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Speaker 1: Oh hey, there, it's me Jack. You've caught me unwinding,

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Speaker 1: enjoying a large goblet of delicious eggnog, untangling my brain,

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Speaker 1: gaining five to fifteen pounds of eggnog while we unwind.

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Speaker 1: Here at Daily Zeitgeist, in addition to publishing our normal

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Speaker 1: year end episodes and Santo's University, etc. We've decided to

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Speaker 1: take the opportunity to count down the top ten episodes

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Speaker 1: of the year published over the next ten days. The

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Speaker 1: ten days that will be off Monday through Friday, two

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Speaker 1: weeks in a row. How Jack, how did you guys

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Speaker 1: determine the top ten episodes? They were all equally incredible. Well,

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Speaker 1: we used a little something called democracy. Ever heard of it?

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Speaker 1: Depending on when you listen to this episode, that might

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Speaker 1: not be such a rhetorical question. But anyways, we let

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Speaker 1: you vote on the most listened to episodes of the

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Speaker 1: year to see what you liked best. And you're about

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Speaker 1: to hear your answers. Just ten bangers right in a row.

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Speaker 1: We've got a trending episode in the mix. We got

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Speaker 1: a lot of good ones and at number one, well,

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Speaker 1: let's just say you'll find out, especially if this is

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Speaker 1: the number one episode we're putting this same bumper at

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Speaker 1: the start of all ten, so we hope you enjoy it.

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Speaker 1: We hope you enjoyed listening to this year of TDZ

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Speaker 1: as much as we enjoyed making it. And we will

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Speaker 1: see you all in twenty twenty five. We hope you

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Speaker 1: have a RESTful holiday. I couldnot fucking sleep all night,

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Speaker 1: all night.

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Speaker 2: I've been up like a fucking owl, like THEO Vaughn

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Speaker 2: was telling Donald Trump interview. I should I'll have you

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Speaker 2: like an owl, man, you'd be your own fucking not

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Speaker 2: you being a street light light, just like and that's good.

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Speaker 1: No, that's good, that's good. And that feeling you like

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Speaker 1: that feeling.

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Speaker 2: No, no, no, I do not want to feel like

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Speaker 2: a vampire with a heart condition. And yeah, for the record,

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Speaker 2: it's not because I was doing cocaine. This had a

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Speaker 2: been a low grade anxiety that kept me up all night.

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Speaker 1: Is that what they're calling it? Yeah, okay, okay, all right,

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Speaker 1: and I'm like an Now.

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Speaker 2: We call that, we call that poor people's cocaine anxiety.

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Speaker 1: Yeah, it's just a low great anxiety, the poor man's cocaine.

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

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Speaker 1: Episode five of dirt Ally's Like I Say, production of iHeartRadio.

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Speaker 1: This is a podcast where we take a deep dive

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Speaker 1: into American share consciousness. And it is Friday, August thirtieth,

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Speaker 1: twenty twenty four. Yeah, yes, last day.

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Speaker 2: No, well thirty, they're thirty one days. They're thirty one

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Speaker 2: days half August exactly. Also, Hey, guess guess who I

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Speaker 2: get to shout out today? Shout up to my dad

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Speaker 2: is his bay, seventy years old up in this place.

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Speaker 1: Congrats to you. I was just at a friend of

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Speaker 1: my youngests and they have an amazing work from your

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Speaker 1: dad on their wall. Oh they do? Yeah?

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Speaker 2: Yeah, oh well you know, yeah, you're very white. My

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Speaker 2: dad is internationally known and locally respected as an artist.

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Speaker 2: So I appreciate, appreciate the support from everybody. But also

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Speaker 2: August thirtieth is National Beach Day, National Grief Awareness Day,

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Speaker 2: National Toasted Marshmallow Day, and for all you college fans,

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Speaker 2: it's National College Colors Day. I am not wearing mine,

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Speaker 2: Sadly I should normally maybe kind of I need, I

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Speaker 2: need some more gray, But I feel like I wear

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Speaker 2: like my Alma matership because I'm like, well, I gave

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Speaker 2: these people so much money, like I have to. I

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Speaker 2: need to get something out of it. We're like, they

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Speaker 2: should brand me.

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Speaker 1: Yeah, yeah, the amount of money I gave them. Thank you,

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Speaker 1: thank you, thank you. Yes, all right, Well, my name

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Speaker 1: is Jack O'Brien aka my Doughnuts. Bring jd Vance to

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Speaker 1: the yard and he's like, whatever makes sense, damn right,

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Speaker 1: whatever makes sense? That one courtesy and Macaroni on the

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Speaker 1: discord new jd Vance flub just dropped. I'm sure we

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Speaker 1: talked about it yesterday's trending, but his uh.

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Speaker 2: Did you did you see?

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Speaker 1: Don't worry any every everybody. I'm not gonna take my

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Speaker 1: shirt off. Okay, it's a banger.

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Speaker 2: It's another was being booed by firefighters, so he just silence. Yeah, yeah, yeah.

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

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Speaker 1: co host, mister Miles Gras.

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Speaker 2: It's Miles Gray aka whale was beached.

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Speaker 3: YEA be headed it with the chainsaw and it strapped

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Speaker 3: it to the top.

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Speaker 2: Of the car.

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Speaker 3: Whale was beached ya because I'm a normal hunter and

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Speaker 3: whales are cool.

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Speaker 2: You know that they are? Oh wales? Okay, shout out

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Speaker 2: to clearly Dot Universe for that creep.

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Speaker 1: Tlc RFK hunter. Another creep.

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Speaker 2: Yeah, a true, true creep. But yeah, that whale was

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Speaker 2: beached and hey whale. As Michael Knowles on the Daily

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Speaker 2: Wires and whales are.

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Speaker 1: Cool and he's a hunter, so it's normal. Find a

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Speaker 1: new angle exactly, chainsong of whales head does normal? Find

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Speaker 1: a new angle? Mis whales juice? Thrill Whale juice is wonderful.

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

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Speaker 1: by a poet and a lawyer who is the co

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Speaker 1: founder and executive director of Partners for Justice, which is

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Speaker 1: designed to create a new model of collaborative public defense

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Speaker 1: designed to empower You probably read her deep dive on

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Speaker 1: Twitter into Project twenty twenty five. Please welcome to the show.

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Speaker 1: Emily Galvin.

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Speaker 4: O'monza very happy to be here, and I'm so sad

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Speaker 4: that I don't have an Internet supplied jingle or joke

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Speaker 4: to go along. I'm just sitting here like horrified at

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Speaker 4: you guys having unearthed my deep poet history.

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Speaker 1: Yeah, you do a little googling. Do the math is

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Speaker 1: a public you are a published poet.

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Speaker 4: I am math. Poetry is the most marketable genre of literature.

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Speaker 4: I don't know if it's the top seller. Yeah.

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Speaker 2: Yeah, you were doing math poetry.

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Speaker 4: Yes, poetry is math, right, like when you have a

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Speaker 4: rhythm of word, you know, the same way music is math.

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Speaker 4: Poetry is math. I just started with other math first

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Speaker 4: and then tried to create poetic forms that adhere to

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Speaker 4: that math again.

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Speaker 2: Real bang Yeah wow, Okay, that's like some like tool

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Speaker 2: type shit you like, I'm using sacred geometry to write down.

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Speaker 1: Yeah.

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Speaker 4: Yeah. If only I could be in the same category

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Speaker 4: as tool, I would be. I mean, that's that's the best.

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Speaker 4: That's the that's the height of my poetic career. You

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Speaker 4: 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. So we we did reach out to

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Speaker 1: you on this length of project, your project twenty twenty

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Speaker 1: five Twitter thread, Deep Dive, and then we found out,

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Speaker 1: like we have a bunch of mutual friends and we're

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Speaker 1: a big fan of your work otherwise, but the project

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Speaker 1: twenty twenty five thing, you were just like kind of

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Speaker 1: reading it because you can read and comprehend lots of text,

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Speaker 1: and you're like, oh, this is worse than I imagined.

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Speaker 4: Yeah, No, what makes the project twenty twenty five texts

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Speaker 4: really really dangerous is that it's written to sound super normal,

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Speaker 4: and it's also incredibly long. It's like nine hundred pages long.

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Speaker 4: And so if you are a layperson who's just a

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Speaker 4: little bit concerned about what the Heritage Foundation might be

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Speaker 4: putting out there, because you recognize that they were heavily

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Speaker 4: influential in Donald Trump's last administration, and you see that

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Speaker 4: a lot of his administration cronies had contributed to this document,

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Speaker 4: you want to peruse it. It might not seem as

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Speaker 4: scary as it actually is because it's written again to

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Speaker 4: sound very very normal and that sort of like policies.

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Speaker 4: But having you know, gone to law school and been

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Speaker 4: forced to read lots of stuff, I think it was

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Speaker 4: a good use of time to try to kind of

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Speaker 4: get in there and translate for people some of the

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Speaker 4: scariest aspects of the policy plan. And then I got

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Speaker 4: really far in there and wrote like a four hundred

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Speaker 4: tweet thread about how bad it is and also how insane,

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Speaker 4: like they're weird obsession with boyfriends and like how scared

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Speaker 4: of boyfriends they are?

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

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Speaker 4: My yead, So they think that single moms are terrible, right,

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Speaker 4: super popular parsil like, let's all hate on single moms.

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Speaker 4: They had this fixation on fatherhood. They're of course very

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Speaker 4: very interested in preserving the nuclear, heteronormative family. But from

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Speaker 4: that flows this like weird paragraph where they actually talk

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Speaker 4: about how dangerous like a single mom is bad, but

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Speaker 4: like a single mom with a boyfriend it's the worst

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Speaker 4: possible outcome for children and the like. This part is

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Speaker 4: actually not done in like very elegant policies. They like

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Speaker 4: actually hate on boyfriends for a while. It's just there

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Speaker 4: are these twists deep within the document that are working.

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Speaker 2: It's like, yeah, they're probably named Craig, and like they

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Speaker 2: eat your cereal and like and they don't even ask

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Speaker 2: you when you're twelve or when maybe your kid is

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Speaker 2: twelve whatever. Maybe my life is bleeding into what I'm writing.

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Speaker 4: Here's the policy equivalent. If you're not my real dad,

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Speaker 4: it's just right.

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Speaker 2: Yeah, which is so weird, but yeah, but I mean

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Speaker 2: so many conservative men have that energy of like you're

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Speaker 2: not my real dad, and you're like, wow, dude, we

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Speaker 2: weren't even talking about anything related to that, and you

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Speaker 2: went with that.

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Speaker 1: Okay, okay, you just.

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Speaker 4: Needed to take it down like six notches.

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Speaker 1: Yes, right, yeah, yeah, absolutely, all right, Well, we will

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Speaker 1: link off to the entire thread in the footnotes. We

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Speaker 1: are going to get to know you a little bit

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Speaker 1: better in a moment. First, a couple of things that

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Speaker 1: we're talking about later on when we get to the news.

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Speaker 1: The mainstream media, it has been pointed out recently, seem

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Speaker 1: to actually be for all the talk of there being

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Speaker 1: a anti Trump bias, they really seem to help him

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Speaker 1: in a lot of So we just want to cover

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Speaker 1: a couple small examples. The right has their new case

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Speaker 1: closed winning strategy against Harris Waltz. This time it is

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Speaker 1: taking down Harris's Donald's job story in the least convincing

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Speaker 1: way possible.

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Speaker 2: Oh, she's being mixed Swift voted to.

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Speaker 1: Be mix Swift voted yep. And then we're gonna talk

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Speaker 1: about a company called Axon. Now, I know you're hearing

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Speaker 1: the name Axon and you're like, that company sounds like

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Speaker 1: they do good in the world and probably not scary.

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Speaker 1: Axon is actually a terrifying fucking company that is like

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Speaker 1: the private arm of America's police, and they are experimenting

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Speaker 1: with using AI to help police do more damage. So

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Speaker 1: we're gonna talk about that and just some of the

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Speaker 1: other shit that they've done there. There is a photograph

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Speaker 1: that our writer JM put in the doc that is

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Speaker 1: their CEO addressing a crowd, and he is not him,

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Speaker 1: Like the person addressing the crowd is in a all

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Speaker 1: black suit and has a motorcycle helmet on and then

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Speaker 1: an iPad strapped to the front with his face on it,

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Speaker 1: but is like gesture as he delivers the speech from wherever,

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Speaker 1: bun whatever, like volcanic layer he's at. The person is

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Speaker 1: like gesturing with his words too, So it's like a

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Speaker 1: weird avatar situation that is just it's all so very

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Speaker 1: on the nose. Might also talk about this New York

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Speaker 1: Times article about alternative policing and just where we're at

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Speaker 1: with the mainstream media when it comes to, you know,

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Speaker 1: things that aren't police, that aren't armed police, and how

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Speaker 1: that's being talked about these days in the mainstream all

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Speaker 1: that plenty more. But first, Emily, we do like to

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Speaker 1: ask our guest, what is something from your search history

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

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Speaker 4: Oh man, So it's actually really right that you guys

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Speaker 4: asked me this because a few months ago Okay, so

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Speaker 4: the story is gonna get weird. A deer impaled itself

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Speaker 4: on my colleague's fence, oh no, and my colleague, of course,

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Speaker 4: shared a photograph with our entire team to ask Jesus,

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Speaker 4: what do I do now there's a dead deer on

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Speaker 4: my fence. And it just so happened that I dipped

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Speaker 4: into my own Google search history to demonstrate how ready

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Speaker 4: for this topic I actually was, and I took a

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Speaker 4: screen grap I'm actually going to show you guys to

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Speaker 4: prove that it's really a screen grab of my search

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Speaker 4: results from that day, which were as follows wordle how

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Speaker 4: to gut a deer at home, Oh no, how to

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Speaker 4: dress a deer at home? Inflation Reduction Act Rebates twenty

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Speaker 4: twenty four Massachusetts driving directions to the Magical Bridge Playground.

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Speaker 4: That's there.

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Speaker 2: You go, wait, what's the Magical Bridge break? I'm like,

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Speaker 2: more like, what's the Magical Bridge?

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Speaker 4: You know, it's actually really cool. In this area where

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Speaker 4: I was living in California at time, because I teach

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Speaker 4: at Stanford during the winter, there is this playground that

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Speaker 4: was actually designed to be really accessible for kids with disabilities.

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Speaker 4: And it turned out that the playground that's accessible. Is

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Speaker 4: actually the best playground ever made, and it's everybody's favorite playground.

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Speaker 4: So that's the one I was taking my kid too.

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Speaker 1: Yeah, this place looks like a fucking theme park.

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Speaker 4: It's amazing. There's like three, there's more than three of them.

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Speaker 4: They're they're popping up everywhere. They're they're the next generation

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Speaker 4: of playgrounds.

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Speaker 2: Really wow wow wow wow.

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Speaker 1: Okay. I like to see that.

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Speaker 2: And you can tell it's got that like tartan on

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Speaker 2: the ground, Like that makes it real soft and sunshine.

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Speaker 4: Like, yeah, you hurt their little heads on like when

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Speaker 4: they've grown up and it was just all concrete.

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Speaker 2: Yeah, or i'd get like wood moults stuck under my

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Speaker 2: toenails or something because like try to go barefoot down

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Speaker 2: the slide.

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Speaker 4: Yeah, the slide burns you, and then the wood impales

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Speaker 4: at the bottom and that.

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Speaker 2: Makes them stronger, and then you get that nice aroma

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Speaker 2: of like smoky playground for sure.

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Speaker 1: Read yeah, safe for my kids. The tartan. And also

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Speaker 1: I feel like I jump higher on it, and so

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Speaker 1: I like to show that off when I'm at the playground. Well,

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Speaker 1: I'm gonna dunk on these monkey bars. Oh it's five

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Speaker 1: feet high.

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Speaker 4: Cristiana Ronaldo of the of the Mugical Bridge Playground.

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Speaker 1: Yeah, why was your partners or the person who texted you,

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Speaker 1: Why was their fence so sharp? Is that a thing

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Speaker 1: that is normal?

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Speaker 4: Great question. I don't know. They live in the South.

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Speaker 4: I can't speak to Southern fence practices. I don't I

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Speaker 4: have a lot of fence experience. I've myself constructed a

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Speaker 4: lot of four string barb wire fence grown up in

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Speaker 4: ranch culture, but never something with an impalable top.

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Speaker 2: I have real questions, right, yeah, yeah.

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Speaker 1: Like it is that by design? Is he like I'm

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Speaker 1: gonna leave the deer there to tell the other deer

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Speaker 1: what happens when you try to come on our property?

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Speaker 1: Or is it just like an accident because it was

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Speaker 1: so I'm.

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Speaker 4: Gonna go with the former. I'm gonna decide that whoever

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Speaker 4: install that fence wanted it to be a place where

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Speaker 4: you could impale ahead just really.

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Speaker 2: Yeah, oh yeah, like what like in Game of Thrones

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Speaker 2: when like Kalisi takes over that place and like the

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Speaker 2: heads are like on pikes and stuff or body Yeah yeah, I.

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Speaker 4: Guess, or in real life in human history.

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Speaker 1: Yeah, yeah, that was like everywhere that was just interior

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Speaker 1: or exterior decorating.

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Speaker 4: Back in the day, need like a wisconce and then

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Speaker 4: a head.

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Speaker 1: Yeah, it was like we like to around Christmas, have

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Speaker 1: points set as the rest of your heads, just heads

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Speaker 1: decorating everything. What is? What's something you think is underrated?

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Speaker 4: So I I thought about this question a lot. I

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Speaker 4: think that tea time is underrated. And I'm gonna make

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Speaker 4: a defensive of tea time because when I see say

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Speaker 4: tea time, people usually think of sort of like stodgy, British,

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Speaker 4: pinky raised, unpleasantly meticulous. Okay, tea time is supposed to

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Speaker 4: be an incredible spread at like four o'clock in the afternoon,

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Speaker 4: where if you are like me, you are most ravenous.

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Speaker 4: There should be pastries and cakes and like say everythings.

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Speaker 4: My husband is Bolivian. Bolivians really do tea time like

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Speaker 4: they do They will fill your table at tea time

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Speaker 4: and then dinner is like a light snack. I think

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Speaker 4: this is a really underrated way of living one's life

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Speaker 4: because that's when I actually want to just become a

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Speaker 4: complete glutton and move my way across a full table

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Speaker 4: at four o'clock in the afternoon, when I'm just when

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Speaker 4: I've had it with the world. So I think tea time,

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Speaker 4: we should bring tea time back.

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Speaker 1: Wow, I didn't know.

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Speaker 2: Like Bolivians are really I'm just reading about these they

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Speaker 2: got salon reading about these Bolivia, these Olivions.

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Speaker 1: They love the tea time.

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Speaker 4: It comes on tea time.

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Speaker 1: It's a whole.

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Speaker 4: It's very elegant, it's very comforting.

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Speaker 1: Yeah, four o'clock is snack time for me. Like I'm

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Speaker 1: just wondering, Like I feel like I basically recreated this

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Speaker 1: in my own life, but with a snack drawer because

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Speaker 1: you know, being raised Catholic, I have shame and so

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Speaker 1: like I just have all the snacks, but they're like

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Speaker 1: in a drawer, and I just like eat over the

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Speaker 1: drawer all the different snacksave it in there. It's my

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Speaker 1: tea time shame drawer. Wait, what do you wait? What

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Speaker 1: do you got in there?

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Speaker 2: Like loose bread slices or just loose breads all scale,

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Speaker 2: a couple of pieces of wonderbread.

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Speaker 1: No, I got got chips, I got some some pretzels,

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Speaker 1: I got cashews, maybe maybe some sorted nuts in there,

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Speaker 1: and then you know, I'll bring out one bag of

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Speaker 1: either chips or pretzels. At a time to accompany you know,

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Speaker 1: some things from the cold cut drawer, Oh wow, or

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Speaker 1: some you know or hummus, how dignified. Wait, what's this

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Speaker 1: one at a time?

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Speaker 2: What's the spread out of Bolivion tea time?

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Speaker 1: Emily?

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Speaker 4: Oh my god. So you're obviously gonna have different beverages,

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Speaker 4: including tea, but you're also going to have both an

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Speaker 4: array of savory and an array of sweet options. Okay,

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Speaker 4: And I am going to state right here that asking

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Speaker 4: an actual Bolivion would get you a better answer, being

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Speaker 4: of course being there, you know, here without a Bolivion

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Speaker 4: on the call, I'm gonna highlight like something which are

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Speaker 4: a breakfast food as one of the best Bolivian foods

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Speaker 4: you can get. You can get a ton in like

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Speaker 4: the uh DC metro area, a ton of Bolivian's there.

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Speaker 4: You can get them sort of all across Virginia and

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Speaker 4: some in California. But Southanias is basically like a like

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Speaker 4: a savory pastry full of delicious stew and you can

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Speaker 4: kind of bite off the end and sip the stew

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Speaker 4: and then eat the pastry.

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Speaker 1: Oh it's souping.

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Speaker 2: Because like when I see a picture, I'm like, oh,

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Speaker 2: this looks like anada.

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Speaker 4: But oh my god, you will never touch another.

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Speaker 5: No.

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Speaker 4: South is our next level. We have which are like

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Speaker 4: a like a cheese pastry that are really fluffy and delicious.

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Speaker 4: They're kind of like a powdered casual but better better,

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Speaker 4: Oh way better, way better.

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Speaker 1: Okay, okay, go on, go on.

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Speaker 4: Will I'm here to talk about Lyvian food and I

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Speaker 4: will tell you some of the best in the world. No,

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Speaker 4: they're gonna have all kinds of like real dishes, like meats,

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Speaker 4: prepared meats, and and maybe a stew. And then you're

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Speaker 4: also going to have a lot of like cakes and

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Speaker 4: pastries and the sort of usual.

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Speaker 1: Like just started sweating, like.

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Speaker 2: Yeah, because I love pald occasual. Like when I first

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Speaker 2: had that the first I'm like, my god, what the

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Speaker 2: fuck are we doing up here? Like I love and

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Speaker 2: now seeing the other one we say is the fact

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Speaker 2: that it's sort of like a soup dumpling, but the

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Speaker 2: Bolivian version was like first you got to take the

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Speaker 2: bite and then get the soup out and then keep going.

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Speaker 2: I'm also intrigued by the structural integrity of the pastry

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Speaker 2: that can contain a stew within it.

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Speaker 1: This comes down to scale.

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Speaker 4: I mean the pastry is like really robust and like

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Speaker 4: almost has a slight sweetness and thick chewiness to it.

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Speaker 4: But it's also like you're gonna get judged on your

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Speaker 4: skill level, Like some people are beginners and they need

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Speaker 4: to use a utensil or they might get stew on

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Speaker 4: them once they're real pro. You're just holding that saltania

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Speaker 4: in one hand and like longboarding down the road, no

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Speaker 4: stew anywhere on.

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Speaker 1: Your person bottle. Yeah, what is something you think is overrated?

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Speaker 4: I'm gonna stay with my food theme. I actually think

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Speaker 4: we've gotten to the point where where brunches is overrated.

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Speaker 4: I think, yeah, like everybody's doing brunch every weekend, and

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Speaker 4: it's getting the point where it's just like flat, flabby

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Speaker 4: breakfast food at a different time of day, and I'm

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Speaker 4: no longer excited by it. I'm no longer inspired over it.

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Speaker 2: I think brunch like loses its appeal the earlier I

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Speaker 2: wake up. Like when I was like younger and like

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Speaker 2: like you know, going out and shit like that, and

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Speaker 2: I'd wake up lane, I'm like, yeah, brunch, but yeah,

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Speaker 2: let's eat at one that's breakfast. But now I'm like,

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Speaker 2: not already eight or it's depends on you know, if

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Speaker 2: there's an occasion.

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Speaker 1: But yeah, I get that.

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Speaker 2: I guess is that is that maybe one of our

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00:20:32,560 --> 00:20:35,399
Speaker 2: latest food fads that's going away now it is a brunch.

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00:20:35,960 --> 00:20:37,159
Speaker 4: Oh, it'll never go away.

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00:20:37,320 --> 00:20:38,560
Speaker 1: Yeah maybe maybe.

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00:20:38,600 --> 00:20:40,600
Speaker 4: Actually the right answer to how to live one's life

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Speaker 4: is to only have brunch and tea. I mean maybe breakfast,

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Speaker 4: lunch and dinner.

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Speaker 1: Or two meals. Yeah yeah, yeah, yeah, two meals. Give

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Speaker 1: me a brunch, give me a tea, give me that tea.

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Speaker 1: I mean, does the enthusiasm for tea just like make

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Speaker 1: you less likely to have anything at dinner? I feel

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Speaker 1: like dinner becomes an afterthought at that point. Well, snack

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Speaker 1: dinner like spread for tea. Okay, I like that. Yeah,

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Speaker 1: brunch is not a natural time for me to be hungry.

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Speaker 1: If I've eaten breakfast, then like brunch is not.

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Speaker 2: Real, or you do the thing where you wake up

416
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Speaker 2: you're like, fuck, dude, brunch is in four hours and

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Speaker 2: you're like I don't want to like go and not

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Speaker 2: eat anything. So then you're like walking this tightrope of

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00:21:20,080 --> 00:21:22,840
Speaker 2: not eating before or showing up. Hangary, You're like this,

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00:21:23,200 --> 00:21:23,920
Speaker 2: show up me.

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Speaker 1: Yeah, Yeah, all right, let's take a quick break and

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Speaker 1: we'll be right back. And we're back. We're back. And yeah.

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Speaker 1: So there's been some talk it's being called sane washing

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00:21:49,680 --> 00:21:52,680
Speaker 1: when it comes to the Trump campaign, like that how

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00:21:52,720 --> 00:21:59,399
Speaker 1: the mainstream media covers President Trump former President Trump's long

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00:21:59,600 --> 00:22:04,320
Speaker 1: ramble laying press conferences. Yeah and yeah, but it just

427
00:22:04,840 --> 00:22:07,879
Speaker 1: it does feel like there's a different standard when it

428
00:22:07,880 --> 00:22:11,800
Speaker 1: comes to him, possibly because of the glut of insanity

429
00:22:11,880 --> 00:22:15,440
Speaker 1: that is like coming at us, or possibly just because

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Speaker 1: I don't know that you want to make it a

431
00:22:19,400 --> 00:22:22,080
Speaker 1: good a good game. Everyone wants to see a good game,

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00:22:22,200 --> 00:22:24,000
Speaker 1: so we gotta we gotta make sure it's close.

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00:22:24,160 --> 00:22:25,840
Speaker 2: No one likes to blow out, so we got to

434
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Speaker 2: prop up the orange guy who's deteriorating before our eyes.

435
00:22:30,080 --> 00:22:32,520
Speaker 2: But yeah, like you, like you said Aaron Rupar, like

436
00:22:32,560 --> 00:22:35,000
Speaker 2: on a lot of his videos that he clips out

437
00:22:35,000 --> 00:22:37,320
Speaker 2: and puts out on Twitter, like whenever he cut Like

438
00:22:37,440 --> 00:22:39,320
Speaker 2: we've we've played a couple of those clips where trumb

439
00:22:39,320 --> 00:22:40,879
Speaker 2: will be rambling on they'll cut back to someone in

440
00:22:40,920 --> 00:22:43,400
Speaker 2: the student like what he means to say is actually

441
00:22:43,440 --> 00:22:47,000
Speaker 2: this not that Hannibal elector was a real person and

442
00:22:47,280 --> 00:22:47,960
Speaker 2: a good guy.

443
00:22:48,040 --> 00:22:51,520
Speaker 1: What he means a late great Hannibal elector represents our democracy.

444
00:22:52,000 --> 00:22:54,120
Speaker 1: He just wants to talk about all the ways.

445
00:22:53,800 --> 00:22:57,159
Speaker 2: He's a poet actually. But like two recent events in

446
00:22:57,200 --> 00:22:59,800
Speaker 2: the presidential race have kind of underscored how the mainstream

447
00:22:59,800 --> 00:23:02,639
Speaker 2: met tries to normalize Trump and like his circus of

448
00:23:02,680 --> 00:23:05,879
Speaker 2: political aids. So this week, obviously he made headlines for

449
00:23:05,960 --> 00:23:09,080
Speaker 2: insisting on taking photos and filming a TikTok video for

450
00:23:09,160 --> 00:23:12,199
Speaker 2: his campaign in a section of Arlington National Cemetery that

451
00:23:12,320 --> 00:23:14,919
Speaker 2: prohibits that very thing. In fact, it's a violation of

452
00:23:14,960 --> 00:23:19,200
Speaker 2: federal law to use a military cemetery for campaign purposes.

453
00:23:19,480 --> 00:23:21,879
Speaker 2: So while this was happening happening, there was a press

454
00:23:21,880 --> 00:23:25,000
Speaker 2: release that came out of the Trump campaign to sort

455
00:23:25,000 --> 00:23:28,040
Speaker 2: of like paper things over, and journalist Brandon Friedman he

456
00:23:28,080 --> 00:23:30,720
Speaker 2: pointed out on Twitter how the Trump campaign's press release

457
00:23:30,760 --> 00:23:32,359
Speaker 2: had a very dumb typo in it.

458
00:23:32,480 --> 00:23:32,800
Speaker 1: Quote.

459
00:23:32,800 --> 00:23:36,560
Speaker 2: In the statement, campaign manager Chris Losovita incorrectly used the

460
00:23:36,560 --> 00:23:43,160
Speaker 2: word hollowed instead of hallowed. On this hollowed hallowed out, Yeah,

461
00:23:43,240 --> 00:23:48,720
Speaker 2: knock on a hollowed gesture, hallowed ground exactly right, precise

462
00:23:49,080 --> 00:23:53,879
Speaker 2: precisely Fridian slip like so axios the Daily Beasts, they

463
00:23:53,880 --> 00:23:56,440
Speaker 2: added the sort of parenthetical sick to sort of say,

464
00:23:56,480 --> 00:23:58,879
Speaker 2: like if they misused the word, this is what they

465
00:23:58,920 --> 00:24:01,399
Speaker 2: meant to say. But if few other organizations, as you

466
00:24:01,440 --> 00:24:04,600
Speaker 2: pointed out, sort of caught the misuse but then corrected

467
00:24:04,640 --> 00:24:08,520
Speaker 2: it on behalf of the Trump campaign, like CNN did,

468
00:24:08,640 --> 00:24:10,879
Speaker 2: and just like they're like, they meant hallowed, dude, just

469
00:24:10,960 --> 00:24:13,160
Speaker 2: change it so people don't make like, you know, point

470
00:24:13,160 --> 00:24:16,400
Speaker 2: out they made a typo. The New York Times predictably

471
00:24:16,720 --> 00:24:19,639
Speaker 2: ran the story with the typo unedited, and then but

472
00:24:19,680 --> 00:24:21,600
Speaker 2: like so if you searched in Google, you'd be like, oh, yeah,

473
00:24:21,640 --> 00:24:23,600
Speaker 2: they wrote hollowed haha. But when you click it, they

474
00:24:23,640 --> 00:24:27,440
Speaker 2: republished it and edit it, edited it to be hallowed

475
00:24:27,560 --> 00:24:30,160
Speaker 2: without any sort of reference to the fact that there

476
00:24:30,240 --> 00:24:32,120
Speaker 2: was a typo. And then they had, you know, dud

477
00:24:32,240 --> 00:24:34,879
Speaker 2: you basically like, we're doing some copy intern stuff for

478
00:24:34,960 --> 00:24:37,240
Speaker 2: the camp the Trump campaign. But this is like a

479
00:24:37,280 --> 00:24:40,879
Speaker 2: subtle example, but like worth noting because these like small

480
00:24:40,920 --> 00:24:44,480
Speaker 2: accommodations are at the very least bad journalism and at

481
00:24:44,560 --> 00:24:47,600
Speaker 2: best being like, no, we're helping them because like we

482
00:24:48,040 --> 00:24:50,160
Speaker 2: they we just need them to look a little.

483
00:24:50,000 --> 00:24:52,760
Speaker 1: Bit more like together than they obviously are.

484
00:24:53,160 --> 00:24:56,679
Speaker 2: So the other thing that has been like being pointed

485
00:24:56,680 --> 00:25:00,280
Speaker 2: out across the media is from like the CNN shit,

486
00:25:00,480 --> 00:25:02,760
Speaker 2: Like during the DNC, they would have these panels of

487
00:25:02,800 --> 00:25:06,000
Speaker 2: like quote undecided voters to be like, well, so what

488
00:25:06,040 --> 00:25:09,320
Speaker 2: did you think about that you're undecided? And right after

489
00:25:09,480 --> 00:25:12,080
Speaker 2: Kamala Harris gave her acceptance speech, they spoke to a

490
00:25:12,119 --> 00:25:16,160
Speaker 2: panel of supposedly undecided voters in Pennsylvania, and one man

491
00:25:16,560 --> 00:25:19,120
Speaker 2: was clearly an outlier, Like after the speech, he's like, I.

492
00:25:19,040 --> 00:25:20,399
Speaker 1: Don't know, that thing was like bad.

493
00:25:20,520 --> 00:25:22,480
Speaker 2: Most people are like, yeah, that was that was pretty good.

494
00:25:22,560 --> 00:25:25,280
Speaker 2: That that wasn't that wasn't fun? That yeah, that was fine.

495
00:25:25,680 --> 00:25:28,080
Speaker 2: He's like, nah, there's nothing. There's a big nothing burger.

496
00:25:28,400 --> 00:25:30,080
Speaker 2: And then when the panel, like the person who was

497
00:25:30,119 --> 00:25:32,679
Speaker 2: hosting the panel said, has anyone decided yet after this

498
00:25:32,760 --> 00:25:35,360
Speaker 2: speech who they're going to vote for? This guy immediately

499
00:25:35,400 --> 00:25:37,119
Speaker 2: raised his hand. He's like, yeah, I'm voting for Trump.

500
00:25:37,880 --> 00:25:41,800
Speaker 2: They're like, oh, okay. Midas Touch looked into this guy

501
00:25:41,960 --> 00:25:45,080
Speaker 2: and his social media is like littered with MAGA crap,

502
00:25:45,400 --> 00:25:48,520
Speaker 2: Like he's very much clearly like a Trump supporter.

503
00:25:48,880 --> 00:25:50,120
Speaker 1: And when they pressed.

504
00:25:49,800 --> 00:25:52,080
Speaker 2: CNN and him on it, they both kind of had

505
00:25:52,119 --> 00:25:55,440
Speaker 2: conflicting stories. The man said, yeah, dude, I told CNN

506
00:25:55,520 --> 00:25:57,399
Speaker 2: I was a Trump like, I'm a Trump guy, and

507
00:25:57,440 --> 00:25:59,320
Speaker 2: they just asked if I could keep an open mind,

508
00:25:59,400 --> 00:26:01,320
Speaker 2: and I said, yeah, I can't keep an open mind.

509
00:26:01,359 --> 00:26:05,159
Speaker 2: So I went and they called me undecided. CNN was like, well, technically,

510
00:26:05,200 --> 00:26:07,879
Speaker 2: when we spoke to him, he said he hadn't decided

511
00:26:07,960 --> 00:26:11,119
Speaker 2: who he was gonna support, so we invited him to

512
00:26:11,200 --> 00:26:14,520
Speaker 2: speak on the panel. And that's where you're just like,

513
00:26:14,600 --> 00:26:17,080
Speaker 2: what the Like, I'm always confused when they do these

514
00:26:17,119 --> 00:26:21,160
Speaker 2: like undecided sort of panels. I'm like, who, like, who

515
00:26:21,240 --> 00:26:24,120
Speaker 2: really are these people? Like are they really that undecided?

516
00:26:24,119 --> 00:26:27,840
Speaker 2: Because they seem pretty informed for being undecided. And then

517
00:26:27,840 --> 00:26:29,639
Speaker 2: I'm like, what is it that you're waiting for on

518
00:26:29,680 --> 00:26:32,160
Speaker 2: either side? For you'd be like, all right, Trump said

519
00:26:32,160 --> 00:26:34,480
Speaker 2: the thing I needed to hear, all right, the Democrats

520
00:26:34,480 --> 00:26:37,680
Speaker 2: said the thing I needed to hear. And this could

521
00:26:37,680 --> 00:26:39,639
Speaker 2: be like a one off or like a you know,

522
00:26:39,720 --> 00:26:43,160
Speaker 2: simple mistake, but like, you know, Parker macloy pointed out

523
00:26:43,160 --> 00:26:46,240
Speaker 2: that CNN has like a pattern of this shit, Like

524
00:26:46,320 --> 00:26:49,840
Speaker 2: in twenty fifteen, they had a roundtable with Trump supporters

525
00:26:49,880 --> 00:26:52,080
Speaker 2: where like a woman went on like a viral tirade

526
00:26:52,119 --> 00:26:55,520
Speaker 2: against Obama and the issue here. It's just that this

527
00:26:55,640 --> 00:26:58,359
Speaker 2: wasn't like some fox brained normal person. This was like

528
00:26:58,400 --> 00:27:02,200
Speaker 2: a sitting New Hampshire leg Just later Bertha who tried

529
00:27:02,200 --> 00:27:04,840
Speaker 2: to keep Obama off the New Hampshire ballot, who's just

530
00:27:04,880 --> 00:27:09,359
Speaker 2: presenting as just a citizen in New Hampshire. Then in

531
00:27:09,400 --> 00:27:12,879
Speaker 2: twenty eighteen, CNN also had a discussion with quote five

532
00:27:13,000 --> 00:27:17,080
Speaker 2: conservative women from Florida to discuss the sexual assault allegations

533
00:27:17,080 --> 00:27:22,320
Speaker 2: against Brett Kavanaugh, and the women's responses were, yeah, here,

534
00:27:22,359 --> 00:27:26,359
Speaker 2: I'll I'll just play the supposed five conservative women from

535
00:27:26,600 --> 00:27:29,880
Speaker 2: Florida talking about the allegations against Kavanaugh a.

536
00:27:29,840 --> 00:27:32,600
Speaker 4: Show of hands. How many of you believe Judge Kavanaugh

537
00:27:32,600 --> 00:27:33,840
Speaker 4: when he says this didn't happen?

538
00:27:34,720 --> 00:27:40,120
Speaker 6: Abaleena Believa, believe Abalea. How can we believe the word

539
00:27:40,160 --> 00:27:42,920
Speaker 6: of a woman or something that happened thirty six years ago?

540
00:27:43,000 --> 00:27:47,200
Speaker 6: When this guy has an impeccable reputation. It wasn't nobody

541
00:27:47,359 --> 00:27:48,919
Speaker 6: nobody that has spoken.

542
00:27:48,640 --> 00:27:49,560
Speaker 1: Ill will about him.

543
00:27:49,600 --> 00:27:51,800
Speaker 5: Everyone that speaks about him. This guy's an altar boy,

544
00:27:52,359 --> 00:27:53,200
Speaker 5: you know, a scout.

545
00:27:53,400 --> 00:27:56,120
Speaker 6: He's you know, because one woman made an allegation.

546
00:27:56,400 --> 00:27:58,320
Speaker 5: Sorry I don't buy it, but in the grand scheme

547
00:27:58,320 --> 00:28:00,960
Speaker 5: of things, my goodness you there was no intercourse.

548
00:28:01,480 --> 00:28:05,520
Speaker 6: There was maybe a time. Can we really thirty six

549
00:28:05,600 --> 00:28:08,159
Speaker 6: years later she still step on that had it happened.

550
00:28:08,560 --> 00:28:10,560
Speaker 5: I mean, we're talking about a fifteen year old girl,

551
00:28:10,680 --> 00:28:14,160
Speaker 5: which I respect, you know, I'm a woman. I respect.

552
00:28:14,600 --> 00:28:16,920
Speaker 5: We're talking about a seventeen year old boy in high

553
00:28:16,960 --> 00:28:19,480
Speaker 5: school with a stots ll running high Tell me what

554
00:28:19,640 --> 00:28:21,200
Speaker 5: boy hasn't done this in high school.

555
00:28:21,720 --> 00:28:25,399
Speaker 2: So the thing is a huge Journalists looked into these people,

556
00:28:25,880 --> 00:28:29,600
Speaker 2: and at least three of them are political operatives, like

557
00:28:29,600 --> 00:28:32,840
Speaker 2: like one woman like was hosting fundraisers for the GOP.

558
00:28:33,359 --> 00:28:36,240
Speaker 2: Another was running for like was a candidate for office.

559
00:28:36,560 --> 00:28:38,440
Speaker 2: So they had people that were like part of like

560
00:28:38,520 --> 00:28:41,320
Speaker 2: the GOP, like machinery go in there to sort of

561
00:28:41,400 --> 00:28:43,720
Speaker 2: provide intellectual cover for people to be like, yeah, whatever

562
00:28:43,720 --> 00:28:45,239
Speaker 2: happened to our kabnall was not that bad. I mean,

563
00:28:45,240 --> 00:28:49,040
Speaker 2: these five normal people just said that it's nothing, so

564
00:28:49,120 --> 00:28:51,800
Speaker 2: maybe maybe it is. They just framed them as like

565
00:28:52,440 --> 00:28:56,720
Speaker 2: some people with conservatives, Yeah, just as conservatives that were

566
00:28:56,760 --> 00:28:59,520
Speaker 2: living in Florida and their take on it. So it's

567
00:28:59,560 --> 00:29:04,000
Speaker 2: just a yeah, it's just an odd, odd practice, but

568
00:29:04,240 --> 00:29:06,600
Speaker 2: may be quite intentional. But I guess it depends on

569
00:29:06,640 --> 00:29:09,200
Speaker 2: how you look at things, Emily, how do you sort

570
00:29:09,240 --> 00:29:12,520
Speaker 2: of see this kind of journalism with what's your take

571
00:29:12,560 --> 00:29:12,760
Speaker 2: on that?

572
00:29:13,200 --> 00:29:15,400
Speaker 4: Well, it's not happening in a vacuum. When I look

573
00:29:15,400 --> 00:29:17,840
Speaker 4: at this, what I see, honestly is I'm a trial lawyer.

574
00:29:17,840 --> 00:29:20,920
Speaker 4: I see the jury selection process, right, which is a

575
00:29:20,920 --> 00:29:21,480
Speaker 4: similar space.

576
00:29:21,560 --> 00:29:21,640
Speaker 2: Right.

577
00:29:21,640 --> 00:29:23,560
Speaker 4: It's this space where we're all pretending to be neutral,

578
00:29:23,600 --> 00:29:25,680
Speaker 4: and we have no pre existing beliefs, and we're coming

579
00:29:25,680 --> 00:29:27,560
Speaker 4: in here with a totally open mind. And yet everyone

580
00:29:27,600 --> 00:29:29,840
Speaker 4: walked through the door totally looked at my client was like,

581
00:29:29,880 --> 00:29:33,640
Speaker 4: I wonder what that person did? Right, So we also

582
00:29:33,760 --> 00:29:37,479
Speaker 4: see real restrictions on who's invited to be part of

583
00:29:37,520 --> 00:29:39,479
Speaker 4: that process. Like you got to look at how media

584
00:29:39,640 --> 00:29:41,680
Speaker 4: put out the call for people to sign up for

585
00:29:41,760 --> 00:29:43,640
Speaker 4: opportunities like this, the same way you got to look

586
00:29:43,640 --> 00:29:46,200
Speaker 4: at how you know in the jury system, people with

587
00:29:46,240 --> 00:29:48,400
Speaker 4: prior convictions are excluded, People who aren't on the voter

588
00:29:48,440 --> 00:29:50,880
Speaker 4: rolls are excluded. People who may not have a driver's

589
00:29:50,920 --> 00:29:52,720
Speaker 4: license can be excluded, people who don't have a mailing

590
00:29:52,720 --> 00:29:54,800
Speaker 4: address are excluded, So you get these juries that are

591
00:29:54,840 --> 00:29:58,080
Speaker 4: sort of made wealthier and wider and more conservative by

592
00:29:58,080 --> 00:30:00,480
Speaker 4: the ways in which people are even invited to attend.

593
00:30:01,240 --> 00:30:04,280
Speaker 4: And then that's sort of distilled into an even more

594
00:30:04,400 --> 00:30:09,560
Speaker 4: sort of pro prosecution extract through the process of questioning people.

595
00:30:09,600 --> 00:30:10,959
Speaker 4: And then if a person's like, I don't know if

596
00:30:11,000 --> 00:30:12,520
Speaker 4: I can be fair, the judge is like, you can

597
00:30:12,600 --> 00:30:15,960
Speaker 4: keep an open mind, right, same, it's the CNN question, right, right.

598
00:30:16,080 --> 00:30:17,800
Speaker 4: You've told us who you are and what you believe in,

599
00:30:18,000 --> 00:30:20,040
Speaker 4: but you can sett all that aside, can't you.

600
00:30:20,520 --> 00:30:26,400
Speaker 2: Right, So I'm as huh, I can keep an open

601
00:30:26,440 --> 00:30:27,480
Speaker 2: mind here? Yeah, I think so.

602
00:30:27,760 --> 00:30:28,160
Speaker 1: I think so.

603
00:30:28,960 --> 00:30:31,960
Speaker 2: But that's not a problem for me brother in law technically.

604
00:30:32,320 --> 00:30:32,880
Speaker 1: But yeah.

605
00:30:33,120 --> 00:30:36,400
Speaker 4: So yeah, Like who's who's the producer, who's setting up

606
00:30:36,400 --> 00:30:39,680
Speaker 4: the process through which these people appear, and who's the

607
00:30:39,720 --> 00:30:42,000
Speaker 4: person not doing like a basic social media.

608
00:30:41,840 --> 00:30:44,320
Speaker 2: Search, right, because like in that instance of the guy

609
00:30:44,440 --> 00:30:46,440
Speaker 2: like in the undecided, like I mean whatever, that guy

610
00:30:46,600 --> 00:30:48,600
Speaker 2: just there's like I get to be on TV or whatever,

611
00:30:48,640 --> 00:30:51,320
Speaker 2: Like I mean that that that's clearly on the producers,

612
00:30:51,360 --> 00:30:54,040
Speaker 2: like you're saying, of how they're selecting people and whether

613
00:30:54,120 --> 00:30:56,680
Speaker 2: they they are doing it intentionally or just don't care

614
00:30:57,080 --> 00:30:58,760
Speaker 2: because they're like, I don't know, they said they were, man,

615
00:30:58,760 --> 00:30:59,880
Speaker 2: I'm just trying to get five people.

616
00:30:59,680 --> 00:31:00,960
Speaker 1: In the room so they could talk.

617
00:31:01,600 --> 00:31:03,760
Speaker 2: And yeah, I guess maybe it was a mistake for

618
00:31:03,800 --> 00:31:05,840
Speaker 2: me to reach out to my friend who like runs

619
00:31:05,880 --> 00:31:08,920
Speaker 2: the local Republican party to ask if they knew five

620
00:31:09,000 --> 00:31:11,000
Speaker 2: people who wanted to be on camera for CNN.

621
00:31:11,400 --> 00:31:13,800
Speaker 4: But yeah, it's also like what's the utility, Like, what

622
00:31:13,880 --> 00:31:16,640
Speaker 4: are we really gaining? It's not a scientific process. This

623
00:31:16,720 --> 00:31:19,400
Speaker 4: group of people doesn't necessarily represent or speak for the

624
00:31:19,480 --> 00:31:22,560
Speaker 4: average undecided voter. Now we know that they're maybe not

625
00:31:22,600 --> 00:31:25,280
Speaker 4: even undecided at all. Maybe they're just a political operative

626
00:31:25,440 --> 00:31:28,800
Speaker 4: who has a good, you know, makeup face. Right, I

627
00:31:28,840 --> 00:31:33,360
Speaker 4: don't understand what the average viewer is gaining from these events.

628
00:31:33,880 --> 00:31:36,560
Speaker 2: Yeah, right, it's always meant to I think I don't know,

629
00:31:36,600 --> 00:31:38,320
Speaker 2: like half the time when I see those panels or

630
00:31:38,320 --> 00:31:41,120
Speaker 2: people undecided, like like I said that, they seem to know.

631
00:31:41,720 --> 00:31:45,040
Speaker 2: They don't seem like low information voters, you know, like,

632
00:31:45,120 --> 00:31:47,640
Speaker 2: and so then I'm like, I that's where I'm like,

633
00:31:47,680 --> 00:31:50,959
Speaker 2: these people sound like they're basically Democrats or Republicans who

634
00:31:51,000 --> 00:31:53,520
Speaker 2: are being like I don't know, but probably this.

635
00:31:53,920 --> 00:31:56,600
Speaker 4: Low information voters would be great. Like, honestly, you put

636
00:31:56,600 --> 00:31:59,479
Speaker 4: people on there who first of all normalize being a

637
00:31:59,520 --> 00:32:01,680
Speaker 4: low inform voter, make it okay to be like, hey,

638
00:32:01,680 --> 00:32:03,600
Speaker 4: I actually don't know about this or this issue is

639
00:32:03,600 --> 00:32:06,000
Speaker 4: confusing me, and I'd like a better explanation, and then

640
00:32:06,360 --> 00:32:09,360
Speaker 4: present an opportunity for the mass media audience to also

641
00:32:09,480 --> 00:32:11,400
Speaker 4: receive that explanation, so that people It's the same way

642
00:32:11,400 --> 00:32:12,920
Speaker 4: as high school teacher might say, like, if you have

643
00:32:12,960 --> 00:32:15,400
Speaker 4: a question, somebody else probably has the same question. Please

644
00:32:15,440 --> 00:32:17,560
Speaker 4: ask your question. We could do that. I don't know

645
00:32:17,560 --> 00:32:18,600
Speaker 4: why we're doing this instead.

646
00:32:18,680 --> 00:32:21,800
Speaker 2: Yeah, no stupid questions. The thing that every good professor

647
00:32:21,840 --> 00:32:24,440
Speaker 2: will tell you or teacher who's like no, no ask, ask

648
00:32:24,760 --> 00:32:26,760
Speaker 2: because you got to know, or else, yeah, you ask

649
00:32:26,880 --> 00:32:29,360
Speaker 2: weird stuff or learn weird stuff because you don't ask.

650
00:32:29,920 --> 00:32:33,120
Speaker 1: There's that sketch and everybody's in la the John Mulaney

651
00:32:33,160 --> 00:32:36,280
Speaker 1: series where they're like doing a daily show style like

652
00:32:36,400 --> 00:32:40,600
Speaker 1: interview with a guy who's like saying really stupid shit

653
00:32:40,920 --> 00:32:44,080
Speaker 1: about like Trump and his support for Trump, and then

654
00:32:44,160 --> 00:32:46,640
Speaker 1: they like follow him home and he's like, yeah, no,

655
00:32:46,720 --> 00:32:49,480
Speaker 1: I'm stupid. On TV for a living that's like my thing.

656
00:32:50,160 --> 00:32:53,000
Speaker 1: I actually got interviewed by Borat a couple of years

657
00:32:53,000 --> 00:32:55,600
Speaker 1: ago that was a career highlight, and like he's just

658
00:32:55,640 --> 00:32:58,240
Speaker 1: like I have this like room that I keep in

659
00:32:58,280 --> 00:33:00,680
Speaker 1: my house that looks like shit and has like a

660
00:33:00,720 --> 00:33:03,480
Speaker 1: Confederate flag up and then but like I keep that

661
00:33:03,560 --> 00:33:06,400
Speaker 1: stuff separate, like I actually live with my family and

662
00:33:06,440 --> 00:33:10,200
Speaker 1: the kids, like we have this nice house that Yeah,

663
00:33:10,760 --> 00:33:14,400
Speaker 1: I feel like, yeah, these are political operatives essentially, Like

664
00:33:14,440 --> 00:33:18,520
Speaker 1: it's the real, the real version of that, except you know,

665
00:33:19,120 --> 00:33:23,600
Speaker 1: obviously they are employed and like working within these massive

666
00:33:23,640 --> 00:33:27,440
Speaker 1: parties too write convey what those parties need conveyed.

667
00:33:27,760 --> 00:33:29,320
Speaker 2: But I think it's also yeah, it sort of shows

668
00:33:29,360 --> 00:33:31,400
Speaker 2: too how we talk about how like a lot of

669
00:33:31,760 --> 00:33:35,040
Speaker 2: media outlets just aren't able to reckon with real issues

670
00:33:35,080 --> 00:33:36,960
Speaker 2: because of the fact that they're so entrenched in a

671
00:33:37,000 --> 00:33:38,200
Speaker 2: lot of these systems themselves.

672
00:33:38,240 --> 00:33:40,120
Speaker 1: They're like, yeah, I think this is this is good enough.

673
00:33:40,400 --> 00:33:42,360
Speaker 1: Can we actually speak objectively about that?

674
00:33:42,600 --> 00:33:45,400
Speaker 2: I don't know, but you know, this is like I think, yeah,

675
00:33:45,400 --> 00:33:47,360
Speaker 2: this is where a lot of the media is falling

676
00:33:47,440 --> 00:33:49,720
Speaker 2: short at a time when people really need to have

677
00:33:49,920 --> 00:33:53,640
Speaker 2: like the truth, which we don't get all the time.

678
00:33:53,800 --> 00:33:56,120
Speaker 4: I also don't know what objective necessarily looks like, because

679
00:33:56,120 --> 00:33:58,560
Speaker 4: you're right, when you're deeply entrenched in the system, it's very,

680
00:33:58,680 --> 00:34:02,360
Speaker 4: very hard to see it's equilibrium from the outside. I'm

681
00:34:02,720 --> 00:34:06,080
Speaker 4: a like devoted NPR listener. I go running in the

682
00:34:06,080 --> 00:34:07,960
Speaker 4: morning and I pop on Morning Edition and I'm very

683
00:34:08,000 --> 00:34:10,400
Speaker 4: happy to hear it. But like, even on NPR, there

684
00:34:10,400 --> 00:34:12,480
Speaker 4: have been these few times where they're covering, you know,

685
00:34:12,560 --> 00:34:14,719
Speaker 4: a democratic event, a Republican event, and they'll be like, well,

686
00:34:14,719 --> 00:34:16,640
Speaker 4: they talked about the economy, which is a bad issue

687
00:34:16,640 --> 00:34:19,200
Speaker 4: for Democrats, and I'm like, is it, Yeah, it is

688
00:34:19,360 --> 00:34:21,759
Speaker 4: actually like listen to Bill Clinton, a person of whom

689
00:34:21,800 --> 00:34:23,560
Speaker 4: I am not always a fan, but with Bill Clinton,

690
00:34:23,560 --> 00:34:27,120
Speaker 4: who like spelled out how great the economy is built

691
00:34:27,120 --> 00:34:30,239
Speaker 4: by Democrats over the last several decades have been, and

692
00:34:30,280 --> 00:34:31,920
Speaker 4: it's weird to hear that coming from NPR. I think

693
00:34:31,920 --> 00:34:34,839
Speaker 4: it's like their gesture towards equilibrium that doesn't actually speak

694
00:34:34,880 --> 00:34:35,600
Speaker 4: to truth.

695
00:34:35,960 --> 00:34:38,200
Speaker 1: Right, Yeah, I think that's a problem with the mainstream

696
00:34:38,239 --> 00:34:41,879
Speaker 1: media that we'll also get to on you know, policing,

697
00:34:42,200 --> 00:34:44,640
Speaker 1: and you know, they there are these things that they

698
00:34:44,840 --> 00:34:48,799
Speaker 1: just assume are bad for progressives that everyone disagrees with

699
00:34:48,960 --> 00:34:52,959
Speaker 1: and they just do a very surface level pass over

700
00:34:53,080 --> 00:34:56,200
Speaker 1: those things, just being like, yeah, well those things that

701
00:34:56,400 --> 00:35:00,680
Speaker 1: everybody assumes about progressive ideas around this are true, and

702
00:35:00,719 --> 00:35:02,719
Speaker 1: like we just have to take that into account as

703
00:35:02,719 --> 00:35:05,680
Speaker 1: opposed to like digging into some ways that they can

704
00:35:05,719 --> 00:35:09,760
Speaker 1: be proven not true. Right, Yeah, MPR drives me fucking crazy.

705
00:35:10,520 --> 00:35:12,880
Speaker 1: All right, let's take a quick break and we're going

706
00:35:12,960 --> 00:35:17,640
Speaker 1: to come back and talk about policing and Axon finally

707
00:35:17,680 --> 00:35:20,200
Speaker 1: find out a little bit more about this cool company

708
00:35:20,360 --> 00:35:34,359
Speaker 1: named Axon, and we're back. We're back, all right. So

709
00:35:34,400 --> 00:35:37,400
Speaker 1: that you may have seen this story that AI police

710
00:35:37,400 --> 00:35:44,160
Speaker 1: reports are here to save the police from doing police work. Basically,

711
00:35:44,719 --> 00:35:47,080
Speaker 1: it's only a matter of time, you know, it was

712
00:35:47,120 --> 00:35:50,880
Speaker 1: only a matter of time until the two of the

713
00:35:50,920 --> 00:35:55,880
Speaker 1: shittier things on the planet, AI and policing joined forces.

714
00:35:56,320 --> 00:35:59,239
Speaker 1: In this case, the AI helps them churn out recaps

715
00:35:59,239 --> 00:36:03,520
Speaker 1: of incidents using bodycam footage, thus sparing the officers from

716
00:36:03,560 --> 00:36:07,920
Speaker 1: having to pen lengthy reports. And the cops like in

717
00:36:08,040 --> 00:36:10,400
Speaker 1: talking about it, the ones that they're like interviewing for

718
00:36:10,480 --> 00:36:13,640
Speaker 1: these puff pieces on the technology are like, I can't

719
00:36:13,640 --> 00:36:17,840
Speaker 1: write for shit, come basically an idiot and this thing

720
00:36:17,960 --> 00:36:21,319
Speaker 1: made me like it. This is a quote from one

721
00:36:21,320 --> 00:36:23,880
Speaker 1: of the stories. It was a better report than I

722
00:36:23,920 --> 00:36:27,040
Speaker 1: could have ever written, and it was one hundred percent accurate.

723
00:36:27,200 --> 00:36:30,400
Speaker 1: It flowed better, better than I could have ever written.

724
00:36:30,520 --> 00:36:34,440
Speaker 1: That's first of all, it's supposed to be a rough draft, Like,

725
00:36:34,560 --> 00:36:37,719
Speaker 1: it's not supposed to be replaced the reports that you're

726
00:36:37,760 --> 00:36:40,279
Speaker 1: writing the post to give you a rough draft that

727
00:36:40,360 --> 00:36:43,440
Speaker 1: you then like work backwards on. Nah, gotta cut corners.

728
00:36:43,960 --> 00:36:46,800
Speaker 1: How it's called draft one, by the way, that's the

729
00:36:46,920 --> 00:36:49,440
Speaker 1: name of the technology as draft one. And he's like,

730
00:36:49,719 --> 00:36:52,759
Speaker 1: this is the goddamn best thing, best version of a

731
00:36:52,800 --> 00:36:54,759
Speaker 1: report I've ever seen. EI.

732
00:36:54,920 --> 00:36:58,719
Speaker 2: How important are these like police reports in terms of,

733
00:36:59,040 --> 00:37:01,800
Speaker 2: you know, like what people intersect with the justice system,

734
00:37:02,160 --> 00:37:05,200
Speaker 2: Like is it how vital are these and how much

735
00:37:05,280 --> 00:37:08,680
Speaker 2: like how much room is there for you know, dubious

736
00:37:08,760 --> 00:37:11,560
Speaker 2: shit to pop into these kinds of police reports.

737
00:37:11,880 --> 00:37:13,840
Speaker 4: All Right, to be clear, they're already largely made of

738
00:37:13,920 --> 00:37:16,080
Speaker 4: dubious shit. Like let's let's start from there.

739
00:37:16,160 --> 00:37:18,120
Speaker 1: Okay, there we go, Thank you. Things.

740
00:37:18,440 --> 00:37:20,960
Speaker 4: These things vary really really wildly from place to place.

741
00:37:21,040 --> 00:37:24,000
Speaker 4: So when I started out as a public defender, I

742
00:37:24,040 --> 00:37:27,360
Speaker 4: was in Santa Clara County, California, in which the police

743
00:37:27,400 --> 00:37:31,080
Speaker 4: are trained to write reports. So when a thing happens

744
00:37:31,080 --> 00:37:32,719
Speaker 4: and the police are there, they'll like write down what

745
00:37:32,760 --> 00:37:34,680
Speaker 4: they saw, and then the second top there will write

746
00:37:34,719 --> 00:37:36,120
Speaker 4: down what he saw. And then they talked to a

747
00:37:36,160 --> 00:37:37,600
Speaker 4: witness and they write down with the witness set, and

748
00:37:37,640 --> 00:37:40,279
Speaker 4: all in all you get this packet, which is really

749
00:37:40,320 --> 00:37:43,160
Speaker 4: really helpful if we are going to believe that the

750
00:37:43,239 --> 00:37:46,080
Speaker 4: legal system is in any way about finding truth, right, Like,

751
00:37:46,120 --> 00:37:48,719
Speaker 4: you want to have detailed accounts from the people who

752
00:37:48,760 --> 00:37:50,880
Speaker 4: are there about what they what they heard, and what

753
00:37:50,920 --> 00:37:53,160
Speaker 4: they saw. I then went out to New York to

754
00:37:53,280 --> 00:37:56,160
Speaker 4: work at Bronx Defenders, and that's when I learned that

755
00:37:56,200 --> 00:38:00,680
Speaker 4: the NYPD is essentially like really really really good at

756
00:38:00,719 --> 00:38:03,960
Speaker 4: not writing stuff down. When you get an NYPD discovery packet,

757
00:38:03,960 --> 00:38:06,600
Speaker 4: it's like a whole bunch of pages, but all of

758
00:38:06,640 --> 00:38:09,080
Speaker 4: the pages have the same one line copy pasted on them.

759
00:38:09,080 --> 00:38:10,759
Speaker 4: It's like, at the time and place of occurrence the

760
00:38:10,800 --> 00:38:11,880
Speaker 4: incident did occur.

761
00:38:13,640 --> 00:38:17,640
Speaker 1: Anything, and that is when the suspected perpetrator did occur

762
00:38:17,840 --> 00:38:22,160
Speaker 1: onto the occurrence and it happened that at that moment

763
00:38:22,400 --> 00:38:27,600
Speaker 1: in the geographical location in question, heretofore it's just like

764
00:38:27,920 --> 00:38:28,359
Speaker 1: it's ill.

765
00:38:28,960 --> 00:38:31,440
Speaker 4: Yeah, we can have a whole conversation about like the

766
00:38:31,480 --> 00:38:34,680
Speaker 4: police attraction to big words. They don't quite use it.

767
00:38:35,080 --> 00:38:37,040
Speaker 4: You want to have a great time. They've got the

768
00:38:37,120 --> 00:38:41,120
Speaker 4: defense attorney ask a cop on the stand what furtive means?

769
00:38:41,719 --> 00:38:45,400
Speaker 4: They love saying every doing furtive movements, But like, what

770
00:38:45,400 --> 00:38:47,320
Speaker 4: what is what is furtive to you?

771
00:38:48,800 --> 00:38:49,000
Speaker 1: Now?

772
00:38:49,640 --> 00:38:54,640
Speaker 4: Indeed, this whole situation is furtive. So when you get

773
00:38:54,680 --> 00:38:57,840
Speaker 4: to a place where essentially nothing is written down, you

774
00:38:57,640 --> 00:39:01,879
Speaker 4: had you create a systemic problem, which is, in order

775
00:39:01,920 --> 00:39:05,120
Speaker 4: for me to get any information to protect an accused

776
00:39:05,120 --> 00:39:09,080
Speaker 4: person and protect their US constitutional rights, I'm going to

777
00:39:09,160 --> 00:39:11,279
Speaker 4: need to create a legal process to find out more

778
00:39:11,360 --> 00:39:14,600
Speaker 4: about what this CoP's claims actually are, which means I

779
00:39:14,640 --> 00:39:17,040
Speaker 4: may have to demand hearings that I don't actually need,

780
00:39:17,120 --> 00:39:19,040
Speaker 4: Like I might have to file suppression hearings that I

781
00:39:19,040 --> 00:39:20,800
Speaker 4: don't actually need, just to get the cop on the

782
00:39:20,840 --> 00:39:23,560
Speaker 4: witness stand just so I can cross examine them about

783
00:39:23,560 --> 00:39:26,560
Speaker 4: what the heck they're saying they saw and did. So

784
00:39:26,640 --> 00:39:29,479
Speaker 4: it's really really really inefficient, and it's bad for truth

785
00:39:29,520 --> 00:39:31,640
Speaker 4: and it's bad for justice, Like it's very very bad

786
00:39:31,680 --> 00:39:34,160
Speaker 4: for any semblance of accuracy in the system, and it

787
00:39:34,200 --> 00:39:36,479
Speaker 4: causes massive delays. So all of this is to say

788
00:39:36,920 --> 00:39:41,239
Speaker 4: bad discovery is a huge driver of our system being

789
00:39:41,320 --> 00:39:43,560
Speaker 4: inept at creating any semblance of truth.

790
00:39:44,000 --> 00:39:44,239
Speaker 1: Right.

791
00:39:44,800 --> 00:39:48,280
Speaker 4: It's also like you have to remember that police writing reports.

792
00:39:48,280 --> 00:39:51,080
Speaker 4: There's kind of a double edged sword here because police

793
00:39:51,080 --> 00:39:54,880
Speaker 4: get a ton of overtime out of writing reports if

794
00:39:54,880 --> 00:39:56,680
Speaker 4: they make an arrest at the end of their shift

795
00:39:57,000 --> 00:39:58,400
Speaker 4: and they get to sit at their desk for the

796
00:39:58,400 --> 00:40:01,400
Speaker 4: next three hours like carefully inscribing documents with at the

797
00:40:01,400 --> 00:40:05,239
Speaker 4: time and place of occurrence the event and again very

798
00:40:05,239 --> 00:40:08,520
Speaker 4: I cursive. Yeah, they make a ton of overtime doing that.

799
00:40:08,680 --> 00:40:12,399
Speaker 4: So I think I mean when I say a ton,

800
00:40:12,440 --> 00:40:14,480
Speaker 4: I mean like millions and millions. Wherever you are in

801
00:40:14,480 --> 00:40:16,880
Speaker 4: the country. You should google who the highest paid public

802
00:40:16,880 --> 00:40:18,960
Speaker 4: employee in your jurisdiction was, and there's like a decent

803
00:40:19,040 --> 00:40:20,360
Speaker 4: chance it was a cop who made a lot of

804
00:40:20,400 --> 00:40:22,000
Speaker 4: overtime a few years ago. It was like a port

805
00:40:22,040 --> 00:40:23,520
Speaker 4: authority cop in New York City.

806
00:40:24,160 --> 00:40:24,640
Speaker 1: Wow.

807
00:40:24,760 --> 00:40:27,120
Speaker 4: Yeah, just like over a million bucks in overtime. And

808
00:40:28,480 --> 00:40:30,319
Speaker 4: so when I think about what AI would do to

809
00:40:30,360 --> 00:40:32,200
Speaker 4: this process, I think of a couple of things. One

810
00:40:33,280 --> 00:40:36,520
Speaker 4: it's less accurate because it's not giving you the police

811
00:40:36,560 --> 00:40:39,440
Speaker 4: officers impressions of what happened. It's giving you the AIS

812
00:40:39,480 --> 00:40:41,600
Speaker 4: impressions of what happened. And this is even assuming the

813
00:40:41,600 --> 00:40:45,000
Speaker 4: AI doesn't hallucinate, which, as we know, like AIS make

814
00:40:45,040 --> 00:40:48,640
Speaker 4: stuff up all the time. So yeah, like, if you're

815
00:40:48,640 --> 00:40:51,160
Speaker 4: going to totally hand over your faith to a robot

816
00:40:51,200 --> 00:40:54,239
Speaker 4: to tell you what happened in a video and abandon

817
00:40:54,320 --> 00:40:57,400
Speaker 4: the idea that human perception is necessary to interpret what

818
00:40:57,440 --> 00:40:59,879
Speaker 4: happened in a video, you're also leaving by the side

819
00:40:59,880 --> 00:41:01,480
Speaker 4: of the road things that I might need to know

820
00:41:01,520 --> 00:41:04,759
Speaker 4: about the CoP's ability to perceive about what the cop

821
00:41:04,840 --> 00:41:08,280
Speaker 4: was focused on. What like, For example, in a police report,

822
00:41:08,400 --> 00:41:11,799
Speaker 4: let's say the whole report is written about I don't know,

823
00:41:12,160 --> 00:41:15,160
Speaker 4: somebody's way of driving a car in a DUI case,

824
00:41:15,800 --> 00:41:17,279
Speaker 4: and none of it's about the fact that when the

825
00:41:17,280 --> 00:41:21,799
Speaker 4: person got totally furtively furtively across you know, when they

826
00:41:21,840 --> 00:41:24,080
Speaker 4: get out of the car, maybe everything they did at

827
00:41:24,080 --> 00:41:26,640
Speaker 4: that point was fine. Maybe they're talking, fine, walking fine,

828
00:41:26,719 --> 00:41:30,000
Speaker 4: don't have any sort of symptoms of intoxication. If the

829
00:41:30,160 --> 00:41:32,920
Speaker 4: entire report is about the driving, then I get to

830
00:41:32,960 --> 00:41:35,719
Speaker 4: cross examine the cop On, Like why why didn't you

831
00:41:35,719 --> 00:41:39,400
Speaker 4: talk about what happened after that? What's like, their omissions

832
00:41:39,400 --> 00:41:43,080
Speaker 4: can be really really important to a jury to decide

833
00:41:43,120 --> 00:41:45,880
Speaker 4: who's lying, who's telling the truth. You take the human

834
00:41:46,000 --> 00:41:49,640
Speaker 4: perception out of that, and you take away this fundamental thing.

835
00:41:49,680 --> 00:41:52,040
Speaker 4: Our system is designed to have twelve people tell you

836
00:41:52,320 --> 00:41:56,040
Speaker 4: if another person is lying, right, twelve people can't tell

837
00:41:56,040 --> 00:41:58,600
Speaker 4: you if an ai is lying or hallucinating. I mean,

838
00:41:58,600 --> 00:42:01,760
Speaker 4: it's just it takes us even farther from the system

839
00:42:01,840 --> 00:42:04,799
Speaker 4: having utility. And I get that in the system we

840
00:42:04,880 --> 00:42:08,759
Speaker 4: are going to consistently prioritize the efficiency of punishment over

841
00:42:08,800 --> 00:42:12,440
Speaker 4: the semblance of truth. But especially with the involvement of Axon,

842
00:42:12,560 --> 00:42:14,640
Speaker 4: which has a grotesque history, I'd be more than happy

843
00:42:14,680 --> 00:42:18,960
Speaker 4: to Yeah, this is like five alarm fire.

844
00:42:19,360 --> 00:42:22,040
Speaker 2: Wait, you're saying a company that used to be called Taser.

845
00:42:24,360 --> 00:42:27,200
Speaker 1: I like that they went from Taser obviously like trying

846
00:42:27,200 --> 00:42:28,920
Speaker 1: to cover up the fact that they're the company that

847
00:42:28,960 --> 00:42:32,000
Speaker 1: invented the taser that for some reason as a negative

848
00:42:32,000 --> 00:42:36,520
Speaker 1: connotation with it to Axon is like so fucking aggressive.

849
00:42:37,000 --> 00:42:39,000
Speaker 4: Well, it's also you know, that's a nerve. Axon is

850
00:42:39,000 --> 00:42:41,399
Speaker 4: what the electrical current runs down that stimulates the next

851
00:42:41,400 --> 00:42:44,040
Speaker 4: nerve cell. So it's still like we're gonna zapia. It's

852
00:42:44,120 --> 00:42:46,359
Speaker 4: just we're gonna zapA for people who took ap bio.

853
00:42:46,800 --> 00:42:49,320
Speaker 2: That's right exactly. It's like the version of using furtive.

854
00:42:49,400 --> 00:42:51,200
Speaker 2: They're like, what if we just clashed it up?

855
00:42:53,840 --> 00:42:57,319
Speaker 1: I would you throw in the additional thing? And this

856
00:42:57,400 --> 00:43:00,840
Speaker 1: might be like not this might be a controversial statement,

857
00:43:00,880 --> 00:43:04,680
Speaker 1: but I personally don't want to get like it. So

858
00:43:04,800 --> 00:43:08,120
Speaker 1: the CEO of Axon, who is a company behind this

859
00:43:08,880 --> 00:43:11,680
Speaker 1: AI technology, will talk about other stuff there behind brag

860
00:43:11,719 --> 00:43:14,640
Speaker 1: that the AI spares cops from the tedious work of

861
00:43:14,760 --> 00:43:19,800
Speaker 1: spending half their day doing data entry. I don't want

862
00:43:20,400 --> 00:43:24,799
Speaker 1: police to be like out roaming the streets more with

863
00:43:24,880 --> 00:43:29,080
Speaker 1: their guns, like ready to get like suspicious about whatever

864
00:43:29,280 --> 00:43:33,400
Speaker 1: comes across their plate while they're sufficiently bored, Like I

865
00:43:33,400 --> 00:43:35,239
Speaker 1: feel like this is a job that we want to

866
00:43:35,360 --> 00:43:40,960
Speaker 1: have a healthy amount of like downtime where they're reflecting

867
00:43:41,000 --> 00:43:44,920
Speaker 1: on what they've done and like having to think about

868
00:43:44,960 --> 00:43:48,799
Speaker 1: that and account for it. And this technology seems to

869
00:43:48,800 --> 00:43:51,600
Speaker 1: be designed to like what if the police were like

870
00:43:51,880 --> 00:43:56,480
Speaker 1: even more gas and less breaks like built into it.

871
00:43:56,520 --> 00:43:59,000
Speaker 1: What if it was just more they don't really even

872
00:43:59,040 --> 00:44:01,120
Speaker 1: have to think about it. The machines there to like

873
00:44:01,239 --> 00:44:04,560
Speaker 1: just document what they did. We want to beless now,

874
00:44:04,719 --> 00:44:08,200
Speaker 1: you know, yeah, you know exactly more frictionless policing aka

875
00:44:08,440 --> 00:44:11,960
Speaker 1: just like out there fucking shit up more of the time.

876
00:44:12,080 --> 00:44:12,319
Speaker 1: I think.

877
00:44:12,320 --> 00:44:14,239
Speaker 2: The other thing that's really interesting too is like to

878
00:44:14,280 --> 00:44:17,719
Speaker 2: your point, Emily, you know, the overtime is where a

879
00:44:17,719 --> 00:44:20,000
Speaker 2: lot of budgets go, and a lot of people they

880
00:44:20,040 --> 00:44:22,160
Speaker 2: make their they make that money. We're like, how does

881
00:44:22,160 --> 00:44:24,960
Speaker 2: that cop have that fucking car and like a boat

882
00:44:25,000 --> 00:44:26,840
Speaker 2: and all this other stuff. It's like, yeah, dude, the

883
00:44:26,880 --> 00:44:30,759
Speaker 2: overtime's wacky that they're never like this will actually help

884
00:44:30,840 --> 00:44:33,520
Speaker 2: cut down on costs. They're more just like, dude, it's

885
00:44:33,520 --> 00:44:35,400
Speaker 2: gonna help the cops, dude, so they don't have to

886
00:44:35,440 --> 00:44:38,080
Speaker 2: be bored at work, you know, and like you think

887
00:44:38,120 --> 00:44:39,799
Speaker 2: the way to sell it to people who might be

888
00:44:39,800 --> 00:44:42,839
Speaker 2: more progressive, like guess what, man, this could actually save

889
00:44:42,880 --> 00:44:44,400
Speaker 2: a lot of money because now they don't have the

890
00:44:44,440 --> 00:44:47,920
Speaker 2: time to do, you know, claim as much overtime. But again,

891
00:44:47,960 --> 00:44:50,680
Speaker 2: that's that's part of the appeal. So they'll just be like, no, man,

892
00:44:50,719 --> 00:44:52,680
Speaker 2: it just makes their job easier so they can keep

893
00:44:52,719 --> 00:44:57,200
Speaker 2: you the citizen safe, all right, next question, You're right.

894
00:44:57,120 --> 00:45:00,000
Speaker 4: Though, it's also sort of exposing this terrible choice.

895
00:45:00,160 --> 00:45:00,319
Speaker 1: Right.

896
00:45:00,719 --> 00:45:03,600
Speaker 4: We have set up policing policy so that the vast

897
00:45:03,640 --> 00:45:07,000
Speaker 4: majority of police time is spent on things that most

898
00:45:07,040 --> 00:45:09,320
Speaker 4: people don't actually care about. So when you ask the

899
00:45:09,400 --> 00:45:12,400
Speaker 4: people what are they like scared out, it's like burglary, robbery,

900
00:45:12,440 --> 00:45:15,080
Speaker 4: sexual assault, murder. And when you look at how police

901
00:45:15,080 --> 00:45:16,719
Speaker 4: spend their time, the vast majority of it is on

902
00:45:16,840 --> 00:45:20,120
Speaker 4: like noise complaints and unfounded calls and like somebody was

903
00:45:20,120 --> 00:45:23,520
Speaker 4: peeing outside and trespassing, and sometimes on what I sort

904
00:45:23,560 --> 00:45:25,520
Speaker 4: of think of as like police manufactured crime, which is

905
00:45:25,560 --> 00:45:28,720
Speaker 4: like convincing someone with a substance use problem to score

906
00:45:28,800 --> 00:45:31,359
Speaker 4: some drugs and also score for the undercover who will

907
00:45:31,360 --> 00:45:32,640
Speaker 4: then arrest them for a felony.

908
00:45:33,040 --> 00:45:33,640
Speaker 1: Right right.

909
00:45:33,719 --> 00:45:36,360
Speaker 4: And the reason we don't want them on the street

910
00:45:36,400 --> 00:45:40,160
Speaker 4: more is because they are out there on the street, armed, dangerous,

911
00:45:40,600 --> 00:45:44,120
Speaker 4: and not investigating the things that people really care about.

912
00:45:44,160 --> 00:45:46,279
Speaker 4: If you look at clearance rates, a lot of people

913
00:45:46,320 --> 00:45:48,520
Speaker 4: don't know what clearance rates are, but it's the rate

914
00:45:48,520 --> 00:45:51,200
Speaker 4: at which police are able to close cases. And in

915
00:45:51,239 --> 00:45:53,279
Speaker 4: any jurisdiction, you can search for your local clearance rates.

916
00:45:53,320 --> 00:45:55,480
Speaker 4: You'd be like, all right, how many rape cases are

917
00:45:55,560 --> 00:46:01,319
Speaker 4: my local police even closing? Got most places thirteen to

918
00:46:01,360 --> 00:46:06,360
Speaker 4: twenty percent. This is a whole other feminist sobeb.

919
00:46:06,480 --> 00:46:10,279
Speaker 1: Ninety is totally across the entire country. There's like at

920
00:46:10,360 --> 00:46:13,279
Speaker 1: least ninety that they've closed in the past decade.

921
00:46:13,680 --> 00:46:16,600
Speaker 4: Seriously, and that's because it's a policy choice. It's a

922
00:46:16,680 --> 00:46:20,040
Speaker 4: choice from police leadership about what they're going to dedicate

923
00:46:20,080 --> 00:46:22,120
Speaker 4: resources to. And if, yeah, if the answer was, okay,

924
00:46:22,160 --> 00:46:25,520
Speaker 4: they're not going to spend their time writing trespassing reports,

925
00:46:25,960 --> 00:46:29,560
Speaker 4: but instead, we're going to dedicate real efforts to how

926
00:46:29,560 --> 00:46:34,320
Speaker 4: about wage theft or large scale pollution of poisoning entire towns.

927
00:46:34,320 --> 00:46:35,799
Speaker 4: We're going to set the cops on that. If they

928
00:46:35,800 --> 00:46:38,440
Speaker 4: were going to investigate crimes of the powerful against the

929
00:46:38,520 --> 00:46:41,759
Speaker 4: citizenry instead of writing reports, I might feel differently about it,

930
00:46:41,800 --> 00:46:42,759
Speaker 4: But I don't think that's the plan.

931
00:46:43,640 --> 00:46:46,239
Speaker 1: Yeah, never has been. Yeah, But I do just want

932
00:46:46,280 --> 00:46:47,920
Speaker 1: to get a little bit more into the history of ACXON.

933
00:46:48,040 --> 00:46:52,480
Speaker 1: So they were taser. They made their initial money with

934
00:46:53,000 --> 00:46:57,600
Speaker 1: selling tasers and then body cams. When that became the

935
00:46:57,640 --> 00:47:02,680
Speaker 1: solution to police brutality corruption, they went with body cams,

936
00:47:02,760 --> 00:47:06,960
Speaker 1: and they basically have a monopoly for which they've been sued.

937
00:47:07,239 --> 00:47:10,000
Speaker 1: They made four hundred and sixty one million dollars in

938
00:47:10,040 --> 00:47:14,799
Speaker 1: the first quarter of twenty twenty four alone. They're also

939
00:47:14,840 --> 00:47:17,920
Speaker 1: the same company that made headlines for endeavoring to solve

940
00:47:18,080 --> 00:47:22,800
Speaker 1: school shootings with Taser equipped drones. That plan was paused

941
00:47:23,080 --> 00:47:26,799
Speaker 1: when the majority of Axon's ethics board resigned in protests.

942
00:47:27,160 --> 00:47:31,160
Speaker 1: But I think probably the most relevant and also, like

943
00:47:31,200 --> 00:47:36,440
Speaker 1: I mentioned, their CEO gives speeches via remote iPad, I

944
00:47:36,520 --> 00:47:40,040
Speaker 1: use an avatar man glued to the front of the

945
00:47:40,200 --> 00:47:41,200
Speaker 1: motorcycle helmet.

946
00:47:41,480 --> 00:47:45,560
Speaker 4: They also created excited delirium in parts that are due to.

947
00:47:45,520 --> 00:47:47,879
Speaker 1: That, Yes, so I wanted to talk about that because

948
00:47:47,880 --> 00:47:51,040
Speaker 1: I also think like that feels very relevant to this,

949
00:47:51,360 --> 00:47:56,279
Speaker 1: because this is them getting involved in police narrative and

950
00:47:56,360 --> 00:48:01,000
Speaker 1: how police justify what they're doing, and they were involved.

951
00:48:01,640 --> 00:48:05,360
Speaker 1: You actually have a great video on this on your Twitter, Emily,

952
00:48:05,480 --> 00:48:09,719
Speaker 1: where you talk about how about their role in the

953
00:48:09,800 --> 00:48:15,080
Speaker 1: creation of and the proliferation of the term excited delirium,

954
00:48:15,400 --> 00:48:17,920
Speaker 1: which is something we covered a while back, but I

955
00:48:17,920 --> 00:48:20,719
Speaker 1: think it's always worth kind of refreshing people's memory of

956
00:48:20,760 --> 00:48:22,719
Speaker 1: what is excited delirium.

957
00:48:23,120 --> 00:48:26,160
Speaker 4: So excited delirium is a made up medical diagnosis that

958
00:48:26,280 --> 00:48:29,879
Speaker 4: was originally invented in a sort of predictably racist way

959
00:48:29,960 --> 00:48:33,359
Speaker 4: in Miami many decades ago, where a doctor claimed that

960
00:48:33,600 --> 00:48:35,960
Speaker 4: people were dying of excited delirium, the sort of state

961
00:48:36,000 --> 00:48:38,880
Speaker 4: of mania that caused them to behave really erradically and

962
00:48:38,920 --> 00:48:42,440
Speaker 4: aggressively and dangerously and then they perish, They just expire.

963
00:48:43,040 --> 00:48:45,520
Speaker 4: And it turned out that many of the women who

964
00:48:45,520 --> 00:48:48,319
Speaker 4: are originally alleged to have excited delirium had actually been

965
00:48:48,360 --> 00:48:52,160
Speaker 4: killed by a serial killer. But this idea that people

966
00:48:52,160 --> 00:48:55,440
Speaker 4: could become so worked up that they are dangerous and

967
00:48:55,480 --> 00:49:00,319
Speaker 4: then they die was seized upon by police because in

968
00:49:00,400 --> 00:49:03,120
Speaker 4: police encounters where there is a need to justify use

969
00:49:03,120 --> 00:49:05,760
Speaker 4: of force, it is very useful for them to claim

970
00:49:05,800 --> 00:49:09,160
Speaker 4: that the person they used force against was dangerously worked

971
00:49:09,239 --> 00:49:11,920
Speaker 4: up and had this medical thing where they became a

972
00:49:12,040 --> 00:49:14,680
Speaker 4: risk to everybody's safety and they had to be taste

973
00:49:14,760 --> 00:49:18,080
Speaker 4: and then oh, when they died from a heart attack,

974
00:49:18,200 --> 00:49:20,960
Speaker 4: it wasn't because they got a massive vault of electricity.

975
00:49:21,520 --> 00:49:25,160
Speaker 4: It was because they died of excited delirium. So excited delirium,

976
00:49:25,160 --> 00:49:27,279
Speaker 4: which is not accepted by the way by doctors, like

977
00:49:27,360 --> 00:49:30,680
Speaker 4: medical associations are like, that's totally not a thing. Psychiatrical

978
00:49:30,960 --> 00:49:32,560
Speaker 4: associations the same deal, not a thing.

979
00:49:32,880 --> 00:49:36,800
Speaker 1: But there was that one panel that you feel like,

980
00:49:36,840 --> 00:49:38,720
Speaker 1: we're good here. No need to look into the panel

981
00:49:38,800 --> 00:49:40,359
Speaker 1: or who funded that. I think we're good.

982
00:49:40,520 --> 00:49:42,279
Speaker 4: Yeah, No need to look at how many doctors on

983
00:49:42,320 --> 00:49:44,719
Speaker 4: the panel were put there by Axon to no need

984
00:49:44,719 --> 00:49:46,680
Speaker 4: to connect this to no need to also think about

985
00:49:46,719 --> 00:49:49,560
Speaker 4: how much this reduces Axon's liability right, because if deaths

986
00:49:49,560 --> 00:49:51,920
Speaker 4: are caused by excited delirium and not caused by a taser,

987
00:49:52,400 --> 00:49:53,719
Speaker 4: they're not going to have they're not going to be

988
00:49:53,719 --> 00:49:57,600
Speaker 4: able to be successfully sued. But it's actually become a

989
00:49:57,680 --> 00:50:01,120
Speaker 4: serious epidemic in this country of police use excited delirium

990
00:50:01,120 --> 00:50:03,759
Speaker 4: to justify it, not only taser use of force, but

991
00:50:04,400 --> 00:50:06,560
Speaker 4: the use of paramedics as a weapon, like we saw

992
00:50:06,600 --> 00:50:09,440
Speaker 4: in the Elijah McClain case, where the police had paramedics

993
00:50:09,440 --> 00:50:13,359
Speaker 4: inject Elijah McLain with a lethal dose of sedatives under

994
00:50:13,440 --> 00:50:17,200
Speaker 4: this false diagnosis of excited delirium, so that that seed

995
00:50:17,280 --> 00:50:22,839
Speaker 4: that Axon planted in eight in legitimizing this diagnosis has

996
00:50:22,920 --> 00:50:25,440
Speaker 4: now caused many many deaths and is continuing to cause

997
00:50:25,480 --> 00:50:26,719
Speaker 4: deaths around the country.

998
00:50:26,760 --> 00:50:29,400
Speaker 2: Right because like they'll hit people like ketamine and stuff,

999
00:50:29,480 --> 00:50:31,640
Speaker 2: and then like they like I was reading a statistic

1000
00:50:31,680 --> 00:50:32,920
Speaker 2: that a lot of those people end up having to

1001
00:50:32,960 --> 00:50:35,920
Speaker 2: be intubated because it's so severe, and they're like, I

1002
00:50:35,920 --> 00:50:37,560
Speaker 2: don't know, man, wait, the guy was excited. I mean

1003
00:50:37,600 --> 00:50:39,920
Speaker 2: then they also said the same thing about George Floyd too.

1004
00:50:40,000 --> 00:50:42,200
Speaker 1: Yeah, that was like very early on, like it's excited.

1005
00:50:42,320 --> 00:50:44,319
Speaker 2: I don't know what you want to say, man, let's

1006
00:50:44,440 --> 00:50:48,640
Speaker 2: let's just move on. So then like so axin for them,

1007
00:50:48,800 --> 00:50:50,840
Speaker 2: it's just more because they're sort of like, hey, we

1008
00:50:50,960 --> 00:50:53,160
Speaker 2: love what you guys do. Let's help out because this

1009
00:50:53,239 --> 00:50:55,799
Speaker 2: also helps justify the use of our products. Like is

1010
00:50:55,840 --> 00:50:58,239
Speaker 2: that sort of like their main motivation and like sort

1011
00:50:58,239 --> 00:51:03,160
Speaker 2: of pushing the excited delirium sort of craze along.

1012
00:51:03,600 --> 00:51:05,359
Speaker 4: I think it's also a legal shield. I mean, if

1013
00:51:05,360 --> 00:51:07,239
Speaker 4: I'm good to Let's say I lose a loved one

1014
00:51:07,239 --> 00:51:09,480
Speaker 4: who was taste and I want a sue Taser for

1015
00:51:09,560 --> 00:51:11,919
Speaker 4: marketing a product as non lethal that was in fact

1016
00:51:12,000 --> 00:51:15,280
Speaker 4: lethal to my loved one. And they say the medical

1017
00:51:15,320 --> 00:51:18,000
Speaker 4: examiner certificate doesn't say that your loved one died of

1018
00:51:18,040 --> 00:51:22,000
Speaker 4: an electric shock. The medical examiner certificate says excited delirium,

1019
00:51:22,040 --> 00:51:24,279
Speaker 4: So you can't actually get money from us in a

1020
00:51:24,320 --> 00:51:28,879
Speaker 4: civil suit or settlement. So it's it's covering them from

1021
00:51:28,960 --> 00:51:32,799
Speaker 4: being financially responsible for deaths they cause. And the same

1022
00:51:32,800 --> 00:51:35,040
Speaker 4: thing for police. I mean, if the police are getting

1023
00:51:35,120 --> 00:51:36,920
Speaker 4: the police could be sued in the same case, right,

1024
00:51:37,000 --> 00:51:39,279
Speaker 4: the pe taser for the device to the police for

1025
00:51:39,320 --> 00:51:42,160
Speaker 4: the action. But either way, if the EMMYS certificate says

1026
00:51:42,160 --> 00:51:44,840
Speaker 4: this person died of excited delirium, it's a liability.

1027
00:51:44,360 --> 00:51:48,359
Speaker 2: Shield, right, Yeah, and disproportionately applied to Black men.

1028
00:51:49,080 --> 00:51:51,160
Speaker 1: Yes a lot of the time. Yes, it's a way

1029
00:51:51,200 --> 00:51:55,239
Speaker 1: for the police to justify that why they're scared.

1030
00:51:54,640 --> 00:51:58,399
Speaker 4: It's really reliant on racist tropes right on the adultification

1031
00:51:58,480 --> 00:52:00,680
Speaker 4: of black children. First of all, this this child is

1032
00:52:00,719 --> 00:52:03,560
Speaker 4: a risk to me because I'm perceiving this child as

1033
00:52:03,600 --> 00:52:08,480
Speaker 4: older because of racial bias, but also the racist myth

1034
00:52:08,680 --> 00:52:12,480
Speaker 4: of dangerousness of black men in an excited state. I mean,

1035
00:52:12,480 --> 00:52:17,800
Speaker 4: this is totally playing on long term American racist tropes

1036
00:52:18,360 --> 00:52:21,800
Speaker 4: and sanitizing them with a fake medical diagnosis.

1037
00:52:21,280 --> 00:52:24,799
Speaker 2: Right yeah, Yeah, it sounds like something you'd get at

1038
00:52:24,840 --> 00:52:29,520
Speaker 2: like Willy Wonka's chocolate factory and excited delirium for you

1039
00:52:29,640 --> 00:52:30,120
Speaker 2: and you're like.

1040
00:52:30,160 --> 00:52:33,120
Speaker 1: Right, ooh yeah, and that's why, and that's why we

1041
00:52:33,200 --> 00:52:36,120
Speaker 1: had to drown him in the chocolate river. Yes, exactly.

1042
00:52:36,800 --> 00:52:39,760
Speaker 1: Just a couple more details about is it Axin Axon.

1043
00:52:40,080 --> 00:52:43,400
Speaker 1: I'm gonna call them Axon because that feels sufficiently violent

1044
00:52:43,640 --> 00:52:48,000
Speaker 1: and sinister. Their CEO is like founding story. I just

1045
00:52:48,160 --> 00:52:52,200
Speaker 1: like founding stories for companies and CEOs because they are

1046
00:52:53,239 --> 00:52:57,160
Speaker 1: like the most full of shit things in America and

1047
00:52:57,239 --> 00:52:59,440
Speaker 1: like most widely believed people. It was founded in the

1048
00:53:00,200 --> 00:53:02,680
Speaker 1: like everything was founded in a garage. No, it wasn't

1049
00:53:03,160 --> 00:53:07,600
Speaker 1: founded in like their rich dad's second home that was

1050
00:53:07,680 --> 00:53:11,960
Speaker 1: behind you his first mansion. But anyways, the CEO repeatedly

1051
00:53:12,000 --> 00:53:14,680
Speaker 1: told the story that he started the company because his

1052
00:53:14,800 --> 00:53:17,640
Speaker 1: two high school friends were shot and killed. He played

1053
00:53:18,000 --> 00:53:21,000
Speaker 1: high school football with them. It's just like two guys

1054
00:53:21,040 --> 00:53:24,480
Speaker 1: that he like knew about who were like four or

1055
00:53:24,480 --> 00:53:26,960
Speaker 1: five years older than him. Yeah, they weren't even he

1056
00:53:27,000 --> 00:53:29,000
Speaker 1: never went to high school with him. Yeah, but like

1057
00:53:29,040 --> 00:53:31,880
Speaker 1: it's just you know, for him, he's like and man like,

1058
00:53:31,960 --> 00:53:35,319
Speaker 1: that's the closest that I like kids who were at

1059
00:53:35,360 --> 00:53:38,920
Speaker 1: your high school before you, is like such a stretch

1060
00:53:39,080 --> 00:53:43,040
Speaker 1: to be like that's also they the workplace culture includes

1061
00:53:43,120 --> 00:53:47,600
Speaker 1: group tasings and tattooing sessions in which employees are inked

1062
00:53:47,640 --> 00:53:52,200
Speaker 1: with corporate insignia. And by the way, the drone thing.

1063
00:53:52,840 --> 00:53:55,279
Speaker 1: While they were like all right fine when their entire

1064
00:53:55,280 --> 00:53:59,760
Speaker 1: ethics board resigned, they did by a drone company recently.

1065
00:54:00,080 --> 00:54:03,120
Speaker 1: So there it seems like what well their mouth says,

1066
00:54:03,160 --> 00:54:07,440
Speaker 1: all right, fine, god, their their money is saying that

1067
00:54:07,520 --> 00:54:13,920
Speaker 1: they're full, full steam ahead on the taser drones front. Yeah,

1068
00:54:13,960 --> 00:54:18,320
Speaker 1: so just all sorts of wild shit there, like truly

1069
00:54:18,640 --> 00:54:22,880
Speaker 1: the most dystopian, like a bunch of tattoo branded like

1070
00:54:23,280 --> 00:54:28,879
Speaker 1: corporate people, guy with motorcycle helmet, iPad face.

1071
00:54:28,920 --> 00:54:33,440
Speaker 2: Like righting, like knowing murder victims. Yeah, it's all I mean,

1072
00:54:33,560 --> 00:54:36,480
Speaker 2: they're in a way, it all does feel very appropriate.

1073
00:54:36,520 --> 00:54:38,560
Speaker 2: That then it's like and now that's what I like

1074
00:54:38,600 --> 00:54:40,719
Speaker 2: to do is help other people lie about stuff and

1075
00:54:41,239 --> 00:54:43,960
Speaker 2: I just make money. And the institutional investment in this

1076
00:54:44,000 --> 00:54:46,440
Speaker 2: company is wild too. Oh yeah, it's like because they

1077
00:54:46,480 --> 00:54:47,960
Speaker 2: know they're like wait, how much they make in Q one?

1078
00:54:48,400 --> 00:54:51,400
Speaker 1: Okay? Yeah, yeah, but just generally I just want to

1079
00:54:51,560 --> 00:54:54,080
Speaker 1: like kind of get your take, Emily. There was recently

1080
00:54:54,160 --> 00:54:58,680
Speaker 1: this New York Times article about a The headline is

1081
00:54:58,840 --> 00:55:01,640
Speaker 1: what a group of posed to police blow the whistle

1082
00:55:01,719 --> 00:55:06,360
Speaker 1: on its founder, And it was like this AI app

1083
00:55:06,800 --> 00:55:10,160
Speaker 1: that was like, we're gonna re like create an alternative

1084
00:55:10,160 --> 00:55:14,399
Speaker 1: to the police by taking people's you know, complaints and

1085
00:55:14,800 --> 00:55:17,640
Speaker 1: routing them to like some of these other police alternatives.

1086
00:55:17,960 --> 00:55:21,640
Speaker 1: It turned out to be like the founder just like

1087
00:55:21,680 --> 00:55:23,520
Speaker 1: didn't have the ability to pull it off and was

1088
00:55:23,560 --> 00:55:27,240
Speaker 1: spending some of the money on like clothing and vacations

1089
00:55:27,320 --> 00:55:30,560
Speaker 1: that you know, like that I'm drifting. Sure you can

1090
00:55:30,640 --> 00:55:38,120
Speaker 1: find scams in any nonprofit like category, but the way

1091
00:55:38,160 --> 00:55:40,360
Speaker 1: the New York Times writes about it and like frames

1092
00:55:40,480 --> 00:55:46,239
Speaker 1: this article is that whole argument of like, oh, you

1093
00:55:46,280 --> 00:55:48,759
Speaker 1: think the police are bad at their jobs, Let's see

1094
00:55:48,800 --> 00:55:52,440
Speaker 1: what you say when you're being robbed, is like basically

1095
00:55:53,520 --> 00:55:57,120
Speaker 1: the whole thesis of the argument. And what it comes

1096
00:55:57,160 --> 00:55:59,520
Speaker 1: down to if you read the article is like one

1097
00:55:59,600 --> 00:56:01,920
Speaker 1: person and on the team is like I didn't want

1098
00:56:01,920 --> 00:56:03,759
Speaker 1: to turn him over to the police because I like,

1099
00:56:04,000 --> 00:56:07,360
Speaker 1: he's a black man, and I fear what would happen

1100
00:56:07,400 --> 00:56:10,400
Speaker 1: to him? And then another person is like, yeah, but

1101
00:56:10,440 --> 00:56:13,400
Speaker 1: I did turn him over to the attorney general because

1102
00:56:13,400 --> 00:56:16,720
Speaker 1: I know that, Like you don't usually call the police

1103
00:56:16,719 --> 00:56:20,160
Speaker 1: on white collar crime because they won't do shit. So anyways,

1104
00:56:20,239 --> 00:56:23,319
Speaker 1: like the attorney general is working on investigation. It might

1105
00:56:23,360 --> 00:56:26,520
Speaker 1: be civil, it might be criminal, but the way they

1106
00:56:26,600 --> 00:56:30,040
Speaker 1: framed it is so much like based on this bad

1107
00:56:30,080 --> 00:56:34,560
Speaker 1: faith reading of any criticism of the police. And it

1108
00:56:34,680 --> 00:56:38,120
Speaker 1: just feels generally like the tone of the mainstream media

1109
00:56:38,800 --> 00:56:44,080
Speaker 1: and the Democratic Party recently is like, boy, those protests

1110
00:56:44,120 --> 00:56:48,960
Speaker 1: in twenty twenty were you know, unpopular, let's never fight again,

1111
00:56:49,040 --> 00:56:53,319
Speaker 1: babe to the police, and it's like, I don't know,

1112
00:56:53,440 --> 00:56:58,719
Speaker 1: it's just so fucking frustrating, Like and meanwhile, police killings

1113
00:56:59,000 --> 00:57:01,960
Speaker 1: haven't have just like stayed the same or gone up,

1114
00:57:02,640 --> 00:57:05,719
Speaker 1: so like, where where are we with this? Like what

1115
00:57:05,960 --> 00:57:08,839
Speaker 1: you know, there were some programs that were funded that

1116
00:57:08,920 --> 00:57:13,400
Speaker 1: like worked really well, Like Denver had a controlled trial

1117
00:57:13,440 --> 00:57:16,000
Speaker 1: of a program that provides housing subsidies to people at

1118
00:57:16,080 --> 00:57:19,520
Speaker 1: risk of homelessness and found a forty percent reduction and arrests.

1119
00:57:19,840 --> 00:57:23,680
Speaker 1: Like there's all these cool examples they get like dashed

1120
00:57:23,720 --> 00:57:26,000
Speaker 1: off really quickly in a New York Times article that

1121
00:57:26,200 --> 00:57:30,160
Speaker 1: like has a counter point for everything that might suggest that,

1122
00:57:30,320 --> 00:57:34,440
Speaker 1: like there could be alternatives to our fucking terrible idea

1123
00:57:34,520 --> 00:57:37,360
Speaker 1: of a system that if you've been to any other

1124
00:57:37,400 --> 00:57:39,280
Speaker 1: country in the world, you're like, oh, wow, why do

1125
00:57:39,360 --> 00:57:41,120
Speaker 1: we do it the way we do it? But yeah,

1126
00:57:41,160 --> 00:57:42,919
Speaker 1: I'm just curious to hear your thoughts on like where

1127
00:57:42,920 --> 00:57:45,720
Speaker 1: we're at in our conversation in the mainstream.

1128
00:57:46,360 --> 00:57:50,240
Speaker 4: So first of all, we're really lucky in this one way,

1129
00:57:50,560 --> 00:57:54,280
Speaker 4: which is that we are overrun with cool solutions. Like

1130
00:57:54,320 --> 00:57:56,480
Speaker 4: I'm writing a book right now, Like my book it's

1131
00:57:56,480 --> 00:57:58,640
Speaker 4: coming out in twenty twenty six. It's going to be

1132
00:57:58,680 --> 00:58:01,640
Speaker 4: a layperson's guy to the criminal legal system in all

1133
00:58:01,720 --> 00:58:06,320
Speaker 4: of its horribleness, and also solutions, Like I'm going to

1134
00:58:06,400 --> 00:58:08,440
Speaker 4: spend two thirds of the book on problems, and then

1135
00:58:08,440 --> 00:58:10,600
Speaker 4: I'm going to present a whole bunch of solutions. I

1136
00:58:10,640 --> 00:58:13,360
Speaker 4: had originally intended to write one chapter on solutions. I'm

1137
00:58:14,400 --> 00:58:16,320
Speaker 4: now at like page eighty eight of one hundred of

1138
00:58:16,320 --> 00:58:18,640
Speaker 4: all of these solutions because there are just so many

1139
00:58:19,480 --> 00:58:23,520
Speaker 4: fantastic things happening that have better data than the status quo,

1140
00:58:23,880 --> 00:58:28,240
Speaker 4: Like we don't have data strongly suggesting that police are

1141
00:58:28,800 --> 00:58:34,880
Speaker 4: a feasible preventative mechanism. Police can disappear problems. They can

1142
00:58:34,960 --> 00:58:37,160
Speaker 4: take people and put them in spaces where they are

1143
00:58:37,200 --> 00:58:39,560
Speaker 4: no longer visible to the general public and where they

1144
00:58:39,600 --> 00:58:41,800
Speaker 4: may be then violently harmed in ways that make them

1145
00:58:41,840 --> 00:58:43,760
Speaker 4: more likely to engage in crime in the future. So

1146
00:58:44,080 --> 00:58:46,479
Speaker 4: police may be sort of like temporarily making a problem

1147
00:58:46,560 --> 00:58:48,520
Speaker 4: disappear in a way that long term makes it worse.

1148
00:58:48,640 --> 00:58:50,680
Speaker 4: We have that data, have a ton of data on

1149
00:58:50,800 --> 00:58:53,480
Speaker 4: like the Star program in Denver, or cahoots in Oregon,

1150
00:58:53,640 --> 00:58:56,000
Speaker 4: or you know, other alternatives to police popping up around

1151
00:58:56,040 --> 00:58:58,760
Speaker 4: the country. Massive public support for this. Most voters would

1152
00:58:58,800 --> 00:59:03,600
Speaker 4: love to have mental health first responders, and actually most cops,

1153
00:59:03,640 --> 00:59:05,000
Speaker 4: if you ask them, are like, yes, I would like

1154
00:59:05,040 --> 00:59:07,000
Speaker 4: to also no longer be treated like I'm a trained

1155
00:59:07,040 --> 00:59:09,480
Speaker 4: social worker, because I'm not one, and I would like

1156
00:59:09,520 --> 00:59:11,960
Speaker 4: that to not be part of my job. What's really

1157
00:59:12,240 --> 00:59:16,280
Speaker 4: what bugs me about the perspective you just described, right,

1158
00:59:16,280 --> 00:59:17,880
Speaker 4: which is like, Oh, these people who don't want to

1159
00:59:17,960 --> 00:59:22,120
Speaker 4: use the police, what happens when they need the police? Well, okay,

1160
00:59:23,440 --> 00:59:26,840
Speaker 4: when we on election day hear from voters that they

1161
00:59:26,840 --> 00:59:29,680
Speaker 4: are scared to go to their local polling place because

1162
00:59:29,680 --> 00:59:33,600
Speaker 4: there are proud Boys outside the polling place, intimidating potential voters.

1163
00:59:34,600 --> 00:59:38,960
Speaker 4: No one is saying, well, it's your problem if you

1164
00:59:38,960 --> 00:59:40,800
Speaker 4: don't like the proud Boys, don't you just have a

1165
00:59:40,840 --> 00:59:42,320
Speaker 4: way to work out. No, we say, okay, this is

1166
00:59:42,360 --> 00:59:45,320
Speaker 4: a problem because people have a legitimate fear. It is

1167
00:59:45,320 --> 00:59:49,080
Speaker 4: a legitimate fear of an organized effort which is intimidating

1168
00:59:49,120 --> 00:59:51,280
Speaker 4: and threatening harm to the general public. And because the

1169
00:59:51,280 --> 00:59:54,520
Speaker 4: general public is afraid, we the government should probably take

1170
00:59:54,520 --> 00:59:57,920
Speaker 4: action to protect the general public. The blind spot with

1171
00:59:57,960 --> 01:00:02,439
Speaker 4: regard to when that organ harmful force is a governmental

1172
01:00:02,480 --> 01:00:06,240
Speaker 4: body is obscene. So by blaming people who are like, hey,

1173
01:00:06,280 --> 01:00:08,400
Speaker 4: I actually I'm nervous about calling the police on my

1174
01:00:08,640 --> 01:00:11,800
Speaker 4: black boss because black men get killed by police at

1175
01:00:11,800 --> 01:00:14,920
Speaker 4: ignordinate rates, and also not to mention that subject to

1176
01:00:14,960 --> 01:00:19,080
Speaker 4: illegitimate prosecutions and overcharging and charge stacking and longer sentences

1177
01:00:19,120 --> 01:00:22,400
Speaker 4: and the incredible damage even of a pretop process. And

1178
01:00:22,400 --> 01:00:24,080
Speaker 4: by the way, I'm saying this with great care because

1179
01:00:24,120 --> 01:00:26,240
Speaker 4: here's a person who's accused and has not been found

1180
01:00:26,240 --> 01:00:30,960
Speaker 4: guilty of anything. So really weighing, hey, do I want

1181
01:00:31,000 --> 01:00:35,720
Speaker 4: to subject this person to all of these risks, or

1182
01:00:35,880 --> 01:00:38,960
Speaker 4: is there a better way for me to seek accountability

1183
01:00:39,000 --> 01:00:46,480
Speaker 4: and truth without those risks of lethality, injustice, ruinousness. That's

1184
01:00:46,520 --> 01:00:49,000
Speaker 4: a fantastic thing for an ordinary citizen to be considering

1185
01:00:49,000 --> 01:00:50,880
Speaker 4: in any government that doesn't say, you know what, I'm

1186
01:00:50,880 --> 01:00:53,320
Speaker 4: going to consider that with you, and I'm going to

1187
01:00:53,360 --> 01:00:56,160
Speaker 4: acknowledge that your fears are real and the problems you

1188
01:00:56,240 --> 01:00:59,480
Speaker 4: highlight are real and work on these problems to come

1189
01:00:59,520 --> 01:01:02,040
Speaker 4: up with something better. Is abrogating its duty to the

1190
01:01:02,040 --> 01:01:05,320
Speaker 4: public in favor of the optics of being pro cop.

1191
01:01:06,160 --> 01:01:11,320
Speaker 2: Right, Yeah, yeah, the pro cop turn that's happened in

1192
01:01:11,360 --> 01:01:13,400
Speaker 2: like the Democratic Party. I mean it's not that they

1193
01:01:13,400 --> 01:01:16,600
Speaker 2: were anti but like, just I saw you retweet an

1194
01:01:16,680 --> 01:01:19,400
Speaker 2: article or a thread about how the platform changed, because

1195
01:01:19,800 --> 01:01:21,400
Speaker 2: I was like, I was definitely looking at a lot

1196
01:01:21,400 --> 01:01:23,200
Speaker 2: of the I was really interested in the foreign policy

1197
01:01:23,240 --> 01:01:25,120
Speaker 2: stuff that was in the platform, and I was like, oh, wow,

1198
01:01:25,200 --> 01:01:27,720
Speaker 2: like you just a ton of one eighties here compared

1199
01:01:27,760 --> 01:01:31,000
Speaker 2: to twenty twenty. And then reading sort of the excerpts

1200
01:01:31,000 --> 01:01:35,200
Speaker 2: on what was happening with policing was also very like, Oh,

1201
01:01:35,520 --> 01:01:38,680
Speaker 2: we're really embracing this thing about being like, let's not

1202
01:01:38,840 --> 01:01:41,920
Speaker 2: talk about the death penalty anymore. Let's I know we

1203
01:01:41,920 --> 01:01:45,160
Speaker 2: were talking about choke holds. Let's like really tamp that down.

1204
01:01:45,240 --> 01:01:49,160
Speaker 2: And it really is wild how much it's become because

1205
01:01:49,160 --> 01:01:51,400
Speaker 2: I think obviously this whole election is set up to

1206
01:01:51,440 --> 01:01:54,840
Speaker 2: be we have a prosecutor and a felon, and so

1207
01:01:55,080 --> 01:01:57,800
Speaker 2: because of that framing, we're going to really lean into

1208
01:01:57,920 --> 01:02:01,160
Speaker 2: a lot of this, like the like the the prosecutorial

1209
01:02:01,240 --> 01:02:04,240
Speaker 2: aspects of this and also be make it feel like yeah, man,

1210
01:02:04,320 --> 01:02:08,040
Speaker 2: like we're the cops again and that's okay. That was

1211
01:02:08,080 --> 01:02:11,080
Speaker 2: just kind of like, I mean, I was very cynical

1212
01:02:11,120 --> 01:02:12,880
Speaker 2: in twenty twenty when I saw this sort of like

1213
01:02:13,000 --> 01:02:15,040
Speaker 2: uptaking me, like, yeah, we really need to do something,

1214
01:02:15,400 --> 01:02:17,640
Speaker 2: and that's the most that will happen. I will say

1215
01:02:17,680 --> 01:02:19,800
Speaker 2: that we need to do something. But now to see

1216
01:02:19,920 --> 01:02:22,440
Speaker 2: like really forimally stripped out, you're like, oh, right, right, right,

1217
01:02:22,440 --> 01:02:25,520
Speaker 2: this was never a real concern. But I'm how do

1218
01:02:25,560 --> 01:02:27,959
Speaker 2: you sort of perceive that sort of like shift now

1219
01:02:28,080 --> 01:02:30,240
Speaker 2: or at least now that you know, even in their

1220
01:02:30,320 --> 01:02:33,320
Speaker 2: written platforms, it's just sort of like, yeah, those are

1221
01:02:33,480 --> 01:02:35,480
Speaker 2: those are those are problems, but you know we can

1222
01:02:35,840 --> 01:02:37,160
Speaker 2: address them at some point later.

1223
01:02:37,560 --> 01:02:39,360
Speaker 4: I mean, the death penalty thing, I really don't get

1224
01:02:39,440 --> 01:02:41,720
Speaker 4: because Harris has been against the death penalty for a

1225
01:02:41,720 --> 01:02:44,800
Speaker 4: lot of her career, was criticized as ag for upholding

1226
01:02:44,800 --> 01:02:47,920
Speaker 4: the law instead of acting on a moral objection. That

1227
01:02:47,960 --> 01:02:51,360
Speaker 4: she has to death penalty which is super expensive and

1228
01:02:51,440 --> 01:02:53,640
Speaker 4: has resulted in the death of a lot of innocent

1229
01:02:53,680 --> 01:02:56,320
Speaker 4: people because our system gets it wrong a lot, because

1230
01:02:56,360 --> 01:02:59,760
Speaker 4: of things like junk science and bad eyewin as ideas

1231
01:02:59,840 --> 01:03:02,960
Speaker 4: and insufficient funding of public defense. So let's just like Cavin,

1232
01:03:03,000 --> 01:03:05,040
Speaker 4: this is like, I don't I really don't get the

1233
01:03:05,080 --> 01:03:08,360
Speaker 4: Democratic Party stepping away from opposing the death penalty. I

1234
01:03:08,400 --> 01:03:12,160
Speaker 4: think polling on it has not changed dramatically, Like Americans

1235
01:03:12,200 --> 01:03:15,080
Speaker 4: are not like rabidly pro death penalty now. So I

1236
01:03:15,120 --> 01:03:17,200
Speaker 4: really don't get it. But here's what I'll say about

1237
01:03:17,200 --> 01:03:22,960
Speaker 4: the prosecutor versus pellent thing. It's being treated as a

1238
01:03:23,080 --> 01:03:30,040
Speaker 4: sort of vicious backing a violent force against crime, but

1239
01:03:30,120 --> 01:03:33,840
Speaker 4: it doesn't have to be. Prosecutors are unique among lawyers.

1240
01:03:33,960 --> 01:03:35,960
Speaker 4: Rarely will you hear me say nice things about prosecutors.

1241
01:03:36,000 --> 01:03:38,440
Speaker 4: I'm going to now say some nice things about prosecutors.

1242
01:03:38,880 --> 01:03:41,880
Speaker 4: They have an ethical duty to do justice. That is

1243
01:03:41,920 --> 01:03:44,439
Speaker 4: a unique ethical duty. No other kind of lawyer has

1244
01:03:44,520 --> 01:03:45,160
Speaker 4: that duty.

1245
01:03:45,880 --> 01:03:46,040
Speaker 1: Now.

1246
01:03:46,080 --> 01:03:48,240
Speaker 4: I just got done teaching a course to some really

1247
01:03:48,240 --> 01:03:50,520
Speaker 4: talented law students, and in one of my exercises, I

1248
01:03:50,560 --> 01:03:52,080
Speaker 4: made half of them be defense lawyers and half of

1249
01:03:52,080 --> 01:03:54,920
Speaker 4: them be prosecutors. And I told the prosecutors in a

1250
01:03:54,920 --> 01:03:57,760
Speaker 4: bail argument, you have this unique ethical duty. You have

1251
01:03:57,800 --> 01:04:00,000
Speaker 4: to do justice, and not just justice for the peace

1252
01:04:00,000 --> 01:04:03,240
Speaker 4: people who were harmed in a crime or who you

1253
01:04:03,280 --> 01:04:05,400
Speaker 4: think of as part of the community. You have to

1254
01:04:05,400 --> 01:04:08,920
Speaker 4: do justice for everybody. That includes the accused person and

1255
01:04:08,960 --> 01:04:11,160
Speaker 4: their family and their kids and their loved ones. That

1256
01:04:11,200 --> 01:04:15,440
Speaker 4: includes everybody. When you talk for the people, you represent everybody.

1257
01:04:15,880 --> 01:04:18,000
Speaker 4: And when I told them that their assignment would be

1258
01:04:18,000 --> 01:04:22,600
Speaker 4: graded on how well they were able to consider everyone's

1259
01:04:22,680 --> 01:04:27,240
Speaker 4: needs safety and justice, they got up there on the

1260
01:04:27,280 --> 01:04:29,400
Speaker 4: record and did radically different things than I've ever seen

1261
01:04:29,440 --> 01:04:31,640
Speaker 4: a prosecutor do in real life. And largely we're thinking

1262
01:04:31,720 --> 01:04:35,000
Speaker 4: of restorative solutions and root causes and like how they

1263
01:04:35,040 --> 01:04:38,160
Speaker 4: could heal a community instead of just punishing and disappearing

1264
01:04:38,160 --> 01:04:43,440
Speaker 4: a person. If what prosecutor means is somebody who is

1265
01:04:43,640 --> 01:04:47,760
Speaker 4: enshrined with governmental authority to do justice for everyone in

1266
01:04:47,800 --> 01:04:51,560
Speaker 4: the community, including people who might be opposed to that

1267
01:04:51,800 --> 01:04:55,120
Speaker 4: very prosecutor. I think it could actually be a very

1268
01:04:55,200 --> 01:04:59,760
Speaker 4: powerful encapsulation of the best version of a leader, right

1269
01:05:00,000 --> 01:05:02,360
Speaker 4: person who's going to take this seriously and care for

1270
01:05:02,440 --> 01:05:04,640
Speaker 4: all of our well being and yes, stand up to

1271
01:05:05,400 --> 01:05:09,040
Speaker 4: abuses of people with less power, which is really what

1272
01:05:09,040 --> 01:05:11,240
Speaker 4: we would want prosecutors to stand up to the most.

1273
01:05:11,280 --> 01:05:14,200
Speaker 4: I think certainly it's not being done that way. I

1274
01:05:14,240 --> 01:05:17,720
Speaker 4: think the rhetoric sucks. I think half of Americans have

1275
01:05:17,760 --> 01:05:20,280
Speaker 4: had a loved one locked up. I just think that

1276
01:05:20,360 --> 01:05:23,400
Speaker 4: the rhetoric doesn't have to change. If it was made smarter.

1277
01:05:23,720 --> 01:05:26,360
Speaker 4: In order to be smarter, though, the policy would not

1278
01:05:26,480 --> 01:05:28,640
Speaker 4: have to shift towards tough on crime. It would have

1279
01:05:28,680 --> 01:05:34,040
Speaker 4: to shift towards evidence based, root cause thinking and solutions

1280
01:05:34,080 --> 01:05:36,600
Speaker 4: that shift us towards something better than our shitty status quo.

1281
01:05:37,320 --> 01:05:39,720
Speaker 2: Yeah, yeah, yeah, and now it just feels like, now

1282
01:05:39,760 --> 01:05:42,400
Speaker 2: let's embrace the status quo and bring it closer and

1283
01:05:42,400 --> 01:05:45,360
Speaker 2: closer and closer. But yeah, that's a yeah, such a

1284
01:05:46,720 --> 01:05:49,760
Speaker 2: the whole time, I'm like, wow, it Like. The thing

1285
01:05:49,800 --> 01:05:51,560
Speaker 2: that really makes you think a lot too, is like,

1286
01:05:51,600 --> 01:05:54,800
Speaker 2: we have so many people who are prosecutors that ascended politics,

1287
01:05:55,160 --> 01:05:57,560
Speaker 2: and that's why when Kantazi Brown Jackson was like the

1288
01:05:57,600 --> 01:06:00,720
Speaker 2: first public defender who had sat on the Supreme Court.

1289
01:06:00,760 --> 01:06:02,400
Speaker 2: I was liked, is that true?

1290
01:06:02,840 --> 01:06:05,960
Speaker 4: Oh my god, Oh writ large the federal bench is

1291
01:06:06,000 --> 01:06:08,120
Speaker 4: largely prosecuted. I actually read. I wrote a really meant

1292
01:06:08,160 --> 01:06:11,720
Speaker 4: email today Guys like my local representative, who I love,

1293
01:06:11,920 --> 01:06:14,760
Speaker 4: is like moving to run for the state a different

1294
01:06:14,760 --> 01:06:18,520
Speaker 4: state office, and he endorsed. It's a two candidate race.

1295
01:06:18,520 --> 01:06:20,480
Speaker 4: I live in a place with major housing issues. There's

1296
01:06:20,520 --> 01:06:22,400
Speaker 4: just not enough housing for people, cost of housing or

1297
01:06:22,440 --> 01:06:25,080
Speaker 4: too high. And one of the candidates is a housing organizer,

1298
01:06:25,200 --> 01:06:28,160
Speaker 4: a local housing organizer, and the other candidate is a prosecutor.

1299
01:06:29,320 --> 01:06:33,880
Speaker 4: Guess who've got the entressment prosecutor? And I wrote them

1300
01:06:33,880 --> 01:06:36,800
Speaker 4: a note being like, like, come on, this housing is

1301
01:06:37,120 --> 01:06:39,600
Speaker 4: the issue of our region. If you are going to

1302
01:06:39,680 --> 01:06:44,000
Speaker 4: make prosecution once again a blind path to power, you

1303
01:06:44,080 --> 01:06:46,600
Speaker 4: at least have to justify why you are overlooking someone

1304
01:06:46,640 --> 01:06:48,959
Speaker 4: whose life work is in the zone we most need.

1305
01:06:49,160 --> 01:06:50,840
Speaker 4: And the thing that bugs me about it the most

1306
01:06:50,960 --> 01:06:53,520
Speaker 4: is that it tells young people. I mean, in my work,

1307
01:06:53,800 --> 01:06:56,040
Speaker 4: I work with public defenders all over the country, and

1308
01:06:56,080 --> 01:06:58,840
Speaker 4: I help them expand the practic their practice and expand

1309
01:06:58,840 --> 01:07:00,520
Speaker 4: what they can offer their clients, and I place a

1310
01:07:00,520 --> 01:07:03,920
Speaker 4: lot of new professionals, usually young people, into jobs in

1311
01:07:03,920 --> 01:07:06,520
Speaker 4: public defense, and as they start out their careers, I'm

1312
01:07:06,520 --> 01:07:08,680
Speaker 4: looking at how they think of their career trajectory. They're

1313
01:07:08,680 --> 01:07:10,520
Speaker 4: doing great things, like I'm going to learn all about

1314
01:07:10,520 --> 01:07:13,560
Speaker 4: how fucked up America's public systems are and I'm going

1315
01:07:13,600 --> 01:07:15,960
Speaker 4: to carry that knowledge into my own change making career.

1316
01:07:16,280 --> 01:07:16,520
Speaker 1: Right.

1317
01:07:16,920 --> 01:07:19,640
Speaker 4: But to everybody else, the vast majority of young people,

1318
01:07:19,680 --> 01:07:23,360
Speaker 4: future lawyers, who are not like these dedicated, brilliant advocates,

1319
01:07:23,880 --> 01:07:26,400
Speaker 4: they think, Okay, I'll be a prosecutor for like two

1320
01:07:26,480 --> 01:07:30,120
Speaker 4: years and then I'll get my elected office. If I

1321
01:07:30,280 --> 01:07:34,280
Speaker 4: just incarcerate young black men and separate families and crush

1322
01:07:34,280 --> 01:07:36,800
Speaker 4: people's dreams and lives and maybe cause a few deaths,

1323
01:07:37,360 --> 01:07:40,000
Speaker 4: then I could be a state senter.

1324
01:07:40,160 --> 01:07:40,400
Speaker 1: Right.

1325
01:07:40,720 --> 01:07:43,160
Speaker 2: I've been betted right, and I've done the work right,

1326
01:07:43,560 --> 01:07:44,440
Speaker 2: And it's we shouldn't.

1327
01:07:44,440 --> 01:07:48,720
Speaker 4: We shouldn't make change making power reliant on willingness to

1328
01:07:48,760 --> 01:07:49,640
Speaker 4: harm others.

1329
01:07:50,640 --> 01:07:52,880
Speaker 1: Yeah, all right, sounds like we have a lot of

1330
01:07:52,880 --> 01:07:56,680
Speaker 1: work to do. Emily Galvin Almansa, what a pleasure having

1331
01:07:56,720 --> 01:08:00,080
Speaker 1: you on the daily zeitgeist? Where can people find you?

1332
01:08:00,160 --> 01:08:02,360
Speaker 1: Follow you, support your work and all that good stuff.

1333
01:08:02,640 --> 01:08:05,880
Speaker 4: Well, if they want to support expanding and improving public

1334
01:08:05,880 --> 01:08:08,680
Speaker 4: defense around the country, really transforming what we mean by

1335
01:08:08,680 --> 01:08:11,480
Speaker 4: public defense and getting more help for poor people with

1336
01:08:11,520 --> 01:08:14,960
Speaker 4: housing and employment and benefits and transportation and all the

1337
01:08:14,960 --> 01:08:18,320
Speaker 4: things that people actually need, they can go to www.

1338
01:08:18,479 --> 01:08:22,479
Speaker 4: Dot Partners for Justice dot org where they can learn

1339
01:08:22,520 --> 01:08:25,960
Speaker 4: all about our work to support public defenders nationally. They

1340
01:08:25,960 --> 01:08:30,160
Speaker 4: can also catch us on Twitter at at PFJ Underscore

1341
01:08:30,280 --> 01:08:33,599
Speaker 4: USA or Instagram at Partners for Justice, or they can

1342
01:08:33,640 --> 01:08:35,880
Speaker 4: follow and I guess I should say, and they can

1343
01:08:35,920 --> 01:08:41,600
Speaker 4: follow my much spicy tweets at Galvin Almonza it's just

1344
01:08:41,680 --> 01:08:45,360
Speaker 4: my last name. Do a weekly video on things that

1345
01:08:45,400 --> 01:08:47,280
Speaker 4: are awful in our legal system. So if people want

1346
01:08:47,280 --> 01:08:49,679
Speaker 4: to get like that, like spike of outrage once a week,

1347
01:08:49,760 --> 01:08:51,320
Speaker 4: come on Twitter with me and I will I will

1348
01:08:51,320 --> 01:08:51,960
Speaker 4: give you a spike.

1349
01:08:52,400 --> 01:08:55,080
Speaker 1: Yeah, but it's not just blind outrage. You also have

1350
01:08:55,320 --> 01:08:57,920
Speaker 1: solutions and ideas for things to do, so I do

1351
01:08:58,120 --> 01:09:01,240
Speaker 1: I highly recommend. Is there work of media that you've

1352
01:09:01,280 --> 01:09:01,879
Speaker 1: been enjoying.

1353
01:09:02,000 --> 01:09:04,439
Speaker 4: Okay, I'm gonna be really nerdy, guys. There was a

1354
01:09:04,479 --> 01:09:07,040
Speaker 4: paper that came out a couple months ago from Vida B. Johnson,

1355
01:09:07,240 --> 01:09:10,519
Speaker 4: who is a lawyer, and she wrote a paper called

1356
01:09:10,760 --> 01:09:14,840
Speaker 4: Whom do Prosecutors Protect? And I know that mostly people

1357
01:09:14,880 --> 01:09:16,559
Speaker 4: are not like you know, what I'm waiting for is

1358
01:09:16,600 --> 01:09:19,640
Speaker 4: the next hot law paper to drop. And I'm going

1359
01:09:19,680 --> 01:09:21,760
Speaker 4: to just dive into that, bastard and roll it on.

1360
01:09:22,000 --> 01:09:24,240
Speaker 4: But it's really good and it's really accessible, and it

1361
01:09:24,280 --> 01:09:27,679
Speaker 4: details every single way in which the kind of problematic

1362
01:09:27,720 --> 01:09:29,840
Speaker 4: incentives we've been talking about prosecution as a past, the

1363
01:09:29,920 --> 01:09:34,000
Speaker 4: power and the inter reliance between prosecutors and police, are

1364
01:09:34,160 --> 01:09:37,280
Speaker 4: robbing ordinary Americans of their chance at justice. And it's

1365
01:09:37,320 --> 01:09:38,200
Speaker 4: a really good paper.

1366
01:09:38,720 --> 01:09:43,040
Speaker 1: Damn that sounds good. Amazing. Miles, Where can people find you?

1367
01:09:43,479 --> 01:09:46,240
Speaker 1: And what is the latest legal brief that you've been enjoyed?

1368
01:09:46,680 --> 01:09:49,280
Speaker 2: Yeah, let me give me a second about the legal brief.

1369
01:09:50,320 --> 01:09:52,679
Speaker 2: I just found this one, the Pelican Brief.

1370
01:09:53,000 --> 01:09:54,919
Speaker 1: Oh hell yeah too, dude.

1371
01:09:55,600 --> 01:09:59,680
Speaker 2: You can find me, you can find men?

1372
01:10:01,160 --> 01:10:03,719
Speaker 1: Is that? Oh yeah, that actually makes sense? Huh huh.

1373
01:10:03,760 --> 01:10:05,680
Speaker 2: Thanks for that little factoid. I'm gonna take that to

1374
01:10:05,720 --> 01:10:08,439
Speaker 2: the take that to the bar tonight. Umm. You can

1375
01:10:08,479 --> 01:10:10,200
Speaker 2: find me at Miles of Great on Twitter and Instagram.

1376
01:10:10,240 --> 01:10:12,400
Speaker 2: You can find Jack and I on the basketball podcast

1377
01:10:12,400 --> 01:10:14,519
Speaker 2: Miles and Jack got mad boosties. You could also find

1378
01:10:14,520 --> 01:10:16,920
Speaker 2: me talking about ninety day fiance on four to twenty

1379
01:10:16,960 --> 01:10:21,599
Speaker 2: day Fiance a tweet I like, oh man, so uh

1380
01:10:21,960 --> 01:10:26,120
Speaker 2: the you know libs of TikTok person Chiachik tweeted out

1381
01:10:26,160 --> 01:10:28,719
Speaker 2: a few days ago. It said, I'm looking for parents

1382
01:10:28,760 --> 01:10:31,280
Speaker 2: anywhere in Ohio who have kids in public schools to

1383
01:10:31,320 --> 01:10:34,200
Speaker 2: be eyes and ears on the ground. Your identity will

1384
01:10:34,200 --> 01:10:37,280
Speaker 2: remain anonymous and protected. Please DM me if you fit

1385
01:10:37,360 --> 01:10:41,880
Speaker 2: this criteria. Patton Oswalt quote tweeted this and said, Chaia,

1386
01:10:42,240 --> 01:10:44,800
Speaker 2: I am so glad you're doing this. There's a boy

1387
01:10:44,840 --> 01:10:48,439
Speaker 2: in our neighborhood, Elliott, a child of divorce who we

1388
01:10:48,520 --> 01:10:51,080
Speaker 2: think is hiding an alien in his closet with the

1389
01:10:51,120 --> 01:10:53,800
Speaker 2: help of his siblings Gertie and Michael.

1390
01:10:57,120 --> 01:10:59,360
Speaker 1: Me hitting her with that et.

1391
01:10:59,760 --> 01:11:02,800
Speaker 2: But yeah, that is one of my favorite tweets recent

1392
01:11:02,880 --> 01:11:04,800
Speaker 2: Leave Ohio public schools.

1393
01:11:04,400 --> 01:11:07,720
Speaker 1: Alone, y'all. That's I am. I am a product of

1394
01:11:07,720 --> 01:11:10,760
Speaker 1: Ohio public schools. They do. They do fine work every

1395
01:11:10,800 --> 01:11:13,960
Speaker 1: once in a while, all right, tweet I've been enjoying

1396
01:11:14,320 --> 01:11:17,920
Speaker 1: Katie at Skatie for twenty tweeted they should call that

1397
01:11:17,960 --> 01:11:22,080
Speaker 1: guy Edgar allan poem because of all those poems he

1398
01:11:22,120 --> 01:11:27,960
Speaker 1: did similarly smart yeah yeah, yeah, yeah, yeah great. And

1399
01:11:28,000 --> 01:11:31,479
Speaker 1: then Brandy Jensen tweeted, I love when an it guy

1400
01:11:31,560 --> 01:11:37,720
Speaker 1: refers to my laptop as your machine is kind of

1401
01:11:37,720 --> 01:11:42,280
Speaker 1: cool here it makes it sound so cool. You can

1402
01:11:42,320 --> 01:11:45,519
Speaker 1: find me on Twitter at Jack Underscore Obrian. You can

1403
01:11:45,560 --> 01:11:47,760
Speaker 1: find us on Twitter at Daily Zeigeist, where at the

1404
01:11:47,880 --> 01:11:50,400
Speaker 1: Daily Zeitgeist on Instagram. We have a Facebook fan page.

1405
01:11:50,400 --> 01:11:53,800
Speaker 1: On website Dailyzeikeist dot com. We post our episodes and

1406
01:11:53,960 --> 01:11:56,280
Speaker 1: our footnotes no link off to the information that we

1407
01:11:56,280 --> 01:11:58,760
Speaker 1: talked about in today's episode, as well as the song

1408
01:11:58,800 --> 01:12:01,240
Speaker 1: that we think you might enjoy, Hey Miles with long

1409
01:12:01,320 --> 01:12:02,719
Speaker 1: do you think people might enjoy?

1410
01:12:03,240 --> 01:12:06,800
Speaker 2: I stumbled stumbled across a producer by the name of

1411
01:12:06,920 --> 01:12:10,200
Speaker 2: Harrison and just going through some of their tracks, and

1412
01:12:10,240 --> 01:12:12,960
Speaker 2: there's this one track that's really popular of his that's

1413
01:12:13,000 --> 01:12:16,679
Speaker 2: called Selfish High Heels and it's with him Young Bay

1414
01:12:16,760 --> 01:12:20,040
Speaker 2: and mac Ross eighty two ninety nine. But the sound

1415
01:12:20,120 --> 01:12:23,360
Speaker 2: of it is like eighties like Japanese city pop kind

1416
01:12:23,400 --> 01:12:25,679
Speaker 2: of stuff from the eighties, but like a little bit

1417
01:12:25,880 --> 01:12:29,120
Speaker 2: more like modern and futuristic. It's kind of trippy. So

1418
01:12:29,200 --> 01:12:32,080
Speaker 2: I really enjoyed it. So this is Selfish high Heels

1419
01:12:32,120 --> 01:12:34,080
Speaker 2: by Young Bay and Harrison and.

1420
01:12:34,040 --> 01:12:37,200
Speaker 1: It also creates like the next pixeart movie about the

1421
01:12:37,240 --> 01:12:40,519
Speaker 1: answer Fromorphic Shoes. Yeah, and you got the you know,

1422
01:12:40,760 --> 01:12:45,880
Speaker 1: funky sneakers, the selfish high heels silly slippers. I don't know,

1423
01:12:46,200 --> 01:12:48,720
Speaker 1: you guys do the work. I waste any more of

1424
01:12:48,800 --> 01:12:51,920
Speaker 1: your time. The kind of high high tops like they

1425
01:12:52,000 --> 01:12:53,200
Speaker 1: smoke a little weed, you know.

1426
01:12:53,320 --> 01:12:54,000
Speaker 2: Oh.

1427
01:12:54,040 --> 01:12:56,320
Speaker 1: The DAILIESE Guy is the production of iHeartRadio. For more

1428
01:12:56,360 --> 01:12:58,559
Speaker 1: podcasts from my Heart Radio, visit the iHeart Radio app,

1429
01:12:58,600 --> 01:13:01,280
Speaker 1: Apple podcast, or wherever you your favorite shows. That is

1430
01:13:01,320 --> 01:13:02,800
Speaker 1: going to do it for us this week. We are

1431
01:13:02,880 --> 01:13:06,600
Speaker 1: back on Tuesday after Labor Day to tell you what

1432
01:13:06,680 --> 01:13:09,040
Speaker 1: was trending over the long weekend and we will talk

1433
01:13:09,080 --> 01:13:10,200
Speaker 1: to you all then. Bye.

1434
01:13:10,520 --> 01:13:10,640
Speaker 4: By