,280 --> 00:32:41,960
Speaker 1: their sense of urgency and like what the base is doing. Granted,

553
00:32:42,040 --> 00:32:44,760
Speaker 1: yes there were there was an armed protest outside of

554
00:32:44,800 --> 00:32:47,360
Speaker 1: an FBI office in Arizona. There was that guy in

555
00:32:47,480 --> 00:32:51,360
Speaker 1: Cincinnati who you know, was killed by the FBI like

556
00:32:51,360 --> 00:32:54,080
Speaker 1: after he attacked the FBI office, and there have been

557
00:32:54,160 --> 00:32:57,040
Speaker 1: multiple people have made threats. But in terms of like

558
00:32:57,160 --> 00:33:00,000
Speaker 1: physical space here, I just want to play this other

559
00:33:00,280 --> 00:33:02,680
Speaker 1: clip because this is Eric Trump on the show too,

560
00:33:03,160 --> 00:33:05,440
Speaker 1: and he's also trying to be like, man, the energy

561
00:33:05,520 --> 00:33:08,000
Speaker 1: out there, folks, you gotta see it. It's it's it's

562
00:33:08,040 --> 00:33:10,640
Speaker 1: really something else. And it just feels there's something the

563
00:33:10,720 --> 00:33:14,239
Speaker 1: anger among the mega base exactly. And like also just

564
00:33:14,280 --> 00:33:16,680
Speaker 1: like that everyone's like everyone's head and head is in

565
00:33:16,680 --> 00:33:20,000
Speaker 1: the game. So this is Eric Trump insisting that it's

566
00:33:20,040 --> 00:33:23,320
Speaker 1: so bad that people were just crying at his feet

567
00:33:23,320 --> 00:33:25,120
Speaker 1: when him and Laura were out for dinner. They're not

568
00:33:25,120 --> 00:33:27,560
Speaker 1: even talking about any other Republican candidates because they have

569
00:33:27,600 --> 00:33:30,040
Speaker 1: all kind of disappeared. They're not even in the equation.

570
00:33:30,520 --> 00:33:32,640
Speaker 1: I mean, last night I had an argument between two

571
00:33:32,720 --> 00:33:34,960
Speaker 1: people in a restaurant who are trying to buy Laura

572
00:33:34,960 --> 00:33:37,600
Speaker 1: and I dinner to apologize to what the you know,

573
00:33:37,640 --> 00:33:41,400
Speaker 1: for what the United States government has done to our family, Sean.

574
00:33:41,520 --> 00:33:45,760
Speaker 1: I mean, you wouldn't believe the energy out um m hm.

575
00:33:46,840 --> 00:33:51,120
Speaker 1: So I mean you wouldn't people trying to buy us

576
00:33:51,240 --> 00:33:56,520
Speaker 1: dinner because they're so like that's just like the persecution

577
00:33:56,640 --> 00:33:59,920
Speaker 1: is so bad, famous person. The level of persecution is

578
00:34:00,040 --> 00:34:04,960
Speaker 1: so bad. It's at free dinners bad. Okay, folks. We

579
00:34:05,000 --> 00:34:07,560
Speaker 1: need your heads in the game, folks. The energy out

580
00:34:07,560 --> 00:34:10,480
Speaker 1: here that you're not showing us that we need because

581
00:34:10,480 --> 00:34:13,400
Speaker 1: we're panic is bad. And that's what I'm saying. Like

582
00:34:14,000 --> 00:34:16,640
Speaker 1: there was another thing of there was they tried to organize,

583
00:34:16,640 --> 00:34:18,880
Speaker 1: like Magga people try to organize the protest outside of

584
00:34:18,920 --> 00:34:20,920
Speaker 1: an FBI office. They had to cancel because nobody went.

585
00:34:21,280 --> 00:34:23,319
Speaker 1: And I really think that what we heard in that

586
00:34:23,320 --> 00:34:26,360
Speaker 1: clip yesterday from Laura Ingram it might be one of

587
00:34:26,360 --> 00:34:29,400
Speaker 1: the most accurate sort of details right now, which is

588
00:34:29,440 --> 00:34:34,240
Speaker 1: like they are kind of tired of doing this over

589
00:34:34,320 --> 00:34:38,480
Speaker 1: and over again. Obviously the them being like in the

590
00:34:38,600 --> 00:34:42,480
Speaker 1: Victims Lane is like their preferred mode of outrage. But

591
00:34:42,560 --> 00:34:45,040
Speaker 1: even then there's like something where like I don't know,

592
00:34:45,080 --> 00:34:46,440
Speaker 1: I can just hear it with like Laura English, like,

593
00:34:46,480 --> 00:34:47,920
Speaker 1: I don't know, man, I think people are tired of

594
00:34:48,000 --> 00:34:50,600
Speaker 1: like I think she was talking about herself too, like

595
00:34:50,760 --> 00:34:53,880
Speaker 1: I don't want I much rather go like get behind

596
00:34:54,040 --> 00:34:56,520
Speaker 1: a real fascist who knows what they're doing, rather than

597
00:34:56,520 --> 00:34:59,200
Speaker 1: this like circus that I'm a party too right now.

598
00:34:59,640 --> 00:35:04,239
Speaker 1: I mean there anyway that they're also just I don't know,

599
00:35:04,320 --> 00:35:07,960
Speaker 1: Like there's this tweet as dude Ben Wexler, he said,

600
00:35:08,000 --> 00:35:11,040
Speaker 1: there's nothing more nutty than the couple that couple hours

601
00:35:11,160 --> 00:35:14,120
Speaker 1: where all of mega is waiting silently to find out

602
00:35:14,160 --> 00:35:17,279
Speaker 1: what they're supposed to think. And he he tweeted that

603
00:35:17,360 --> 00:35:21,120
Speaker 1: like when all this like nuclear secrets ship went down,

604
00:35:21,320 --> 00:35:23,799
Speaker 1: but I feel like they haven't really like figured out

605
00:35:23,960 --> 00:35:27,920
Speaker 1: what they're supposed to think because the nuclear secrets thing

606
00:35:28,000 --> 00:35:31,400
Speaker 1: is just really bad and they're like like that, so

607
00:35:31,520 --> 00:35:34,759
Speaker 1: they're they're kind of panicking that like there's there's not

608
00:35:34,840 --> 00:35:38,520
Speaker 1: really an angle on this, and it's precisely like it

609
00:35:38,640 --> 00:35:42,640
Speaker 1: fits so neatly into this box that was the but

610
00:35:42,800 --> 00:35:46,560
Speaker 1: her emails world they were so consumed with, and they

611
00:35:46,600 --> 00:35:49,760
Speaker 1: really have. That's why that that clip from Representative Turner

612
00:35:49,760 --> 00:35:51,759
Speaker 1: where they're like you were you were really going hard

613
00:35:51,800 --> 00:35:55,080
Speaker 1: and Hillary Clinton, where's that energy? And all I can

614
00:35:55,160 --> 00:35:57,840
Speaker 1: just like this is different? And then do a word

615
00:35:57,920 --> 00:36:01,440
Speaker 1: salad that wasn't going to take nuclear secrets. No, I

616
00:36:01,440 --> 00:36:07,560
Speaker 1: would never take I would never do that. Oh yes,

617
00:36:08,360 --> 00:36:11,480
Speaker 1: And or we get the thing where people keep evoking

618
00:36:11,520 --> 00:36:13,839
Speaker 1: that Nixon quote. If the president does it, then it's

619
00:36:13,880 --> 00:36:19,319
Speaker 1: not illegal. But uh huh, like nothing is quite. That's

620
00:36:19,320 --> 00:36:22,000
Speaker 1: why it's like really interesting. What they're gonna coalesce around.

621
00:36:22,040 --> 00:36:24,239
Speaker 1: And I think it's just easier right now is just

622
00:36:24,280 --> 00:36:27,719
Speaker 1: to go like this is January six, like the same

623
00:36:27,760 --> 00:36:30,759
Speaker 1: thing they're they're busted, like they lost the election, or

624
00:36:30,760 --> 00:36:33,319
Speaker 1: in this case, it sounds like they have evidence of

625
00:36:33,400 --> 00:36:37,120
Speaker 1: like him actually doing this, this this crime. They're cornered

626
00:36:37,440 --> 00:36:39,040
Speaker 1: and they want to fight their way out of it,

627
00:36:39,920 --> 00:36:42,560
Speaker 1: and it seems like that's kind of the reflex of

628
00:36:42,600 --> 00:36:44,600
Speaker 1: them is to get like, Okay, well now we're in

629
00:36:44,640 --> 00:36:47,600
Speaker 1: a corner. Just start being like the d o j

630
00:36:47,840 --> 00:36:51,359
Speaker 1: is trying to declare war on you you and send

631
00:36:51,400 --> 00:36:54,400
Speaker 1: all these weird fundraising texts trying to shame like MAGA

632
00:36:54,560 --> 00:36:58,719
Speaker 1: donors being like we thought you were a patriot. What happened?

633
00:36:58,840 --> 00:37:03,400
Speaker 1: Donald Trump's way eating on you to help. So it's

634
00:37:03,560 --> 00:37:06,400
Speaker 1: um a lot of a lot of confusion at the moment.

635
00:37:06,480 --> 00:37:09,000
Speaker 1: But are there other indicators other than like, you know,

636
00:37:09,080 --> 00:37:12,640
Speaker 1: them not showing up to protest the FBI and get

637
00:37:12,680 --> 00:37:15,799
Speaker 1: their faces like entered into a database that will make

638
00:37:15,840 --> 00:37:18,840
Speaker 1: it so they never bore a flight ever again without

639
00:37:18,920 --> 00:37:22,279
Speaker 1: like three hours worth of checks. Are there is there

640
00:37:22,320 --> 00:37:27,880
Speaker 1: other indications of like flagging energy other than Laura Ingram

641
00:37:28,000 --> 00:37:32,839
Speaker 1: seeming tired and Tucker Carlson's because if you look, you know,

642
00:37:32,880 --> 00:37:34,880
Speaker 1: like suddenly it's it's wild. Like a lot of the

643
00:37:34,920 --> 00:37:38,520
Speaker 1: conservative posters came out and said, like, hey, actually, Trump's

644
00:37:38,280 --> 00:37:41,200
Speaker 1: he's he's putting a smash onto Santis, right, That's what

645
00:37:41,239 --> 00:37:43,440
Speaker 1: I said, That's what I saw, And so I assumed

646
00:37:43,480 --> 00:37:45,840
Speaker 1: that this was like going to be invigorating. But that

647
00:37:45,920 --> 00:37:49,360
Speaker 1: was also mostly I think that storyline came up before

648
00:37:50,040 --> 00:37:53,600
Speaker 1: people were like it was nuclear secrets and like there's

649
00:37:53,640 --> 00:37:57,640
Speaker 1: this whole ongoing storyline with the Saudias that like he

650
00:37:57,719 --> 00:38:01,160
Speaker 1: was trying to steal nuclear secrets or like reveal nuclear

651
00:38:01,239 --> 00:38:05,439
Speaker 1: secrets to the Saudiast for financial gain, which would be

652
00:38:06,560 --> 00:38:10,000
Speaker 1: one of the most dangerous things that any any single

653
00:38:10,320 --> 00:38:15,520
Speaker 1: human being could do to the current globe. Like that,

654
00:38:15,800 --> 00:38:19,760
Speaker 1: that's that is it? That would be a wild Yeah.

655
00:38:20,840 --> 00:38:23,600
Speaker 1: I mean, if you want to say, flagging energy. I mean,

656
00:38:23,640 --> 00:38:26,560
Speaker 1: you know, you see the Democrats all very like, yes,

657
00:38:26,640 --> 00:38:30,960
Speaker 1: the polls look favorable fucking finally for Democrats. Uh, and

658
00:38:31,000 --> 00:38:34,280
Speaker 1: they're like, is this a backlash to you know, allowing

659
00:38:34,560 --> 00:38:36,920
Speaker 1: human rights to be rolled back and curtailed by the

660
00:38:36,960 --> 00:38:39,440
Speaker 1: Supreme Court? Who knows, but guess what, we'll take it.

661
00:38:39,880 --> 00:38:43,479
Speaker 1: So the pendulum is like even electorally moving in that tracks.

662
00:38:43,560 --> 00:38:46,719
Speaker 1: I think the problem is the Republicans loved when it

663
00:38:46,840 --> 00:38:51,279
Speaker 1: was like Joe Byron's inflation bonanza and they could keep

664
00:38:51,520 --> 00:38:55,480
Speaker 1: hitting that button. But now they got them being like

665
00:38:55,760 --> 00:39:02,239
Speaker 1: fuck the police, and they're like this is They're like,

666
00:39:02,280 --> 00:39:03,799
Speaker 1: we were back in the funk out the blue a

667
00:39:03,840 --> 00:39:06,480
Speaker 1: second ago. Now it's fun the defund the FBI, And

668
00:39:06,480 --> 00:39:09,080
Speaker 1: I'm and then again we're like ironically, I mean, sure

669
00:39:08,920 --> 00:39:11,880
Speaker 1: you've got something there, but maybe you don't have the

670
00:39:11,920 --> 00:39:14,840
Speaker 1: full picture. But I think it's just a it's difficult

671
00:39:14,840 --> 00:39:17,279
Speaker 1: because you're not You're also in a time when there

672
00:39:17,280 --> 00:39:20,480
Speaker 1: are plenty of like lived experiences that you could use

673
00:39:20,560 --> 00:39:24,320
Speaker 1: to turn into like more populist talking points. They're completely

674
00:39:24,360 --> 00:39:27,000
Speaker 1: just focused on defending Trump. And then like, you know,

675
00:39:27,320 --> 00:39:29,200
Speaker 1: sure there are those conservatives who are like yeah, man,

676
00:39:29,280 --> 00:39:30,719
Speaker 1: leave them alone. And there are other people who I'm

677
00:39:30,719 --> 00:39:34,080
Speaker 1: sure probably like fun man, like life's sucking hard, Like

678
00:39:34,360 --> 00:39:37,080
Speaker 1: does someone have a solution here? And it's yeah. And

679
00:39:37,080 --> 00:39:39,719
Speaker 1: in that sense, I'm sure it's it's not energizing if

680
00:39:39,719 --> 00:39:44,640
Speaker 1: you're somewhat if you're not all in on you know, fascism. Yeah,

681
00:39:45,320 --> 00:39:48,480
Speaker 1: all right, well, I mean we we don't know, we'll

682
00:39:48,520 --> 00:39:51,799
Speaker 1: we'll keep an eye on it, but it's gonna be Yeah,

683
00:39:51,840 --> 00:39:53,840
Speaker 1: it's gonna I'm sure we're gonna see a lot more

684
00:39:54,120 --> 00:39:58,000
Speaker 1: you know, thrashing from that side before, not though, for sure,

685
00:39:58,040 --> 00:40:00,799
Speaker 1: because that's just that's what we see. And yeah, I

686
00:40:00,800 --> 00:40:02,200
Speaker 1: don't know. I mean, it's like one of those things

687
00:40:02,239 --> 00:40:06,520
Speaker 1: where I'm never gonna be like, yeah, karma has arrived

688
00:40:07,200 --> 00:40:11,520
Speaker 1: because you never fucking no, it's display America. Yeah exactly.

689
00:40:11,880 --> 00:40:15,120
Speaker 1: But like I said, I will revel in these moments

690
00:40:15,120 --> 00:40:19,560
Speaker 1: where I see that they're they're they're struggling with this

691
00:40:19,600 --> 00:40:21,840
Speaker 1: one before, Like I really hated it when everything was

692
00:40:21,840 --> 00:40:23,719
Speaker 1: like that. It's fake news. It gives a ship, move on,

693
00:40:23,800 --> 00:40:26,919
Speaker 1: nothing fucking fucking effect us. Well, move on. I don't

694
00:40:26,960 --> 00:40:29,759
Speaker 1: care if they're like allegations of assault against this guy.

695
00:40:29,840 --> 00:40:33,640
Speaker 1: We move on now. They're like, well, I don't know,

696
00:40:34,880 --> 00:40:37,400
Speaker 1: and that is I mean, that's that's different. And I

697
00:40:37,440 --> 00:40:42,319
Speaker 1: will welcome that mhm, that I did return his passports. Yes,

698
00:40:42,600 --> 00:40:46,600
Speaker 1: that's three of them. Two. It's the little winds, the

699
00:40:46,680 --> 00:40:50,160
Speaker 1: little winds that that maybe maybe he'll that'll that'll get

700
00:40:50,200 --> 00:40:52,600
Speaker 1: them through the day. Yeah, all right, let's take a

701
00:40:52,680 --> 00:40:55,480
Speaker 1: quick break. We'll come back and we'll talk about bias

702
00:40:55,600 --> 00:41:08,680
Speaker 1: in the media and where it comes from. H And

703
00:41:08,760 --> 00:41:13,520
Speaker 1: we're back, And how's your pointing out there's a threat

704
00:41:13,560 --> 00:41:16,480
Speaker 1: on Twitter that's you know, pointing to something that we

705
00:41:16,600 --> 00:41:19,480
Speaker 1: we've talked about before. I think like the yeah, the

706
00:41:20,000 --> 00:41:25,760
Speaker 1: you know, insidiousness of unpaid internships being sort of a

707
00:41:25,840 --> 00:41:28,680
Speaker 1: formal like, well, you gotta do a year of unpaid

708
00:41:28,719 --> 00:41:32,480
Speaker 1: internships to to get the gig. And it's like, well,

709
00:41:32,640 --> 00:41:35,640
Speaker 1: but how how do I do that when I the

710
00:41:36,120 --> 00:41:40,279
Speaker 1: the game in this country is I need job to survive? Yeah, yeah, yeah, yeah.

711
00:41:40,400 --> 00:41:42,760
Speaker 1: If you could do like eighteen months of no money,

712
00:41:43,800 --> 00:41:47,120
Speaker 1: then we we may open the doors. And this author,

713
00:41:47,200 --> 00:41:51,279
Speaker 1: David Dennis Jr. Black author, journalist, and he's he had

714
00:41:51,280 --> 00:41:52,960
Speaker 1: this stread. He's like, I've been writing about this for

715
00:41:53,000 --> 00:41:55,640
Speaker 1: a long time. But I just want to keep underscoring

716
00:41:55,680 --> 00:41:58,360
Speaker 1: how the you know, when people are talking about like

717
00:41:58,400 --> 00:42:00,560
Speaker 1: what's going on in our newsroom, was like, why are

718
00:42:00,600 --> 00:42:04,560
Speaker 1: we both sides thing like the most like grotesque creatures

719
00:42:04,680 --> 00:42:07,880
Speaker 1: on the planet or like giving them like the the

720
00:42:07,960 --> 00:42:11,600
Speaker 1: deference of like someone that absolutely doesn't deserve it, talking

721
00:42:11,600 --> 00:42:13,600
Speaker 1: about how we really need to look at how the

722
00:42:13,680 --> 00:42:16,560
Speaker 1: newsrooms are filled. Specifically in his experience, is trying to

723
00:42:16,600 --> 00:42:20,000
Speaker 1: break in as like a newsroom journalist, and in many

724
00:42:20,080 --> 00:42:23,200
Speaker 1: media and I think many media operations are like this.

725
00:42:23,239 --> 00:42:25,359
Speaker 1: It's not just journalism. It can be video, it can

726
00:42:25,360 --> 00:42:29,880
Speaker 1: be film, television, publishing. If you want to be an agent,

727
00:42:30,360 --> 00:42:33,200
Speaker 1: there will be like this thing. It's like the unpaid

728
00:42:33,239 --> 00:42:37,799
Speaker 1: internship that's your way in. And he's saying that when

729
00:42:37,840 --> 00:42:39,760
Speaker 1: you look at like places like the New York Times

730
00:42:39,800 --> 00:42:42,480
Speaker 1: or these other big newsrooms, and why are people are

731
00:42:42,520 --> 00:42:44,400
Speaker 1: like why how are they so out of touch? How

732
00:42:44,400 --> 00:42:47,520
Speaker 1: are they like like barely having a beat of sweat

733
00:42:47,640 --> 00:42:50,920
Speaker 1: looking at like like our you know, rapid loss of

734
00:42:51,040 --> 00:42:54,680
Speaker 1: rights or extremism in the country. It's because the pipeline

735
00:42:55,080 --> 00:42:57,520
Speaker 1: to that place is just already out of step with

736
00:42:57,560 --> 00:43:00,000
Speaker 1: the lived reality of Americans and he's talking about how

737
00:43:00,480 --> 00:43:03,799
Speaker 1: your way in is typically an unpaid internship, and if

738
00:43:03,840 --> 00:43:07,239
Speaker 1: you are someone who doesn't have the support system to

739
00:43:07,360 --> 00:43:11,120
Speaker 1: not make money while working eight hours a day, that

740
00:43:11,160 --> 00:43:13,959
Speaker 1: means your opportunities dwindle. But the people who are able

741
00:43:14,000 --> 00:43:17,480
Speaker 1: to do that tend to be affluent, typically white Americans

742
00:43:17,680 --> 00:43:19,960
Speaker 1: who are able to you know, be like, yeah, oh yeah,

743
00:43:20,000 --> 00:43:21,719
Speaker 1: my parents can support me for two years if I

744
00:43:21,760 --> 00:43:24,120
Speaker 1: don't work, and so that way I'm you know, I'm

745
00:43:24,200 --> 00:43:26,160
Speaker 1: I'm blessed enough to get my foot in the door

746
00:43:26,239 --> 00:43:28,880
Speaker 1: to get into these new newsrooms. And he's saying, because

747
00:43:28,880 --> 00:43:31,560
Speaker 1: of that, you just get this one note thinking where

748
00:43:31,560 --> 00:43:33,360
Speaker 1: a lot of people are looking at life through the

749
00:43:33,400 --> 00:43:36,600
Speaker 1: same narrow perspective and thus report the news from that

750
00:43:36,760 --> 00:43:39,920
Speaker 1: vantage point, and they all respond to the criticisms the

751
00:43:39,960 --> 00:43:41,920
Speaker 1: same way because they're all kind of coming from the

752
00:43:41,960 --> 00:43:44,840
Speaker 1: same place. And I was just like, oh right, that

753
00:43:44,840 --> 00:43:47,640
Speaker 1: that that is that that makes the perfect sense. And

754
00:43:47,680 --> 00:43:50,120
Speaker 1: he's saying, that's why they're spending a lot of inches

755
00:43:50,160 --> 00:43:53,680
Speaker 1: talking about culture war stuff or cancel culture when a

756
00:43:53,680 --> 00:43:57,440
Speaker 1: lot of people are like, what about inequality? What about

757
00:43:57,600 --> 00:44:01,360
Speaker 1: police violence? What about all of these other threats that

758
00:44:01,400 --> 00:44:04,680
Speaker 1: we're facing and yeah, like it just saying that because

759
00:44:04,719 --> 00:44:07,040
Speaker 1: of that, and for if we constantly put people through

760
00:44:07,080 --> 00:44:09,120
Speaker 1: this first hoop, which is, are you affluent enough to

761
00:44:09,160 --> 00:44:11,439
Speaker 1: not you know, make money for a year and a half,

762
00:44:11,920 --> 00:44:13,520
Speaker 1: then of course we're going to get this kind of

763
00:44:13,680 --> 00:44:16,240
Speaker 1: thinking across the board. And like you're saying, in many industries,

764
00:44:16,239 --> 00:44:18,640
Speaker 1: I'm sure many people have seen this, not just in media,

765
00:44:18,680 --> 00:44:21,680
Speaker 1: but anywhere that uses on paid internships. You know, it

766
00:44:21,719 --> 00:44:25,120
Speaker 1: reminds me of, um, this observation that was made in

767
00:44:25,160 --> 00:44:27,319
Speaker 1: the early days of computing in this country. There was

768
00:44:27,360 --> 00:44:31,920
Speaker 1: this computer scientist named George Conway who noticed that the

769
00:44:32,000 --> 00:44:35,440
Speaker 1: structure of a piece of software always reflected the team

770
00:44:35,640 --> 00:44:38,080
Speaker 1: that created it, and it came to be known as

771
00:44:38,160 --> 00:44:42,040
Speaker 1: Conway's law, which was basically like software always exhibits the

772
00:44:42,080 --> 00:44:45,120
Speaker 1: features of the people who created it, the team that

773
00:44:45,200 --> 00:44:48,040
Speaker 1: created it. And I think that's that's like true in

774
00:44:48,080 --> 00:44:51,920
Speaker 1: every field, right, Like what you see being produced is

775
00:44:51,960 --> 00:44:54,480
Speaker 1: a reflection of the makeup of the people that are

776
00:44:54,520 --> 00:44:58,360
Speaker 1: producing it. And I think it goes even beyond internships.

777
00:44:58,400 --> 00:45:01,440
Speaker 1: I mean, like I'm thinking about publishing. I mean, salaries

778
00:45:01,480 --> 00:45:05,440
Speaker 1: and publishing are like notoriously really low, and publishing is

779
00:45:05,440 --> 00:45:08,160
Speaker 1: in new York City. So if so, basically, you know,

780
00:45:08,200 --> 00:45:10,400
Speaker 1: the sort of the whole industry is premised on the

781
00:45:10,440 --> 00:45:13,839
Speaker 1: idea that you are going to have some kind of

782
00:45:14,880 --> 00:45:17,960
Speaker 1: cushion or you're gonna have some kind of way to

783
00:45:18,000 --> 00:45:20,040
Speaker 1: live in New York City on this like rock bottom

784
00:45:20,080 --> 00:45:23,640
Speaker 1: salary for years while you like work your way up

785
00:45:23,680 --> 00:45:26,120
Speaker 1: the ladder. So it's like no surprise at all that

786
00:45:26,200 --> 00:45:29,360
Speaker 1: publishing is as homogeneous as it is, right or And

787
00:45:29,360 --> 00:45:31,680
Speaker 1: they also speak to the fact of, like even making

788
00:45:31,640 --> 00:45:33,719
Speaker 1: a way in a newsroom, you're gonna have to come

789
00:45:33,840 --> 00:45:38,360
Speaker 1: past editor after editor, and if you realize an editor

790
00:45:38,440 --> 00:45:41,560
Speaker 1: likes this perspective a lot more than that perspective, just

791
00:45:41,600 --> 00:45:44,359
Speaker 1: by virtue of being a worker, you're going to be like, well,

792
00:45:44,360 --> 00:45:46,480
Speaker 1: my work is going to fit into that because I

793
00:45:46,520 --> 00:45:49,920
Speaker 1: want to please my boss. And even then you can

794
00:45:49,920 --> 00:45:51,960
Speaker 1: start to see all these other patterns that emerge in

795
00:45:52,080 --> 00:45:53,840
Speaker 1: there too, And like to your point, I think it

796
00:45:53,920 --> 00:45:57,360
Speaker 1: might have been Michael Crichton who was like pointing out that,

797
00:45:57,400 --> 00:45:59,919
Speaker 1: like there's a like a huge problem like in sign

798
00:46:00,000 --> 00:46:03,440
Speaker 1: typic research where English is like the standardized language for

799
00:46:03,480 --> 00:46:07,520
Speaker 1: scientific research and by every like for so much scientific

800
00:46:07,560 --> 00:46:10,279
Speaker 1: thought to be processed in one language is going to

801
00:46:10,400 --> 00:46:13,520
Speaker 1: create a lot of rigidity and could potentially create a

802
00:46:13,560 --> 00:46:16,400
Speaker 1: problem down the road that people aren't seeing because everyone

803
00:46:16,520 --> 00:46:20,400
Speaker 1: is forced to think about problems in English versus the

804
00:46:20,600 --> 00:46:24,000
Speaker 1: syntax or structure of many other languages. Shows that people

805
00:46:24,040 --> 00:46:27,359
Speaker 1: even problem solve in different ways because of language that

806
00:46:27,520 --> 00:46:29,800
Speaker 1: you kind of see the same thing, Like if people

807
00:46:29,840 --> 00:46:33,000
Speaker 1: are continually being one note, then they're going to fail

808
00:46:33,080 --> 00:46:36,839
Speaker 1: to actually identify things because of this just sort of

809
00:46:37,040 --> 00:46:39,600
Speaker 1: like a pattern of like normativity that comes through with

810
00:46:39,920 --> 00:46:41,480
Speaker 1: sort of getting the same kind of people the same

811
00:46:41,520 --> 00:46:45,760
Speaker 1: backgrounds and things like, Yeah, that Conway law thing is interesting.

812
00:46:46,320 --> 00:46:49,239
Speaker 1: I wasn't familiar with that, Like just the idea that

813
00:46:49,360 --> 00:46:53,319
Speaker 1: like when so many small decisions are making up kind

814
00:46:53,320 --> 00:46:57,040
Speaker 1: of the overall structure, the overall like substance of a thing,

815
00:46:57,160 --> 00:47:01,000
Speaker 1: that like the people making those decisions just necessarily kind

816
00:47:01,000 --> 00:47:05,120
Speaker 1: of imprint themselves on the sort of d n a

817
00:47:05,200 --> 00:47:08,440
Speaker 1: of any of any program totally. I mean, if you

818
00:47:08,440 --> 00:47:11,239
Speaker 1: think about like the architecture of Twitter, like Twitter was

819
00:47:11,280 --> 00:47:15,480
Speaker 1: created by a group of white men who you know,

820
00:47:15,560 --> 00:47:17,560
Speaker 1: when they were growing up with like bulletin boards in

821
00:47:17,600 --> 00:47:23,200
Speaker 1: the eighties, didn't experience harassment. They didn't experience like abuse,

822
00:47:23,440 --> 00:47:26,560
Speaker 1: you know, in chatters and bulletin boards and so like,

823
00:47:26,680 --> 00:47:29,520
Speaker 1: they didn't even think about like building that into the

824
00:47:29,600 --> 00:47:33,520
Speaker 1: architecture of Twitter because they hadn't experienced it. So like,

825
00:47:33,719 --> 00:47:36,959
Speaker 1: if you if you'd had like a more diverse group

826
00:47:37,000 --> 00:47:39,880
Speaker 1: of people in that room, you know, when when Twitter

827
00:47:39,960 --> 00:47:42,960
Speaker 1: was getting created, I think we would see like a

828
00:47:43,000 --> 00:47:46,719
Speaker 1: different product architecture. And I mean similarly, I wonder, you know,

829
00:47:46,880 --> 00:47:48,920
Speaker 1: if there had been a more diverse group of people

830
00:47:48,960 --> 00:47:51,880
Speaker 1: in the early days of Facebook, Like would what if

831
00:47:51,920 --> 00:47:55,160
Speaker 1: there had been people from you know, a variety of

832
00:47:55,239 --> 00:47:59,839
Speaker 1: different nationalities and backgrounds and countries, Like would would there

833
00:47:59,880 --> 00:48:02,560
Speaker 1: have been more of like an immune system built in

834
00:48:02,640 --> 00:48:04,840
Speaker 1: to the product architecture because you would have had a

835
00:48:04,840 --> 00:48:07,880
Speaker 1: more diverse group of people. Start thinking about how this

836
00:48:07,960 --> 00:48:09,839
Speaker 1: was going to play out in the world. If one

837
00:48:09,840 --> 00:48:12,040
Speaker 1: of the people who had been like rated as not

838
00:48:12,200 --> 00:48:16,640
Speaker 1: hot on Mark Zuckerberg's face smash had gone on to

839
00:48:16,719 --> 00:48:20,760
Speaker 1: invent Facebook instead of Mark Zuckerberg, Like, but we'd probably

840
00:48:20,840 --> 00:48:23,560
Speaker 1: live in a better world. Yea, that architecture is built

841
00:48:23,560 --> 00:48:29,560
Speaker 1: on horny rate and we have problems because that base

842
00:48:30,000 --> 00:48:32,960
Speaker 1: idea turned into this other thing. Yeah yeah, yeah, maybe,

843
00:48:33,640 --> 00:48:36,200
Speaker 1: but yeah I mean it is like I just even

844
00:48:36,200 --> 00:48:38,000
Speaker 1: think about I know, like there are two anchors on

845
00:48:38,120 --> 00:48:40,680
Speaker 1: MSNBC who like kind of fit this description, where like

846
00:48:40,719 --> 00:48:43,120
Speaker 1: they both went to the most expensive high schools in

847
00:48:43,239 --> 00:48:47,640
Speaker 1: l A. Connected families, and yeah, are like at the

848
00:48:48,000 --> 00:48:51,120
Speaker 1: at the apex of like television journalism. But I'm sure

849
00:48:51,160 --> 00:48:54,000
Speaker 1: they had I mean, they're talented people. But again, there's

850
00:48:54,040 --> 00:48:57,200
Speaker 1: always like there's so many people that just in general

851
00:48:57,200 --> 00:48:59,520
Speaker 1: in the public, we don't realize how much of a

852
00:48:59,640 --> 00:49:01,719
Speaker 1: lego most of these people have had all the time.

853
00:49:02,200 --> 00:49:04,600
Speaker 1: And just you know, that's a whole other issue too

854
00:49:04,640 --> 00:49:06,799
Speaker 1: that feeds into people's ideas of self forth and we're like,

855
00:49:06,840 --> 00:49:09,759
Speaker 1: you're only looking at these very privileged cases a lot

856
00:49:09,760 --> 00:49:11,960
Speaker 1: of the time. Wouldn't it be amazing? I think about

857
00:49:11,960 --> 00:49:13,919
Speaker 1: this a lot, Like in the arts and creative work

858
00:49:13,960 --> 00:49:16,120
Speaker 1: in general, wouldn't it be amazing if there was like

859
00:49:16,320 --> 00:49:20,120
Speaker 1: radical transparency and you could see, when you you know,

860
00:49:20,160 --> 00:49:23,560
Speaker 1: observe someone whatever what they were doing in their career,

861
00:49:23,600 --> 00:49:27,600
Speaker 1: you could also see like what, like what the money

862
00:49:27,640 --> 00:49:32,560
Speaker 1: situation was that created the possibility for that achievement. I

863
00:49:32,640 --> 00:49:36,400
Speaker 1: think that would be so illuminating. Who knows, I'm sure

864
00:49:36,440 --> 00:49:39,560
Speaker 1: with like wearable technology, like there will soon enough you

865
00:49:39,560 --> 00:49:42,160
Speaker 1: can like look at someone, but that's fucking creepy. But

866
00:49:42,200 --> 00:49:46,880
Speaker 1: it'd be like wearable technology was invented by somebody whose

867
00:49:47,080 --> 00:49:51,680
Speaker 1: parents were in the prison and like millionaires off the

868
00:49:51,719 --> 00:49:54,440
Speaker 1: prison industrial complex, and they were like, what if we

869
00:49:54,480 --> 00:49:59,600
Speaker 1: could take this even further right and monitor people where

870
00:49:59,600 --> 00:50:02,880
Speaker 1: they are before we put them in prison like that,

871
00:50:03,000 --> 00:50:05,680
Speaker 1: that's that's just a guess, right. Oh. I was just

872
00:50:05,719 --> 00:50:08,319
Speaker 1: thinking just what you were saying about science and how

873
00:50:08,880 --> 00:50:11,760
Speaker 1: even just are talking about science and thinking about science

874
00:50:11,800 --> 00:50:14,880
Speaker 1: is kind of constrained by the fact that it's only

875
00:50:14,920 --> 00:50:17,440
Speaker 1: in one language, with one set of syntax and one

876
00:50:17,840 --> 00:50:21,640
Speaker 1: kind of way of of expression. Makes me think about

877
00:50:22,320 --> 00:50:26,040
Speaker 1: the fact that in like the field of ecology, from

878
00:50:26,120 --> 00:50:30,520
Speaker 1: most of the twentieth century, ecologists thought about the world

879
00:50:30,640 --> 00:50:34,800
Speaker 1: primarily as being governed by competition, like the natural world

880
00:50:34,920 --> 00:50:38,280
Speaker 1: is being basically a competitive landscape red tooth and claw,

881
00:50:39,040 --> 00:50:45,759
Speaker 1: you know, species vying for limited resources. And some scholars

882
00:50:45,880 --> 00:50:50,960
Speaker 1: have like proposed that the reason that they totally overlooked cooperation,

883
00:50:51,400 --> 00:50:55,920
Speaker 1: which it turns out is actually fundamental to like every species,

884
00:50:56,239 --> 00:51:00,920
Speaker 1: like life on Earth, is that the science just themselves

885
00:51:00,960 --> 00:51:07,960
Speaker 1: were existed within this like heavily competitive scientific establishment, so

886
00:51:08,080 --> 00:51:10,719
Speaker 1: like their whole their the conceptual frameworks that they were

887
00:51:10,800 --> 00:51:13,719
Speaker 1: drawing from, we're all competitive. So that's what they saw

888
00:51:13,840 --> 00:51:16,520
Speaker 1: out in the world, when in fact, like every species

889
00:51:16,560 --> 00:51:20,400
Speaker 1: depends on some kind of mutual, mutually beneficial relationship. But

890
00:51:20,760 --> 00:51:24,520
Speaker 1: they literally couldn't see it because it was kids stuff

891
00:51:24,560 --> 00:51:27,200
Speaker 1: where you're talking about that's in our newsrooms are like

892
00:51:27,280 --> 00:51:30,680
Speaker 1: my uncle's my uncle voted for Trump. He's not that racist.

893
00:51:30,800 --> 00:51:33,239
Speaker 1: Let me do a place on this so people understand

894
00:51:33,280 --> 00:51:37,480
Speaker 1: my uncle is not that bad or whatever. Or these

895
00:51:37,480 --> 00:51:40,160
Speaker 1: are why these things of inequality might might not hit

896
00:51:40,239 --> 00:51:43,680
Speaker 1: my radar because they never they never came that close

897
00:51:43,719 --> 00:51:45,239
Speaker 1: to me where I even considered it. I mean, I

898
00:51:45,280 --> 00:51:47,560
Speaker 1: know it's out there, but for my lived experience, that's

899
00:51:47,600 --> 00:51:50,000
Speaker 1: completely different. And I can see how all of that

900
00:51:50,080 --> 00:51:52,000
Speaker 1: just sort of shapes, you know, just I've seen a

901
00:51:52,040 --> 00:51:56,120
Speaker 1: handful of YouTube videos of the police acting badly, but

902
00:51:56,560 --> 00:51:59,680
Speaker 1: I've also some of those police have gotten dealt with,

903
00:51:59,840 --> 00:52:02,920
Speaker 1: And you know, I've never been nervous when the police

904
00:52:02,920 --> 00:52:06,239
Speaker 1: pulled me over. So I think we're good here, and

905
00:52:06,920 --> 00:52:10,080
Speaker 1: we can go back to using the police as our

906
00:52:10,239 --> 00:52:15,480
Speaker 1: primary source for literally every story about crime and what's

907
00:52:15,520 --> 00:52:20,040
Speaker 1: happening in the city basically, and they're like friend and

908
00:52:20,080 --> 00:52:22,160
Speaker 1: not even taking from the context of the police, like

909
00:52:22,200 --> 00:52:24,400
Speaker 1: you know, they're telling you that to like keep the

910
00:52:24,480 --> 00:52:27,920
Speaker 1: near like the fear up. So there's the budgets increase,

911
00:52:27,960 --> 00:52:30,320
Speaker 1: and there's more like being like, yeah, man, how to

912
00:52:30,360 --> 00:52:32,640
Speaker 1: control man? Just give them all the money they need.

913
00:52:33,160 --> 00:52:39,040
Speaker 1: But yeah, unpaid internships ban them. Shout out to unpaid internships.

914
00:52:41,360 --> 00:52:46,480
Speaker 1: The Economist, which I have to imagine has an unpaid

915
00:52:46,520 --> 00:52:50,480
Speaker 1: internship program just released or didn't just this was earlier,

916
00:52:50,480 --> 00:52:52,320
Speaker 1: but I just I saw it in the real estate

917
00:52:52,400 --> 00:52:56,200
Speaker 1: section of CNBC. I don't know how I entered that

918
00:52:56,400 --> 00:53:00,560
Speaker 1: unholy healthscape, but the real estate section of the NBC

919
00:53:01,239 --> 00:53:04,719
Speaker 1: got my attention with the top ten. Uh, these are

920
00:53:04,760 --> 00:53:07,279
Speaker 1: the top ten best and worst places to live in

921
00:53:07,320 --> 00:53:10,800
Speaker 1: the world. And you won't find the US on either list.

922
00:53:11,160 --> 00:53:15,479
Speaker 1: That was the headline. And so the study is done

923
00:53:15,480 --> 00:53:20,960
Speaker 1: by the Economist Intelligence Unit, presumably with I. I don't

924
00:53:20,960 --> 00:53:23,080
Speaker 1: know where they're getting there here. I guess it's an

925
00:53:23,080 --> 00:53:27,359
Speaker 1: independently run like survey. So first, let's just leave behind

926
00:53:27,400 --> 00:53:30,319
Speaker 1: the fact that the Economist Intelligence Unit thought it was

927
00:53:30,360 --> 00:53:33,799
Speaker 1: cool to steal the valor of the CIA and other

928
00:53:33,880 --> 00:53:39,520
Speaker 1: intelligence agencies. Uh and you know, like that that is

929
00:53:39,600 --> 00:53:43,400
Speaker 1: what they're drawing on their intelligence unit. But the map

930
00:53:43,760 --> 00:53:47,960
Speaker 1: is straight. So they have a map City Livability Index

931
00:53:48,080 --> 00:53:53,400
Speaker 1: March Most livable, least livable. Every single one of the

932
00:53:53,480 --> 00:53:59,799
Speaker 1: least livable cities are in the global South, and all

933
00:53:59,840 --> 00:54:04,000
Speaker 1: of the most livable are like in Western Europe and

934
00:54:04,160 --> 00:54:09,520
Speaker 1: like just traditionally like the United States, Western Europe, New Zealand,

935
00:54:09,920 --> 00:54:15,799
Speaker 1: and then you you can find a couple in Australia shockingly,

936
00:54:16,880 --> 00:54:23,040
Speaker 1: and it's just it's it's not even like subtle bias, right,

937
00:54:23,360 --> 00:54:27,600
Speaker 1: it's the you know. So a writer and the Guardian

938
00:54:27,920 --> 00:54:31,600
Speaker 1: who's from Nigeria pointed out was just comparing you know,

939
00:54:31,840 --> 00:54:35,560
Speaker 1: they had lived in Lagos and you know, Vienna was

940
00:54:35,640 --> 00:54:39,000
Speaker 1: ranked the number one most livable city in the world.

941
00:54:39,480 --> 00:54:44,080
Speaker 1: Lagos was on the bottom ten list. Yeah, and it

942
00:54:44,160 --> 00:54:47,960
Speaker 1: was like it lacks culture. Yeah. So this writer is

943
00:54:48,040 --> 00:54:51,239
Speaker 1: pointing out Vienna scored a nineties six out of one

944
00:54:51,320 --> 00:54:56,759
Speaker 1: hundred on their h Culture and Environment category. Well Mozart,

945
00:54:56,960 --> 00:55:00,680
Speaker 1: I mean haled from there. So that's straight up. Lagos

946
00:55:00,760 --> 00:55:04,120
Speaker 1: was fifty three point five and you know they I'm

947
00:55:04,200 --> 00:55:08,719
Speaker 1: guessing they wouldn't have noted that Phila Couti like, and

948
00:55:09,000 --> 00:55:13,160
Speaker 1: you know a lot of his and Afroby like has

949
00:55:13,239 --> 00:55:17,840
Speaker 1: a lasting legacy there. And it's just like the things

950
00:55:17,920 --> 00:55:22,759
Speaker 1: that they are overlooking and the things like it just

951
00:55:23,160 --> 00:55:25,920
Speaker 1: it reeks of like, yeah, well I asked like my

952
00:55:26,000 --> 00:55:29,120
Speaker 1: friends who lived here, and they didn't ask a single

953
00:55:29,280 --> 00:55:32,360
Speaker 1: like person of color or a person who couldn't afford

954
00:55:32,400 --> 00:55:36,399
Speaker 1: to live through an unpaid internship like to respond to

955
00:55:36,440 --> 00:55:40,400
Speaker 1: this survey let alone. You know that they're like crime

956
00:55:40,680 --> 00:55:44,160
Speaker 1: statistics and things like that that are you know, those

957
00:55:44,200 --> 00:55:50,720
Speaker 1: self reported police statistics that are bullshit, you know, are

958
00:55:50,719 --> 00:55:56,440
Speaker 1: are self serving police propaganda. I think that's really interesting

959
00:55:57,200 --> 00:55:58,920
Speaker 1: the idea, right, like because when you look at it,

960
00:55:58,920 --> 00:56:01,560
Speaker 1: and even in that or this writer and the Guardian

961
00:56:01,640 --> 00:56:03,560
Speaker 1: is just talking about like it's so clear that their

962
00:56:03,600 --> 00:56:06,799
Speaker 1: idea of culture is like was it at the met

963
00:56:06,640 --> 00:56:10,120
Speaker 1: the MoMA or something like that, Like that's what culture is,

964
00:56:10,640 --> 00:56:13,319
Speaker 1: and you know the this author is like points out

965
00:56:13,360 --> 00:56:16,480
Speaker 1: rightfully it's like, yeah, you know Picasso was actually inspired

966
00:56:16,480 --> 00:56:18,880
Speaker 1: by like African artists too, write like do you're aware

967
00:56:18,880 --> 00:56:21,960
Speaker 1: of that? Like that that's like the origin of culture

968
00:56:22,680 --> 00:56:27,440
Speaker 1: is actually not in Europe, Right, it's really odd. But yeah,

969
00:56:27,560 --> 00:56:30,320
Speaker 1: like it's weird because it's almost like they're they're trying

970
00:56:30,360 --> 00:56:33,000
Speaker 1: harder to like explain to you why these places you

971
00:56:33,280 --> 00:56:36,920
Speaker 1: like that the like you know, developed economies or like

972
00:56:36,960 --> 00:56:42,120
Speaker 1: ignoring like it's justified. Actually of course, you know, of course, yeah,

973
00:56:42,120 --> 00:56:45,520
Speaker 1: it's this is it is like the well, we put

974
00:56:45,520 --> 00:56:51,319
Speaker 1: a scientific we we used a scientific method to evaluate

975
00:56:51,600 --> 00:56:54,759
Speaker 1: the worth of these places, like a publication that calls

976
00:56:54,800 --> 00:57:01,560
Speaker 1: itself the economist is assigning like economic value to these places,

977
00:57:02,000 --> 00:57:05,360
Speaker 1: and then you know it is ultimately an article about

978
00:57:05,360 --> 00:57:12,000
Speaker 1: real estate and like assigning value to places entire cities

979
00:57:12,120 --> 00:57:16,360
Speaker 1: based on you know, rankings and I don't know, like

980
00:57:16,800 --> 00:57:19,440
Speaker 1: real estate. Just the the longer we've done this show

981
00:57:19,800 --> 00:57:22,680
Speaker 1: and the you know, if you listen to this American

982
00:57:22,720 --> 00:57:27,960
Speaker 1: Life or watch John Oliver, you've witnessed real estate agents anywhere.

983
00:57:28,040 --> 00:57:33,320
Speaker 1: Like it's like there was this American Life episode where

984
00:57:33,320 --> 00:57:36,520
Speaker 1: they sent a white couple to an apartment in New

985
00:57:36,600 --> 00:57:38,360
Speaker 1: York City and a black couple, and the black couple

986
00:57:38,440 --> 00:57:41,320
Speaker 1: was sent away and told there were no apartments, and

987
00:57:41,480 --> 00:57:44,720
Speaker 1: the white couple were immediately shown a bunch of nice

988
00:57:44,720 --> 00:57:48,480
Speaker 1: apartments in the same in the same building and you know,

989
00:57:48,600 --> 00:57:51,880
Speaker 1: you just have like the John Oliver did a similar

990
00:57:52,000 --> 00:57:56,400
Speaker 1: like undercover thing or showed I guess similar undercover footage,

991
00:57:56,440 --> 00:58:01,000
Speaker 1: and like the real estate agent just speaking in you know,

992
00:58:01,160 --> 00:58:03,960
Speaker 1: hushed tones, being like well, this is not these are

993
00:58:03,960 --> 00:58:06,480
Speaker 1: not the type of neighbors that you would want to

994
00:58:06,640 --> 00:58:10,240
Speaker 1: live next to. The like hurt, the hurt the property

995
00:58:10,320 --> 00:58:15,080
Speaker 1: value like this, it's just like it is alive and

996
00:58:15,200 --> 00:58:19,880
Speaker 1: well and it's not just small off the record conversations.

997
00:58:19,960 --> 00:58:24,280
Speaker 1: They have an entire like white supremacist apparatus that is

998
00:58:24,920 --> 00:58:29,160
Speaker 1: enforcing their funked up ideals with you know, seemingly scientific

999
00:58:29,960 --> 00:58:34,680
Speaker 1: evaluations of cities, and it's it's just so out in

1000
00:58:34,720 --> 00:58:37,120
Speaker 1: the open, but like done in terms that are just

1001
00:58:37,200 --> 00:58:40,240
Speaker 1: like I don't know that I I don't know how

1002
00:58:40,280 --> 00:58:44,840
Speaker 1: this article is excusable or like how it's still acceptable.

1003
00:58:45,000 --> 00:58:49,080
Speaker 1: And in the modern world. It also just seems like

1004
00:58:49,800 --> 00:58:54,600
Speaker 1: an insanely narrow way of thinking about it. If you

1005
00:58:54,600 --> 00:58:57,000
Speaker 1: if you think that there's just some kind of like universal,

1006
00:58:57,760 --> 00:59:02,720
Speaker 1: universally good city that where everyone is, you know, for everyone,

1007
00:59:02,840 --> 00:59:06,760
Speaker 1: because like cities are very different depending on who you are.

1008
00:59:06,800 --> 00:59:08,840
Speaker 1: I mean, I live in Minneapolis. This is a city

1009
00:59:08,880 --> 00:59:12,000
Speaker 1: that's often very highly rated for things like literacy and

1010
00:59:12,320 --> 00:59:15,200
Speaker 1: you know, bookstores and theater seats, and we have some

1011
00:59:15,280 --> 00:59:18,040
Speaker 1: of the worst racial disparities in the country in terms

1012
00:59:18,040 --> 00:59:23,080
Speaker 1: of education and income and you know, housing segregation and

1013
00:59:23,120 --> 00:59:26,040
Speaker 1: so like who you ask, you know, is this a

1014
00:59:26,040 --> 00:59:29,440
Speaker 1: good city to live in? Is gonna really determine what

1015
00:59:29,560 --> 00:59:32,640
Speaker 1: the answer to that question is? Right, Yeah, And it

1016
00:59:32,640 --> 00:59:35,200
Speaker 1: seems like they're only they're like asking a very specific

1017
00:59:35,320 --> 00:59:39,800
Speaker 1: kind of like middle class type consumer of like oh yeah,

1018
00:59:39,840 --> 00:59:41,880
Speaker 1: I love Vienna, Oh yeah, I love this place, and

1019
00:59:41,920 --> 00:59:45,600
Speaker 1: not to knock those places, but yeah, like when everyone

1020
00:59:45,680 --> 00:59:49,040
Speaker 1: is sort of looking if most people agree that culture

1021
00:59:49,160 --> 00:59:51,800
Speaker 1: is equated or you know, it's it's all about or

1022
00:59:51,800 --> 00:59:55,800
Speaker 1: it's defined by being in a museum collection like at

1023
00:59:55,800 --> 00:59:58,400
Speaker 1: a your in a European collection or something like that,

1024
00:59:58,480 --> 01:00:01,320
Speaker 1: then yeah, that's that's a very specific version. Like someone

1025
01:00:01,360 --> 01:00:03,640
Speaker 1: like me who also likes music, I would be just

1026
01:00:03,760 --> 01:00:08,080
Speaker 1: as interested to go to like Legos to see the

1027
01:00:08,120 --> 01:00:10,040
Speaker 1: shrine where a fellow COUTI is like, you know, his

1028
01:00:10,080 --> 01:00:12,240
Speaker 1: son still performs and things like that appeals to me

1029
01:00:12,280 --> 01:00:14,040
Speaker 1: more so, Like it's funny when I read that article

1030
01:00:14,160 --> 01:00:16,720
Speaker 1: from this where the Nigerian person is giving their critique.

1031
01:00:16,720 --> 01:00:18,880
Speaker 1: I'm like, yeah, that just sounds weight. Your description of

1032
01:00:18,960 --> 01:00:21,760
Speaker 1: Nigeria is way better because they probably didn't ask somebody

1033
01:00:21,800 --> 01:00:25,280
Speaker 1: who's like, you were stationed in Nigeria for a few years,

1034
01:00:25,320 --> 01:00:26,960
Speaker 1: what was how was that like for you? And they're like,

1035
01:00:27,000 --> 01:00:28,920
Speaker 1: oh it was okay. I don't know if I'd go

1036
01:00:28,960 --> 01:00:33,200
Speaker 1: back and like okay, thank you. Yeah. Not to mentioned,

1037
01:00:33,240 --> 01:00:36,640
Speaker 1: like Vienna had like the highest rate of anti Semitic incidents,

1038
01:00:36,680 --> 01:00:39,600
Speaker 1: like this year, like it had like a record number

1039
01:00:39,640 --> 01:00:43,280
Speaker 1: of anti Semitic incidents, And I don't know if that's

1040
01:00:43,360 --> 01:00:46,800
Speaker 1: factored into this calculation about Vienna being livable for everybody

1041
01:00:46,800 --> 01:00:52,520
Speaker 1: either maybe not damn the economist. Yeah, I don't know.

1042
01:00:52,960 --> 01:00:57,480
Speaker 1: It's interesting that this is a open like the all

1043
01:00:57,520 --> 01:01:00,160
Speaker 1: you have to do is look at the map. The

1044
01:01:00,240 --> 01:01:03,439
Speaker 1: map is just like not not subtle. It's not it's

1045
01:01:03,480 --> 01:01:08,240
Speaker 1: not not subtle whatsoever. Basically, whoa wow, all these places

1046
01:01:08,320 --> 01:01:12,240
Speaker 1: the US invaded, right, yeah, yeah, all these places the

1047
01:01:12,360 --> 01:01:15,560
Speaker 1: US invaded and that have like been the target of

1048
01:01:15,760 --> 01:01:21,440
Speaker 1: CIA and you know, economic like gangsterism by the United States,

1049
01:01:21,480 --> 01:01:26,120
Speaker 1: like the weird that those places have don't don't fit,

1050
01:01:26,520 --> 01:01:30,600
Speaker 1: don't score well on the economists like s a T

1051
01:01:31,000 --> 01:01:38,840
Speaker 1: for city livability. Yeah, well expected better from you. Relationship ended? Yeah,

1052
01:01:39,120 --> 01:01:42,040
Speaker 1: all right, Well Jessica, it's been a true pleasure having

1053
01:01:42,080 --> 01:01:45,640
Speaker 1: you on the daily zeitgeist. Where can people find you?

1054
01:01:45,760 --> 01:01:49,160
Speaker 1: Follow you all that good stuff? Oh? Thank you? Um, well,

1055
01:01:49,280 --> 01:01:52,720
Speaker 1: you can find me on my website, Jessica or Dell

1056
01:01:52,800 --> 01:01:56,080
Speaker 1: dot com. You can find my book The End of

1057
01:01:56,200 --> 01:02:00,600
Speaker 1: Bias A Beginning anywhere books are sold. Support local independent

1058
01:02:00,600 --> 01:02:04,280
Speaker 1: bookstores please, and yeah. The book looks at approaches that

1059
01:02:04,320 --> 01:02:07,959
Speaker 1: have been shown to change people's behavior, like measurably cause

1060
01:02:08,040 --> 01:02:10,520
Speaker 1: people to behave in ways that are more fair and

1061
01:02:10,560 --> 01:02:13,160
Speaker 1: more just like some of the examples that we talked about.

1062
01:02:13,240 --> 01:02:15,360
Speaker 1: So I would love to hear from you if you

1063
01:02:15,440 --> 01:02:19,520
Speaker 1: have a chance to check it out. And if you, oh,

1064
01:02:19,640 --> 01:02:22,080
Speaker 1: if you choose to read the book in a book club,

1065
01:02:22,200 --> 01:02:25,560
Speaker 1: I am able. As far as I'm able, I will

1066
01:02:25,640 --> 01:02:30,160
Speaker 1: zoom into your book club and answer questions and chat

1067
01:02:30,320 --> 01:02:33,320
Speaker 1: chat with people. So if you would like to do

1068
01:02:33,400 --> 01:02:36,200
Speaker 1: something like that, hit me up on my website. You

1069
01:02:36,240 --> 01:02:38,960
Speaker 1: can contact me directly to talk about that as well.

1070
01:02:39,280 --> 01:02:43,080
Speaker 1: And great great person to have to speak with for sure.

1071
01:02:44,080 --> 01:02:46,520
Speaker 1: Is there a tweet or some other work of social

1072
01:02:46,560 --> 01:02:51,440
Speaker 1: media you've been enjoying. Uh, I wouldn't say like enjoying necessarily,

1073
01:02:51,480 --> 01:02:53,880
Speaker 1: but I think it's important. There was a tweet that

1074
01:02:53,920 --> 01:02:59,160
Speaker 1: came out pretty recently referring to a study of preschool

1075
01:02:59,160 --> 01:03:03,400
Speaker 1: teachers where these teachers were asked to show they were

1076
01:03:03,400 --> 01:03:07,600
Speaker 1: asked to look at a video of preschoolers to note misbehavior.

1077
01:03:07,800 --> 01:03:10,760
Speaker 1: But actually the study was a study of eyes and

1078
01:03:10,880 --> 01:03:13,720
Speaker 1: it was used eye tracking to watch where the teachers

1079
01:03:13,760 --> 01:03:17,360
Speaker 1: looked on screen. And there was a white boy, a

1080
01:03:17,400 --> 01:03:19,880
Speaker 1: white girl, a black boy, and a black girl, and

1081
01:03:19,920 --> 01:03:23,320
Speaker 1: they tracked where the preschool teacher's eyes were looking on

1082
01:03:23,360 --> 01:03:28,560
Speaker 1: the screen, and they found that the teachers spent significantly

1083
01:03:28,600 --> 01:03:32,840
Speaker 1: more time watching specifically the black boy. There wasn't more

1084
01:03:32,960 --> 01:03:36,080
Speaker 1: misbehavior coming from him, but that's where their eyes went.

1085
01:03:36,120 --> 01:03:38,760
Speaker 1: And so it kind of connects to our conversation earlier

1086
01:03:39,120 --> 01:03:43,800
Speaker 1: about how teachers are. You know that those early relationships

1087
01:03:43,840 --> 01:03:47,760
Speaker 1: are setting the stage for you know, future trust or

1088
01:03:47,840 --> 01:03:50,640
Speaker 1: lack of trust and respect or lack of respect, And

1089
01:03:50,680 --> 01:03:53,960
Speaker 1: so I think that study is is pretty important to

1090
01:03:54,480 --> 01:04:00,160
Speaker 1: think about. Damn Miles, where can people find you and

1091
01:04:00,240 --> 01:04:02,520
Speaker 1: follow you on? What's a tweet you've been enjoying? You

1092
01:04:02,560 --> 01:04:06,760
Speaker 1: can find me on Twitter and Instagram at Miles of Gray.

1093
01:04:06,960 --> 01:04:09,960
Speaker 1: Also obviously check out Myles and Jack got Mad, boost These,

1094
01:04:09,960 --> 01:04:12,400
Speaker 1: the NBA podcast, and if you like ninety day fiance

1095
01:04:12,760 --> 01:04:17,440
Speaker 1: catch me on four twenty day Fiancee. Uh, let's see

1096
01:04:17,520 --> 01:04:21,240
Speaker 1: a tweet that I like. I had one word. Oh,

1097
01:04:21,360 --> 01:04:23,520
Speaker 1: this is just a link to an article. I thought

1098
01:04:23,560 --> 01:04:27,120
Speaker 1: it was just very interesting and makes it warms my

1099
01:04:27,160 --> 01:04:29,800
Speaker 1: heart about gen Z and again Taylor Lorenz at Taylor

1100
01:04:29,840 --> 01:04:33,680
Speaker 1: Lorenz wrote an article about gen Z TikTok creators that

1101
01:04:33,720 --> 01:04:37,120
Speaker 1: are turning against Amazon, and the tweet is a coalition

1102
01:04:37,120 --> 01:04:39,640
Speaker 1: of TikTok creators is pledging to cease all work with Amazon,

1103
01:04:39,680 --> 01:04:42,560
Speaker 1: including shutting down storefronts and halting new partnerships until the

1104
01:04:42,560 --> 01:04:45,720
Speaker 1: company meets the demands of the Amazon Labor Union. Uh.

1105
01:04:45,760 --> 01:04:48,240
Speaker 1: And I was like, damn, it can be about his

1106
01:04:48,320 --> 01:04:53,439
Speaker 1: zoomers there we go. Um, so yeah, I'm gonna gonna

1107
01:04:53,440 --> 01:04:55,600
Speaker 1: read that one later if I if I howk up,

1108
01:04:55,680 --> 01:04:58,080
Speaker 1: if I pony up the money to read the Washington Post.

1109
01:04:59,080 --> 01:05:02,880
Speaker 1: You can find me on Twitter at Jack Underscore O'Brien

1110
01:05:03,280 --> 01:05:07,240
Speaker 1: and on Miles and Jack. I'm at boost E's uh tweet.

1111
01:05:07,240 --> 01:05:11,240
Speaker 1: I've been enjoying. Ron Ivor tweeted, I squat through fifteen

1112
01:05:11,400 --> 01:05:16,440
Speaker 1: I bench pressed to Okay, well, well, well you're lifting weights.

1113
01:05:16,600 --> 01:05:19,600
Speaker 1: I'm lifting my homie spirits with little jokes and tom

1114
01:05:19,640 --> 01:05:24,200
Speaker 1: foolery and then Greta Gremlins. Barbie tweeted, ET is god

1115
01:05:24,240 --> 01:05:28,520
Speaker 1: to your eighties blockbuster, but it's cultural impact has diminished

1116
01:05:28,520 --> 01:05:32,720
Speaker 1: because it's deeply uncool nostalgia bros won't funk with it

1117
01:05:32,880 --> 01:05:35,680
Speaker 1: the same way they funk with Back to the Future whatever,

1118
01:05:35,800 --> 01:05:39,960
Speaker 1: because it means engaging with a completely sincere, emotionally devastating

1119
01:05:40,040 --> 01:05:44,760
Speaker 1: kids movie. And I agree, ET needs a needs a second,

1120
01:05:45,200 --> 01:05:48,400
Speaker 1: a second cultural life, and I think it's getting it.

1121
01:05:48,440 --> 01:05:50,480
Speaker 1: I think they're putting it out in Imax as a

1122
01:05:50,840 --> 01:05:57,360
Speaker 1: like I always felt he was the ship. Talk about

1123
01:05:57,400 --> 01:06:03,280
Speaker 1: Emplican back, Yeah right, I remember balling, yeah man. You

1124
01:06:03,360 --> 01:06:06,640
Speaker 1: can find us on Twitter at Daily zey Geist. Were

1125
01:06:06,640 --> 01:06:09,240
Speaker 1: at the Daily Ziegeist on Instagram. We have Facebook fan

1126
01:06:09,280 --> 01:06:11,600
Speaker 1: page on our website Daily zike Guys dot com, where

1127
01:06:11,640 --> 01:06:15,000
Speaker 1: we post our episodes on our foot Nope, we link

1128
01:06:15,000 --> 01:06:17,280
Speaker 1: off to the information that we talked about in today's episode,

1129
01:06:17,280 --> 01:06:19,800
Speaker 1: as well as a song that we think you might enjoy.

1130
01:06:20,200 --> 01:06:22,080
Speaker 1: Mile as a song. Do we thank people might enjoy it?

1131
01:06:22,520 --> 01:06:25,040
Speaker 1: This is just like kind of very by. The track

1132
01:06:25,120 --> 01:06:27,320
Speaker 1: from this artist is called New Ages and you a

1133
01:06:27,520 --> 01:06:32,720
Speaker 1: gest And I just did tracks that like start off

1134
01:06:32,720 --> 01:06:36,479
Speaker 1: with Alan Watts spoken word, just such a specific kind

1135
01:06:36,520 --> 01:06:39,560
Speaker 1: of vibe and it just reminds me of being in

1136
01:06:39,640 --> 01:06:42,480
Speaker 1: college when I first read the book by Alan wattson

1137
01:06:42,840 --> 01:06:47,120
Speaker 1: what's the funk Man? Like, what are we talking about here? Uh?

1138
01:06:47,120 --> 01:06:49,600
Speaker 1: And this track you hear his voice and it's called Dreams.

1139
01:06:50,200 --> 01:06:53,160
Speaker 1: But the production is really kind of cool, so it's yeah,

1140
01:06:53,280 --> 01:06:56,400
Speaker 1: check this out, New Ages with Dreams, all right. Well.

1141
01:06:56,480 --> 01:06:58,840
Speaker 1: Daily Zych is a production by Heart Radio for More

1142
01:06:58,960 --> 01:07:01,439
Speaker 1: podcast for my Heart Radio, visit the I Heart Radio app,

1143
01:07:01,440 --> 01:07:04,280
Speaker 1: Apple podcast, or wherever you listen to your favorite shows.

1144
01:07:04,320 --> 01:07:05,960
Speaker 1: That's gonna do it for us this morning. But we're

1145
01:07:06,000 --> 01:07:08,880
Speaker 1: back this afternoon to tell you what's trended and we'll

1146
01:07:08,880 --> 01:07:11,040
Speaker 1: talk to you all then. Bye bye By