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Speaker 1: I couldnot fucking sleep all night, all night, been up

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Speaker 1: like a fucking owl, like THEO Vaughn was telling Donald.

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Speaker 2: Trump, I should have you like an owl.

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Speaker 1: Oh man, you be your own fucking not you being

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Speaker 1: a street light light, just like and that's good. No,

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Speaker 1: that's good, that's good.

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Speaker 2: And that feeling you like that feeling.

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Speaker 1: No, no, no, no, I do not want to feel

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Speaker 1: like a vampire with a heart condition. And yeah, for

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Speaker 1: the record, it's not because I was doing cocaine. This

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Speaker 1: had been a low, great anxiety that kept me up

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Speaker 1: all night.

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Speaker 2: What they're calling it, Okay, okay, I'm like an owl.

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Speaker 1: We call that. We call that poor people's cocaine anxiety. Yeah,

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Speaker 1: just a.

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Speaker 2: Little anxiety, the poor man's cocaine. Hello the Internet, and

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Speaker 2: welcome to season three, p. Fifty three, Episode five of

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Speaker 2: Daly's Like I Say production of iHeartRadio. This is a

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Speaker 2: podcast where we take a deep dive into American share consciousness.

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Speaker 2: And it is Friday, August thirtieth.

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Speaker 1: Twenty twenty four. Yeah, yes, last day. No, well, thirty

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Speaker 1: they're thirty one day. They're thirty one days half August exactly. Also, Hey,

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Speaker 1: guess guess who I get to shout out today, shout

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Speaker 1: out to my dad is his way seventy years old

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Speaker 1: up in this place. Congrats to you.

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Speaker 2: I was just at a friend of my youngests and

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Speaker 2: they have an amazing work from your dad on their wall.

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Speaker 1: Oh they do? Yeah yeah, oh well you know, yeah,

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Speaker 1: you're very white. My dad is internationally known and locally

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Speaker 1: respected as an artist. So I appreciate, appreciate the support

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Speaker 1: from everybody. But also August thirtieth is National Beach Day,

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Speaker 1: National Grief Awareness Daytional Toasted Marshmallow Day, and for all

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Speaker 1: you college fans, it's National College Colors Day. I am

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Speaker 1: not wearing mine, sadly I should normally maybe kind of

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Speaker 1: I need, I need some more gray. But I feel

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Speaker 1: like I wear like my alma matership because I'm like, well,

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Speaker 1: I gave these people so much money, like I have to.

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Speaker 1: I need to get something out of it, where like

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Speaker 1: they should brand me.

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Speaker 2: Yeah yeah, the amount of money I gave them.

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Speaker 1: Thank you, thank you, thank you. Yes.

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Speaker 2: All right. Well, my name is Jack O'Brien aka my Doughnuts,

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Speaker 2: bring jd Vance to the yard. And he's like, whatever

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Speaker 2: makes sense, damn right, whatever makes sense. That one courtesy

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Speaker 2: and Macaroni on the discord. What new jd vance flub

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Speaker 2: just dropped? I'm sure we talked about it yesterday's trending,

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Speaker 2: but his uh did you did you see? Don't worry

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Speaker 2: any every everybody. I'm not gonna take my shirt off. Okay,

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Speaker 2: it's a banger. It's another Certifi.

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Speaker 1: He was being booed by firefighters, so he just silence. Yeah. Yeah, yeah.

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

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Speaker 2: co host, mister Miles.

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Speaker 1: It's Miles Gray a k. A whale was beached? Yeah,

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Speaker 1: be headed it with the chainsaw and it strapped it

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Speaker 1: to the top of the car. Whale was beached? Yeah,

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Speaker 1: because I'm a normal hunter and whales are cool. You

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Speaker 1: know that they are whales? Okay, shout out to cleoly

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Speaker 1: dot universe for that creep. Tlc RFK hunter another creep. Yeah,

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Speaker 1: a true true creep. But yeah, that whale was beached

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Speaker 1: and hey, whale. As Michael Knowles on the Daily Wires,

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Speaker 1: and whales are.

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Speaker 2: Cool and he's a hunter, so it's normal. Find a

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Speaker 2: new angle exactly, chainsong of whales head? Does normal? Find

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Speaker 2: a new angle? Miles whale juice thrill whale juice is wonderful.

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Speaker 2: Thrilled to be joined in our third seat by a

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Speaker 2: poet and a lawyer who is the co founder and

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Speaker 2: executive director of Partners for Justice, which is designed to

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Speaker 2: create a new model of collaborative public defense designed to

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Speaker 2: empower You probably read her deep dive on Twitter into

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Speaker 2: Project twenty twenty five. Please welcome to the show, Emily

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Speaker 2: Galvin Omonza.

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Speaker 3: Very happy to be here, and I'm so sad that

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Speaker 3: I don't have an Internet supplied jingle or joke to

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Speaker 3: go along. I'm just sitting here like horrified at you

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Speaker 3: guys having unearthed my deep poet history.

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Speaker 1: Yeah.

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Speaker 2: Yeah, you do a little googling do the math is

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Speaker 2: a public You are a published poet.

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Speaker 3: I am. Math Poetry is the most marketable genre of literature.

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Speaker 3: I don't know if it's a top seller.

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Speaker 1: Yeah, yeah, you were doing math poetry.

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Speaker 3: Yes, poetry is math, right, like when you have a

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Speaker 3: rhythm of word, you know, the same way music's math.

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Speaker 3: Poetry is math. I just started with other math first

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Speaker 3: and then tried to create poetic forms that adhere to

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Speaker 3: that math again.

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Speaker 1: Real bang yeah wow, Okay, that's like some like tool

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Speaker 1: type shit. You're like I'm using a sacred geometry to

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Speaker 1: write my words down. Yeah yeah, yeah.

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Speaker 3: Oh if only I could be in the same category

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Speaker 3: as Tool, I would be. I mean, that's that's the best,

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Speaker 3: that's the that's the height of my poetic career. You

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Speaker 3: just you just made it right there.

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Speaker 1: Yeah, that's exactly right. I get it. I get it.

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Speaker 1: I get it.

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Speaker 2: So we did reach out to you on the strength

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Speaker 2: of project your Project twenty twenty five, Twitter thread, Deep Dive,

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Speaker 2: and and then we found out, like, we have a

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Speaker 2: bunch of mutual friends and we're a big fan of

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Speaker 2: your work otherwise, but the Project twenty twenty five thing,

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Speaker 2: you were just like kind of reading it because you

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Speaker 2: can read and comprehend lots of text, and you're like, oh,

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Speaker 2: this is worse than I imagined.

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Speaker 3: Yeah.

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Speaker 4: No.

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Speaker 3: What makes the Project twenty twenty five texts really really

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Speaker 3: dangerous is that it's written to sound super normal, and

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Speaker 3: it's also in credibly long. It's like nine hundred pages long.

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Speaker 3: And so if you are a lay person who's just

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Speaker 3: a little bit concerned about what the Heritage Foundation might

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Speaker 3: be putting out there, because you recognize that they were

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Speaker 3: heavily influential in Donald Trump's last administration, and you see

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Speaker 3: that a lot of his administration cronies had contributed to

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Speaker 3: this document. You want to peruse it, it might not

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Speaker 3: seem as scary as it actually is, because it's written

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Speaker 3: again to sound very very normal and that sort of

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Speaker 3: like policy speak. But having you know, gone to law

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Speaker 3: school and been forced to read lots of stuff, I

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Speaker 3: think it was a good use of time to try

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Speaker 3: to kind of get in there and translate for people

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Speaker 3: some of the scariest aspects of the policy plan. And

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Speaker 3: then I got really far in there and wrote like

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Speaker 3: a four hundred tweet thread about how bad it is

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Speaker 3: and also how insane, like they're weird obsession with boyfriends

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Speaker 3: and like how scared of boyfriends they are?

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Speaker 1: Yeah, wait, what is their obsession with boyfriends?

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Speaker 2: Oh?

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Speaker 1: My yead?

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Speaker 3: So they think that single terrible, right, super popular PRESI

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Speaker 3: let's all hate on single moms. Yeah, they had this

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Speaker 3: fixation on fatherhood. They're of course very very interested in

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Speaker 3: preserving the nuclear heteronormative family, right, But from that flows

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Speaker 3: this like weird paragraph where they actually talk about how dangerous,

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Speaker 3: like a single mom is bad, Like a single mom

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Speaker 3: with a boyfriend it's the worst possible outcome for children,

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Speaker 3: and like this part is actually not done in like

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Speaker 3: very elegant policy speak. They like actually hate on boyfriends

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Speaker 3: for a while. It's just there are these twists deep

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Speaker 3: within the document that are worth.

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Speaker 1: It's like, yeah, they're probably named Craig, and like they

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Speaker 1: eat your cereal and like and they don't even ask

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Speaker 1: you when you're twelve or when maybe your kid is

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Speaker 1: twelve whatever. Maybe my life is bleeding into what I'm

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Speaker 1: writing here policy equivalent.

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Speaker 3: If you're not my real dad, it's just right, yeah.

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Speaker 1: Which is so weird. But yeah, but I mean so

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Speaker 1: many conservative men have that energy of like you're not

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Speaker 1: my real dad, and you're like, wow, dude, you weren't

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Speaker 1: even talking about anything related to that, and you went

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Speaker 1: with that. Okay, okay, you seed to.

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Speaker 3: Say get down like six notches, yes.

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Speaker 1: Right, yeah, yeah, absolutely, all right.

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Speaker 2: Well, we will link off to the entire thread in

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Speaker 2: the footnotes. We are going to get to know you

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Speaker 2: a little bit better in a moment.

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Speaker 1: First.

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Speaker 2: A couple of things that we're talking about later on

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Speaker 2: when we get to the news. The mainstream media, it

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Speaker 2: has been pointed out recently, seem to actually be for

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Speaker 2: all the talk of there being a anti Trump bias,

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Speaker 2: they really seem to help him in a lot of ways.

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Speaker 2: So we just want to cover a couple small examples.

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Speaker 2: The Right has their new case closed winning strategy against

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Speaker 2: Harris Waltz. This time it is taking down Harris's Donald's

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Speaker 2: job story in the least convincing way possible.

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Speaker 1: Oh, she's being mixed. Swift voted to.

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Speaker 2: Be mc Swift voted yep. And then we're going to

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Speaker 2: talk about a company called Axon. Now I know you're

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Speaker 2: hearing the name Axon and you're like, that company sounds

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Speaker 2: like they do good in the world and probably not scary.

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Speaker 2: Axon is actually a terrifying fucking company that is like

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Speaker 2: the private arm of America's police, and they are experimenting

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Speaker 2: with using AI to help police do more damage. So

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Speaker 2: we're going to talk about that and just some of

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Speaker 2: the other shit that they've done. There is a photograph

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Speaker 2: that our writer JM put in the doc that is

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Speaker 2: their CEO addressing a crowd and he is not him.

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Speaker 2: Like the person addressing the crowd is in an all

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Speaker 2: black suit and has a motorcycle helmet on and then

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Speaker 2: an iPad strapped to the front with his face on it,

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Speaker 2: but is like gesture as he delivers the speech from wherever,

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Speaker 2: bunt whatever, like volcanic layer. He's at. The person is

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Speaker 2: like gesturing with his words too, So it's like a

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Speaker 2: weird avatar situation. Sick is just it's all so very.

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Speaker 1: On the nose.

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Speaker 2: Also talked about this New York Times article about alternative

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Speaker 2: policing and just where we're at with the mainstream media

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Speaker 2: when it comes to, you know, things that aren't police,

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Speaker 2: that aren't armed police, and how that's being talked about

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Speaker 2: these days in the mainstream All that plenty more. But first, Emily,

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Speaker 2: we do like to ask our guest, what is something

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Speaker 2: from your search history that's revealing about who you are?

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Speaker 3: Oh man, So it's actually really great that you guys

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Speaker 3: asked me this because a few months ago, Okay, so

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Speaker 3: the story is gonna get weird, a deer impaled itself

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Speaker 3: on my colleague's fence. Oh no, And my colleague, of course,

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Speaker 3: shared a photograph with our entire team to ask Jesus,

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Speaker 3: what do I do now? There's a dead deer on

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Speaker 3: my fence, And it just so happened that I dipped

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Speaker 3: into my own Google search history to demonstrate how ready

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Speaker 3: for this topic? I actually was, and I took a

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Speaker 3: screen grap I'm actually going to show you guys to

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Speaker 3: prove that it's really a screen grab of my search

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Speaker 3: results from that day, which were as follows wordle how

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Speaker 3: to gut a deer at home? Oh no, how to

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Speaker 3: dress a deer at home? Inflation Reduction Act Rebates twenty

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Speaker 3: twenty four Massachusetts driving directions to the Magical Bridge Playground.

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Speaker 3: That's there? You go, wo.

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Speaker 1: Wait, what's the Magical Bridge break? I'm like, more like,

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Speaker 1: what's the Magical Bridge?

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Speaker 3: You know it's there. It's actually really cool. In this

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Speaker 3: area where I was living in California at the time,

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Speaker 3: because I teach at Stanford during the winter, there is

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Speaker 3: this playground that was actually designed to be really accessible

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Speaker 3: for kids with disabilities. And it turned out that the

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Speaker 3: playground that's accessible is actually the best playground ever made,

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Speaker 3: and it's everybody's favorite playground. So that's the one I

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Speaker 3: was taking my kid to.

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Speaker 2: Yeah, this place.

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Speaker 1: Looks like a fucking theme park.

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Speaker 3: It's amazing. There's like three there's more than three of them.

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Speaker 3: They're they're popping up everywhere. They're they're the next generation

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Speaker 3: of playgrounds.

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Speaker 1: Really wow. Wow, wow wow. Okay, I like to see that,

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Speaker 1: and you can tell us gott that like tartan on

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Speaker 1: the ground, like that makes it real soft and sunshine,

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Speaker 1: like yeah, yeah, hurt.

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Speaker 3: Their little heads on, like when they've grown up and

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Speaker 3: it was just all concrete.

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Speaker 1: Yeah, or i'd get like wood mults stuck under my

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Speaker 1: toenails or something, because like try to go barefoot down

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Speaker 1: the slide.

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Speaker 3: Yes, the slide burns you, and then the wood impales

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Speaker 3: at the bottom and that makes them.

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Speaker 1: Stronger and then you get that nice aroma of like

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Speaker 1: smoky playground for sure.

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Speaker 2: Yeah, safe for my kids, the tartan. And also I

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Speaker 2: feel like I jump higher on it, and so I

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Speaker 2: like to show that off when I'm at the playground, like, look,

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Speaker 2: I'm gonna dunk on these monkey bars.

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Speaker 1: Oh it's five feet high.

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Speaker 3: Cristiana Ronaldo of the Magical Bridge Playground.

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Speaker 1: Yeah.

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Speaker 2: Why was your partners or the person who texted you?

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Speaker 2: Why was their fence so sharp? Is that a thing

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Speaker 2: that is normal?

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Speaker 3: Great question. I don't know. They live in the South.

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Speaker 3: I can't speak to Southern fence practices. I don't I

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Speaker 3: have a lot of fence experience. I've myself constructed a

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Speaker 3: lot of four string barb wire fence grown up in

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Speaker 3: ranch culture, but never something with an impalable top.

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Speaker 1: I have real questions, right, yeah, yeah, like that is it?

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Speaker 1: Is that by design?

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Speaker 2: Is he like I'm going to leave the deer there

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Speaker 2: to tell the other deer what happens when you try

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Speaker 2: to come on our property? Or is it just like

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Speaker 2: an accident because it was, So I'm.

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Speaker 3: Going to go with the former. I'm going to decide

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Speaker 3: that whoever install that defence wanted it to be a

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Speaker 3: place where you could impale ahead just really.

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Speaker 1: Yeah yeah, oh yeah, like what like in Game of

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Speaker 1: Thrones when like Kalisi takes over that place and all

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Speaker 1: like the heads are like on pikes and stuff or

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Speaker 1: yeah yeah, I.

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Speaker 3: Guess, or in real life in human history, yeah.

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Speaker 2: Yeah, that was like everywhere that was just interior or

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Speaker 2: exterior decorating.

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Speaker 3: Back in the day need like a wisconce and then

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Speaker 3: a head.

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Speaker 2: Yeah, and it was like we like to around Christmas

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Speaker 2: have points set. He has the rest of your heads

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Speaker 2: just heads decorating everything.

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Speaker 1: What is uh? What's something you think is underrated?

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Speaker 3: So I I thought about this question a lot. I

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Speaker 3: think that tea time is underrated. And I'm going to

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Speaker 3: make a defensive of tea time because when I see time,

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Speaker 3: say tea time, people usually think of sort of like

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Speaker 3: stodgy British, pinky raised, unpleasantly meticulous. Okay, tea time is

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Speaker 3: supposed to be an incredible spread at like four o'clock

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Speaker 3: in the afternoon, where if you are like me, you

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Speaker 3: are most ravenous. There should be pastries and cakes and

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Speaker 3: like sa every things. My husband is Bolivian. Bolivians really

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Speaker 3: do tea Time like they do. They will fill your

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Speaker 3: table at tea time and then dinner is like a

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Speaker 3: light snack. I think this is a really underrated way

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Speaker 3: of living one's life because that's when I actually want

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Speaker 3: to just become a complete glutton and move my way

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Speaker 3: across a full table at four o'clock in the afternoon,

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Speaker 3: when I'm just when I've had it with the world.

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Speaker 3: So I think tea Time we should bring tea Time back.

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Speaker 1: Wow, I didn't know like Bolivians are really I'm just

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Speaker 1: reading about these. They got salon the Olivion. They love

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Speaker 1: the tea time.

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Speaker 3: Tea Time, it's a whole it's very elegant, it's very comforting.

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Speaker 2: Yeah, snack time. I'm for me, like I'm just wondering,

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Speaker 2: Like I feel like I basically recreated this in my

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Speaker 2: own life, but with a snack drawer, because you know,

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Speaker 2: being raised Catholic, I have shame and so like I

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Speaker 2: just have all the snacks, but they're like in a drawer,

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Speaker 2: and I just like eat over the drawer all.

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Speaker 1: The different snacks.

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Speaker 2: Bullets leave it in there, my tea time, shame drawer.

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Speaker 1: Wait, what do you wait? What do you got in there?

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Speaker 1: Like loose bread slices or just loose breads all stale,

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Speaker 1: A couple of pieces of wonderbread.

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Speaker 2: No, I got got chips, I got some some pretzels,

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Speaker 2: I got cashoes, maybe maybe some sort of nuts in there.

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Speaker 2: And then you know, I'll bring out one bag of

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Speaker 2: either chips or pretzels at a time to accompany you know,

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Speaker 2: some things from the.

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Speaker 1: Cold cut drawer.

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Speaker 2: Oh wow, or some you know or hummus, how dignified?

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Speaker 1: Wait, what's a dead time? What's the spread out of

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Speaker 1: Bolivian tea time? Emily?

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Speaker 3: Oh my god. So you're obviously gonna have different beverages,

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Speaker 3: including tea, but you're also going to have both an

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Speaker 3: array of savory and an array of sweet options. Okay,

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Speaker 3: and I'm going to state right here that asking an

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Speaker 3: actual Bolivion would get you a better answer, being of

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Speaker 3: course being there, you know here without a Bolivion on

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Speaker 3: the call. I'm going to highlights like Southenia's, which are

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Speaker 3: a breakfast food as one of the best Bolivian foods

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Speaker 3: you can get. You can get a ton in like

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Speaker 3: the DC metro area, a ton of Bolivians there. You

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Speaker 3: can get them sort of all across Virginia and some

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Speaker 3: in California. But South Anias is basically like a like

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Speaker 3: a savory pastry full of delicious stew and you can

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Speaker 3: kind of bite off the end and sip the stew

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Speaker 3: and then eat the pastry.

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Speaker 1: Oh shit, it's souping because like when I see a picture,

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Speaker 1: I'm like, oh, this looks like an empanada, but.

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Speaker 3: Oh my god, you will never touch another panada now, Like, no,

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Speaker 3: South ania is our next level.

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Speaker 1: Okay.

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Speaker 3: We have counez, which are like a like a cheese

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Speaker 3: pastry that are really fluffy and delicious. They're kind of

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Speaker 3: like a powered casual but better, oh better than oh

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Speaker 3: way better, way better.

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Speaker 1: Okay, Okay, go on, go on, will I'm.

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Speaker 3: Here to talk about Lyvian food, and I will tell

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Speaker 3: you some of the best in the world. No, they're

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Speaker 3: gonna have all kinds of like real dishes, like meats,

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Speaker 3: prepared meats, and maybe a stew. And then you're also

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Speaker 3: gonna have a lot of like cakes and pastries and

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Speaker 3: the sort of usual like.

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Speaker 2: Yeah, so I just started sweating, like the peel meme.

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Speaker 1: Yeah, because I love paladaccasual. Like when I first had

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Speaker 1: that the first I'm like, my god, what the fuck

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Speaker 1: are we doing up here? Like I love and now

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Speaker 1: seeing the other one we say is the fact that

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Speaker 1: it's sort of like a soup dumpling, but the Bolivian

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Speaker 1: version was like first you got to take the bite

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Speaker 1: and then get the soup out and then keep going.

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Speaker 1: I'm also intrigued by the structural integrity of the pastry

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Speaker 1: that can contain a stew within it. This comes down

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Speaker 1: to scale.

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Speaker 3: I mean the pastry is like really robust and like

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Speaker 3: almost has a slight sweetness and thick chewiness to it.

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Speaker 3: But it's also like you're gonna get judged on your

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Speaker 3: skill level, Like some people are beginners and they need

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Speaker 3: to use a utensil or they might get stew on

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Speaker 3: them once they're real pro you're just holding that southenia

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Speaker 3: on one hand and like longboarding down the road. Uh

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Speaker 3: no stew anywhere on your person.

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Speaker 1: Bottle?

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Speaker 2: Yeah, what is something you think is overrated?

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Speaker 3: I'm gonna stay with my food theme. I actually think

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Speaker 3: we've gotten to the point where where brunches is overrated.

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Speaker 3: I think, yeah, what do you think Everybody's doing brunch

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Speaker 3: every weekend? And it's getting the point whe it's just

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Speaker 3: like flat, flabby breakfast food at a different time of day,

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Speaker 3: and I'm no longer excited by it. I'm no longer

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Speaker 3: inspired over it.

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Speaker 1: I think brunch like loses its appeal the earlier I

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Speaker 1: wake up, Like when I was like younger and like

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Speaker 1: like you know, going out and ship like that, and

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Speaker 1: I'd wake up Lane, I'm like, yeah, brunch, but yeah,

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Speaker 1: let's eat at one that's breakfast. But now I'm like,

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Speaker 1: not that already eight or it's depends on, you know,

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Speaker 1: if there's an occasion, but yeah, I get that. I

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Speaker 1: guess is that is that maybe one of our latest

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Speaker 1: food fads that's going only now it was a brunch.

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Speaker 3: Oh it'll never go away? Yeah, maybe maybe actually the

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00:19:06,320 --> 00:19:07,959
Speaker 3: right answer to how to live one's life is to

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00:19:08,000 --> 00:19:10,800
Speaker 3: only have brunch and tea. I mean maybe breakfast, lunch

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Speaker 3: and dinner.

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Speaker 1: Two meals.

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00:19:12,160 --> 00:19:15,280
Speaker 2: Yeah yeah, yeah yeah, two meals. Give me a brunch,

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00:19:15,560 --> 00:19:17,120
Speaker 2: give me a tea, give me that tea. I mean,

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00:19:17,280 --> 00:19:21,600
Speaker 2: does the enthusiasm for tea just like make you less

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Speaker 2: likely to have anything at dinner? I feel like dinner

392
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Speaker 2: becomes an afterthought at that point. A little snack for dinner,

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Speaker 2: spread for tea.

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Speaker 1: Okay, I like that.

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Speaker 2: Yeah, brunch is not a natural time for me to

396
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Speaker 2: be hungry. If I've eaten breakfast, then like brunch is

397
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Speaker 2: not real.

398
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Speaker 1: Or you do the thing where you wake up you're like, fuck, dude,

399
00:19:40,480 --> 00:19:43,400
Speaker 1: brunch is in four hours, and you're like, I don't

400
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Speaker 1: want to like go and not eat anything. So then

401
00:19:45,560 --> 00:19:48,120
Speaker 1: you're like walking this tight rope of not eating before

402
00:19:48,359 --> 00:19:51,480
Speaker 1: or showing up hangary and you're like this showing up mean.

403
00:19:51,560 --> 00:19:56,240
Speaker 2: Yeah, yeah, all right, Uh, let's take a quick break

404
00:19:56,480 --> 00:20:00,480
Speaker 2: and we'll be right back. Mm hm.

405
00:20:08,320 --> 00:20:12,400
Speaker 1: And we're back. We're back. And yeah.

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00:20:12,400 --> 00:20:16,639
Speaker 2: So there's been some talk it's being called sane washing

407
00:20:16,840 --> 00:20:19,840
Speaker 2: when it comes to the Trump campaign, like that, how

408
00:20:19,880 --> 00:20:26,560
Speaker 2: the mainstream media covers President Trump former President Trump's long

409
00:20:26,800 --> 00:20:32,080
Speaker 2: rambling press conferences. Yeah and yeah, but it just it

410
00:20:32,119 --> 00:20:35,280
Speaker 2: does feel like there's a different standard when it comes

411
00:20:35,280 --> 00:20:39,119
Speaker 2: to him, possibly because of the glut of insanity that

412
00:20:39,240 --> 00:20:43,359
Speaker 2: is like coming at us, or possibly just because I

413
00:20:43,400 --> 00:20:46,800
Speaker 2: don't know that you want to make it a good

414
00:20:47,119 --> 00:20:49,240
Speaker 2: a good game. Everyone wants to see a good game,

415
00:20:49,359 --> 00:20:51,160
Speaker 2: so we gotta we gotta make sure it's close.

416
00:20:51,320 --> 00:20:53,240
Speaker 1: No one likes to blowout, So we got to prop

417
00:20:53,359 --> 00:20:57,600
Speaker 1: up the orange guy who's deteriorating before our eyes. But yeah,

418
00:20:57,640 --> 00:20:59,840
Speaker 1: like you, like you said Aaron Rupar like on a

419
00:20:59,880 --> 00:21:02,440
Speaker 1: lot of his videos that he clips out and puts

420
00:21:02,440 --> 00:21:05,240
Speaker 1: out on Twitter, like whenever he cut Like, We've played

421
00:21:05,240 --> 00:21:07,000
Speaker 1: a couple of those clips where Trump will be rambling

422
00:21:07,040 --> 00:21:08,480
Speaker 1: on and they'll cut back to someone in the student

423
00:21:08,560 --> 00:21:11,159
Speaker 1: Like what he means to say is actually this not

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00:21:11,240 --> 00:21:15,119
Speaker 1: that Hannibal Elector was a real person and a good guy.

425
00:21:15,200 --> 00:21:15,720
Speaker 1: What do he means A.

426
00:21:15,800 --> 00:21:19,720
Speaker 2: Late great Hannibal Elector represents our democracy? I just wanted

427
00:21:19,840 --> 00:21:20,600
Speaker 2: to talk about.

428
00:21:20,960 --> 00:21:24,320
Speaker 1: He's a poet actually, But like, two recent events in

429
00:21:24,320 --> 00:21:27,040
Speaker 1: the presidential race have kind of underscored how the mainstream

430
00:21:27,080 --> 00:21:29,800
Speaker 1: media tries to normalize Trump and like his circus of

431
00:21:29,840 --> 00:21:33,040
Speaker 1: political aids. So this week, obviously he made headlines for

432
00:21:33,119 --> 00:21:36,240
Speaker 1: insisting on taking photos and filming a TikTok video for

433
00:21:36,320 --> 00:21:39,359
Speaker 1: his campaign in a section of Arlington National Cemetery that

434
00:21:39,480 --> 00:21:42,080
Speaker 1: prohibits that very thing. In fact, it's a violation of

435
00:21:42,119 --> 00:21:46,320
Speaker 1: federal law to use a military cemetery for campaign purposes.

436
00:21:46,640 --> 00:21:49,040
Speaker 1: So while this was happening happening, there was a press

437
00:21:49,040 --> 00:21:52,160
Speaker 1: release that came out of the Trump campaign to sort

438
00:21:52,160 --> 00:21:55,199
Speaker 1: of like paper things over, and journalist Brandon Friedman he

439
00:21:55,240 --> 00:21:57,880
Speaker 1: pointed out on Twitter how the Trump campaign's press release

440
00:21:57,920 --> 00:22:00,680
Speaker 1: had a very dumb typo in it. Quote the statement

441
00:22:00,800 --> 00:22:05,520
Speaker 1: campaign manager Chris Losovita incorrectly used the word hollowed instead

442
00:22:05,520 --> 00:22:08,040
Speaker 1: of hallowed on this.

443
00:22:08,320 --> 00:22:12,520
Speaker 2: Hollowed hallowed out, Yeah, knocked on like a hollowed gesture,

444
00:22:13,359 --> 00:22:18,159
Speaker 2: hallowed ground exactly right, precise almost precisely Fredian slip.

445
00:22:18,920 --> 00:22:21,600
Speaker 1: Like so Axios, the Daily Beasts, they added the sort

446
00:22:21,600 --> 00:22:24,080
Speaker 1: of parenthetical sick to sort of say like that they

447
00:22:24,119 --> 00:22:26,679
Speaker 1: misused the word this is what they meant to say.

448
00:22:26,880 --> 00:22:29,720
Speaker 1: But a few other organizations, as you pointed out, sort

449
00:22:29,760 --> 00:22:33,120
Speaker 1: of caught the misuse but then corrected it. On behalf

450
00:22:33,160 --> 00:22:36,320
Speaker 1: of the Trump campaign, like CNN did, and just like

451
00:22:36,320 --> 00:22:38,639
Speaker 1: they're like they meant hallowed, dude, just change it so

452
00:22:38,680 --> 00:22:40,960
Speaker 1: people don't make like, you know, point out they made

453
00:22:40,960 --> 00:22:44,520
Speaker 1: a typo. The New York Times predictably ran the story

454
00:22:44,560 --> 00:22:47,159
Speaker 1: with the typo unedited, and then but like so if

455
00:22:47,200 --> 00:22:48,919
Speaker 1: you searched in Google, you'd be like, oh, yeah, they

456
00:22:48,920 --> 00:22:51,440
Speaker 1: wrote hollowed haha. But when you click it, they republished

457
00:22:51,480 --> 00:22:55,080
Speaker 1: it and edit it, edited it to be hallowed without

458
00:22:55,119 --> 00:22:57,440
Speaker 1: any sort of reference to the fact that there was

459
00:22:57,480 --> 00:22:59,879
Speaker 1: a typo. And then they had you know, dud you basically,

460
00:23:00,240 --> 00:23:02,679
Speaker 1: we're doing some copy intern stuff for the camp, the

461
00:23:02,680 --> 00:23:05,840
Speaker 1: Trump campaign. But this is like a subtle example, but

462
00:23:05,960 --> 00:23:09,199
Speaker 1: like worth noting because these like small accommodations are at

463
00:23:09,200 --> 00:23:12,719
Speaker 1: the very least bad journalism and at best being like, no,

464
00:23:12,800 --> 00:23:16,560
Speaker 1: we're helping them because like they we just need them

465
00:23:16,560 --> 00:23:19,160
Speaker 1: to look a little bit more like together than they

466
00:23:19,160 --> 00:23:22,879
Speaker 1: obviously are. So the other thing that has been like

467
00:23:23,200 --> 00:23:26,600
Speaker 1: being pointed out across the media is from like the

468
00:23:26,640 --> 00:23:29,320
Speaker 1: CNN shit, Like during the DNC they would have these

469
00:23:29,400 --> 00:23:33,000
Speaker 1: panels of like quote undecided voters to be like, well,

470
00:23:33,000 --> 00:23:36,120
Speaker 1: so would you think about that You're undecided. And right

471
00:23:36,200 --> 00:23:39,159
Speaker 1: after Kamala Harris gave her acceptance speech, they spoke to

472
00:23:39,160 --> 00:23:43,000
Speaker 1: a panel of supposedly undecided voters in Pennsylvania, and one

473
00:23:43,080 --> 00:23:46,080
Speaker 1: man was clearly an outlier. Like after the speech, he's like,

474
00:23:46,119 --> 00:23:48,080
Speaker 1: I don't know, that thing was like bad. Most people

475
00:23:48,119 --> 00:23:49,960
Speaker 1: are like, yeah, that was that was pretty good. That

476
00:23:49,960 --> 00:23:52,399
Speaker 1: that wasn't that wasn't fun. That yeah, that was fine.

477
00:23:52,840 --> 00:23:55,240
Speaker 1: He's like, nah, there's nothing. There's a big nothing burger.

478
00:23:55,560 --> 00:23:57,240
Speaker 1: And then when the panel, like the person who was

479
00:23:57,280 --> 00:23:59,880
Speaker 1: hosting the panel said, has anyone decided yet after this

480
00:24:00,080 --> 00:24:02,520
Speaker 1: beach who they're going to vote for? This guy immediately

481
00:24:02,560 --> 00:24:04,280
Speaker 1: raised his hand. He's like, yeah, I'm voting for Trump.

482
00:24:05,040 --> 00:24:08,960
Speaker 1: They're like, oh, okay. Midas Touch looked into this guy

483
00:24:09,119 --> 00:24:12,240
Speaker 1: and his social media is like littered with MAGA crap,

484
00:24:12,560 --> 00:24:16,159
Speaker 1: Like he's very much clearly like a Trump supporter. And

485
00:24:16,200 --> 00:24:18,800
Speaker 1: when they pressed CNN and him on it, they both

486
00:24:18,880 --> 00:24:21,720
Speaker 1: kind of had conflicting stories. The man said, yeah, dude,

487
00:24:21,720 --> 00:24:23,800
Speaker 1: I told CNN I was a Trump like, I'm a

488
00:24:23,840 --> 00:24:25,719
Speaker 1: Trump guy, and they just asked if I could keep

489
00:24:25,760 --> 00:24:27,840
Speaker 1: an open mind, and I said, yeah, I can keep

490
00:24:27,840 --> 00:24:30,080
Speaker 1: an open mind. So I went and they called me undecided.

491
00:24:30,480 --> 00:24:33,239
Speaker 1: CNN was like, well, technically, when we spoke to him,

492
00:24:33,280 --> 00:24:36,360
Speaker 1: he said he hadn't decided who he was gonna support,

493
00:24:36,720 --> 00:24:40,360
Speaker 1: so we invited him to speak on the panel. And

494
00:24:40,520 --> 00:24:43,080
Speaker 1: that's where you're just like, what the Like, I'm always

495
00:24:43,160 --> 00:24:46,440
Speaker 1: confused when they do these like undecided sort of panels.

496
00:24:46,560 --> 00:24:49,439
Speaker 1: I'm like, who, Like, who really are these people? Like

497
00:24:49,520 --> 00:24:53,080
Speaker 1: are they really that undecided? Because they seem pretty informed

498
00:24:53,440 --> 00:24:55,800
Speaker 1: for being undecided. And then I'm like, what is it

499
00:24:55,840 --> 00:24:57,639
Speaker 1: that you're waiting for on either side for you to

500
00:24:57,720 --> 00:24:59,840
Speaker 1: be like? All right? Trump said the thing I needed

501
00:24:59,840 --> 00:25:02,479
Speaker 1: to do here, all right? The Democrats that the thing

502
00:25:02,520 --> 00:25:05,280
Speaker 1: I needed to hear. And this could be like a

503
00:25:05,320 --> 00:25:08,879
Speaker 1: one off or like a you know, simple mistake, but like,

504
00:25:08,880 --> 00:25:11,480
Speaker 1: you know, Parker mcloy pointed out that CNN has like

505
00:25:11,520 --> 00:25:14,959
Speaker 1: a pattern of this shit, Like in twenty fifteen, they

506
00:25:15,000 --> 00:25:17,640
Speaker 1: had a roundtable with Trump supporters where like a woman

507
00:25:17,720 --> 00:25:20,720
Speaker 1: went on like a viral tirade against Obama and the

508
00:25:20,800 --> 00:25:23,719
Speaker 1: issue here. It's just that this wasn't like some fox

509
00:25:23,800 --> 00:25:26,960
Speaker 1: brained normal person. This was like a sitting New Hampshire

510
00:25:27,080 --> 00:25:30,719
Speaker 1: legislator Birther who tried to keep Obama off the New

511
00:25:30,760 --> 00:25:34,959
Speaker 1: Hampshire ballot, who's just presenting as just a citizen in

512
00:25:35,000 --> 00:25:38,720
Speaker 1: New Hampshire. Then in twenty eighteen, CNN also had a

513
00:25:38,760 --> 00:25:42,560
Speaker 1: discussion with quote five conservative women from Florida to discuss

514
00:25:42,640 --> 00:25:46,600
Speaker 1: the sexual assault allegations against Brett Kavanaugh, and the women's

515
00:25:46,680 --> 00:25:50,679
Speaker 1: responses were, yeah, here, I just I'll just play the

516
00:25:50,760 --> 00:25:55,760
Speaker 1: supposed five conservative women from Florida talking about the allegations

517
00:25:55,760 --> 00:25:57,040
Speaker 1: against Kavanaugh a.

518
00:25:57,000 --> 00:25:59,679
Speaker 3: Show of hands. How many of you believe Judge Kavanaugh

519
00:25:59,720 --> 00:26:05,520
Speaker 3: when he's says this didn't happen, Abalida believe believe Abalea.

520
00:26:05,600 --> 00:26:08,399
Speaker 5: How can we believe the word of a woman or

521
00:26:08,400 --> 00:26:11,200
Speaker 5: something that happened thirty six years ago? When this guy

522
00:26:11,240 --> 00:26:15,239
Speaker 5: has an impeccable reputation. There wasn't nobody, nobody that has

523
00:26:15,280 --> 00:26:18,000
Speaker 5: spoken ill will about him. Everyone that speaks about him.

524
00:26:18,040 --> 00:26:20,800
Speaker 5: This guy's an altar boy, you know, a scout, he's

525
00:26:20,920 --> 00:26:23,920
Speaker 5: you know, because one woman made an allegation, sorry I

526
00:26:23,920 --> 00:26:25,840
Speaker 5: don't buy it, but in the grand scheme of things,

527
00:26:25,880 --> 00:26:28,119
Speaker 5: my goodness you, there was no intercourse.

528
00:26:28,640 --> 00:26:32,639
Speaker 3: There was maybe a tip. Can we really thirty six

529
00:26:32,760 --> 00:26:35,400
Speaker 3: years later, she's still step on that had it happened?

530
00:26:35,720 --> 00:26:37,720
Speaker 4: I mean, we're talking about a fifteen year old girl,

531
00:26:37,840 --> 00:26:41,320
Speaker 4: which I respect, you know, I'm a woman. I respect.

532
00:26:41,760 --> 00:26:44,080
Speaker 4: We're talking about a seventeen year old boy in high

533
00:26:44,119 --> 00:26:46,639
Speaker 4: school with his thoughts ll running high Tell me what

534
00:26:46,800 --> 00:26:48,359
Speaker 4: boy hasn't done this in high school?

535
00:26:48,840 --> 00:26:52,560
Speaker 1: So the thing is a HU journalist looked into these people,

536
00:26:53,040 --> 00:26:56,760
Speaker 1: and at least three of them are political operatives, like

537
00:26:56,760 --> 00:26:59,919
Speaker 1: like one woman like was hosting fundraisers for the GOP,

538
00:27:00,520 --> 00:27:03,400
Speaker 1: another was running for like was a candidate for office.

539
00:27:03,720 --> 00:27:05,600
Speaker 1: So they had people that were like part of like

540
00:27:05,640 --> 00:27:08,479
Speaker 1: the GOP, like machinery go in there to sort of

541
00:27:08,560 --> 00:27:10,880
Speaker 1: provide intellectual cover for people to be like, yeah, whatever

542
00:27:10,880 --> 00:27:12,399
Speaker 1: happened to our kavanall is out that bad? I mean

543
00:27:12,400 --> 00:27:16,200
Speaker 1: these five normal people just said that it's nothing, so

544
00:27:16,280 --> 00:27:18,959
Speaker 1: maybe maybe it is. They just framed them as like

545
00:27:19,600 --> 00:27:23,880
Speaker 1: some people with conservatives, Yeah, just as conservatives that were

546
00:27:23,920 --> 00:27:26,680
Speaker 1: living in Florida and their take on it. So it's

547
00:27:26,720 --> 00:27:30,960
Speaker 1: just a very Yeah, it's just an odd, odd practice

548
00:27:31,119 --> 00:27:33,639
Speaker 1: but may be quite intentional. But I guess it depends

549
00:27:33,680 --> 00:27:36,119
Speaker 1: on how you look at things. Emily, how do you

550
00:27:36,160 --> 00:27:39,680
Speaker 1: sort of see this kind of journalism? What's your take

551
00:27:39,720 --> 00:27:40,760
Speaker 1: on that, Well.

552
00:27:40,640 --> 00:27:42,760
Speaker 3: It's not happening in a vacuum. When I look at this,

553
00:27:42,840 --> 00:27:45,040
Speaker 3: what I see, honestly is I'm a trial lawyer. I

554
00:27:45,040 --> 00:27:48,800
Speaker 3: see the jury selection process, right, which is a similar space. Right,

555
00:27:48,920 --> 00:27:50,719
Speaker 3: was this space where we're all pretending to be neutral,

556
00:27:50,760 --> 00:27:52,840
Speaker 3: and we have no pre existing beliefs, and we're coming

557
00:27:52,840 --> 00:27:54,720
Speaker 3: in here with a totally open mind, and yet everyone

558
00:27:54,760 --> 00:27:57,000
Speaker 3: walked through the door totally looked at my client was like,

559
00:27:57,040 --> 00:28:00,840
Speaker 3: I wonder what that person did. Right, So we also

560
00:28:00,920 --> 00:28:04,639
Speaker 3: see real restrictions on who's invited to be part of

561
00:28:04,680 --> 00:28:06,639
Speaker 3: that process. Like you got to look at how media

562
00:28:06,800 --> 00:28:08,840
Speaker 3: put out the call for people to sign up for

563
00:28:08,920 --> 00:28:10,800
Speaker 3: opportunities like this, the same way you got to look

564
00:28:10,800 --> 00:28:13,359
Speaker 3: at how you know in the jury system, people with

565
00:28:13,400 --> 00:28:15,560
Speaker 3: prior convictions are excluded, People who aren't on the voter

566
00:28:15,600 --> 00:28:18,080
Speaker 3: rolls are excluded. People who may not have a driver's

567
00:28:18,080 --> 00:28:19,880
Speaker 3: license can be excluded, people who don't have a mailing

568
00:28:19,880 --> 00:28:21,959
Speaker 3: address are excluded. So you get these juries that are

569
00:28:22,000 --> 00:28:25,199
Speaker 3: sort of made wealthier and wider and more conservative by

570
00:28:25,240 --> 00:28:27,600
Speaker 3: the ways in which people are even invited to attend.

571
00:28:28,400 --> 00:28:31,439
Speaker 3: And then that's sort of distilled into an even more

572
00:28:31,560 --> 00:28:36,359
Speaker 3: sort of pro prosecution extract through the process of questioning

573
00:28:36,400 --> 00:28:38,000
Speaker 3: people and then if a person's like, I don't know

574
00:28:38,000 --> 00:28:39,480
Speaker 3: if I can be fair, the judge is like, you

575
00:28:39,520 --> 00:28:42,760
Speaker 3: can keep an open mind, right, Same, it's the CNN question, right,

576
00:28:43,240 --> 00:28:44,960
Speaker 3: You've told us who you are and what you believe in,

577
00:28:45,160 --> 00:28:47,200
Speaker 3: but you can sett all that aside, can't you.

578
00:28:47,680 --> 00:28:53,840
Speaker 1: Right, So I'm a cause I can keep an open mind.

579
00:28:54,040 --> 00:28:56,440
Speaker 1: Yeah I think so, I think so wrong, But that's

580
00:28:56,480 --> 00:29:00,000
Speaker 1: not a problem for me brother in law technically. But yeah,

581
00:29:00,320 --> 00:29:00,840
Speaker 1: so yeah, like.

582
00:29:00,840 --> 00:29:04,280
Speaker 3: Who's who's the producer, who's setting up the process through

583
00:29:04,280 --> 00:29:07,800
Speaker 3: which these people appear, and who's the person not doing

584
00:29:07,840 --> 00:29:09,160
Speaker 3: like a basic social media.

585
00:29:09,000 --> 00:29:11,480
Speaker 1: Search, right, because like in that instance of the guy

586
00:29:11,600 --> 00:29:13,640
Speaker 1: like in the undecided, Like I mean whatever, that guy

587
00:29:13,760 --> 00:29:15,760
Speaker 1: just there's like I get to be on TV or whatever,

588
00:29:15,800 --> 00:29:18,719
Speaker 1: Like I mean, that's clearly on the producers, like you're

589
00:29:18,760 --> 00:29:22,120
Speaker 1: saying of how they're selecting people and whether they they

590
00:29:22,120 --> 00:29:24,680
Speaker 1: are doing it intensely or just don't care because they're

591
00:29:24,720 --> 00:29:26,080
Speaker 1: like I don't know, they said they were, man, I'm

592
00:29:26,080 --> 00:29:27,520
Speaker 1: just trying to get five people in the room so

593
00:29:27,560 --> 00:29:30,040
Speaker 1: they could talk. And yeah, I guess maybe it was

594
00:29:30,240 --> 00:29:32,120
Speaker 1: a mistake for me to reach out to my friend

595
00:29:32,120 --> 00:29:35,440
Speaker 1: who like runs the local Republican Party to ask if

596
00:29:35,440 --> 00:29:37,600
Speaker 1: they knew five people who wanted to be on camera

597
00:29:37,640 --> 00:29:38,240
Speaker 1: for CNN.

598
00:29:38,560 --> 00:29:40,960
Speaker 3: But yeah, it's also like what's the utility, Like, what

599
00:29:41,040 --> 00:29:43,800
Speaker 3: are we really gaining? It's not a scientific process. This

600
00:29:43,880 --> 00:29:46,560
Speaker 3: group of people doesn't necessarily represent or speak for the

601
00:29:46,640 --> 00:29:49,719
Speaker 3: average undecided voter. Now we know that they're maybe not

602
00:29:49,760 --> 00:29:52,440
Speaker 3: even undecided at all. Maybe they're just a political operative

603
00:29:52,600 --> 00:29:55,960
Speaker 3: who has a good, you know, makeup face. Right, I

604
00:29:56,000 --> 00:30:00,719
Speaker 3: don't understand what the average viewer is gaining fraze events.

605
00:30:01,040 --> 00:30:03,719
Speaker 1: Yeah, right, it's always meant to. I think I don't know,

606
00:30:03,760 --> 00:30:05,480
Speaker 1: like half the time when I see those panels or

607
00:30:05,480 --> 00:30:08,280
Speaker 1: people undecided, like like I said that, they seem to know,

608
00:30:08,880 --> 00:30:12,200
Speaker 1: they don't seem like low information voters, you know, like

609
00:30:12,280 --> 00:30:14,800
Speaker 1: and so then I'm like, I that's where I'm like,

610
00:30:14,840 --> 00:30:18,120
Speaker 1: these people sound like they're basically Democrats or Republicans who

611
00:30:18,160 --> 00:30:20,680
Speaker 1: are being like I don't know, but probably this.

612
00:30:21,080 --> 00:30:23,760
Speaker 3: Low information voters would be great. Like honestly, you put

613
00:30:23,800 --> 00:30:26,640
Speaker 3: people on there who first of all normalize being a

614
00:30:26,680 --> 00:30:28,840
Speaker 3: low information voter, make it okay to be like, hey,

615
00:30:28,840 --> 00:30:30,760
Speaker 3: I actually don't know about this or this issue is

616
00:30:30,760 --> 00:30:33,160
Speaker 3: confusing me, and I'd like a better explanation and then

617
00:30:33,520 --> 00:30:36,520
Speaker 3: present an opportunity for the mass media audience to also

618
00:30:36,640 --> 00:30:38,560
Speaker 3: receive that explanation, so that people it's the same way

619
00:30:38,560 --> 00:30:40,080
Speaker 3: as high school teacher might say, like, if you have

620
00:30:40,120 --> 00:30:42,560
Speaker 3: a question, somebody else probably has the same question. Please

621
00:30:42,600 --> 00:30:44,720
Speaker 3: ask your question. We could do that. I don't know

622
00:30:44,720 --> 00:30:45,760
Speaker 3: why we're doing this instead.

623
00:30:45,840 --> 00:30:48,960
Speaker 1: Yeah, no stupid questions. The thing that every good professor

624
00:30:49,000 --> 00:30:51,160
Speaker 1: will tell you or teacher who's like, no, no ask,

625
00:30:51,360 --> 00:30:53,719
Speaker 1: ask because you got to know, or else, yeah, you

626
00:30:53,760 --> 00:30:56,480
Speaker 1: ask weird stuff or learn weird stuff because you don't ask.

627
00:30:57,080 --> 00:31:00,200
Speaker 2: There's that sketch. And everybody's in la the John Malay

628
00:31:00,320 --> 00:31:03,440
Speaker 2: series where they're like doing a daily show style like

629
00:31:03,560 --> 00:31:07,800
Speaker 2: interview with a guy who's like saying really stupid shit

630
00:31:08,080 --> 00:31:11,280
Speaker 2: about like Trump and his support for Trump, and then

631
00:31:11,320 --> 00:31:13,800
Speaker 2: they like follow him home and he's like, yeah, no,

632
00:31:13,880 --> 00:31:16,680
Speaker 2: I'm stupid on TV for a living. That's like my thing.

633
00:31:16,880 --> 00:31:20,160
Speaker 2: I actually got interviewed by Borat a couple of years

634
00:31:20,160 --> 00:31:22,920
Speaker 2: ago that was a career highlight, and like he's just like,

635
00:31:23,200 --> 00:31:25,520
Speaker 2: I have this like room that I keep in my

636
00:31:25,600 --> 00:31:28,400
Speaker 2: house that looks like shit and has like a Confederate

637
00:31:28,400 --> 00:31:31,440
Speaker 2: flag up and then but like I keep that stuff separate,

638
00:31:31,520 --> 00:31:34,040
Speaker 2: Like I actually live with my family and the kids,

639
00:31:34,880 --> 00:31:38,920
Speaker 2: Like we have this nice house that Yeah, I feel like, yeah,

640
00:31:39,000 --> 00:31:42,080
Speaker 2: these are political operative essentially, like it's.

641
00:31:42,080 --> 00:31:44,080
Speaker 1: The real, the real version of.

642
00:31:44,000 --> 00:31:48,440
Speaker 2: That, except you know, obviously they are employed and like

643
00:31:48,480 --> 00:31:53,959
Speaker 2: working within these massive parties to convey what those parties

644
00:31:53,960 --> 00:31:54,600
Speaker 2: need to conveyed.

645
00:31:54,920 --> 00:31:56,480
Speaker 1: But I think it's also yeah, it sort of shows

646
00:31:56,520 --> 00:31:58,560
Speaker 1: too how we talk about how like a lot of

647
00:31:58,920 --> 00:32:02,160
Speaker 1: media outlets just aren't able to reckon with real issues

648
00:32:02,240 --> 00:32:04,120
Speaker 1: because of the fact that they're so entrenched in a

649
00:32:04,160 --> 00:32:06,120
Speaker 1: lot of these systems themselves. They're like, yeah, I think

650
00:32:06,160 --> 00:32:08,560
Speaker 1: this is this is good enough. Can we actually speak

651
00:32:08,600 --> 00:32:11,560
Speaker 1: objectively about that? I don't know, but you know this

652
00:32:11,680 --> 00:32:13,560
Speaker 1: is like I think, yeah, this is where a lot

653
00:32:13,600 --> 00:32:15,680
Speaker 1: of the media is falling short at a time when

654
00:32:15,720 --> 00:32:19,640
Speaker 1: people really need to have like the truth, which we

655
00:32:19,800 --> 00:32:20,800
Speaker 1: don't get all the time.

656
00:32:21,000 --> 00:32:23,280
Speaker 3: I also don't know what objective necessarily looks like, because

657
00:32:23,280 --> 00:32:25,400
Speaker 3: you're right, when you're deeply entrenched in the system, it's

658
00:32:25,520 --> 00:32:29,120
Speaker 3: very very hard to see it's equilibrium from the outside.

659
00:32:29,360 --> 00:32:33,160
Speaker 3: I'm a like devoted NPR listener. I go running in

660
00:32:33,160 --> 00:32:34,959
Speaker 3: the morning and I pop on Morning edition and I'm

661
00:32:35,040 --> 00:32:37,280
Speaker 3: very happy to hear it. But like, even on NPR,

662
00:32:37,440 --> 00:32:39,640
Speaker 3: there have been these few times where they're covering, you know,

663
00:32:39,720 --> 00:32:41,880
Speaker 3: a democratic event or Republican event, and they'll be like, well,

664
00:32:41,880 --> 00:32:43,760
Speaker 3: they talked about the economy, which is a bad issue

665
00:32:43,760 --> 00:32:44,760
Speaker 3: for Democrats, and I'm like.

666
00:32:44,920 --> 00:32:45,200
Speaker 1: Is it.

667
00:32:45,440 --> 00:32:48,360
Speaker 3: Yeah, it is actually like listen to Bill Clinton, a

668
00:32:48,400 --> 00:32:50,040
Speaker 3: person of whom I am not always a fan, but

669
00:32:50,040 --> 00:32:53,400
Speaker 3: with Bill Clinton, who like spelled out how great the

670
00:32:53,480 --> 00:32:56,640
Speaker 3: economy is built by Democrats over the last several decades

671
00:32:56,680 --> 00:32:58,840
Speaker 3: have been, and it's weird to hear that coming from NPR.

672
00:32:58,880 --> 00:33:01,360
Speaker 3: I think it's like their gesture towards equilibrium that doesn't

673
00:33:01,360 --> 00:33:02,760
Speaker 3: actually speak to truth.

674
00:33:03,120 --> 00:33:03,440
Speaker 1: Right, Yeah.

675
00:33:03,480 --> 00:33:05,800
Speaker 2: I think that's a problem with the mainstream media that

676
00:33:05,880 --> 00:33:09,959
Speaker 2: we'll also get to on you know, policing, and you know,

677
00:33:10,080 --> 00:33:13,040
Speaker 2: they they are these things that they just assume are

678
00:33:13,240 --> 00:33:17,000
Speaker 2: bad for progressives that everyone disagrees with, and they just

679
00:33:17,120 --> 00:33:21,440
Speaker 2: do a very surface level pass over those things, just

680
00:33:21,440 --> 00:33:25,040
Speaker 2: being like, yeah, well those things that everybody assumes about

681
00:33:25,080 --> 00:33:28,320
Speaker 2: progressive ideas around this are true, and like we just

682
00:33:28,400 --> 00:33:30,440
Speaker 2: have to take that into account as opposed to like

683
00:33:30,480 --> 00:33:35,360
Speaker 2: digging into some ways that they can be proven not true. Right, Yeah,

684
00:33:35,520 --> 00:33:38,800
Speaker 2: MPR drives me fucking crazy. All right, let's take a

685
00:33:38,880 --> 00:33:41,160
Speaker 2: quick break and we're going to come back and talk

686
00:33:41,160 --> 00:33:45,920
Speaker 2: about policing and Axon finally find out a little bit

687
00:33:45,960 --> 00:34:00,840
Speaker 2: more about this cool company named Axon, and we're we're back,

688
00:34:00,960 --> 00:34:03,280
Speaker 2: all right, So that you may have seen this story

689
00:34:03,560 --> 00:34:07,320
Speaker 2: that AI police reports are here to save the police

690
00:34:07,800 --> 00:34:13,520
Speaker 2: from doing police work. Basically, it's only a matter of time,

691
00:34:13,840 --> 00:34:15,759
Speaker 2: you know. It was only a matter of time until

692
00:34:16,160 --> 00:34:20,400
Speaker 2: the two of the shittier things on the planet, AI

693
00:34:20,920 --> 00:34:24,960
Speaker 2: and policing joined forces. In this case, the AI helps

694
00:34:25,000 --> 00:34:29,319
Speaker 2: them churn out recaps of incidents using bodycam footage, thus

695
00:34:29,360 --> 00:34:34,080
Speaker 2: sparing the officers from having to pen lengthy reports. And

696
00:34:34,120 --> 00:34:36,440
Speaker 2: the cops like in talking about it, the ones that

697
00:34:36,480 --> 00:34:39,520
Speaker 2: they're like interviewing for these puff pieces on the technology

698
00:34:39,560 --> 00:34:43,920
Speaker 2: are like, I can't write for shit, come basically an idiot,

699
00:34:44,280 --> 00:34:47,759
Speaker 2: and this thing made me like it. This is a

700
00:34:47,840 --> 00:34:50,359
Speaker 2: quote from one of the stories. It was a better

701
00:34:50,440 --> 00:34:52,640
Speaker 2: report than I could have ever written, and it was

702
00:34:52,680 --> 00:34:56,160
Speaker 2: one hundred percent accurate. It flowed better, better than I

703
00:34:56,200 --> 00:34:59,920
Speaker 2: could have ever written. That's first of all, it's supposed

704
00:34:59,960 --> 00:35:02,880
Speaker 2: to to be a rough draft, like it's not supposed

705
00:35:02,880 --> 00:35:06,040
Speaker 2: to replace the reports that you're writing the post. They

706
00:35:06,200 --> 00:35:08,800
Speaker 2: give you a rough draft that you then like work backwards.

707
00:35:09,040 --> 00:35:12,200
Speaker 1: Nah n, I gotta cut corners and how like it's

708
00:35:12,200 --> 00:35:14,360
Speaker 1: called draft one, by the way, that's the name of

709
00:35:14,400 --> 00:35:16,080
Speaker 1: the technology is draft one.

710
00:35:16,160 --> 00:35:19,200
Speaker 2: And he's like, this is the goddamn best thing, best

711
00:35:19,320 --> 00:35:21,000
Speaker 2: version of a report I've ever seen.

712
00:35:21,719 --> 00:35:25,879
Speaker 1: And how important are these like police reports in terms of,

713
00:35:26,200 --> 00:35:28,960
Speaker 1: you know, like when people intersect with the justice system,

714
00:35:29,280 --> 00:35:32,359
Speaker 1: Like is it how vital are these and how much

715
00:35:32,440 --> 00:35:35,840
Speaker 1: like how much room is there for you know, dubious

716
00:35:35,880 --> 00:35:38,760
Speaker 1: ship to pop into these kinds of police reports.

717
00:35:39,000 --> 00:35:41,000
Speaker 3: All right, to be clear, they're already largely made of

718
00:35:41,080 --> 00:35:43,239
Speaker 3: dubious shit, Like let's let's start from there.

719
00:35:43,280 --> 00:35:44,360
Speaker 1: Okay, there we go, thank you.

720
00:35:44,920 --> 00:35:48,080
Speaker 3: Things. These things vary really really wildly from place to place.

721
00:35:48,200 --> 00:35:51,160
Speaker 3: So when I started out as a public defender, I

722
00:35:51,200 --> 00:35:54,520
Speaker 3: was in Santa Clara County, California, in which the police

723
00:35:54,560 --> 00:35:58,240
Speaker 3: are trained to write reports. So when a thing happens

724
00:35:58,239 --> 00:35:59,799
Speaker 3: and the police are there, they'll like write down what

725
00:35:59,800 --> 00:36:01,839
Speaker 3: they saw, and then the second cop there will write

726
00:36:01,880 --> 00:36:03,279
Speaker 3: down what he saw, and then they talked to a

727
00:36:03,320 --> 00:36:04,759
Speaker 3: witness and they write down with the witness set and

728
00:36:04,800 --> 00:36:07,439
Speaker 3: all and all you get this packet, which is really

729
00:36:07,480 --> 00:36:10,320
Speaker 3: really helpful if we are going to believe that the

730
00:36:10,400 --> 00:36:13,239
Speaker 3: legal system is in any way about finding truth, right, Like,

731
00:36:13,280 --> 00:36:15,920
Speaker 3: you want to have detailed accounts from the people who

732
00:36:15,920 --> 00:36:18,040
Speaker 3: are there about what they what they heard, and what

733
00:36:18,040 --> 00:36:20,360
Speaker 3: they saw. I then went out to New York to

734
00:36:20,400 --> 00:36:23,320
Speaker 3: work at Bronx Defenders, and that's when I learned that

735
00:36:23,360 --> 00:36:27,839
Speaker 3: the NYPD is essentially like really really really good at

736
00:36:27,880 --> 00:36:31,120
Speaker 3: not writing stuff down. When you get an NYPD discovery packet,

737
00:36:31,120 --> 00:36:33,759
Speaker 3: it's like a whole bunch of pages, but all of

738
00:36:33,800 --> 00:36:36,200
Speaker 3: the pages have the same one line copy pasted on them.

739
00:36:36,239 --> 00:36:37,920
Speaker 3: It's like, at the time and place of occurrence the

740
00:36:37,960 --> 00:36:39,040
Speaker 3: incident did occur.

741
00:36:39,560 --> 00:36:44,440
Speaker 6: Yes, anything, and that is when the suspected perpetrator did

742
00:36:44,480 --> 00:36:48,640
Speaker 6: occur onto the occurrence, and it would happen that at

743
00:36:48,640 --> 00:36:53,960
Speaker 6: that moment in her jo graphical location in question heretofore.

744
00:36:54,200 --> 00:36:55,520
Speaker 2: It's just like it's ill.

745
00:36:56,120 --> 00:36:58,600
Speaker 3: Yeah, we could have a whole conversation about like the

746
00:36:58,640 --> 00:37:01,840
Speaker 3: police attraction to big words. They don't quite use it.

747
00:37:02,239 --> 00:37:05,120
Speaker 3: You want to have a great time as a defense attorney,

748
00:37:05,719 --> 00:37:09,040
Speaker 3: ask a cop on the stand what furtive means. They

749
00:37:09,080 --> 00:37:12,880
Speaker 3: love saying every furtive movements, but like, what what is

750
00:37:13,040 --> 00:37:18,160
Speaker 3: what is furtive to you? Now, indeed, this whole situation

751
00:37:18,320 --> 00:37:22,399
Speaker 3: is furtive. So when you get to a place where

752
00:37:22,480 --> 00:37:26,720
Speaker 3: essentially nothing is written down you you create a systemic problem,

753
00:37:26,840 --> 00:37:30,560
Speaker 3: which is, in order for me to get any information

754
00:37:30,640 --> 00:37:35,280
Speaker 3: to protect an accused person and protect their US constitutional rights,

755
00:37:35,840 --> 00:37:37,680
Speaker 3: I'm going to need to create a legal process to

756
00:37:37,719 --> 00:37:40,600
Speaker 3: find out more about what this CoP's claims actually are,

757
00:37:40,960 --> 00:37:43,360
Speaker 3: which means I may have to demand hearings that I

758
00:37:43,400 --> 00:37:45,640
Speaker 3: don't actually need, Like I might have to file suppression

759
00:37:45,719 --> 00:37:47,520
Speaker 3: hearings that I don't actually need, just to get the

760
00:37:47,560 --> 00:37:49,600
Speaker 3: cop on the witness stand just so I can cross

761
00:37:49,640 --> 00:37:52,359
Speaker 3: examine them about what the heck they're saying they saw

762
00:37:52,400 --> 00:37:55,919
Speaker 3: and did. So it's really really really inefficient, and it's

763
00:37:55,960 --> 00:37:58,040
Speaker 3: bad for truth and it's bad for justice, Like it's

764
00:37:58,160 --> 00:38:01,120
Speaker 3: very very bad for any simple of accuracy in the system,

765
00:38:01,120 --> 00:38:03,239
Speaker 3: and it causes massive delays. So all of this is

766
00:38:03,280 --> 00:38:07,680
Speaker 3: to say, bad discovery is a huge driver of our

767
00:38:07,719 --> 00:38:10,720
Speaker 3: system being inept at creating any semblance of truth.

768
00:38:11,160 --> 00:38:11,399
Speaker 1: Right.

769
00:38:11,920 --> 00:38:15,440
Speaker 3: It's also like you have to remember that police writing reports.

770
00:38:15,440 --> 00:38:18,200
Speaker 3: There's kind of a double edged sword here because police

771
00:38:18,239 --> 00:38:22,040
Speaker 3: get a ton of overtime out of writing reports if

772
00:38:22,040 --> 00:38:23,840
Speaker 3: they make an arrest at the end of their shift

773
00:38:24,160 --> 00:38:25,560
Speaker 3: and they get to sit at their desk for the

774
00:38:25,560 --> 00:38:28,560
Speaker 3: next three hours, like carefully inscribing documents with at the

775
00:38:28,560 --> 00:38:30,000
Speaker 3: time and place of occurrence the event.

776
00:38:29,840 --> 00:38:32,920
Speaker 1: Is right and over again, very cursive.

777
00:38:33,920 --> 00:38:35,920
Speaker 3: Yeah, they make a ton of overtime doing that. So

778
00:38:36,840 --> 00:38:39,640
Speaker 3: I think, I mean when I say a ton, I

779
00:38:39,680 --> 00:38:42,000
Speaker 3: mean like millions and millions. Wherever you are in the country,

780
00:38:42,000 --> 00:38:44,520
Speaker 3: you should google who the highest paid public employee in

781
00:38:44,520 --> 00:38:46,520
Speaker 3: your jurisdiction was, and there's like a decent chance it

782
00:38:46,560 --> 00:38:48,040
Speaker 3: was a cop who made a lot of overtime. A

783
00:38:48,040 --> 00:38:49,879
Speaker 3: few years ago. It was like a port authority cop

784
00:38:49,920 --> 00:38:50,680
Speaker 3: in New York City.

785
00:38:51,320 --> 00:38:51,800
Speaker 1: Wow.

786
00:38:51,920 --> 00:38:54,279
Speaker 3: Yeah, just like over a million bucks in overtime. And

787
00:38:55,640 --> 00:38:57,480
Speaker 3: so when I think about what AI would do to

788
00:38:57,520 --> 00:38:59,359
Speaker 3: this process, I think of a couple of things. One,

789
00:39:00,440 --> 00:39:03,680
Speaker 3: it's less accurate because it's not giving you the police

790
00:39:03,719 --> 00:39:06,600
Speaker 3: officer's impressions of what happened. It's giving you the AI's

791
00:39:06,640 --> 00:39:08,759
Speaker 3: impressions of what happened. And this is even assuming the

792
00:39:08,760 --> 00:39:12,160
Speaker 3: AI doesn't hallucinate, which, as we know, like AIS make

793
00:39:12,200 --> 00:39:15,799
Speaker 3: stuff up all the time. So yeah, like if you're

794
00:39:15,800 --> 00:39:18,320
Speaker 3: going to totally hand over your faith to a robot

795
00:39:18,360 --> 00:39:21,400
Speaker 3: to tell you what happened in a video and abandon

796
00:39:21,480 --> 00:39:24,560
Speaker 3: the idea that human perception is necessary to interpret what

797
00:39:24,600 --> 00:39:27,120
Speaker 3: happened in a video, you're also leaving by the side

798
00:39:27,120 --> 00:39:28,640
Speaker 3: of the road things that I might need to know

799
00:39:28,680 --> 00:39:31,879
Speaker 3: about the cops ability to perceive about what the cop

800
00:39:32,000 --> 00:39:35,440
Speaker 3: was focused on what Like For example, in a police report,

801
00:39:35,560 --> 00:39:38,759
Speaker 3: let's say the whole report is written about I don't

802
00:39:38,800 --> 00:39:42,319
Speaker 3: know somebody's way of driving a car in a DUI case,

803
00:39:42,960 --> 00:39:44,440
Speaker 3: and none of it's about the fact that when the

804
00:39:44,440 --> 00:39:48,960
Speaker 3: person got totally furtively furtively across you know, when they

805
00:39:49,000 --> 00:39:51,239
Speaker 3: get out of the car, maybe everything they did at

806
00:39:51,239 --> 00:39:53,800
Speaker 3: that point was fine. Maybe they're talking, fine, walking fine,

807
00:39:53,880 --> 00:39:57,160
Speaker 3: don't have any sort of symptoms of intoxication. If the

808
00:39:57,320 --> 00:40:00,120
Speaker 3: entire report is about the driving, then I can to

809
00:40:00,120 --> 00:40:02,880
Speaker 3: cross examine the cop on like why why didn't you

810
00:40:02,880 --> 00:40:06,719
Speaker 3: talk about what happened after that? Like their omissions can

811
00:40:06,760 --> 00:40:10,879
Speaker 3: be really really important to a jury to decide who's lying,

812
00:40:10,880 --> 00:40:14,000
Speaker 3: who's telling the truth. You take the human perception out

813
00:40:14,000 --> 00:40:16,920
Speaker 3: of that, and you take away this fundamental thing. Our

814
00:40:16,960 --> 00:40:19,640
Speaker 3: system is designed to have twelve people tell you if

815
00:40:19,680 --> 00:40:23,239
Speaker 3: another person is lying, right, twelve people can't tell you

816
00:40:23,320 --> 00:40:25,960
Speaker 3: if an ai is lying or hallucinating. I mean, it's

817
00:40:26,080 --> 00:40:29,840
Speaker 3: just it takes us even farther from the system having utility.

818
00:40:30,280 --> 00:40:32,279
Speaker 3: And I get that in the system we are going

819
00:40:32,320 --> 00:40:36,520
Speaker 3: to consistently prioritize the efficiency of punishment over the semblance

820
00:40:36,520 --> 00:40:39,919
Speaker 3: of truth. But especially with the involvement of Axon, which

821
00:40:39,920 --> 00:40:41,920
Speaker 3: has a grotesque history, I'd be more than happy to

822
00:40:42,600 --> 00:40:46,120
Speaker 3: yeah about this is like five alarm fire.

823
00:40:46,520 --> 00:40:49,200
Speaker 1: Wait, you're saying a company that used to be called Taser.

824
00:40:51,520 --> 00:40:54,360
Speaker 2: I like that they went from Taser obviously, like trying

825
00:40:54,360 --> 00:40:56,080
Speaker 2: to cover up the fact that they're the company that

826
00:40:56,120 --> 00:40:59,120
Speaker 2: invented the taser that for some reason as a negative

827
00:40:59,160 --> 00:41:03,680
Speaker 2: connotation with it to Axon is like so fucking aggressive.

828
00:41:04,160 --> 00:41:06,160
Speaker 3: Well, it's also you know, that's a nerve Axon is

829
00:41:06,160 --> 00:41:08,560
Speaker 3: what the electrical current runs down that stimulates the next

830
00:41:08,560 --> 00:41:11,200
Speaker 3: nerve cell. So it's still like we're gonna zapia. It's

831
00:41:11,280 --> 00:41:13,520
Speaker 3: just we're gonna zapA for people who took ap bio.

832
00:41:13,960 --> 00:41:16,520
Speaker 1: That's right exactly. It's like the version of using furtive.

833
00:41:16,560 --> 00:41:21,040
Speaker 1: They're like, what if we just clashed it up? I

834
00:41:21,080 --> 00:41:21,600
Speaker 1: would just.

835
00:41:21,840 --> 00:41:25,160
Speaker 2: Throw in the additional thing. And this might be like

836
00:41:25,640 --> 00:41:29,239
Speaker 2: not this might be a controversial statement, but I personally

837
00:41:29,480 --> 00:41:33,680
Speaker 2: don't want to get like it. So the CEO of Axon,

838
00:41:34,000 --> 00:41:37,160
Speaker 2: who is a company behind this AI technology, we'll talk

839
00:41:37,200 --> 00:41:39,840
Speaker 2: about other stuff there behind brag that the AI spares

840
00:41:39,880 --> 00:41:43,359
Speaker 2: cops from the tedious work of spending half their day

841
00:41:43,480 --> 00:41:48,680
Speaker 2: doing data entry. I don't want police to be like

842
00:41:49,120 --> 00:41:54,080
Speaker 2: out roaming the streets more with their guns, like ready

843
00:41:54,120 --> 00:41:57,680
Speaker 2: to get like suspicious about whatever comes across their plate

844
00:41:58,160 --> 00:42:01,160
Speaker 2: while they're sufficiently bored. Like I feel like this is

845
00:42:01,200 --> 00:42:04,640
Speaker 2: the job that we want to have a healthy amount

846
00:42:04,680 --> 00:42:09,280
Speaker 2: of like downtime where they're reflecting on what they've done

847
00:42:09,800 --> 00:42:13,480
Speaker 2: and like having to think about that and account for it.

848
00:42:13,880 --> 00:42:17,759
Speaker 2: And this technology seems to be designed to like what

849
00:42:17,800 --> 00:42:21,680
Speaker 2: if the police were like even more gas and less

850
00:42:21,840 --> 00:42:24,400
Speaker 2: breaks like built into it. What if it was just

851
00:42:24,480 --> 00:42:27,040
Speaker 2: more they don't really even have to think about it

852
00:42:27,040 --> 00:42:29,759
Speaker 2: because the machine's there to like just document what they

853
00:42:29,800 --> 00:42:33,439
Speaker 2: did to out you know, yeah, you know, exa more

854
00:42:33,560 --> 00:42:38,120
Speaker 2: frictionless policing aka just like out there fucking shit up

855
00:42:38,320 --> 00:42:39,120
Speaker 2: more of the time.

856
00:42:39,239 --> 00:42:40,920
Speaker 1: I think the other thing that's really interesting too, is

857
00:42:41,160 --> 00:42:44,360
Speaker 1: like to your point Emily. You know, the overtime is

858
00:42:44,400 --> 00:42:46,640
Speaker 1: where a lot of budgets go and a lot of

859
00:42:46,680 --> 00:42:49,080
Speaker 1: people they make their they make that money. We're like,

860
00:42:49,080 --> 00:42:51,560
Speaker 1: how does that cop have that fucking car and like

861
00:42:51,600 --> 00:42:53,919
Speaker 1: a boat and all this other stuff. It's like, yeah, dude,

862
00:42:53,960 --> 00:42:57,760
Speaker 1: the overtime's wacky that they're never like this will actually

863
00:42:57,760 --> 00:43:00,520
Speaker 1: help cut down on costs. They're more just like, dudes,

864
00:43:00,520 --> 00:43:02,440
Speaker 1: it's gonna help the cops, dude, so they don't have

865
00:43:02,480 --> 00:43:04,920
Speaker 1: to be bored at work, you know, and like you

866
00:43:05,040 --> 00:43:06,839
Speaker 1: think the way to sell it to people who might

867
00:43:06,880 --> 00:43:09,800
Speaker 1: be more progressive, like guess what, man, this could actually

868
00:43:09,840 --> 00:43:11,440
Speaker 1: save a lot of money because now they don't have

869
00:43:11,480 --> 00:43:14,520
Speaker 1: the time to do you know, claim as much over time.

870
00:43:14,680 --> 00:43:16,879
Speaker 1: But again that's that's part of the appeal. So they'll

871
00:43:16,920 --> 00:43:18,719
Speaker 1: just be like, no, man, it just makes their job

872
00:43:18,760 --> 00:43:22,440
Speaker 1: easier so they can keep you the citizen safe, all right,

873
00:43:22,600 --> 00:43:23,200
Speaker 1: next question.

874
00:43:23,880 --> 00:43:27,520
Speaker 3: You're right though, it's also sort of exposing this terrible choice. Right,

875
00:43:27,880 --> 00:43:30,760
Speaker 3: we have set up policing policy so that the vast

876
00:43:30,800 --> 00:43:34,160
Speaker 3: majority of police time is spent on things that most

877
00:43:34,200 --> 00:43:36,799
Speaker 3: people don't actually care about. So when you ask them people,

878
00:43:36,800 --> 00:43:39,560
Speaker 3: what are they like scared of it's like burglary, robbery,

879
00:43:39,600 --> 00:43:42,239
Speaker 3: sexual assault, murder. And when you look at how police

880
00:43:42,239 --> 00:43:43,880
Speaker 3: spend their time, the vast majority of it is on

881
00:43:44,000 --> 00:43:47,279
Speaker 3: like noise complaints and unfounded calls and like somebody was

882
00:43:47,280 --> 00:43:50,719
Speaker 3: peeing outside and trespassing, and sometimes on what I sort

883
00:43:50,719 --> 00:43:52,680
Speaker 3: of think of as like police manufactured crime, which is

884
00:43:52,719 --> 00:43:55,880
Speaker 3: like convincing someone with a substance use problem to score

885
00:43:55,960 --> 00:43:58,520
Speaker 3: some drugs and also score for the undercover who will

886
00:43:58,520 --> 00:44:02,440
Speaker 3: then arrest them for a felony. And the reason we

887
00:44:02,440 --> 00:44:05,239
Speaker 3: don't want them on the street more is because they

888
00:44:05,239 --> 00:44:08,200
Speaker 3: are out there on the street, armed, dangerous, and not

889
00:44:08,480 --> 00:44:11,520
Speaker 3: investigating the things that people really care about. If you

890
00:44:11,560 --> 00:44:13,680
Speaker 3: look at clearance rates, a lot of people don't know

891
00:44:13,719 --> 00:44:16,000
Speaker 3: what clearance rates are, but it's the rate at which

892
00:44:16,040 --> 00:44:19,080
Speaker 3: police are able to close cases. And in any jurisdiction,

893
00:44:19,120 --> 00:44:20,719
Speaker 3: you can search for your local clearance rates. You'd be like,

894
00:44:20,719 --> 00:44:23,720
Speaker 3: all right, how many rape cases are my local police

895
00:44:23,800 --> 00:44:24,640
Speaker 3: even closing?

896
00:44:25,080 --> 00:44:28,640
Speaker 1: Got most places thirteen.

897
00:44:28,320 --> 00:44:33,520
Speaker 3: To twenty percent. This is a whole other feminist sobec.

898
00:44:33,640 --> 00:44:37,440
Speaker 2: Ninety is totally across the entire country. There's like at

899
00:44:37,520 --> 00:44:40,439
Speaker 2: least ninety that they've closed in the past decade.

900
00:44:40,840 --> 00:44:43,759
Speaker 3: Seriously, and that's because it's a policy choice. It's a

901
00:44:43,840 --> 00:44:47,200
Speaker 3: choice from police leadership about what they're going to dedicate

902
00:44:47,239 --> 00:44:49,279
Speaker 3: resources to. And if, yeah, if the answer was, okay,

903
00:44:49,320 --> 00:44:52,719
Speaker 3: they're not going to spend their time writing trespassing reports,

904
00:44:53,120 --> 00:44:56,680
Speaker 3: but instead, we're going to dedicate real efforts to how

905
00:44:56,719 --> 00:45:01,480
Speaker 3: about wage theft or large scale pollution of poisoning entire towns.

906
00:45:01,480 --> 00:45:02,920
Speaker 3: We're going to set the cops on that. If they

907
00:45:02,960 --> 00:45:05,600
Speaker 3: were going to investigate crimes of the powerful against the

908
00:45:05,680 --> 00:45:08,880
Speaker 3: citizenry instead of writing reports, I might feel differently about it,

909
00:45:08,960 --> 00:45:09,919
Speaker 3: But I don't think that's the plant.

910
00:45:10,800 --> 00:45:13,400
Speaker 2: Yeah, never has been. Yeah, But I do just want

911
00:45:13,440 --> 00:45:15,160
Speaker 2: to get a little bit more into the history of axons.

912
00:45:15,200 --> 00:45:19,640
Speaker 2: So they were taser. They made their initial money with

913
00:45:20,160 --> 00:45:25,200
Speaker 2: selling tasers, and then bodycams. When that became the solution

914
00:45:25,480 --> 00:45:30,120
Speaker 2: to police brutality corruption, they went with body cams and

915
00:45:30,320 --> 00:45:34,120
Speaker 2: they basically have a monopoly for which they've been sued.

916
00:45:34,400 --> 00:45:37,160
Speaker 2: They made four hundred and sixty one million dollars in

917
00:45:37,200 --> 00:45:41,959
Speaker 2: the first quarter of twenty twenty four alone. They're also

918
00:45:42,000 --> 00:45:45,080
Speaker 2: the same company that made headlines for endeavoring to solve

919
00:45:45,239 --> 00:45:49,960
Speaker 2: school shootings with taser equipped drones. That plan was paused

920
00:45:50,239 --> 00:45:53,960
Speaker 2: when the majority of Axon's ethics board resigned in protests.

921
00:45:54,320 --> 00:45:58,319
Speaker 2: But I think probably the most relevant and also, like

922
00:45:58,360 --> 00:46:03,600
Speaker 2: I mentioned, their CEO gets speeches via remote iPad, I

923
00:46:03,680 --> 00:46:07,200
Speaker 2: use an avatar man glued to the front of the

924
00:46:07,360 --> 00:46:08,360
Speaker 2: motorcycle helmet.

925
00:46:08,640 --> 00:46:12,160
Speaker 3: They also created excited delirium in parts, so alls that

926
00:46:12,200 --> 00:46:12,799
Speaker 3: are due to that.

927
00:46:13,239 --> 00:46:15,120
Speaker 2: Yes, So I wanted to talk about that because I

928
00:46:15,160 --> 00:46:18,880
Speaker 2: also think like that feels very relevant to this because

929
00:46:18,920 --> 00:46:23,719
Speaker 2: this is them getting involved in police narrative and how

930
00:46:23,760 --> 00:46:28,600
Speaker 2: police justify what they're doing, and they were involved. You

931
00:46:28,960 --> 00:46:31,960
Speaker 2: actually have a great video on this on your Twitter,

932
00:46:32,120 --> 00:46:36,759
Speaker 2: Emily where you talk about how about their role in

933
00:46:36,800 --> 00:46:42,160
Speaker 2: the creation of and the proliferation of the term excited delirium,

934
00:46:42,560 --> 00:46:45,040
Speaker 2: which is something we covered a while back, but I

935
00:46:45,080 --> 00:46:47,880
Speaker 2: think it's always worth kind of refreshing people's memory of

936
00:46:47,880 --> 00:46:49,880
Speaker 2: what is excited delirium.

937
00:46:50,280 --> 00:46:53,319
Speaker 3: So excited delirium is a made up medical diagnosis that

938
00:46:53,440 --> 00:46:57,040
Speaker 3: was originally invented in a sort of predictably racist way

939
00:46:57,120 --> 00:47:00,520
Speaker 3: in Miami many decades ago, where a doctor claimed that

940
00:47:00,760 --> 00:47:03,120
Speaker 3: people were dying of excited delirium, the sort of state

941
00:47:03,160 --> 00:47:06,040
Speaker 3: of mania that caused them to behave really erradically and

942
00:47:06,080 --> 00:47:09,600
Speaker 3: aggressively and dangerously and then they perish, They just expire.

943
00:47:10,200 --> 00:47:12,680
Speaker 3: And it turned out that many of the women who

944
00:47:12,680 --> 00:47:15,440
Speaker 3: are originally alleged to have excited delirium had actually been

945
00:47:15,520 --> 00:47:19,320
Speaker 3: killed by a serial killer. But this idea that people

946
00:47:19,320 --> 00:47:22,600
Speaker 3: could become so worked up that they are dangerous and

947
00:47:22,640 --> 00:47:27,440
Speaker 3: then they die was seized upon by police because in

948
00:47:27,560 --> 00:47:30,279
Speaker 3: police encounters where there is a need to justify use

949
00:47:30,280 --> 00:47:32,920
Speaker 3: of force, it is very useful for them to claim

950
00:47:32,960 --> 00:47:36,320
Speaker 3: that the person they used force against was dangerously worked

951
00:47:36,400 --> 00:47:39,080
Speaker 3: up and had this medical thing where they became a

952
00:47:39,200 --> 00:47:41,840
Speaker 3: risk to everybody's safety and they had to be taste

953
00:47:41,920 --> 00:47:45,240
Speaker 3: and then oh, when they died from a heart attack,

954
00:47:45,360 --> 00:47:48,120
Speaker 3: it wasn't because they got a massive vault of electricity.

955
00:47:48,680 --> 00:47:52,320
Speaker 3: It was because they died of excited delirium. So excited delirium,

956
00:47:52,320 --> 00:47:54,440
Speaker 3: which is not accepted by the way, by doctors, like

957
00:47:54,520 --> 00:47:57,839
Speaker 3: medical associations are like, that's totally not a thing. Psychiatrical

958
00:47:58,120 --> 00:47:59,719
Speaker 3: associations the same deal, not a thing.

959
00:48:00,080 --> 00:48:03,600
Speaker 2: But there was that one panel that is a way

960
00:48:03,600 --> 00:48:05,200
Speaker 2: I feel like we're good here. No need to look

961
00:48:05,200 --> 00:48:07,600
Speaker 2: into the panel or who funded that. I think we're good.

962
00:48:07,680 --> 00:48:09,440
Speaker 3: Yeah, no need to look at how many doctors on

963
00:48:09,480 --> 00:48:11,480
Speaker 3: the panel were put there by Axon. No need to,

964
00:48:11,560 --> 00:48:13,439
Speaker 3: no need to connect this to no need to also

965
00:48:13,480 --> 00:48:16,359
Speaker 3: think about how much this reduces Axon's liability, right, because

966
00:48:16,360 --> 00:48:18,479
Speaker 3: if deaths are caused by excited delirium and not caused

967
00:48:18,480 --> 00:48:20,640
Speaker 3: by a taser, they're not going to have they're not

968
00:48:20,640 --> 00:48:24,320
Speaker 3: gonna be able to be successfully sued. But it's actually

969
00:48:24,360 --> 00:48:27,319
Speaker 3: become a serious epidemic in this country of police using

970
00:48:27,360 --> 00:48:30,360
Speaker 3: excited delirium to justify it not only taser use of force,

971
00:48:30,840 --> 00:48:33,520
Speaker 3: but the use of paramedics as a weapon, like we

972
00:48:33,560 --> 00:48:35,919
Speaker 3: saw in the Elijah McLain case where the police had

973
00:48:35,960 --> 00:48:39,759
Speaker 3: paramedics inject Elijah McLain with a lethal dose of sedatives

974
00:48:40,280 --> 00:48:43,759
Speaker 3: under this false diagnosis of excited delirium, so that that

975
00:48:44,000 --> 00:48:49,319
Speaker 3: seed that Axon planted in eight in legitimizing this diagnosis

976
00:48:49,800 --> 00:48:52,400
Speaker 3: has now caused many, many deaths and is continuing to

977
00:48:52,440 --> 00:48:53,879
Speaker 3: cause deaths around the country.

978
00:48:53,920 --> 00:48:56,560
Speaker 1: Right because like they'll hit people like ketamine and stuff,

979
00:48:56,640 --> 00:48:58,800
Speaker 1: and then like they like I was reading a statistic

980
00:48:58,840 --> 00:49:00,160
Speaker 1: that a lot of those people end up having be

981
00:49:00,239 --> 00:49:03,719
Speaker 1: intubated because it's so severe, and they're like, I don't know, man, wait,

982
00:49:03,920 --> 00:49:05,560
Speaker 1: the guy was excited. I mean then they also said

983
00:49:05,560 --> 00:49:07,640
Speaker 1: the same thing about George Floyd too. Yeah, that was

984
00:49:07,680 --> 00:49:09,759
Speaker 1: like very early on, like it's excited. I don't know

985
00:49:09,760 --> 00:49:12,360
Speaker 1: what you want to say, man, let's let's just move on.

986
00:49:12,680 --> 00:49:16,360
Speaker 1: So then like so action in for them, it's just

987
00:49:16,440 --> 00:49:18,440
Speaker 1: more because they're sort of like, hey, we love what

988
00:49:18,480 --> 00:49:20,960
Speaker 1: you guys do. Let's help out because this also helps

989
00:49:21,080 --> 00:49:23,319
Speaker 1: justify the use of our products. Like is that sort

990
00:49:23,360 --> 00:49:26,600
Speaker 1: of like their main motivation and like sort of pushing

991
00:49:26,640 --> 00:49:30,320
Speaker 1: the excited delirium sort of craze along.

992
00:49:30,760 --> 00:49:32,520
Speaker 3: I think it's also a legal shield. I mean, if

993
00:49:32,520 --> 00:49:34,399
Speaker 3: I'm going to let's say I lose a loved one

994
00:49:34,400 --> 00:49:36,640
Speaker 3: who was taste and I want a sue Taser for

995
00:49:36,719 --> 00:49:39,080
Speaker 3: marketing a product as non lethal that was in fact

996
00:49:39,160 --> 00:49:42,440
Speaker 3: lethal to my loved one. And they say, the medical

997
00:49:42,480 --> 00:49:45,160
Speaker 3: examiner certificate doesn't say that your loved one died of

998
00:49:45,200 --> 00:49:49,160
Speaker 3: an electric shock. The medical examiner certificate says excited delirium.

999
00:49:49,200 --> 00:49:51,440
Speaker 3: So you can't actually get money from us in a

1000
00:49:51,480 --> 00:49:56,080
Speaker 3: civil suit or settlement. So it's it's covering them from

1001
00:49:56,120 --> 00:50:00,600
Speaker 3: being financially responsible for deaths they cause. The same for police.

1002
00:50:00,640 --> 00:50:02,839
Speaker 3: I mean, if the police are getting. The police could

1003
00:50:02,840 --> 00:50:05,239
Speaker 3: be sued in the same case, right, the taser for

1004
00:50:05,239 --> 00:50:07,920
Speaker 3: the device to the police for the action. But either way,

1005
00:50:07,960 --> 00:50:10,800
Speaker 3: if the Emmys certificate says this person died of excited delirium,

1006
00:50:10,800 --> 00:50:12,000
Speaker 3: it's a liability.

1007
00:50:11,520 --> 00:50:16,640
Speaker 1: Shield, right, Yeah, and disproportionately applied to black men. Yes,

1008
00:50:16,680 --> 00:50:17,279
Speaker 1: a lot of the time.

1009
00:50:17,880 --> 00:50:19,879
Speaker 2: It's a way for the police to justify that why

1010
00:50:19,920 --> 00:50:22,360
Speaker 2: they why they're scared.

1011
00:50:21,800 --> 00:50:25,560
Speaker 3: It's really reliant on racist tropes right on the adultification

1012
00:50:25,640 --> 00:50:27,840
Speaker 3: of black children. First of all, this this child is

1013
00:50:27,880 --> 00:50:30,719
Speaker 3: a risk to me because I'm perceiving this child as

1014
00:50:30,760 --> 00:50:35,640
Speaker 3: older because of racial bias, but also the racist myth

1015
00:50:35,840 --> 00:50:39,640
Speaker 3: of dangerousness of black men in an excited state. I mean,

1016
00:50:39,640 --> 00:50:44,960
Speaker 3: this is totally playing on long term American racist tropes

1017
00:50:45,520 --> 00:50:48,160
Speaker 3: and sanitizing them with a fake medical diagnosis.

1018
00:50:48,480 --> 00:50:52,080
Speaker 1: Right. Yeah. It sounds like something you'd get at like

1019
00:50:52,160 --> 00:50:57,240
Speaker 1: Willy Wonka's chocolate factory excited delirium for you and you're.

1020
00:50:57,120 --> 00:51:00,279
Speaker 2: Like, oh, yeah, and that's why, and that's why we

1021
00:51:00,360 --> 00:51:02,000
Speaker 2: had to drown him in the chocolate river.

1022
00:51:02,320 --> 00:51:03,280
Speaker 1: Yes, exactly.

1023
00:51:03,960 --> 00:51:06,920
Speaker 2: Just a couple more details about is it axin Axon.

1024
00:51:07,200 --> 00:51:10,560
Speaker 2: I'm gonna call them Axon because that feels sufficiently violent

1025
00:51:10,800 --> 00:51:15,160
Speaker 2: and sinister. Their CEO his like founding story. I just

1026
00:51:15,320 --> 00:51:19,359
Speaker 2: like founding stories for companies and CEOs because they are

1027
00:51:20,400 --> 00:51:24,279
Speaker 2: like the most full of shit things in America and

1028
00:51:24,400 --> 00:51:26,520
Speaker 2: like most widely believed people, like it was founded in

1029
00:51:26,560 --> 00:51:29,520
Speaker 2: the like everything was founded in a garage. No, it

1030
00:51:29,600 --> 00:51:33,759
Speaker 2: wasn't founded in like their rich dad's second home that

1031
00:51:34,680 --> 00:51:38,280
Speaker 2: was behind you his first mansion. But anyways, the CEO

1032
00:51:38,600 --> 00:51:41,360
Speaker 2: repeatedly told the story that he started the company because

1033
00:51:41,760 --> 00:51:44,400
Speaker 2: his two high school friends were shot and killed. He

1034
00:51:44,440 --> 00:51:47,719
Speaker 2: played high school football with them. It's just like two

1035
00:51:47,840 --> 00:51:51,439
Speaker 2: guys that he like knew about who were like four

1036
00:51:51,520 --> 00:51:54,000
Speaker 2: or five years older than him. Yeah, they weren't even

1037
00:51:54,040 --> 00:51:55,920
Speaker 2: he never went to high school with him. Yeah, but

1038
00:51:56,080 --> 00:51:58,239
Speaker 2: like it's just you know, for him, he's like and

1039
00:51:58,400 --> 00:52:02,040
Speaker 2: man like, that's the closest that I like, kids who

1040
00:52:02,080 --> 00:52:05,400
Speaker 2: were at your high school before you is like such

1041
00:52:05,400 --> 00:52:09,280
Speaker 2: a stretch to be like. That's also they The workplace

1042
00:52:09,320 --> 00:52:14,120
Speaker 2: culture includes group tasings and tattooing sessions in which employees

1043
00:52:14,200 --> 00:52:17,880
Speaker 2: are inked with corporate insignia and by the way, the

1044
00:52:18,040 --> 00:52:21,839
Speaker 2: drone thing. While they were like all right fine when

1045
00:52:21,840 --> 00:52:25,640
Speaker 2: their entire ethics board resigned. They did buy a drone

1046
00:52:25,680 --> 00:52:29,680
Speaker 2: company recently, so there it seems like what will their

1047
00:52:29,680 --> 00:52:34,440
Speaker 2: mouth says, all right, fine, god, their money is saying

1048
00:52:34,520 --> 00:52:41,080
Speaker 2: that their full full steam ahead on the Taser drones front. Yeah,

1049
00:52:41,120 --> 00:52:45,480
Speaker 2: so just all sorts of wild shit there, like truly

1050
00:52:45,800 --> 00:52:50,040
Speaker 2: the most dystopian, like a bunch of tattoo branded like

1051
00:52:50,440 --> 00:52:56,040
Speaker 2: corporate people, guy with motorcycle helmet, iPad face.

1052
00:52:56,080 --> 00:53:00,600
Speaker 1: Like ring, like knowing murder victims. Yeah, it's all I mean,

1053
00:53:00,719 --> 00:53:03,640
Speaker 1: they're in a way, it all does feel very appropriate.

1054
00:53:03,680 --> 00:53:05,719
Speaker 1: That then it's like and now that's what I like

1055
00:53:05,760 --> 00:53:07,879
Speaker 1: to do, is help other people lie about stuff and

1056
00:53:08,400 --> 00:53:11,120
Speaker 1: I just make money. And the institutional investment in this

1057
00:53:11,160 --> 00:53:13,600
Speaker 1: company is wild too. Oh yeah, it's like because they

1058
00:53:13,640 --> 00:53:15,799
Speaker 1: know they're like, wait, how much they're makeing Q one? Okay?

1059
00:53:15,880 --> 00:53:17,560
Speaker 1: Yeah yeah, But just.

1060
00:53:17,440 --> 00:53:20,240
Speaker 2: Generally I just want to like kind of get your take, Emily.

1061
00:53:20,320 --> 00:53:24,040
Speaker 2: There was recently this New York Times article about a

1062
00:53:24,800 --> 00:53:28,280
Speaker 2: The headline is what a group opposed to police blow

1063
00:53:28,320 --> 00:53:31,919
Speaker 2: the whistle on its founder, And it was like this

1064
00:53:32,440 --> 00:53:36,719
Speaker 2: AI app that was like, We're gonna like create an

1065
00:53:36,719 --> 00:53:41,120
Speaker 2: alternative to the police by taking people's you know, complaints

1066
00:53:41,320 --> 00:53:44,799
Speaker 2: and routing them to like some of these other police alternatives,

1067
00:53:45,120 --> 00:53:48,799
Speaker 2: it turned out to be like the founder just like

1068
00:53:48,840 --> 00:53:50,680
Speaker 2: didn't have the ability to pull it off and was

1069
00:53:50,719 --> 00:53:54,400
Speaker 2: spending some of the money on like clothing and vacations

1070
00:53:54,480 --> 00:53:57,719
Speaker 2: that you know, like that I'm drifting. Sure, you can

1071
00:53:57,800 --> 00:54:05,279
Speaker 2: find scams in any nonprofit like category, but the way

1072
00:54:05,320 --> 00:54:07,520
Speaker 2: the New York Times writes about it and like frames

1073
00:54:07,600 --> 00:54:13,400
Speaker 2: this article is that whole argument of like, oh, you

1074
00:54:13,440 --> 00:54:15,919
Speaker 2: think the police are bad at their jobs, Let's see

1075
00:54:15,920 --> 00:54:19,600
Speaker 2: what you say when you're being robbed, is like basically

1076
00:54:20,680 --> 00:54:24,279
Speaker 2: the whole thesis of the argument, And what it comes

1077
00:54:24,320 --> 00:54:26,720
Speaker 2: down to if you read the article is like one

1078
00:54:26,800 --> 00:54:29,160
Speaker 2: person on the team is like, I didn't want to

1079
00:54:29,160 --> 00:54:31,359
Speaker 2: turn him over to the police because I like he's

1080
00:54:31,360 --> 00:54:34,879
Speaker 2: a black man and I fear what would happen to him.

1081
00:54:35,200 --> 00:54:37,879
Speaker 2: And then another person is like, yeah, but I did

1082
00:54:38,040 --> 00:54:41,440
Speaker 2: turn him over to the attorney general because I know

1083
00:54:41,600 --> 00:54:44,239
Speaker 2: that like you don't usually call the police on white

1084
00:54:44,239 --> 00:54:47,480
Speaker 2: collar crime because they won't do shit. So anyways, like

1085
00:54:47,560 --> 00:54:51,000
Speaker 2: the attorney general is working on investigation. It might be civil,

1086
00:54:51,040 --> 00:54:54,319
Speaker 2: it might be criminal, But the way they framed it

1087
00:54:54,360 --> 00:54:58,720
Speaker 2: is so much like based on this bad faith reading

1088
00:54:59,040 --> 00:55:02,480
Speaker 2: of any criticism of the police, and it just feels

1089
00:55:02,640 --> 00:55:06,279
Speaker 2: generally like the tone of the mainstream media and the

1090
00:55:07,080 --> 00:55:11,720
Speaker 2: Democratic Party recently is like, boy, those protests in twenty

1091
00:55:11,800 --> 00:55:16,560
Speaker 2: twenty were you know, unpopular. Let's never fight again, babe

1092
00:55:16,840 --> 00:55:20,880
Speaker 2: to the police, and it's like, I don't know, it's

1093
00:55:20,960 --> 00:55:26,440
Speaker 2: just so fucking frustrating. Like and meanwhile, police killings haven't

1094
00:55:26,640 --> 00:55:30,080
Speaker 2: have just like stayed the same or gone up, so like,

1095
00:55:30,920 --> 00:55:33,359
Speaker 2: where where are we with this? Like what you know,

1096
00:55:33,640 --> 00:55:36,560
Speaker 2: there were some programs that were funded that like worked

1097
00:55:36,600 --> 00:55:40,799
Speaker 2: really well, Like Denver had a control trial of a

1098
00:55:40,840 --> 00:55:43,520
Speaker 2: program that provides housing subsidies to people at risk of

1099
00:55:43,520 --> 00:55:47,120
Speaker 2: homelessness and found a forty percent reduction and arrests. Like

1100
00:55:47,400 --> 00:55:51,080
Speaker 2: there's all these cool examples they get like dashed off

1101
00:55:51,120 --> 00:55:53,440
Speaker 2: really quickly in a New York Times article that like

1102
00:55:53,560 --> 00:55:57,560
Speaker 2: has a counterpoint for everything that might suggest that like

1103
00:55:58,000 --> 00:56:01,759
Speaker 2: there could be alternatives to our fucking terrible idea of

1104
00:56:01,800 --> 00:56:04,840
Speaker 2: a system that if you've been to any other country

1105
00:56:04,880 --> 00:56:06,640
Speaker 2: in the world, you're like, oh, wow, why do we

1106
00:56:06,640 --> 00:56:08,480
Speaker 2: do it the way we do it? But yeah, I'm

1107
00:56:08,480 --> 00:56:10,239
Speaker 2: just curious to hear your thoughts on like where we're

1108
00:56:10,280 --> 00:56:12,879
Speaker 2: at in our conversation in the mainstream.

1109
00:56:13,520 --> 00:56:17,400
Speaker 3: So first of all, we're really lucky in this one way,

1110
00:56:17,719 --> 00:56:21,440
Speaker 3: which is that we are overrun with cool solutions. Like

1111
00:56:21,480 --> 00:56:23,640
Speaker 3: I'm writing a book right now, Like my book it's

1112
00:56:23,640 --> 00:56:25,800
Speaker 3: coming out in twenty twenty six. It's going to be

1113
00:56:25,840 --> 00:56:28,799
Speaker 3: a layperson's guide to the criminal legal system in all

1114
00:56:28,880 --> 00:56:33,480
Speaker 3: of its horribleness, and also solutions, Like I'm going to

1115
00:56:33,560 --> 00:56:35,600
Speaker 3: spend two thirds of the book on problems, and then

1116
00:56:35,600 --> 00:56:37,759
Speaker 3: I'm going to present a whole bunch of solutions. I

1117
00:56:37,800 --> 00:56:40,520
Speaker 3: had originally intended to write one chapter on solutions. I'm

1118
00:56:41,560 --> 00:56:43,480
Speaker 3: now at like page eighty eight of one hundred of

1119
00:56:43,480 --> 00:56:45,800
Speaker 3: all of these solutions, because there are just so many

1120
00:56:46,640 --> 00:56:50,759
Speaker 3: fantastic things happening that have better data than the status quo,

1121
00:56:51,040 --> 00:56:55,399
Speaker 3: Like we don't have data strongly suggesting that police are

1122
00:56:55,960 --> 00:57:02,000
Speaker 3: a feasible preventative mechanism. Police can disappear problems. They can

1123
00:57:02,120 --> 00:57:04,319
Speaker 3: take people and put them in spaces where they are

1124
00:57:04,360 --> 00:57:06,719
Speaker 3: no longer visible to the general public and where they

1125
00:57:06,760 --> 00:57:08,960
Speaker 3: may be then violently harmed in ways that make them

1126
00:57:09,000 --> 00:57:10,920
Speaker 3: more likely to engage in crime in the future. So

1127
00:57:11,239 --> 00:57:13,640
Speaker 3: police may be sort of like temporarily making a problem

1128
00:57:13,719 --> 00:57:15,680
Speaker 3: disappear in a way that long term makes it worse.

1129
00:57:15,800 --> 00:57:17,840
Speaker 3: We have that data, get a ton of data on

1130
00:57:17,960 --> 00:57:20,640
Speaker 3: like the Star program in Denver, or cahoots in Oregon,

1131
00:57:20,800 --> 00:57:23,160
Speaker 3: or you know, other alternatives to police popping up around

1132
00:57:23,200 --> 00:57:25,919
Speaker 3: the country. Massive public support for this. Most voters would

1133
00:57:25,960 --> 00:57:30,760
Speaker 3: love to have mental health first responders, and actually most cops,

1134
00:57:30,800 --> 00:57:32,160
Speaker 3: if you ask them, are like, yes, I would like

1135
00:57:32,200 --> 00:57:34,160
Speaker 3: to also no longer be treated like I'm a trained

1136
00:57:34,200 --> 00:57:36,640
Speaker 3: social worker, because I'm not one, and I would like

1137
00:57:36,680 --> 00:57:39,120
Speaker 3: that to not be part of my job. What's really

1138
00:57:39,400 --> 00:57:43,440
Speaker 3: what bugs me about the perspective you just described right,

1139
00:57:43,440 --> 00:57:45,040
Speaker 3: which is like, oh, these people who don't want to

1140
00:57:45,120 --> 00:57:49,280
Speaker 3: use the police, what happens when they need the police? Well, okay,

1141
00:57:50,600 --> 00:57:54,000
Speaker 3: when we on election day hear from voters that they

1142
00:57:54,000 --> 00:57:56,840
Speaker 3: are scared to go to their local polling place because

1143
00:57:56,840 --> 00:58:00,760
Speaker 3: there are proud boys outside the polling place intimidating buttential voters.

1144
00:58:01,760 --> 00:58:06,120
Speaker 3: No one is saying, well, it's your problem if you

1145
00:58:06,120 --> 00:58:07,960
Speaker 3: don't like the proud boys, don't you just have a

1146
00:58:08,000 --> 00:58:09,480
Speaker 3: way to work out. No, we say, okay, this is

1147
00:58:09,520 --> 00:58:12,480
Speaker 3: a problem because people have a legitimate fear. It is

1148
00:58:12,480 --> 00:58:16,240
Speaker 3: a legitimate fear of an organized effort, which is intimidating

1149
00:58:16,280 --> 00:58:18,440
Speaker 3: and threatening harm to the general public. And because the

1150
00:58:18,440 --> 00:58:21,680
Speaker 3: general public is afraid, we the government should probably take

1151
00:58:21,680 --> 00:58:25,000
Speaker 3: action to protect the general public. The blind spot with

1152
00:58:25,120 --> 00:58:29,600
Speaker 3: regard to when that organized harmful force is a governmental

1153
00:58:29,640 --> 00:58:33,400
Speaker 3: body is obscene. So by blaming people who are like, hey,

1154
00:58:33,440 --> 00:58:35,560
Speaker 3: I actually I'm nervous about calling the police on my

1155
00:58:35,800 --> 00:58:38,960
Speaker 3: black boss because black men get killed by police at

1156
00:58:38,960 --> 00:58:42,080
Speaker 3: igordinate rates, and also not to mention that subject to

1157
00:58:42,120 --> 00:58:46,240
Speaker 3: illegitimate prosecutions and overcharging and charge stacking and longer sentences

1158
00:58:46,280 --> 00:58:49,560
Speaker 3: and the incredible damage even of a pretop process. And

1159
00:58:49,560 --> 00:58:51,280
Speaker 3: by the way, I'm saying this with great care because

1160
00:58:51,280 --> 00:58:53,400
Speaker 3: here's a person who's accused and has not been found

1161
00:58:53,400 --> 00:58:58,120
Speaker 3: guilty of anything. So really weighing, hey, do I want

1162
00:58:58,160 --> 00:59:02,880
Speaker 3: to subject this person to all of these risks or

1163
00:59:03,080 --> 00:59:06,120
Speaker 3: is there a better way for me to seek accountability

1164
00:59:06,160 --> 00:59:13,640
Speaker 3: and truth without those risks of lethality, injustice, ruinousness. That's

1165
00:59:13,680 --> 00:59:16,160
Speaker 3: a fantastic thing for an ordinary citizen to be considering.

1166
00:59:16,160 --> 00:59:18,040
Speaker 3: And any government that doesn't say, you know what, I'm

1167
00:59:18,040 --> 00:59:20,480
Speaker 3: going to consider that with you, and I'm going to

1168
00:59:20,520 --> 00:59:23,320
Speaker 3: acknowledge that your fears are real and the problems you

1169
00:59:23,400 --> 00:59:26,640
Speaker 3: highlight are real and work on these problems to come

1170
00:59:26,720 --> 00:59:29,200
Speaker 3: up with something better. Is abrogating its duty to the

1171
00:59:29,200 --> 00:59:32,479
Speaker 3: public in favor of the optics of being pro cop.

1172
00:59:33,320 --> 00:59:38,520
Speaker 1: Right, Yeah, yeah, the pro cop turn that's happened in

1173
00:59:38,520 --> 00:59:40,560
Speaker 1: like the Democratic Party. I mean it's not that they

1174
00:59:40,600 --> 00:59:43,760
Speaker 1: were anti but like just I saw you retweet an

1175
00:59:43,840 --> 00:59:46,560
Speaker 1: article or a thread about how the platform changed, because

1176
00:59:46,960 --> 00:59:48,560
Speaker 1: I was like, I was definitely looking at a lot

1177
00:59:48,560 --> 00:59:50,360
Speaker 1: of the I was really interested in the foreign policy

1178
00:59:50,400 --> 00:59:52,280
Speaker 1: stuff that was in the platform and I was like, oh, wow,

1179
00:59:52,360 --> 00:59:55,000
Speaker 1: like just a ton of one eighties here compared to

1180
00:59:55,120 --> 00:59:58,240
Speaker 1: twenty twenty, and then reading sort of the excerpts on

1181
00:59:58,280 --> 01:00:02,360
Speaker 1: what was happening with policing was also very like, Oh,

1182
01:00:02,680 --> 01:00:05,840
Speaker 1: we're really embracing this thing about being like, let's not

1183
01:00:06,000 --> 01:00:09,080
Speaker 1: talk about the death penalty anymore. Let's I know, we

1184
01:00:09,080 --> 01:00:12,320
Speaker 1: were talking about choke holds. Let's like really tamp that down.

1185
01:00:12,400 --> 01:00:16,320
Speaker 1: And it really is wild how much it's become because

1186
01:00:16,320 --> 01:00:18,560
Speaker 1: I think, obviously this whole election is set up to

1187
01:00:18,600 --> 01:00:22,000
Speaker 1: be we have a prosecutor and a felon, and so

1188
01:00:22,240 --> 01:00:25,160
Speaker 1: because of that framing, we're gonna really lean into a

1189
01:00:25,200 --> 01:00:28,960
Speaker 1: lot of this like the like the prosecutorial aspects of

1190
01:00:29,000 --> 01:00:31,400
Speaker 1: this and also be make it feel like yeah, man,

1191
01:00:31,440 --> 01:00:35,200
Speaker 1: like we're the cops again and that's okay. That was

1192
01:00:35,240 --> 01:00:38,200
Speaker 1: just kind of like, I mean, I was very cynical

1193
01:00:38,280 --> 01:00:40,040
Speaker 1: in twenty twenty when I saw this sort of like

1194
01:00:40,160 --> 01:00:42,240
Speaker 1: upticking me, like, yeah, we really need to do something,

1195
01:00:42,560 --> 01:00:44,800
Speaker 1: and that's the most that will happen. I will say

1196
01:00:44,840 --> 01:00:46,960
Speaker 1: that we need to do something. But now to see

1197
01:00:47,080 --> 01:00:49,560
Speaker 1: like really formally stripped out, you're like, oh, right, right, right,

1198
01:00:49,600 --> 01:00:52,800
Speaker 1: this was never a real concern. But how do you

1199
01:00:52,840 --> 01:00:55,280
Speaker 1: sort of perceive that sort of like shift now or

1200
01:00:55,320 --> 01:00:57,400
Speaker 1: at least the now that you know, even in their

1201
01:00:57,480 --> 01:01:00,360
Speaker 1: written platforms, it's just sort of like, yeah, yeah, those

1202
01:01:00,360 --> 01:01:02,400
Speaker 1: are those are those are problems, but you know we

1203
01:01:02,440 --> 01:01:04,320
Speaker 1: can address them at some point later.

1204
01:01:04,720 --> 01:01:06,520
Speaker 3: I mean, the death penalty thing, I really don't get

1205
01:01:06,600 --> 01:01:08,880
Speaker 3: because Harris has been against the death penalty for a

1206
01:01:08,880 --> 01:01:11,920
Speaker 3: lot of her career, was criticized as ag for upholding

1207
01:01:11,960 --> 01:01:15,080
Speaker 3: the law instead of acting on a moral objection that

1208
01:01:15,120 --> 01:01:18,560
Speaker 3: she has to death penalty, which is super expensive and

1209
01:01:18,600 --> 01:01:21,120
Speaker 3: has resulted in the death of a lot of innocent people.

1210
01:01:21,160 --> 01:01:23,600
Speaker 3: Because our system gets it wrong a lot, because of

1211
01:01:23,680 --> 01:01:27,800
Speaker 3: things like junk science and bad Eyewinness, IDAs and insufficient

1212
01:01:27,800 --> 01:01:30,240
Speaker 3: funding of public defense. So let's just like Cavin, this

1213
01:01:30,320 --> 01:01:32,760
Speaker 3: is like, I don't I really don't get the Democratic

1214
01:01:32,800 --> 01:01:35,800
Speaker 3: Party stepping away from opposing the death penalty. I think

1215
01:01:36,360 --> 01:01:39,480
Speaker 3: polling on it has not changed dramatically, like Americans are

1216
01:01:39,480 --> 01:01:42,480
Speaker 3: not like rapidly pro death penalty now, So I really

1217
01:01:42,480 --> 01:01:44,400
Speaker 3: don't get it. But here's what I'll say about the

1218
01:01:44,440 --> 01:01:50,520
Speaker 3: prosecutor versus pellon thing. It's being treated as a sort

1219
01:01:50,560 --> 01:01:57,400
Speaker 3: of vicious backing a violent force against crime. But it

1220
01:01:57,440 --> 01:02:01,360
Speaker 3: doesn't have to be. Prosecutors are unique among lawyers. Rarely

1221
01:02:01,400 --> 01:02:03,280
Speaker 3: will you hear me say nice things about prosecutors. I'm

1222
01:02:03,280 --> 01:02:06,200
Speaker 3: going to now say some nice things about prosecutors. They

1223
01:02:06,280 --> 01:02:09,160
Speaker 3: have an ethical duty to do justice. That is a

1224
01:02:09,240 --> 01:02:13,200
Speaker 3: unique ethical duty. No other kind of lawyer has that duty. Now.

1225
01:02:13,240 --> 01:02:15,400
Speaker 3: I just got done teaching a course to some really

1226
01:02:15,440 --> 01:02:17,680
Speaker 3: talented law students, and in one of my exercises, I

1227
01:02:17,680 --> 01:02:19,200
Speaker 3: made half of them be defense lawyers and half of

1228
01:02:19,240 --> 01:02:22,080
Speaker 3: them be prosecutors. And I told the prosecutors in a

1229
01:02:22,080 --> 01:02:24,920
Speaker 3: bail argument, you had this unique ethical duty. You have

1230
01:02:24,960 --> 01:02:27,440
Speaker 3: to do justice, and not just justice for the people

1231
01:02:28,040 --> 01:02:30,640
Speaker 3: who were harmed in a crime. Or who you think

1232
01:02:30,640 --> 01:02:32,680
Speaker 3: of as part of the community. You have to do

1233
01:02:32,840 --> 01:02:36,280
Speaker 3: justice for everybody. That includes the accused person and their

1234
01:02:36,280 --> 01:02:39,680
Speaker 3: family and their kids and their loved ones, that includes everybody.

1235
01:02:39,760 --> 01:02:43,160
Speaker 3: When you talk for the people, you represent everybody. And

1236
01:02:43,240 --> 01:02:45,520
Speaker 3: when I told them that their assignment would be graded

1237
01:02:46,360 --> 01:02:50,160
Speaker 3: on how well they were able to consider everyone's needs

1238
01:02:50,560 --> 01:02:54,720
Speaker 3: safety and justice, they got up there on the record

1239
01:02:54,760 --> 01:02:56,680
Speaker 3: and did radically different things than I've ever seen a

1240
01:02:56,680 --> 01:02:58,920
Speaker 3: prosecutor do in real life. And largely we're thinking of

1241
01:02:58,960 --> 01:03:02,439
Speaker 3: restorative solutions and root causes and like how they could

1242
01:03:02,480 --> 01:03:05,720
Speaker 3: heal a community instead of just punishing and disappearing a person.

1243
01:03:06,480 --> 01:03:11,440
Speaker 3: If what prosecutor means is somebody who is enshrined with

1244
01:03:11,560 --> 01:03:15,680
Speaker 3: governmental authority to do justice for everyone in the community,

1245
01:03:15,720 --> 01:03:19,880
Speaker 3: including people who might be opposed to that very prosecutor,

1246
01:03:20,960 --> 01:03:23,600
Speaker 3: I think it could actually be a very powerful encapsulation

1247
01:03:23,760 --> 01:03:27,320
Speaker 3: of the best version of a leader, right, a person

1248
01:03:27,360 --> 01:03:29,760
Speaker 3: who's going to take this seriously and care for all

1249
01:03:29,760 --> 01:03:33,280
Speaker 3: of our well being and yes, stand up to abuses

1250
01:03:33,800 --> 01:03:36,240
Speaker 3: of people with less power, which is really what we

1251
01:03:36,280 --> 01:03:38,520
Speaker 3: would want prosecutors to stand up to the most. I

1252
01:03:38,560 --> 01:03:42,000
Speaker 3: think certainly it's not being done that way. I think

1253
01:03:42,040 --> 01:03:45,040
Speaker 3: the rhetoric sucks. I think half of Americans have had

1254
01:03:45,040 --> 01:03:47,560
Speaker 3: a loved one locked up. I just think that the

1255
01:03:47,640 --> 01:03:50,560
Speaker 3: rhetoric doesn't have to change if it was made smarter.

1256
01:03:50,880 --> 01:03:53,520
Speaker 3: In order to be smarter, though, the policy would not

1257
01:03:53,640 --> 01:03:55,800
Speaker 3: have to shift towards tough on crime. It would have

1258
01:03:55,840 --> 01:04:01,200
Speaker 3: to shift towards evidence based, root cause thinking and solutions

1259
01:04:01,200 --> 01:04:03,760
Speaker 3: that shift us towards something better than our shitty status quo.

1260
01:04:04,480 --> 01:04:06,840
Speaker 1: Yeah, yeah, yeah, and now it just feels like, now

1261
01:04:06,920 --> 01:04:09,520
Speaker 1: let's embrace the status quo and bring it closer and

1262
01:04:09,560 --> 01:04:12,520
Speaker 1: closer and closer. But yeah, that's a yeah, such a

1263
01:04:13,880 --> 01:04:16,880
Speaker 1: the whole time, I'm like, wow, it Like. The thing

1264
01:04:16,920 --> 01:04:18,720
Speaker 1: that really makes you think a lot too, is like,

1265
01:04:18,760 --> 01:04:21,200
Speaker 1: we have so many people who are prosecutors that ascend

1266
01:04:21,240 --> 01:04:24,320
Speaker 1: in politics, and that's why, Like when Kantazi Brown Jackson

1267
01:04:24,400 --> 01:04:27,120
Speaker 1: was like the first public defender who had sat on

1268
01:04:27,120 --> 01:04:29,560
Speaker 1: the Supreme Court, I was liked, is that true?

1269
01:04:30,000 --> 01:04:33,120
Speaker 3: Oh my god, oh writ large the federal bench is

1270
01:04:33,160 --> 01:04:35,280
Speaker 3: largely prosecuted. I actually read. I wrote a really mean

1271
01:04:35,320 --> 01:04:38,880
Speaker 3: email today guys like my local representative, who I love

1272
01:04:39,080 --> 01:04:41,920
Speaker 3: is like moving to run for the state a different

1273
01:04:41,920 --> 01:04:45,680
Speaker 3: state office, and he endorsed it's a two candidate race.

1274
01:04:45,680 --> 01:04:47,640
Speaker 3: I live in a place with major housing issues. There's

1275
01:04:47,680 --> 01:04:49,560
Speaker 3: just not enough housing for people. Cost of housing are

1276
01:04:49,600 --> 01:04:52,240
Speaker 3: too high. And one of the candidates is a housing organizer,

1277
01:04:52,360 --> 01:04:55,320
Speaker 3: a local housing organizer, and the other candidate is a prosecutor.

1278
01:04:56,480 --> 01:05:01,120
Speaker 3: Guess who got the addressment? And I wrote them a

1279
01:05:01,120 --> 01:05:04,360
Speaker 3: note being like, like, come on, this housing is the

1280
01:05:04,560 --> 01:05:07,000
Speaker 3: issue of our region. If you are going to make

1281
01:05:07,080 --> 01:05:11,320
Speaker 3: prosecution once again a blind path to power, you at

1282
01:05:11,400 --> 01:05:14,080
Speaker 3: least have to justify why you are overlooking someone whose

1283
01:05:14,120 --> 01:05:16,400
Speaker 3: life work is in the zone we most need. And

1284
01:05:16,440 --> 01:05:18,160
Speaker 3: the thing that bugs me about it the most is

1285
01:05:18,200 --> 01:05:20,680
Speaker 3: that it tells young people. I mean, in my work,

1286
01:05:20,960 --> 01:05:23,200
Speaker 3: I work at public defenders all over the country, and

1287
01:05:23,240 --> 01:05:26,000
Speaker 3: I help them expand the practic their practice and expand

1288
01:05:26,000 --> 01:05:27,640
Speaker 3: what they can offer their clients. And I place a

1289
01:05:27,680 --> 01:05:31,080
Speaker 3: lot of new professionals, usually young people, into jobs in

1290
01:05:31,080 --> 01:05:33,680
Speaker 3: public defense. And as they start out their careers, I'm

1291
01:05:33,680 --> 01:05:35,800
Speaker 3: looking at how they think of their career trajectory. They're

1292
01:05:35,840 --> 01:05:37,600
Speaker 3: doing great things, like I'm going to learn all about

1293
01:05:37,680 --> 01:05:40,720
Speaker 3: how fucked up America's public systems are and I'm going

1294
01:05:40,760 --> 01:05:43,120
Speaker 3: to carry that knowledge into my own change making career.

1295
01:05:44,080 --> 01:05:46,800
Speaker 3: But to everybody else. The vast majority of young people,

1296
01:05:46,800 --> 01:05:50,520
Speaker 3: future lawyers who are not like these dedicated, brilliant advocates.

1297
01:05:51,040 --> 01:05:53,560
Speaker 3: They think, Okay, I'll be a prosecutor for like two

1298
01:05:53,640 --> 01:05:57,280
Speaker 3: years and then I'll get my elected office. If I

1299
01:05:57,440 --> 01:06:01,440
Speaker 3: just incarcerate young black men and separate families and crush

1300
01:06:01,440 --> 01:06:03,960
Speaker 3: people's dreams and lives and maybe cause a few deaths,

1301
01:06:04,520 --> 01:06:07,120
Speaker 3: then I could be a state senter.

1302
01:06:07,320 --> 01:06:10,320
Speaker 1: Right, I've been vetted right, and I've done the work right,

1303
01:06:10,720 --> 01:06:11,600
Speaker 1: and it's we shouldn't.

1304
01:06:11,600 --> 01:06:15,880
Speaker 3: We shouldn't make change making power reliant on willingness to

1305
01:06:15,920 --> 01:06:16,840
Speaker 3: harm others.

1306
01:06:17,800 --> 01:06:20,040
Speaker 2: Yeah, all right, sounds like we have a lot of

1307
01:06:20,040 --> 01:06:23,840
Speaker 2: work to do. Emily Galvin Almansa, what a pleasure having

1308
01:06:23,880 --> 01:06:27,080
Speaker 2: you on the daily Zeitgeist. Where can people find you,

1309
01:06:27,280 --> 01:06:29,520
Speaker 2: follow you, support your work and all that good stuff.

1310
01:06:29,800 --> 01:06:33,040
Speaker 3: Well, if they want to support expanding and improving public

1311
01:06:33,040 --> 01:06:35,840
Speaker 3: defense around the country, really transforming what we mean by

1312
01:06:35,840 --> 01:06:38,600
Speaker 3: public defense, and getting more help for poor people with

1313
01:06:38,680 --> 01:06:42,120
Speaker 3: housing and employment and benefits and transportation and all the

1314
01:06:42,120 --> 01:06:45,440
Speaker 3: things that people actually need, they can go to www.

1315
01:06:45,640 --> 01:06:49,640
Speaker 3: Dot Partners for Justice dot org where they can learn

1316
01:06:49,680 --> 01:06:53,120
Speaker 3: all about our work to support public defenders nationally. They

1317
01:06:53,120 --> 01:06:57,320
Speaker 3: can also catch us on Twitter at at PFJ Underscore

1318
01:06:57,440 --> 01:07:00,760
Speaker 3: USA or Instagram at Partners for Justice, or they can

1319
01:07:00,800 --> 01:07:03,040
Speaker 3: follow and I guess I should say, and they can

1320
01:07:03,080 --> 01:07:08,680
Speaker 3: follow my much my spicy tweets at Galvin Almonza it's

1321
01:07:08,720 --> 01:07:11,840
Speaker 3: just my last name. It do a weekly video on

1322
01:07:12,160 --> 01:07:14,040
Speaker 3: things that are awful in our legal system. So if

1323
01:07:14,040 --> 01:07:16,360
Speaker 3: people want to get like that, like spike of outrage

1324
01:07:16,360 --> 01:07:18,000
Speaker 3: once a week, come on Twitter with me and I

1325
01:07:18,000 --> 01:07:19,120
Speaker 3: will I will give you a spike.

1326
01:07:19,560 --> 01:07:21,640
Speaker 1: Yeah, but it's not just blind outrage.

1327
01:07:21,640 --> 01:07:24,480
Speaker 2: You also have solutions and ideas for things to do,

1328
01:07:24,640 --> 01:07:27,479
Speaker 2: so I do I highly recommend. Is there a work

1329
01:07:27,480 --> 01:07:29,040
Speaker 2: of media that you've been enjoying?

1330
01:07:29,160 --> 01:07:31,520
Speaker 3: Okay, I'm going to be really nerdy, guys. There was

1331
01:07:31,560 --> 01:07:33,240
Speaker 3: a paper that came out a couple months ago from

1332
01:07:33,320 --> 01:07:36,920
Speaker 3: Vita B. Johnson, who is a lawyer, and she wrote

1333
01:07:36,920 --> 01:07:41,240
Speaker 3: a paper called Whom do Prosecutors Protect? And I know

1334
01:07:41,320 --> 01:07:42,880
Speaker 3: that mostly people are not like you know, what I'm

1335
01:07:42,920 --> 01:07:46,280
Speaker 3: waiting for is the next hot law paper to drop.

1336
01:07:46,360 --> 01:07:48,560
Speaker 3: And I'm going to just dive into that bastard and

1337
01:07:48,640 --> 01:07:51,200
Speaker 3: roll around. But it's really good and it's really accessible,

1338
01:07:51,200 --> 01:07:54,280
Speaker 3: and it details every single way in which the kind

1339
01:07:54,280 --> 01:07:56,680
Speaker 3: of problematic incentives. We've been talking about prosecution as a

1340
01:07:56,720 --> 01:07:59,880
Speaker 3: pastive power and the inter reliance between prosecutors and pull

1341
01:08:00,000 --> 01:08:04,160
Speaker 3: police are robbing ordinary Americans of their chance of justice.

1342
01:08:04,160 --> 01:08:05,320
Speaker 3: And it's a really good paper.

1343
01:08:05,880 --> 01:08:07,880
Speaker 1: Damn that sounds good. Amazing.

1344
01:08:08,120 --> 01:08:11,480
Speaker 2: Miles. Where can people find you? And what is the

1345
01:08:11,560 --> 01:08:13,080
Speaker 2: latest legal brief that you've been in?

1346
01:08:13,840 --> 01:08:16,439
Speaker 1: Yeah, let me give me a second about the legal brief.

1347
01:08:17,520 --> 01:08:20,559
Speaker 1: I just found this one, the Pelican brief. Oh hell,

1348
01:08:20,640 --> 01:08:25,000
Speaker 1: yeah too, dude. You can find me. You can find

1349
01:08:25,040 --> 01:08:29,920
Speaker 1: me at is that? Oh yeah, that actually makes sense.

1350
01:08:30,040 --> 01:08:32,599
Speaker 1: Huh huh. Thanks for that little factoid. I'm gonna take

1351
01:08:32,600 --> 01:08:35,439
Speaker 1: that to the take that to the bar tonight. You

1352
01:08:35,479 --> 01:08:37,360
Speaker 1: can find me at Miles of Great on Twitter and Instagram.

1353
01:08:37,400 --> 01:08:39,479
Speaker 1: You can find Jack and I on the basketball podcast

1354
01:08:39,560 --> 01:08:41,679
Speaker 1: Miles and Jack Got Mad Boosties. You could also find

1355
01:08:41,680 --> 01:08:44,080
Speaker 1: me talking about ninety day fiance on four to twenty

1356
01:08:44,120 --> 01:08:48,880
Speaker 1: Day Fiance, a tweet I like, oh man, so uh

1357
01:08:49,120 --> 01:08:53,240
Speaker 1: the you know libs of TikTok person Chireachik tweeted out

1358
01:08:53,320 --> 01:08:55,840
Speaker 1: a few days ago. It said, I'm looking for parents

1359
01:08:55,920 --> 01:08:58,439
Speaker 1: anywhere in Ohio who have kids in public schools to

1360
01:08:58,479 --> 01:09:01,599
Speaker 1: be eyes and ears on the your identity will remain

1361
01:09:01,640 --> 01:09:05,240
Speaker 1: anonymous and protected. Please DM me if you fit this criteria.

1362
01:09:05,520 --> 01:09:09,720
Speaker 1: Patton Oswalt quote tweeted this and said, Chaia, I am

1363
01:09:09,840 --> 01:09:13,480
Speaker 1: so glad you're doing this. There's a boy in our neighborhood, Elliott,

1364
01:09:13,640 --> 01:09:16,559
Speaker 1: a child of divorce, who we think is hiding an

1365
01:09:16,640 --> 01:09:19,320
Speaker 1: alien in his closet with the help of his siblings

1366
01:09:19,360 --> 01:09:27,800
Speaker 1: Gertie and Michael me hitting her with that et. But yeah,

1367
01:09:27,840 --> 01:09:29,679
Speaker 1: that is one of my favorite tweets.

1368
01:09:29,720 --> 01:09:32,280
Speaker 2: Rees leave Ohio public schools alone, y'all.

1369
01:09:32,520 --> 01:09:34,000
Speaker 1: That's I I.

1370
01:09:33,920 --> 01:09:36,840
Speaker 2: Am a product of Ohio public schools. They do. They

1371
01:09:36,880 --> 01:09:39,559
Speaker 2: do fine work every once in a while, all right,

1372
01:09:39,960 --> 01:09:43,959
Speaker 2: tweet I've been enjoying. Katie at Skatie four twenty tweeted

1373
01:09:44,320 --> 01:09:48,000
Speaker 2: they should call that guy Edgar allan poem because of

1374
01:09:48,040 --> 01:09:54,400
Speaker 2: all those poems he did similarly smart, yeah yeah, yeah,

1375
01:09:54,439 --> 01:09:57,760
Speaker 2: yeah great, and then Brandy Jensen tweeted, I love when

1376
01:09:57,760 --> 01:10:02,000
Speaker 2: an it guy refers to my laptop as your machine.

1377
01:10:04,600 --> 01:10:07,759
Speaker 1: Kind of cool here so cool.

1378
01:10:09,080 --> 01:10:12,439
Speaker 2: You can find me on Twitter at Jack Underscore O Brian.

1379
01:10:12,479 --> 01:10:14,760
Speaker 2: You can find us on Twitter at Daily Zeitgeist where

1380
01:10:14,760 --> 01:10:17,559
Speaker 2: at the Daily Zeitgeist on Instagram. We have a Facebook fanpage.

1381
01:10:17,560 --> 01:10:21,000
Speaker 2: On website dailyzeikeist dot com, we post our episodes and

1382
01:10:21,120 --> 01:10:23,439
Speaker 2: our footnote no link off to the information that we

1383
01:10:23,479 --> 01:10:25,920
Speaker 2: talked about in today's episode, as well as a song

1384
01:10:25,960 --> 01:10:28,559
Speaker 2: that we think you might enjoy Miles a song do

1385
01:10:28,600 --> 01:10:29,880
Speaker 2: you think people might enjoy?

1386
01:10:30,400 --> 01:10:33,960
Speaker 1: I stumbled stumbled across a producer by the name of

1387
01:10:34,080 --> 01:10:37,360
Speaker 1: Harrison and just going through some of their tracks, and

1388
01:10:37,400 --> 01:10:40,120
Speaker 1: there's this one track that's really popular of his that's

1389
01:10:40,160 --> 01:10:43,840
Speaker 1: called Selfish High Heels and it's with him Young Bay

1390
01:10:43,920 --> 01:10:47,240
Speaker 1: and mac Ross eighty two ninety nine. But the sound

1391
01:10:47,280 --> 01:10:50,559
Speaker 1: of it is like eighties like Japanese city pop kind

1392
01:10:50,560 --> 01:10:52,840
Speaker 1: of stuff from the eighties, but like a little bit

1393
01:10:53,040 --> 01:10:56,280
Speaker 1: more like modern and futuristic. It's kind of trippy. So

1394
01:10:56,360 --> 01:10:59,240
Speaker 1: I really enjoyed it. So this is Selfish High Heels

1395
01:10:59,280 --> 01:11:01,240
Speaker 1: by Young Bay and Harrison, and.

1396
01:11:01,200 --> 01:11:04,360
Speaker 2: It also creates like the next Pixheart movie about the

1397
01:11:04,960 --> 01:11:09,000
Speaker 2: promorphic shoes and you got the you know, funky sneakers,

1398
01:11:09,080 --> 01:11:13,400
Speaker 2: the selfish high heels, silly slippers. I don't know, you

1399
01:11:13,720 --> 01:11:16,320
Speaker 2: guys do the work. I gonna waste anymore of your time.

1400
01:11:17,000 --> 01:11:19,439
Speaker 2: The kind of high high tops, like they smoke a

1401
01:11:19,479 --> 01:11:20,360
Speaker 2: little weed you know.

1402
01:11:21,200 --> 01:11:23,200
Speaker 1: The Daily's Eye Guy is the production of iHeartRadio.

1403
01:11:23,240 --> 01:11:25,280
Speaker 2: For more podcasts from my heart Radio, visit the iHeart

1404
01:11:25,360 --> 01:11:27,360
Speaker 2: Radio app, Apple podcast, or wherever you listen to your

1405
01:11:27,400 --> 01:11:29,200
Speaker 2: favorite shows. That is going to do it for us

1406
01:11:29,200 --> 01:11:32,400
Speaker 2: this week. We are back on Tuesday after Labor Day

1407
01:11:33,120 --> 01:11:35,120
Speaker 2: to tell you what was trending over the long weekend

1408
01:11:35,439 --> 01:11:40,120
Speaker 2: and we will talk to you all then Bye.