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

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Speaker 1: two of Guys to production of I Heart Radio. This

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

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Speaker 1: America's share consciousness. It is Tuesday, February second. My name

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Speaker 1: is Jack O'Brien a K. Because we're gonna baja blast,

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Speaker 1: we gonna drink it right, We're gonna do the dupe

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Speaker 1: because it's Daily's I. That is courtesy of Samboni Zamboni,

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

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Speaker 1: co host, Mr Miles grad. Look at his weird hairline.

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Speaker 1: That boy is balding bald. I just came up with

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Speaker 1: that on the spot. I was just thinking about, hold

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Speaker 1: the whole era of of boys family. You mean East

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Speaker 1: Coast family. Yes, never skipped anything now, Yeah that that

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Speaker 1: that's just for me. Not didn't even hit the discord up.

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Speaker 1: That was kind of presumptuous of them to call themselves

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Speaker 1: the East Coast family since, uh, knowing as I do,

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Speaker 1: everybody on the West Coast has East Coast family that

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Speaker 1: they referred to as such. Uh. And also there are

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Speaker 1: all the mafia families, they're Discast family. I mean that's

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Speaker 1: something they'll have to reckon with in their own time. Miles. Uh.

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Speaker 1: It's especially funny that we're having such a dumb conversation

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Speaker 1: here at the top of the show because we are

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Speaker 1: being joined by one of the most impressive guests we've

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Speaker 1: ever had, if not the most impressive. She is running

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Speaker 1: for DA of New York and formerly a contestant on

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Speaker 1: Survivor and the brilliant and talented Eliza Orleans. We're not worthy,

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Speaker 1: We're not worthy. Welcome, Welcome, any thoughts, spanking, thank you,

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Speaker 1: it's great to be here. I'm so sorry I didn't

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Speaker 1: prepare a theme song for myself, um to the tune

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Speaker 1: of you know, pop music. I really I should have

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Speaker 1: been better prepared. I'm not gonna speculate what that will

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Speaker 1: do for your bid for d but I think it'll

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Speaker 1: be good. Can't be good? Yes, I think me singing

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Speaker 1: anywhere would just deter people from voting. And you are

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Speaker 1: joining us from Manhattan, yep, yep, in the midst of

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Speaker 1: Manhattan exactly right now. Yeah. They said we're in like

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Speaker 1: a state of emergency. We shouldn't leave our homes. They're

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Speaker 1: like stopping transit service at two pm. Oh wow, Okay,

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Speaker 1: that sounds real. Yeah. So, I'm sure a lot of

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Speaker 1: our listeners, the first who are just now becoming acquainted

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Speaker 1: with you have the first question that a lot of people,

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Speaker 1: I'm sure I have. It is like, how do you

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Speaker 1: go from Survivor to running for da of New York?

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Speaker 1: How does that path happen? Are they just do they

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Speaker 1: just happen to both be things that you've done? Or uh?

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Speaker 1: Are they connected in any way your brilliant strategic mind. Um,

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Speaker 1: so they both are things. Well, you know, I didn't

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Speaker 1: necessarily see myself running for d A, but I always

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Speaker 1: knew I was going to be a public defender. You know,

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Speaker 1: that was the only job I ever wanted. It was

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Speaker 1: the reason I went to law school. So by the

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Speaker 1: time I went on Survivor the first time, at twenty one,

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Speaker 1: I already knew that I was going to go to

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Speaker 1: law school, that I was going to become a public defender.

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Speaker 1: So it was kind of this you know thing that

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Speaker 1: I did. But it wasn't anything like I didn't anticipate

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Speaker 1: it kind of changing the trajectory of my life in

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Speaker 1: any way. Um, and then went to law school, became

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Speaker 1: a public defender, you know, spent over a decade representing

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Speaker 1: thousands of people fighting against our cruel, unjust criminal legal system,

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Speaker 1: and realized that I needed to do more and we

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Speaker 1: couldn't change the system unless we changed the d A.

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Speaker 1: And so that's kind of what led to my damn. Uh,

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Speaker 1: that's pretty that's pretty awesome. Just years of being a

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Speaker 1: public defender. Were your clients Are the people that you

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Speaker 1: defended familiar with you from Survivor? Was that ever a

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Speaker 1: conversation that happened? That's funny? So mostly not, But for

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Speaker 1: a couple of times. There are probably a couple of

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Speaker 1: times where um, someone after I was representing them looked back,

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Speaker 1: googled me or something, and then found out that I

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Speaker 1: was on Survivor and they were like, oh man, that's

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Speaker 1: my lawyer. Like, uh so there was that, But but

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Speaker 1: mostly no, I don't think it's as popular in New

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Speaker 1: York City as it is in some other places across

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Speaker 1: the country. Right in New York, I feel like you're

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Speaker 1: one of your big issues is about decriminalizing sex work,

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Speaker 1: and I feel like New York has kind of in

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Speaker 1: a sort of put in as a focal point since

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Speaker 1: that story that had come out about the paramedic who

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Speaker 1: was like fired and like I feel like in culture now,

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Speaker 1: we're constantly having moments where people are either they're learning

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Speaker 1: about sex work and why it's not a thing that

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Speaker 1: needs to be criminalized, or what how we look at

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Speaker 1: sex work? Um, and yeah, just how it's like even

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Speaker 1: on your website, it's like the first thing that one

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Speaker 1: of your first noted policies that you're really passionate about.

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Speaker 1: How has that you know, obviously your experience as a

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Speaker 1: public defender i'd imagine shaped your policies like this, But

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Speaker 1: is there something specifically that you really feel Is it

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Speaker 1: New York specific or what is it about your passionate

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Speaker 1: about decriminalizing sex work and the work that the d

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Speaker 1: A does that intersex for you aside from the obvious

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Speaker 1: park Well, yes, so you know, I think that there

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Speaker 1: is there's so much that needs to change in terms

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Speaker 1: of the way we think about sex work. And I'm

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Speaker 1: so grateful to you know, be able to be bringing

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Speaker 1: this conversation to a national stage and for people to

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Speaker 1: be receptive to it, because the first time I was

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Speaker 1: talking about this issue, uh in shortly after I'd become

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Speaker 1: a public defender and I started representing people who were

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Speaker 1: charged with you know, engaging in consensual sex work. And

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Speaker 1: I saw the way that criminalization impacted their lives. I

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Speaker 1: saw how it put their It put them in jeopardy,

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Speaker 1: It put them in danger. It forced interactions with the

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Speaker 1: police that sometimes became violent. You know, they were they

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Speaker 1: were marginalized, they weren't able to seek police help when

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Speaker 1: they were abused or brutalized. They you know, and it

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Speaker 1: criminalized their jobs, and you know, it made them fear

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Speaker 1: prosecution if anything. You know, you can't even report if

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Speaker 1: you're afraid that you yourself are going to be arrested

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Speaker 1: and jailed if you go come forward and say, oh,

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Speaker 1: somebody robbed me, somebody assaulted me, somebody raped me. Um.

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Speaker 1: And so it was something that I felt very strongly

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Speaker 1: about from you know, very early on in my career,

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Speaker 1: but needless to say, in it was not a very

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Speaker 1: popular opinion to take. And I'm so glad that over

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Speaker 1: the last decade plus it's something that really has come

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Speaker 1: to the national stage. And yes, when the woman who

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Speaker 1: was an e MT in New York who was using

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Speaker 1: only fans to to supplement her living because she wasn't

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Speaker 1: being paid enough and was but was doing it of

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Speaker 1: her own accord and her own volition and all of

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Speaker 1: a sudden was shamed by the New York Post. Was

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Speaker 1: you know, was was ostracized, was kind of stigmatized. Has

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Speaker 1: kind of made more people aware of those who are

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Speaker 1: engaging in sex work and saying, hey, we why do

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Speaker 1: we need to be criminalizing this. This isn't making us safe,

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Speaker 1: This isn't you know, doing anything for people. And so

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Speaker 1: I'm I'm so thrilled that you know at the reception

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Speaker 1: that my policy on decriminalizing sex work has gotten awesome. Well,

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Speaker 1: we have a couple of subjects we're talking about today

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Speaker 1: that are kind of broadly systemic things that I'm very

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Speaker 1: interested to hear your thoughts on the sort of I

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Speaker 1: don't know it feels is I'm hearing from people I'm

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Speaker 1: feeling it. Uh, Like there's the mental health crisis. Is

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Speaker 1: you know a lot of people are are dealing with

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Speaker 1: feelings of like this ship is never gonna end. Uh,

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Speaker 1: and then we're seeing uh statistics that indicate that there

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Speaker 1: is a mental health crisis that's getting worse. So we'll

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Speaker 1: talk about that broadly. Uh. We'll also talk about the

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Speaker 1: systemic problem of America's broadband inequality access to broadband and

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Speaker 1: just general that being a a shitty uh system. We

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Speaker 1: will talk about the diet law of past incident, which

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Speaker 1: I'm probably mispronouncing, but it it is this like unsolved

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Speaker 1: mystery that has recently been solved, and we'll talk about

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Speaker 1: why we solved it eleven years ago, so no big deal.

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Speaker 1: But before we get to any of that alliwes, we

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

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Speaker 1: search history that's revealing about who you are? Oh, gosh,

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Speaker 1: revealing about who I am? UM? So I mean the

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Speaker 1: things that like the things I've recently searched to include, like,

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Speaker 1: you know, the weather, because I want to figure out

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Speaker 1: like how soon it's like what the snow situation is,

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Speaker 1: whether I can walk my dog, you know when so

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Speaker 1: he doesn't get buried under snow. Um and uh and

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Speaker 1: then you know, really really I'm constantly looking up things

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Speaker 1: related to UM to criminal justice issues. So like recently,

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Speaker 1: I was just googling UM to confirm because a friend

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Speaker 1: asked and I and I was sure I did know

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Speaker 1: the answer, but I wanted to confirm, which is the

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Speaker 1: cost to incarcerate someone at Ryker's Island for one night?

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Speaker 1: Over nine hundred dollars a night. So it's over three

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Speaker 1: thousand dollars a year to put someone in jail for

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Speaker 1: a year. You think about, like how much we could

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Speaker 1: be investing in people. I mean, we're about to talk

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Speaker 1: about mental health issues, but anyhow that's something that that's relevant. Yes,

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Speaker 1: it's so odd like when you put those figures in

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Speaker 1: front of people, like there's still some people who are like, ah,

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Speaker 1: I'm still not convinced about a more humane way to

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Speaker 1: solve our issues versus throwing away nine nine hundred dollars

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Speaker 1: a night is really anyway putting someone in a dangerous

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Speaker 1: place that is damaging to their than any amount that

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Speaker 1: could have imagined what three hundred thousand dollars would have

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Speaker 1: done at any point for someone who is looking, you know,

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Speaker 1: at a year of being incarcerated, at any point in

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Speaker 1: their life, whether that would have come in the form

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Speaker 1: of services, whether education, social services, whatever, until we get

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Speaker 1: to that point. But it's all about, you know, profiting

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Speaker 1: off of the dysfunction. And that's a perfect example of like,

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Speaker 1: well we don't we don't take care of it, then

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Speaker 1: then we can make there's more money to be spent

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Speaker 1: or made on the other side of it, which is

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Speaker 1: really cool. What is something you think is underrated, Eliza,

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Speaker 1: um under eat? It would be uh nerd ropes. Oh yeah,

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Speaker 1: I ordered a huge box of them and I've been

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Speaker 1: eating them and I'm like, damn, Like these are so

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Speaker 1: good and they're really underrated. I feel like people don't

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Speaker 1: appreciate how delicious nerd ropes are. Yeah, I've I love nerds,

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Speaker 1: and I remember the first time I had seen a

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Speaker 1: nerd rope, it had just it seemed like a bridge

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Speaker 1: too far from me. I was just used to eating

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Speaker 1: the shards of sugar that were nerds. But I think

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Speaker 1: it was until like I on a whim at a

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Speaker 1: movie theater and I basically like by the end of

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Speaker 1: it was just turning into like a ball that I

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Speaker 1: would just eat because it was so good. I feel

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Speaker 1: like nerd ropes are good for regulating the case at

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Speaker 1: which you're eating the nerds, the pace of which you're

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Speaker 1: eating the nerds, because like I have a I have

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Speaker 1: a habit of like when you get the little box

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Speaker 1: of nerds, those are done in a single mouthful. Uh,

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Speaker 1: but even the King's size box of the King. I

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Speaker 1: don't know why I specified the small one. I will

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Speaker 1: I will take I will take the King's size down

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Speaker 1: in like two mouthfuls. Uh. And then it becomes a

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Speaker 1: unpleasant experience, uh for those around me, as I like

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Speaker 1: go through various sugar rushes and crashes and you know,

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Speaker 1: start crying. But the nerd ropes, I feel like, are yeah,

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Speaker 1: they they allow you to take it in experience. The

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Speaker 1: you know, the tannins, the subtle flavor hints that are

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Speaker 1: there in in each bite of nerds. Um Tannon's probably

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Speaker 1: not appropriate, I think that is specific to one, but

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Speaker 1: filled with flavor, delicious, filled with flavor. Thank you. Wait,

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Speaker 1: you're saying you're your house a king size box and nerds.

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Speaker 1: I don't know if I can do the whole kingsize,

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Speaker 1: not like once, but but I get I think most

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Speaker 1: of us know we have those flashbacks to childhood where

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Speaker 1: the nerd boxes were smaller than like the raisin boxes

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Speaker 1: used to get right, the ones that come in there,

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Speaker 1: the ones that like you get for Halloween that comes

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Speaker 1: like variety packs that are just the tiny ones, and

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Speaker 1: then you've got the ones that have the two different sides,

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Speaker 1: like the two different colors, and so that's like a

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Speaker 1: you know, like a little bit bigger, and then and

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Speaker 1: then there's the like king size roll box. Um it's

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Speaker 1: hard to but the nerd ropes are like a really

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Speaker 1: great way of you know, keeping yourself. I can you

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Speaker 1: know that much? And it's delicious And I love the

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Speaker 1: like Gooey Rope part two. Yeah, the Gooey rope part

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Speaker 1: is great. They really nailed it in terms of that.

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Speaker 1: What is what something you think is overrated? Overrated? I

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Speaker 1: would say is copaganda like TV shows that portray police officers,

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Speaker 1: because these shows are basically marketing for police departments, and

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Speaker 1: they glorify and normalize the systemic violence and injustices that

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Speaker 1: are handed out by the police and instead make them

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Speaker 1: heroes and so overrated. What is there a piece of

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Speaker 1: copaganda you've had You've had to have a reckoning with

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Speaker 1: over time as you're like, I realized this is this

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Speaker 1: is just straight nonsense. But but I loved it as

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Speaker 1: a TV show or film. Um, I most of the

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Speaker 1: time watch that stuff in it, Like I I'm no

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Speaker 1: fun to watch legal shows with. I scream at the TV.

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Speaker 1: I'm like that that's not true, that would ever happen,

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Speaker 1: you know, I'm like really unfund So this is why

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Speaker 1: I just like, you know, I just instead watched like

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Speaker 1: reality television there you go, yeah, because like what do

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Speaker 1: we know about the K one visa process one fiance?

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Speaker 1: But watch a procedural with you you're like, oh, that

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Speaker 1: is not Oh my god, the chain of custody on

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Speaker 1: that is absolutely bizarre. Right, that makes sense. I watched

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Speaker 1: den of Thieves over the weekend. I had not seen

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Speaker 1: that that's the Gerard Butler two thousand eighteen uh movie

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Speaker 1: that it's like the protagonists are the l A. Sheriff's Department,

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Speaker 1: um who and like it's there are like vague implications

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Speaker 1: that they're the ones who like have those tattoos and

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Speaker 1: like basically kill people and like have white supremacist ties. Uh,

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Speaker 1: But they like they cut all that out, you know,

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Speaker 1: they make it a little the team a little bit

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Speaker 1: more first. But it's like you're supposed to be rooting

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Speaker 1: for the l A. Sheriff's Department in two thousand eighteen,

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Speaker 1: UM still having trouble, just like that Denzel movie that

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Speaker 1: came out where people are like this is also capaganda.

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Speaker 1: I don't care if it's Denzel, and like we're taking

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Speaker 1: a like Rosie lens to look at policing, but yeah,

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Speaker 1: it's everywhere. Yeah, Choppo trap House was like recommended that.

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Speaker 1: They were like that movie is great. It's like about

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Speaker 1: a dirt bag and it's like, huh, that's the one

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Speaker 1: with Ocean Jackson Jr. Right, Yeah, and yeah that one

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Speaker 1: takes a weird turn and I'm like whatever, it's just

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Speaker 1: like it's just like testosterone fest. It's really there's not

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Speaker 1: much else in it. I haven't seen it, but I'm

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Speaker 1: not going to. Yeah, good watch a watch Bling Empire

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Speaker 1: instead about the real crazy rich Asians of l A.

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Speaker 1: Which I was watching the weekend. How is that? It's wild?

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Speaker 1: You know, it's funny when you see like the you know,

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Speaker 1: just obscene wealth where like people are giving each other

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Speaker 1: gifts and like, yeah, the towel has real gold in it.

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Speaker 1: You're like you gave someone a towel that had gold

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Speaker 1: in it, and You're like that's it's so obscene, And

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Speaker 1: it's just like a whole other dimension of reality that

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Speaker 1: we're not used to. Lies I'm curious as running for

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Speaker 1: d A and saying things like coppaganda, Right. You know

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Speaker 1: a lot of people, as we've seen over the years,

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Speaker 1: like to walk the this weird line of being like,

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Speaker 1: I don't want to make the police mad, but also

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Speaker 1: wanting to also seek justice. Um in terms of and

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Speaker 1: I don't, I don't. You don't need the cast dispersions

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Speaker 1: on the other people running. But it seems like I

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Speaker 1: think there are what eight how many people are running

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Speaker 1: at the moment? Eight people? Is that? Has that been

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Speaker 1: a tone right now for for the other candidates, because

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Speaker 1: I feel like only one is not really about any

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Speaker 1: kind of real reform or seemingly doesn't seem like very

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Speaker 1: forward thinking. But in terms of the race, um, as

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Speaker 1: it is right now, how how many people are sort

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Speaker 1: of have this sort of progressive viewpoint on how things

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Speaker 1: need to change within New York? Well, so, you know

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Speaker 1: what's so interesting? And I was kind of touching on

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Speaker 1: this a little bit when I say that that now,

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Speaker 1: all of a sudden, it's become popular to say things

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Speaker 1: like sex work is work? Um, And I'm so thrilled

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Speaker 1: that people are coming around to the to the progressive

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Speaker 1: views that we should decriminalize sex work, we should decriminalize

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Speaker 1: drug possession, we should end mass incarceration and stop using

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Speaker 1: you know, jail as the default and punitive prison sentences

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Speaker 1: for for low level offenses. But the problem is even

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Speaker 1: people who are now espousing progressive talking points because they're popular,

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Speaker 1: are not necessarily people who truly believe these things. And

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Speaker 1: I think some of the evaluations that have come out

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Speaker 1: recently about the candidates really do kind of delve into

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Speaker 1: these nuanced views of what kinds of changes you would make,

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Speaker 1: and in those like I come out, you know, heade

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Speaker 1: shoulders above everyone else, like I'm the only real progressive

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Speaker 1: in the race who has the authentic commitment from my career,

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Speaker 1: from my experience as being a public defender and fighting

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Speaker 1: against this system of injustice. And it's not like all

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Speaker 1: of a sudden one day I woke up and was like, oh,

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Speaker 1: you know what, I think it'd be popular to say

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Speaker 1: we should end mass incarceration. So that's the pivot I'm

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Speaker 1: going to take. And yeah, I'm gonna hold police accountable.

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Speaker 1: And it's like people who have been working in conjunction

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Speaker 1: with the police to lock people up for their entire career,

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Speaker 1: all of a sudden are saying they're gonna hold police accountable. Well,

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Speaker 1: I've spent every day of my career cross examining police

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Speaker 1: officers and questioning the veracity of the things that they say.

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Speaker 1: Um And so I think that, you know, prioritizing this

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Speaker 1: kind of accountability for the police and these real reforms

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Speaker 1: is incredibly important. But making sure that we're not just

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Speaker 1: listening to someone's memorized talking points, but looking into their record,

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Speaker 1: looking into their history, making sure that this is an

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Speaker 1: authentic commitment to bringing about real change, right, and I

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Speaker 1: can I can tell by the New York Post treatment

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Speaker 1: of you there, you probably are walking the walk because

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Speaker 1: they're painting you as someone who's like this headline most men,

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Speaker 1: most Manhattan DA candidates care most for protecting criminals. Wow,

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Speaker 1: and just like such a disingenuous look on reform, which

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Speaker 1: is like these people just want It's like it's gonna

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Speaker 1: look like a Batman movie where they just open up

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Speaker 1: the asylums and the jails and it's it's it's you know,

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Speaker 1: the people are gonna be running wild when it's really

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Speaker 1: about we have such an inhumane of treating people and

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Speaker 1: we're not actually we're not addressing the root causes of anything.

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Speaker 1: Um So, rather than it, So, you know, credit to you,

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Speaker 1: because all of the New York Post is absolute trash.

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Speaker 1: Listen if the New York Post is criticizing me. I'm

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Speaker 1: obviously doing something right, so I feel I feel great

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Speaker 1: about it. Our pick for Manhattan d A. Then he'd

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Speaker 1: be like, uh, do you do you aspire to one

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Speaker 1: day have like a real shitty New York posts like

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Speaker 1: pun headline about you. I feel I feel like that

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Speaker 1: would be the ultimate honor. Is like some yeah, some pot,

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Speaker 1: they're gonna they're gonna come after me, undoubtedly, like it'll

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Speaker 1: be it'll be a daily occurrence of them just waiting

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Speaker 1: on me. Right, I'm looking forward to it. Yeah, hell yea,

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Speaker 1: all right, let's take a quick break and we'll come

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Speaker 1: right back. And we're back, and uh, let's talk about

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Speaker 1: mental health because I just feel like I'm hearing this

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Speaker 1: more and more. I'm feeling this more and more. Felt

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Speaker 1: like there might be light at the end of the tunnel.

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Speaker 1: Because the vaccines were rolling out and then we just

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Speaker 1: had our worst COVID month ever in January in the

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Speaker 1: United States. The vaccine rollout is a bit of a mess.

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Speaker 1: I definitely don't think we should like feel hopeless. You know,

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Speaker 1: the ingredients of the solution seemed to be somewhat close

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Speaker 1: at hand. Were only ten days into the new administration,

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Speaker 1: but in terms of getting to a place where they're

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Speaker 1: combining those ingredients in a way that actually helps and

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Speaker 1: uh fixes the problem. And there's also a sense in which,

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Speaker 1: like the you know the fact that this is a

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Speaker 1: solution that requires widespread responsible behavior from them from the

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Speaker 1: US population. Uh, And we've just seen NonStop evidence that

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Speaker 1: a large portion of the U s population is actively

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Speaker 1: hostile to the suggestion that they behave responsibly is I

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Speaker 1: don't know, it's a lit's it's got me feeling kind

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Speaker 1: of shitty and like we're taking a couple of steps backwards. Yeah,

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Speaker 1: I mean, especially with like in l A, the disruption

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Speaker 1: that happened at Dodger Stadium, with all these like anti

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Speaker 1: vax er, masker, COVID hoax plandemic people like interrupting the

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Speaker 1: process of vaccinations, like it's one thing to just have

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Speaker 1: your awful take and just be there with it. The

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Speaker 1: name their causes shop mask free. They want to go

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Speaker 1: to grocery stores and shop without a mask. That is

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Speaker 1: their cause, right? Can we see them? They've been everywhere

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Speaker 1: they've come through parts of the valley. They were at

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Speaker 1: the Century City Mall being like we don't need master

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Speaker 1: and I don't know if this is like of some

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Speaker 1: kind of troll from the Chamber of Commerce, because it's

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Speaker 1: it sounds like just the weirdest like thing to be

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Speaker 1: like we gotta shop mask free, y'all, or we gotta

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Speaker 1: do this it nothing is really making sense, But I

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Speaker 1: think that's adding to like the chaos of this entire pandemic. Like,

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Speaker 1: on top of us not having or regaining any sense

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Speaker 1: of normalcy quote unquote, we have other people who are

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Speaker 1: completely taking this in some bizarre direction where it's now

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Speaker 1: like there's opposition to act to treating people with dignity

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Speaker 1: or or treating each other responsibly. Um, so, yeah, it

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Speaker 1: only feels like it's sort of compounding. There was a

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Speaker 1: nurse in Chattanooga who got the vaccine on TV and

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Speaker 1: fainted because you know, like temporarily like when to her knees.

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Speaker 1: That was helped up and explained immediately to the press

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Speaker 1: that was in attendance that she has an overactive legal

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Speaker 1: response which causes like any pain caused you to pass

403
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Speaker 1: out of the population have it. And the anti bax

404
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Speaker 1: er and COVID hoax or conspiracy theorists seized on this

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Speaker 1: to make the case that she had died and they

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Speaker 1: had replaced her with a clone, I guess, or maybe not.

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Speaker 1: Maybe they think that she's just but like they had

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Speaker 1: to do a proof of life video from the hospital

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Speaker 1: to be like, hey, here she is. These are her coworkers.

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Speaker 1: They can vouch for the fact that she's alive and

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Speaker 1: well and still working to save you guys as lives.

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Speaker 1: And the people are like, yeah, nice, try, nice, try. Therefore,

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Speaker 1: what cloning technology is that good? Yet we're failing to

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Speaker 1: give people like organ transplants yep, Like I mean, it's like,

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Speaker 1: just think of the ramifications, right, If we have clones,

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Speaker 1: couldn't we be doing all kinds of other cool stuff

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Speaker 1: with it rather than being like that nurse, let's cover

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Speaker 1: up the vaccination process, So get a clone in here.

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Speaker 1: It's so it's but I get it. Like, you know,

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Speaker 1: like when people are powerless, they're at their most susceptible

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Speaker 1: to getting swept up into some explanation as to why

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Speaker 1: they are powerless, because sometimes the truth is just so

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Speaker 1: hard to fathom. You wouldn't be like, yeah, it's just

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Speaker 1: the billion they're they've been setting up the game to

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Speaker 1: be like this because of X, Y and Z, and

426
00:25:17,000 --> 00:25:20,040
Speaker 1: it's really just unfortunate to see because and I feel

427
00:25:20,080 --> 00:25:22,239
Speaker 1: bad for this nurse who passed out and immediately into like,

428
00:25:22,320 --> 00:25:24,200
Speaker 1: oh my gosh, people are gonna take that the wrong way.

429
00:25:24,240 --> 00:25:26,560
Speaker 1: I need to immediately be like, that's just me, don't worry.

430
00:25:26,560 --> 00:25:28,639
Speaker 1: Has nothing to do with the efficacy of this vaccine

431
00:25:28,760 --> 00:25:31,560
Speaker 1: or whether or not you should take it, and that

432
00:25:31,640 --> 00:25:34,920
Speaker 1: other level of dread that even healthcare practitioners are having

433
00:25:34,920 --> 00:25:39,840
Speaker 1: to experience. Yeah, there's also facts that lead us like

434
00:25:40,080 --> 00:25:43,840
Speaker 1: non human causes for you know, the bad news. There's

435
00:25:44,000 --> 00:25:48,200
Speaker 1: a new variant of the coronavirus that will likely become

436
00:25:48,240 --> 00:25:52,000
Speaker 1: the dominant strain within the US. It's British variant, or

437
00:25:52,000 --> 00:25:57,000
Speaker 1: at least that's what they're describing it as more contagious,

438
00:25:57,119 --> 00:26:01,320
Speaker 1: and researchers think it maybe more dead. Uh. So there's

439
00:26:01,320 --> 00:26:04,359
Speaker 1: a lot of ways that this story is breaking, uh

440
00:26:04,480 --> 00:26:10,000
Speaker 1: in the wrong direction. Um. But there so combining that

441
00:26:10,040 --> 00:26:14,840
Speaker 1: with just the general since I've had uh reading on

442
00:26:14,880 --> 00:26:18,960
Speaker 1: social media from other people from studies that there that

443
00:26:19,160 --> 00:26:22,320
Speaker 1: this is like having a negative impact on on our

444
00:26:22,440 --> 00:26:26,159
Speaker 1: mental health. Uh. There there's a new study in the

445
00:26:26,240 --> 00:26:30,359
Speaker 1: Journal of Sports Medicine, Uh that is not about how

446
00:26:30,600 --> 00:26:32,800
Speaker 1: we're doing in the pandemic. It's about just like our

447
00:26:32,840 --> 00:26:39,919
Speaker 1: baseline heading into this and uh, it found that seventeen

448
00:26:40,000 --> 00:26:42,840
Speaker 1: percent of men, seventeen point eight percent of men, twenty

449
00:26:42,880 --> 00:26:45,320
Speaker 1: seven point six percent of women. And these are like

450
00:26:45,480 --> 00:26:51,360
Speaker 1: young people, their student athletes there, uh, you know, Naval

451
00:26:51,760 --> 00:26:58,000
Speaker 1: Academy UM freshmen were tested and found to have basically

452
00:26:58,000 --> 00:27:01,320
Speaker 1: they were trying to find a baseline were concussion symptoms.

453
00:27:01,520 --> 00:27:04,399
Speaker 1: They were like, so we want to just test people

454
00:27:04,440 --> 00:27:07,720
Speaker 1: who haven't had any concussions, like concuss of activity, like

455
00:27:07,760 --> 00:27:12,920
Speaker 1: no head uh injuries, just to see like what they are,

456
00:27:12,960 --> 00:27:15,640
Speaker 1: like how they compared to people who have. And they

457
00:27:15,640 --> 00:27:19,200
Speaker 1: found that that portion seventeen percent of men, twenty seven

458
00:27:19,240 --> 00:27:22,720
Speaker 1: point six percent of women like basically had symptoms of

459
00:27:22,840 --> 00:27:28,760
Speaker 1: concussions just from general like mental health deterioration like that.

460
00:27:29,240 --> 00:27:33,200
Speaker 1: They tested the same as people who have had head trauma,

461
00:27:33,280 --> 00:27:37,760
Speaker 1: just like from like a general fog of health problems,

462
00:27:38,240 --> 00:27:41,800
Speaker 1: mental health problems and just a lot of like these

463
00:27:41,840 --> 00:27:48,480
Speaker 1: small kind of background problems that I think are hitting America, uh,

464
00:27:48,520 --> 00:27:52,919
Speaker 1: and that don't really get covered all that much or

465
00:27:52,960 --> 00:27:57,639
Speaker 1: when they do. It's like, you know, teens are being

466
00:27:58,240 --> 00:28:01,159
Speaker 1: made depressed by social media. It's not like covering it

467
00:28:01,200 --> 00:28:04,920
Speaker 1: as like a holistic problem. And I mean to Alizada

468
00:28:05,800 --> 00:28:08,359
Speaker 1: points that you were making like there there's an article

469
00:28:08,520 --> 00:28:12,560
Speaker 1: in the Nation that's about the the way that America

470
00:28:12,680 --> 00:28:18,000
Speaker 1: has been approaching this as uh with awareness campaigns like

471
00:28:18,160 --> 00:28:22,160
Speaker 1: trying like just make uh, you know, be aware of

472
00:28:22,840 --> 00:28:26,320
Speaker 1: that meant like trying to destigmatize mental health, which is like,

473
00:28:27,160 --> 00:28:30,280
Speaker 1: you know, there's not there's nothing negative about that, but

474
00:28:30,400 --> 00:28:34,600
Speaker 1: it's also a cheap fix to a problem that is

475
00:28:35,840 --> 00:28:39,040
Speaker 1: you know, staying unfixed like that. A lot of the

476
00:28:39,080 --> 00:28:44,120
Speaker 1: research finds that it's basically caused by inequality, and you know,

477
00:28:44,240 --> 00:28:50,560
Speaker 1: just all the different ways that endemic inequality, internalized racism,

478
00:28:50,760 --> 00:28:55,760
Speaker 1: all of these different things are causing people to be

479
00:28:56,040 --> 00:29:00,160
Speaker 1: less and less mentally healthy. The longer that we go

480
00:29:00,280 --> 00:29:04,080
Speaker 1: forward in this version of reality where we don't have

481
00:29:04,720 --> 00:29:10,840
Speaker 1: communities or communities are dissolving and we don't have the

482
00:29:10,880 --> 00:29:14,200
Speaker 1: protection of like any sort of social institutions, it's just

483
00:29:14,760 --> 00:29:18,360
Speaker 1: we're at the whim of kind of corporations and for sure,

484
00:29:18,440 --> 00:29:20,480
Speaker 1: but I think that it's also exactly I think it's

485
00:29:20,520 --> 00:29:25,760
Speaker 1: also like something that really is um kind of exacerbated

486
00:29:25,800 --> 00:29:28,000
Speaker 1: by the way in which we handle people who are

487
00:29:28,040 --> 00:29:30,320
Speaker 1: in the throes of mental health crisis. So far, too

488
00:29:30,400 --> 00:29:32,760
Speaker 1: many times I've represented people who have been dealing with

489
00:29:32,760 --> 00:29:36,280
Speaker 1: a mental health issue, and you know, because we don't

490
00:29:36,320 --> 00:29:39,880
Speaker 1: have these structures, these social structures to deal with these issues,

491
00:29:40,280 --> 00:29:42,560
Speaker 1: you know, people call the police and so the police

492
00:29:42,600 --> 00:29:46,600
Speaker 1: show up, they have no training, no ability, no you know,

493
00:29:46,760 --> 00:29:48,560
Speaker 1: no way to handle someone in the throws of a

494
00:29:48,640 --> 00:29:52,320
Speaker 1: mental health crisis, and so the situation usually escalates because

495
00:29:52,360 --> 00:29:55,360
Speaker 1: showing up armed and in a situation where you're going

496
00:29:55,400 --> 00:29:57,520
Speaker 1: to be untrained to deal with someone in the throws

497
00:29:57,560 --> 00:30:00,320
Speaker 1: of mental health crisis means that oftentimes it was results

498
00:30:00,400 --> 00:30:03,080
Speaker 1: in you know, some pretty serious violence. And we've even

499
00:30:03,120 --> 00:30:06,200
Speaker 1: seen it result in the in the deaths of people

500
00:30:06,560 --> 00:30:09,320
Speaker 1: who have been you know, killed by the police because

501
00:30:09,320 --> 00:30:11,120
Speaker 1: they were dealing with a mental health crisis and the

502
00:30:11,120 --> 00:30:13,959
Speaker 1: police showed up. And then of course that there's the

503
00:30:13,960 --> 00:30:18,120
Speaker 1: fact that you know, the number one mental health provider

504
00:30:18,280 --> 00:30:21,200
Speaker 1: in New York City is the Department of Corrections. So

505
00:30:21,280 --> 00:30:24,440
Speaker 1: more people are getting mental health treatment in our jails

506
00:30:24,800 --> 00:30:27,880
Speaker 1: than anywhere else in New York City. I mean, that's

507
00:30:27,920 --> 00:30:30,600
Speaker 1: just wrong, that's absolutely wrong, Like that just shouldn't be

508
00:30:30,680 --> 00:30:32,800
Speaker 1: how we're dealing with these things. And as you say,

509
00:30:32,840 --> 00:30:35,160
Speaker 1: mental health issues are on the rise, and so we

510
00:30:35,240 --> 00:30:38,360
Speaker 1: have to have people, especially in our local offices, who

511
00:30:38,360 --> 00:30:40,920
Speaker 1: don't want to just address these things by locking people up,

512
00:30:40,920 --> 00:30:44,920
Speaker 1: which like honestly just exacerbates and deteriorates people's mental health

513
00:30:44,960 --> 00:30:49,160
Speaker 1: condition even further. Um. And then you know, they're released

514
00:30:49,200 --> 00:30:51,200
Speaker 1: back into the community because most people aren't getting locked

515
00:30:51,240 --> 00:30:53,320
Speaker 1: up for life and they're worse off than when they

516
00:30:53,320 --> 00:30:57,560
Speaker 1: went in. Yeah. Um, there's this quote from this guy,

517
00:30:57,640 --> 00:31:00,840
Speaker 1: Rudolph for Child, who is off called the father of

518
00:31:00,880 --> 00:31:05,120
Speaker 1: modern pathology as a study, not as the father of

519
00:31:05,480 --> 00:31:08,680
Speaker 1: the cause of all these pathologies, but he's a politics

520
00:31:08,720 --> 00:31:11,880
Speaker 1: is nothing else but medicine on a large scale. Um.

521
00:31:11,920 --> 00:31:16,240
Speaker 1: And I think that's just, yeah, a very valid point

522
00:31:16,280 --> 00:31:20,160
Speaker 1: that probably doesn't is it isn't super intuitive to a

523
00:31:20,200 --> 00:31:23,440
Speaker 1: lot of Americans, but the the idea that you know,

524
00:31:23,640 --> 00:31:29,280
Speaker 1: these issues that are happening internally in people's minds are

525
00:31:29,880 --> 00:31:35,680
Speaker 1: a result a direct reflection of systemic issues that they're seeing,

526
00:31:35,720 --> 00:31:38,840
Speaker 1: but we're a little bit too close to it sometimes.

527
00:31:39,120 --> 00:31:42,760
Speaker 1: You know, there's the this is water kind of problem

528
00:31:42,800 --> 00:31:47,520
Speaker 1: where we don't don't look around us and see what

529
00:31:48,120 --> 00:31:50,800
Speaker 1: you know, just our natural conditions because we're too close

530
00:31:50,840 --> 00:31:54,120
Speaker 1: to them. Yeah and yeah. The policy issue is so

531
00:31:54,280 --> 00:31:58,080
Speaker 1: funny because people who are at the levers to create

532
00:31:58,120 --> 00:32:02,160
Speaker 1: the legislation to help people still act confused or try

533
00:32:02,240 --> 00:32:04,760
Speaker 1: and point to other things when it's like, you actually

534
00:32:04,880 --> 00:32:08,320
Speaker 1: have the power to change things, but it's really whether

535
00:32:08,400 --> 00:32:12,600
Speaker 1: or not you're able to rock the boat, uh and

536
00:32:12,800 --> 00:32:16,520
Speaker 1: make the wealthiest people a little bit more uncomfortable than

537
00:32:16,600 --> 00:32:19,120
Speaker 1: maybe they're used to. You know, when you think of

538
00:32:19,160 --> 00:32:23,000
Speaker 1: like just as policy as medicine, a lot of the

539
00:32:23,040 --> 00:32:25,600
Speaker 1: countries were able to get their lockdowns or whatever in

540
00:32:25,720 --> 00:32:29,840
Speaker 1: order because they knew we have to subsidize people's lost wages.

541
00:32:30,320 --> 00:32:32,880
Speaker 1: That's how we get That's how we can get compliance

542
00:32:32,920 --> 00:32:36,600
Speaker 1: because we understand if we say world too scary and

543
00:32:36,720 --> 00:32:41,920
Speaker 1: dangerous to work, therefore we will subsidize that lost income

544
00:32:42,040 --> 00:32:44,920
Speaker 1: so we don't create, you know, just screw up the

545
00:32:44,920 --> 00:32:48,280
Speaker 1: economy even more than it has. Yet, no one's actually

546
00:32:48,280 --> 00:32:50,400
Speaker 1: willing to look at that as an actual treatment to

547
00:32:50,520 --> 00:32:53,480
Speaker 1: like this entire pandemic. It's most like, well, how quickly

548
00:32:53,480 --> 00:32:56,000
Speaker 1: can we get these vaccines? And it's like we're whose

549
00:32:56,040 --> 00:32:58,200
Speaker 1: where or whatever. It's like, but you're you're you're also

550
00:32:58,360 --> 00:33:02,600
Speaker 1: there there's this like really uh intentional blind spot um

551
00:33:02,840 --> 00:33:05,520
Speaker 1: to really kind of throw up your hands and be like, well,

552
00:33:06,120 --> 00:33:08,920
Speaker 1: my powers worked to a point, and at that point

553
00:33:09,360 --> 00:33:12,960
Speaker 1: then it's it's not worth it to some politicians. Unfortunately.

554
00:33:13,760 --> 00:33:18,240
Speaker 1: The article also points out that there are studies that

555
00:33:18,240 --> 00:33:24,760
Speaker 1: have found that inequality in a society, uh is associated

556
00:33:24,840 --> 00:33:30,680
Speaker 1: with worst mental health for both the people who have

557
00:33:30,840 --> 00:33:36,320
Speaker 1: less and also the top one percent. It's bad for everybody. Um,

558
00:33:36,440 --> 00:33:41,000
Speaker 1: so you know they I think that they probably think

559
00:33:41,080 --> 00:33:44,320
Speaker 1: that keeping things the way they are is better overall

560
00:33:44,440 --> 00:33:49,760
Speaker 1: for them because they've been again it's the like this

561
00:33:49,800 --> 00:33:52,960
Speaker 1: is water thing where they're they came up and their

562
00:33:53,040 --> 00:33:56,240
Speaker 1: d n A is like American like has first have

563
00:33:56,440 --> 00:33:59,680
Speaker 1: not but they don't know that there's another way that

564
00:33:59,720 --> 00:34:03,840
Speaker 1: it can be which would allow them to be happier

565
00:34:03,880 --> 00:34:09,120
Speaker 1: in a more justin equal world. Um. But yeah, study

566
00:34:09,160 --> 00:34:11,400
Speaker 1: that this article talks about looked at suicide rate and

567
00:34:11,480 --> 00:34:16,000
Speaker 1: minimum wage between and found that for every dollar the

568
00:34:16,040 --> 00:34:19,360
Speaker 1: minimum wages raised, the suicide rate decreased by three point

569
00:34:19,360 --> 00:34:23,960
Speaker 1: five to six percent. Wow. Um yeah, if that's not

570
00:34:24,000 --> 00:34:26,799
Speaker 1: an argument for raising the minimum wage, fuck, it's like

571
00:34:28,040 --> 00:34:31,960
Speaker 1: uh um right, and then we're still talking about a

572
00:34:31,960 --> 00:34:34,920
Speaker 1: figure that we were debating like ten years ago, like

573
00:34:35,080 --> 00:34:39,360
Speaker 1: fifteen dollars was like that one set sail, Like we

574
00:34:39,400 --> 00:34:43,960
Speaker 1: should really be like thirty you know, uh, if we're

575
00:34:43,960 --> 00:34:46,480
Speaker 1: really trying to make things that could because a lot

576
00:34:46,520 --> 00:34:48,359
Speaker 1: of the times too and people like make these other

577
00:34:48,680 --> 00:34:52,319
Speaker 1: examples like well, you can work at McDonald's in Scandinavia

578
00:34:52,400 --> 00:34:55,000
Speaker 1: like Denmark, and you go to all this stuff and people,

579
00:34:55,640 --> 00:34:58,000
Speaker 1: how does it work? It's like, well, for starters, their

580
00:34:58,040 --> 00:35:00,680
Speaker 1: minimum wage is a lot higher because at the very

581
00:35:00,760 --> 00:35:04,319
Speaker 1: least legislators and the government is willing to actually do

582
00:35:04,360 --> 00:35:08,120
Speaker 1: the analysis and say, as a policy, as a nation,

583
00:35:08,520 --> 00:35:10,959
Speaker 1: we think like you should only have to have one

584
00:35:11,120 --> 00:35:15,080
Speaker 1: job to at least have your needs met, rather than

585
00:35:15,120 --> 00:35:17,360
Speaker 1: like how many jobs can you cram in for the

586
00:35:17,440 --> 00:35:20,279
Speaker 1: bare minimum, which is where we're at now. Um, well,

587
00:35:20,320 --> 00:35:22,960
Speaker 1: and don't you level when people are like, well, teachers,

588
00:35:23,320 --> 00:35:25,960
Speaker 1: if we pay people minimum wage of twenty two dollars

589
00:35:25,960 --> 00:35:28,040
Speaker 1: in our teachers don't even make that much. And instead

590
00:35:28,080 --> 00:35:30,279
Speaker 1: of saying, so let's keep the minimum wage down, it's

591
00:35:30,320 --> 00:35:37,440
Speaker 1: like let's pay teachers to everyone everyone more money except

592
00:35:37,560 --> 00:35:41,520
Speaker 1: billionaires and multi millionaires. Do we can we get on

593
00:35:41,560 --> 00:35:45,040
Speaker 1: that page at least you'll like why it's and it's funny. Yeah,

594
00:35:45,080 --> 00:35:48,480
Speaker 1: like you say, there's this these weird arguments that always

595
00:35:48,480 --> 00:35:50,719
Speaker 1: like a reflexive a lot of American people have, which

596
00:35:50,719 --> 00:35:52,239
Speaker 1: is like, well, where are you going to get that,

597
00:35:52,640 --> 00:35:55,160
Speaker 1: or where's that money come from? Or if we pay

598
00:35:55,400 --> 00:35:57,480
Speaker 1: X group, then we gotta pay X group, Or if

599
00:35:57,480 --> 00:36:00,600
Speaker 1: we give these people kindness, then what about the rest

600
00:36:00,600 --> 00:36:03,520
Speaker 1: of us that have suffered? Like well, unfortunately some of

601
00:36:03,600 --> 00:36:05,840
Speaker 1: us had to go through some ship. But I hope

602
00:36:05,920 --> 00:36:08,720
Speaker 1: that's to create a you know, a plurality of people

603
00:36:08,760 --> 00:36:12,560
Speaker 1: who can actually see the possibilities of a kinder future,

604
00:36:13,040 --> 00:36:15,560
Speaker 1: rather than getting hung up on well I didn't get that.

605
00:36:15,719 --> 00:36:18,799
Speaker 1: I didn't get humane treatment, so no one should. And

606
00:36:18,840 --> 00:36:21,520
Speaker 1: if I didn't get it, then what what what's next?

607
00:36:21,600 --> 00:36:23,560
Speaker 1: The teacher is going to be paid their worth for

608
00:36:23,800 --> 00:36:26,799
Speaker 1: educating our children? Oh, like yeah, it's like, well I

609
00:36:26,840 --> 00:36:30,960
Speaker 1: had to have polio. Why should anyone get the polio vaccine? Right? Really,

610
00:36:31,160 --> 00:36:35,000
Speaker 1: it's we really have this like weird like fomo, except

611
00:36:35,040 --> 00:36:41,440
Speaker 1: it's like fear of humane treatment or something after the fact. Yeah, um,

612
00:36:41,640 --> 00:36:45,680
Speaker 1: the study that found that it's bad for top to

613
00:36:45,719 --> 00:36:51,120
Speaker 1: bottom mental health in terms of like earning power. The

614
00:36:51,120 --> 00:36:55,360
Speaker 1: theory that they for it is that it's just anytime

615
00:36:55,480 --> 00:36:59,880
Speaker 1: you are tying your own self worth to an inn

616
00:37:00,000 --> 00:37:03,919
Speaker 1: reaction with another person, and it's like a zero sum

617
00:37:04,400 --> 00:37:09,120
Speaker 1: situation that adds stress to your life, that adds stress

618
00:37:09,160 --> 00:37:13,720
Speaker 1: to your daily experience of the world. And I think

619
00:37:14,440 --> 00:37:21,000
Speaker 1: by extension, living in a world where if you, you know,

620
00:37:21,360 --> 00:37:26,400
Speaker 1: the worst case scenario is uh, losing your job and

621
00:37:26,760 --> 00:37:33,279
Speaker 1: your life being seen as meaningless like socially, uh, just

622
00:37:33,400 --> 00:37:36,400
Speaker 1: like in terms of that external cues that you're getting

623
00:37:36,920 --> 00:37:42,840
Speaker 1: uh losing your job and the health care system being like, sorry,

624
00:37:42,960 --> 00:37:49,480
Speaker 1: you're fucked like that that and that imprints something about

625
00:37:50,239 --> 00:37:53,040
Speaker 1: two people about like our own self worth and like

626
00:37:53,120 --> 00:37:57,240
Speaker 1: about what you know that that is We're not stupid.

627
00:37:57,320 --> 00:38:01,800
Speaker 1: We can read these clues and see that the society

628
00:38:01,840 --> 00:38:06,000
Speaker 1: around us is telling us our lives are meaningless unless

629
00:38:06,040 --> 00:38:10,120
Speaker 1: we are providing some sort of like value to capital

630
00:38:10,440 --> 00:38:15,480
Speaker 1: basically um and and it's and it's apparently radical to

631
00:38:15,560 --> 00:38:19,839
Speaker 1: say more than can you can you create something? Can

632
00:38:19,880 --> 00:38:22,279
Speaker 1: you create some value? Then you can't play the game

633
00:38:22,400 --> 00:38:25,359
Speaker 1: versus oh, hi, you came into this earth, human being,

634
00:38:25,960 --> 00:38:29,600
Speaker 1: Welcome to Earth, where at you know because we have

635
00:38:29,719 --> 00:38:33,360
Speaker 1: these we've evolved over time from you know, uh cave

636
00:38:33,520 --> 00:38:37,759
Speaker 1: people and to tribal people, to countries to governments, that

637
00:38:37,840 --> 00:38:40,719
Speaker 1: we can still have this this basic sentiment, which is

638
00:38:41,160 --> 00:38:44,560
Speaker 1: you are alive, you are valued, and you're and you

639
00:38:44,600 --> 00:38:47,120
Speaker 1: can you you can get a bare minimum like we're

640
00:38:47,160 --> 00:38:50,319
Speaker 1: doing well enough. We've figured out this earth enough that

641
00:38:50,440 --> 00:38:53,759
Speaker 1: we can offer everyone just the basics, like you don't

642
00:38:53,760 --> 00:38:56,880
Speaker 1: worry about your food or staying alive or getting medical

643
00:38:56,920 --> 00:39:00,600
Speaker 1: care or having shelter. You know, that's says we're all

644
00:39:00,600 --> 00:39:03,080
Speaker 1: gonna say, that's what we're all owed as human beings.

645
00:39:03,080 --> 00:39:05,839
Speaker 1: But it's still like we'll hold up. Can you put

646
00:39:05,880 --> 00:39:08,680
Speaker 1: it in fourty hour shift? Okay, well then then then

647
00:39:08,719 --> 00:39:10,680
Speaker 1: we can Now we can talk because you said yes

648
00:39:10,800 --> 00:39:14,160
Speaker 1: versus our you know, can we just redefine what it

649
00:39:14,239 --> 00:39:18,560
Speaker 1: takes to be to owe to dignity? Um in this culture,

650
00:39:18,680 --> 00:39:22,359
Speaker 1: especially like America because we're so self reliant over here.

651
00:39:23,719 --> 00:39:25,799
Speaker 1: All right, let's take one more break and then we'll

652
00:39:25,840 --> 00:39:40,160
Speaker 1: come back and talk about another big systemic thing. And

653
00:39:40,200 --> 00:39:44,480
Speaker 1: we're back and just kind of branching off of the

654
00:39:44,520 --> 00:39:49,120
Speaker 1: previous conversation we were having. UM. You know, we we've

655
00:39:49,160 --> 00:39:53,040
Speaker 1: been uh, talking to our writer JM and just amongst

656
00:39:53,040 --> 00:39:56,759
Speaker 1: ourselves a little bit about like what we wanna kind

657
00:39:56,760 --> 00:40:02,600
Speaker 1: of keep an eye on any backgrounds, uh infrastructural damage

658
00:40:02,880 --> 00:40:06,360
Speaker 1: that has been done by the Trump administration. And one

659
00:40:06,400 --> 00:40:10,640
Speaker 1: of the things we called out was, you know, broadband

660
00:40:10,680 --> 00:40:15,120
Speaker 1: and just generally America has had a broadband problem in

661
00:40:15,239 --> 00:40:18,680
Speaker 1: terms of equality for many years. But um, you know,

662
00:40:18,760 --> 00:40:22,560
Speaker 1: having a small time criminal organization fronted by a grifter

663
00:40:22,719 --> 00:40:25,319
Speaker 1: for the past year four years in charge of the

664
00:40:25,320 --> 00:40:30,759
Speaker 1: country and an opposition party that at times seems to

665
00:40:30,760 --> 00:40:34,719
Speaker 1: be fighting for the status quo. UM, seems like it's

666
00:40:35,440 --> 00:40:38,560
Speaker 1: not going to help that. UM. So I wanted to

667
00:40:38,600 --> 00:40:41,880
Speaker 1: talk about, you know, inequality of internet access in America,

668
00:40:42,480 --> 00:40:46,359
Speaker 1: since that's always been a problem, but especially now that

669
00:40:46,400 --> 00:40:50,320
Speaker 1: it's we're not totally sure when the um pandemic is

670
00:40:50,320 --> 00:40:55,840
Speaker 1: gonna end, UH is going to be more and more important. Yeah,

671
00:40:56,000 --> 00:40:59,520
Speaker 1: that it's a It's such a NonStop issue. I've first

672
00:40:59,520 --> 00:41:04,560
Speaker 1: starter Eliza in Manhattan. How many companies are you beholden

673
00:41:04,640 --> 00:41:08,280
Speaker 1: to it to even have broadband? Is it like two

674
00:41:08,400 --> 00:41:12,440
Speaker 1: at most? Pretty much? I think it's like two, maybe three.

675
00:41:12,600 --> 00:41:14,600
Speaker 1: But yeah, you're you're stuck if you want to have

676
00:41:14,640 --> 00:41:18,200
Speaker 1: broadband access, you're stuck. And there's so many especially now

677
00:41:18,239 --> 00:41:21,480
Speaker 1: with like remote learning and kids having to go to

678
00:41:21,760 --> 00:41:25,319
Speaker 1: online school. It's like kids who are systemically disadvantaged are

679
00:41:25,360 --> 00:41:29,000
Speaker 1: just further disadvantaged because of their lack of broadband access. Right,

680
00:41:29,640 --> 00:41:32,440
Speaker 1: and like the whole process, I mean, because it's funny

681
00:41:32,440 --> 00:41:35,600
Speaker 1: you're talking about, like, you know, obviously a jit Pie

682
00:41:35,680 --> 00:41:37,960
Speaker 1: at the FCC did a heck of a lot to

683
00:41:38,080 --> 00:41:41,480
Speaker 1: make things not very good in terms of making treating

684
00:41:41,480 --> 00:41:44,759
Speaker 1: broadband is like a right, um, But even then, like

685
00:41:44,960 --> 00:41:47,440
Speaker 1: it's been this thing we can never get right because

686
00:41:47,440 --> 00:41:50,359
Speaker 1: it's we're so like you know, like Eliza's talking about,

687
00:41:50,360 --> 00:41:52,520
Speaker 1: are anyone in most cities? No, it's like I don't know,

688
00:41:52,560 --> 00:41:54,000
Speaker 1: these guys are at the will of like two or

689
00:41:54,040 --> 00:41:57,520
Speaker 1: three companies. And then if if you think about the

690
00:41:57,560 --> 00:42:01,200
Speaker 1: other ways in which it exacerbates inequality, like for example,

691
00:42:01,239 --> 00:42:04,040
Speaker 1: in New York with the vaccine rollout, you know, they

692
00:42:04,040 --> 00:42:06,520
Speaker 1: expanded the categories of people and they just release the

693
00:42:06,560 --> 00:42:10,160
Speaker 1: demographics of the people who are actually getting vaccinated, of

694
00:42:10,200 --> 00:42:14,160
Speaker 1: the eligible people, and again it's like people of color

695
00:42:14,200 --> 00:42:16,480
Speaker 1: being left out. Now, why is that, Well, because you

696
00:42:16,480 --> 00:42:19,759
Speaker 1: have to navigate complicated web. You know, you have to

697
00:42:19,800 --> 00:42:21,720
Speaker 1: have Internet access, you have to be able to navigate

698
00:42:21,760 --> 00:42:24,560
Speaker 1: these sites that are not well designed to sign up

699
00:42:24,600 --> 00:42:27,680
Speaker 1: for an appointment. You know, there's no language access, there's

700
00:42:27,680 --> 00:42:31,160
Speaker 1: no accessibility, Like it makes it extremely difficult for people

701
00:42:31,600 --> 00:42:35,880
Speaker 1: who lack access. Yeah, the disparity of Internet access is

702
00:42:36,200 --> 00:42:41,400
Speaker 1: distinctly racialized. Communities of color trail behind their white counterparts

703
00:42:41,400 --> 00:42:44,279
Speaker 1: in terms of Internet access when you're controlling for all

704
00:42:44,360 --> 00:42:47,359
Speaker 1: other factors, Like we talk a lot on the show

705
00:42:47,400 --> 00:42:52,399
Speaker 1: about internalized white supremacy and it's just everywhere, including here

706
00:42:52,640 --> 00:42:55,400
Speaker 1: in medical care. But I mean yeah, when you have

707
00:42:55,440 --> 00:42:58,960
Speaker 1: a top to bottom in all these different industries that

708
00:42:59,040 --> 00:43:03,959
Speaker 1: are then not be holding to an overarching like governmental

709
00:43:04,760 --> 00:43:11,480
Speaker 1: influence er uh, you know regulatory body as America has

710
00:43:11,600 --> 00:43:16,480
Speaker 1: increasingly kind of been pro deregulation more and more. Uh,

711
00:43:17,320 --> 00:43:20,919
Speaker 1: it ends with these sorts of things bleeding through and

712
00:43:21,560 --> 00:43:27,279
Speaker 1: being causing just immense problems. Yeah, it's so much. There's

713
00:43:27,320 --> 00:43:30,799
Speaker 1: systemic racism, yeah, across the board and white supremacy and

714
00:43:30,880 --> 00:43:32,440
Speaker 1: all of it, and it's built into every one of

715
00:43:32,440 --> 00:43:35,680
Speaker 1: our systems. Yeah, and it's funny because like we always

716
00:43:35,719 --> 00:43:40,120
Speaker 1: heard here about like rural broadband we need rural broadband access.

717
00:43:40,239 --> 00:43:42,680
Speaker 1: But a lot of the times, like people like the

718
00:43:43,200 --> 00:43:47,480
Speaker 1: analysis of who is lacking BRIB is completely like fucked up,

719
00:43:47,520 --> 00:43:50,600
Speaker 1: for lack of a better word, because if these these

720
00:43:50,640 --> 00:43:53,960
Speaker 1: maps that indicate where their lapses in coverage are made

721
00:43:54,000 --> 00:43:57,880
Speaker 1: by the telecoms companies, so they're giving a very weird

722
00:43:57,960 --> 00:44:01,680
Speaker 1: take on who needs the access um and just the

723
00:44:01,719 --> 00:44:04,880
Speaker 1: cost that go. It's just it. That was the biggest

724
00:44:04,880 --> 00:44:06,800
Speaker 1: thing for me, Even just reading what James writing is

725
00:44:06,840 --> 00:44:11,880
Speaker 1: like understanding obviously because they are these racial disparities, but

726
00:44:11,960 --> 00:44:15,160
Speaker 1: the version that's touted to most people is like rural.

727
00:44:15,520 --> 00:44:19,600
Speaker 1: It's the people in these rural areas, because how many

728
00:44:19,640 --> 00:44:22,719
Speaker 1: people know how many of us we've I've even been

729
00:44:22,760 --> 00:44:25,440
Speaker 1: a situation even when you're you're younger, you don't have

730
00:44:25,440 --> 00:44:28,320
Speaker 1: money because let's be real, do you have a hundred

731
00:44:28,400 --> 00:44:31,680
Speaker 1: dollars a month for a broadband or sometimes you know

732
00:44:31,719 --> 00:44:34,279
Speaker 1: around that eighties sixty whatever it is. Most of the

733
00:44:34,320 --> 00:44:36,359
Speaker 1: time you're like going in on it with other people,

734
00:44:36,480 --> 00:44:38,960
Speaker 1: be like, hey, let me get your router information so

735
00:44:39,000 --> 00:44:41,480
Speaker 1: I can hop on your WiFi to your neighbor or

736
00:44:41,520 --> 00:44:43,920
Speaker 1: something like that. And so like when you really factor

737
00:44:43,960 --> 00:44:46,839
Speaker 1: that and it's like the problem is much larger than

738
00:44:47,280 --> 00:44:50,360
Speaker 1: these areas where you're saying the wires aren't connecting in

739
00:44:50,400 --> 00:44:55,120
Speaker 1: the Heartland and Spike way. Of contrast, in other countries

740
00:44:55,160 --> 00:44:58,279
Speaker 1: like South Korea and Sweden, the government's build the broadband

741
00:44:58,280 --> 00:45:02,399
Speaker 1: infrastructure uh the way that the government builds highways here.

742
00:45:03,040 --> 00:45:07,080
Speaker 1: Uh and then they allow internet providers to use them. Um.

743
00:45:07,120 --> 00:45:11,480
Speaker 1: So it's like, but but in in America, it's all

744
00:45:11,520 --> 00:45:16,520
Speaker 1: based on these corporations, you know, trusting the free market

745
00:45:16,760 --> 00:45:19,120
Speaker 1: because the argument is like, well, if we have to

746
00:45:19,200 --> 00:45:22,160
Speaker 1: build take fiber optic cables out there to the farm

747
00:45:22,239 --> 00:45:25,480
Speaker 1: in rural areas, that's too costly and therefore there's no

748
00:45:25,760 --> 00:45:28,400
Speaker 1: r o I on it, there's no return on investment.

749
00:45:28,960 --> 00:45:31,080
Speaker 1: So then, because we always talk about this constant on

750
00:45:31,080 --> 00:45:35,000
Speaker 1: the show, if sharehold shareholder value, has this whole country

751
00:45:35,080 --> 00:45:38,680
Speaker 1: held hostage because if they spend too much money providing

752
00:45:38,760 --> 00:45:42,440
Speaker 1: wife like broadband, then they it's it's not as cost

753
00:45:42,480 --> 00:45:46,120
Speaker 1: effective and their profits look smaller. That affects the share price.

754
00:45:46,600 --> 00:45:48,560
Speaker 1: And now we're like, oh, don't do that, because we

755
00:45:48,640 --> 00:45:51,120
Speaker 1: gotta keep this thing up to make sure that the

756
00:45:51,239 --> 00:45:55,240
Speaker 1: shares are being traded without functionally improving anyone's lives, because

757
00:45:55,680 --> 00:45:58,480
Speaker 1: that's just the whole rub of it. And yeah, like

758
00:45:58,560 --> 00:46:01,279
Speaker 1: it's it's it seems like this really ongoing problem where

759
00:46:02,360 --> 00:46:05,360
Speaker 1: where as much as we think that we can regulate

760
00:46:05,360 --> 00:46:07,360
Speaker 1: our way out of it, we're not ever actually gonna

761
00:46:07,400 --> 00:46:08,879
Speaker 1: when are they going to do the thing where they

762
00:46:08,920 --> 00:46:12,120
Speaker 1: look at the utility companies go this is like, this

763
00:46:12,160 --> 00:46:15,279
Speaker 1: is like antitrust town, and y'all are the mayor, and

764
00:46:15,320 --> 00:46:17,080
Speaker 1: we need to figure out how to break this up

765
00:46:17,120 --> 00:46:20,759
Speaker 1: because we can't keep trusting these companies to do the

766
00:46:20,880 --> 00:46:23,480
Speaker 1: right thing. They only do the right thing for the ledgers.

767
00:46:24,880 --> 00:46:28,880
Speaker 1: Comcast just recently extended its fees despite receiving hundreds of

768
00:46:28,880 --> 00:46:31,800
Speaker 1: millions of dollars of public subsidies and new tax breaks.

769
00:46:32,360 --> 00:46:35,520
Speaker 1: And uh, you know, even though Biden says he's on

770
00:46:35,640 --> 00:46:40,880
Speaker 1: this problem, uh, seventeen Comcast executives maxed out their donations

771
00:46:40,920 --> 00:46:45,200
Speaker 1: to Biden, and their top lobbyist hosted his kickoff fundraiser

772
00:46:45,239 --> 00:46:50,520
Speaker 1: in so this is one definitely their goose is this

773
00:46:52,760 --> 00:46:55,040
Speaker 1: is the problem. This is exactly why. So I will

774
00:46:55,080 --> 00:46:57,440
Speaker 1: say in the primary, I was a big supporter of

775
00:46:57,520 --> 00:46:59,840
Speaker 1: Elizabeth Warren, who I thought had the best plans for

776
00:47:00,000 --> 00:47:04,280
Speaker 1: real big structural change, and she introduced up plan related

777
00:47:04,320 --> 00:47:07,960
Speaker 1: to broadband, you know, expanding broadband access. We're basically she

778
00:47:08,120 --> 00:47:11,960
Speaker 1: thought we should you know, offer grants like in the

779
00:47:12,000 --> 00:47:15,680
Speaker 1: amount of I think she said like eighty something billion

780
00:47:15,760 --> 00:47:21,200
Speaker 1: dollars to nonprofits and municipalities to bring access to get

781
00:47:21,239 --> 00:47:24,600
Speaker 1: internet to underserved areas. And that's like she was all about,

782
00:47:24,640 --> 00:47:27,120
Speaker 1: you know, like break up big tech and and make

783
00:47:27,160 --> 00:47:30,839
Speaker 1: sure that broadband is universally available. Um and and I

784
00:47:30,880 --> 00:47:33,360
Speaker 1: think that that was like, uh, you know, she basically

785
00:47:33,400 --> 00:47:35,600
Speaker 1: said we should break up big tech under anti trust laws.

786
00:47:35,640 --> 00:47:38,520
Speaker 1: And like I think that Joe Biden, for you know,

787
00:47:38,680 --> 00:47:41,160
Speaker 1: he's certainly better than the previous occupant of the White House,

788
00:47:41,480 --> 00:47:43,680
Speaker 1: is not going to be a champion in that regard.

789
00:47:44,440 --> 00:47:47,600
Speaker 1: I don't know, Aliza, just because he's all bowed up

790
00:47:47,680 --> 00:47:51,640
Speaker 1: with cost I can't imagine they've never seen that in

791
00:47:51,680 --> 00:47:55,520
Speaker 1: American politics before. Yeah, I mean that's the whole I mean,

792
00:47:55,560 --> 00:47:58,319
Speaker 1: that's like why everything is so perverted, because it's just

793
00:47:58,360 --> 00:48:00,319
Speaker 1: like Nascar, you know what I mean, Like you just

794
00:48:00,600 --> 00:48:04,680
Speaker 1: you you sponsor a congress person to go do your

795
00:48:04,719 --> 00:48:07,200
Speaker 1: bidding in there to make sure your companies should have

796
00:48:07,200 --> 00:48:09,560
Speaker 1: to wear those jackets. No, they really and I've been

797
00:48:09,560 --> 00:48:11,920
Speaker 1: saying that like flippantly, but you should know when you

798
00:48:11,960 --> 00:48:14,400
Speaker 1: go to a floor vote, you wear all those companies

799
00:48:14,400 --> 00:48:17,040
Speaker 1: on your back, so we know, because that would that

800
00:48:17,040 --> 00:48:19,279
Speaker 1: would help you be like, oh, you're voting against this

801
00:48:19,360 --> 00:48:22,359
Speaker 1: broadband thing. Oh and it's not like you know your

802
00:48:22,400 --> 00:48:25,160
Speaker 1: biggest sponsor, Like how on a NASCAR whoever got the

803
00:48:25,160 --> 00:48:27,719
Speaker 1: hood is got the biggest sponsor and you walk in

804
00:48:27,760 --> 00:48:31,120
Speaker 1: there with Comcast, A T and T. Oh right, this

805
00:48:31,200 --> 00:48:34,560
Speaker 1: makes sense even though, And it's funny because there's so

806
00:48:34,600 --> 00:48:38,359
Speaker 1: many representatives who like are right on all these other things,

807
00:48:38,440 --> 00:48:40,440
Speaker 1: and then you're like, I don't understand why they're so

808
00:48:40,480 --> 00:48:44,319
Speaker 1: wrong this thing. And then you look and you're like, oh, right,

809
00:48:44,360 --> 00:48:47,319
Speaker 1: because their state is where this company is headquartered in

810
00:48:47,440 --> 00:48:50,000
Speaker 1: and you can't enter office unless you have their blessing.

811
00:48:52,320 --> 00:48:56,240
Speaker 1: Um well, alies, I know you have to run. I'm

812
00:48:56,320 --> 00:48:59,480
Speaker 1: shocked at how much of your valuable time you let

813
00:48:59,560 --> 00:49:03,359
Speaker 1: us take up. Uh, it's been such a pleasure having you.

814
00:49:03,840 --> 00:49:07,560
Speaker 1: Where can people find out more about you? Contribute to

815
00:49:07,600 --> 00:49:09,920
Speaker 1: your campaign, all that good stuff. Yeah, it was so

816
00:49:09,960 --> 00:49:12,600
Speaker 1: fun chatting with with you both. Thanks for having me

817
00:49:12,680 --> 00:49:16,080
Speaker 1: on today. Um so to find out more. People can

818
00:49:16,120 --> 00:49:19,440
Speaker 1: go to Eliza Orleans dot com. That's E l I

819
00:49:19,719 --> 00:49:24,600
Speaker 1: Z A O R l I n s dot com. Um,

820
00:49:24,640 --> 00:49:28,360
Speaker 1: we are running a fully grassroots campaign. The maximum donation

821
00:49:28,440 --> 00:49:32,840
Speaker 1: in our race is thirty five thousand dollars per individual donor,

822
00:49:33,120 --> 00:49:34,880
Speaker 1: So you think it's a lot that, Like, you know,

823
00:49:35,160 --> 00:49:37,279
Speaker 1: people are maxing out to Biden in the primary, they

824
00:49:37,280 --> 00:49:39,399
Speaker 1: could give him twenty eight hundred. Our max is more

825
00:49:39,440 --> 00:49:42,440
Speaker 1: than ten times that, which is absolutely wild. So there

826
00:49:42,440 --> 00:49:44,200
Speaker 1: are people in our race who are fully funded by

827
00:49:44,239 --> 00:49:46,760
Speaker 1: millionaires and billionaires who don't want to be held accountable

828
00:49:46,760 --> 00:49:49,759
Speaker 1: by the Manhattan District Attorney's office. So you know, we're

829
00:49:49,760 --> 00:49:54,080
Speaker 1: doing it on thousands and thousands and thousands of grassroots donations.

830
00:49:54,160 --> 00:49:55,600
Speaker 1: So you know, if you can ship in a dollar

831
00:49:55,719 --> 00:49:58,160
Speaker 1: five dollars twenty five dollars, or if you can ship

832
00:49:58,160 --> 00:50:00,400
Speaker 1: in thirty five thousand, we will certainly not turn down.

833
00:50:01,640 --> 00:50:03,840
Speaker 1: But um, but but we really do appreciate it. We have,

834
00:50:03,920 --> 00:50:07,920
Speaker 1: you know, over over seventy individual contributions, like the only

835
00:50:07,920 --> 00:50:11,239
Speaker 1: ones with that much you know, grassroots support, and so

836
00:50:11,680 --> 00:50:14,520
Speaker 1: would be so grateful for for any anyone to learn

837
00:50:14,520 --> 00:50:17,759
Speaker 1: more about us in chipping do it's like gang. And

838
00:50:17,800 --> 00:50:19,920
Speaker 1: we also like to ask our guest if there is

839
00:50:20,080 --> 00:50:23,320
Speaker 1: a tweet they've been enjoying, do you have one of those?

840
00:50:23,600 --> 00:50:28,880
Speaker 1: I do? So. This is from at Superman Tracks s

841
00:50:28,960 --> 00:50:30,719
Speaker 1: U p R A M A N t r A

842
00:50:31,000 --> 00:50:37,440
Speaker 1: x Pandemic Day I made bread, Smiley case da, I

843
00:50:37,480 --> 00:50:40,879
Speaker 1: sure do miss my friends. Day three dred and ten.

844
00:50:41,120 --> 00:50:43,279
Speaker 1: The White House appears to be under the control of

845
00:50:43,320 --> 00:50:46,600
Speaker 1: a shirtless man in a Viking helmet. Day three D

846
00:50:46,760 --> 00:50:50,080
Speaker 1: and thirty. Reddit's coordinated attack on Wall Street is going

847
00:50:50,160 --> 00:50:57,440
Speaker 1: as planned. I don't even know it's they think they

848
00:50:57,440 --> 00:51:00,120
Speaker 1: would think we were lying. I couldn't have believe it,

849
00:51:00,239 --> 00:51:03,320
Speaker 1: wouldn't believed it. Oh God, if only we had access

850
00:51:03,360 --> 00:51:05,719
Speaker 1: to these tweets like in a year ago and like nah,

851
00:51:05,760 --> 00:51:09,359
Speaker 1: come on, come on Reddit versus Wall Street. All right,

852
00:51:11,080 --> 00:51:18,000
Speaker 1: all right, Eliza, thank you so much. Alright. Uh, Like

853
00:51:18,160 --> 00:51:21,120
Speaker 1: we mentioned, Aliza had a heart out because she is

854
00:51:21,160 --> 00:51:24,160
Speaker 1: a busy because running for d A also has to

855
00:51:24,239 --> 00:51:25,799
Speaker 1: go easy on me if you if I ever come

856
00:51:25,840 --> 00:51:31,319
Speaker 1: across your courtroom saving the world. But that that was

857
00:51:31,360 --> 00:51:35,719
Speaker 1: super Dope was an amazing person. Uh, she's out of here.

858
00:51:35,800 --> 00:51:45,880
Speaker 1: Let's talk about some dumb bullshit. Uh uh so this

859
00:51:46,080 --> 00:51:52,160
Speaker 1: diet left past story is one that it's it's a

860
00:51:52,160 --> 00:51:57,759
Speaker 1: staple of the um you know, conspiracy theory community. I

861
00:51:57,800 --> 00:52:02,560
Speaker 1: first came across it in an article, uh like I

862
00:52:02,600 --> 00:52:06,480
Speaker 1: think over a decade ago, and cracked that was basically

863
00:52:06,800 --> 00:52:11,680
Speaker 1: miss like big famous unsolved mysteries that totally have solutions,

864
00:52:12,280 --> 00:52:15,800
Speaker 1: um And so here I'll tell you what the mystery

865
00:52:15,880 --> 00:52:19,759
Speaker 1: is all since you're not familiar with it. Um. So,

866
00:52:19,880 --> 00:52:22,879
Speaker 1: ten members of this Uh, this is a real thing.

867
00:52:23,040 --> 00:52:25,480
Speaker 1: This is a real thing. The way you started off,

868
00:52:25,520 --> 00:52:29,240
Speaker 1: it sounded like a weird So it was a stormy

869
00:52:29,360 --> 00:52:35,600
Speaker 1: night and ten. So ten members of like a polytechnic institute,

870
00:52:35,640 --> 00:52:40,000
Speaker 1: like a scientific college, nine students and one sports instructor

871
00:52:40,880 --> 00:52:43,240
Speaker 1: who had fought in World War two, headed up into

872
00:52:43,560 --> 00:52:47,719
Speaker 1: the frigid wilderness. This is like nine One student with

873
00:52:47,840 --> 00:52:51,799
Speaker 1: joint pain, uh, turned back, and the rest, led by

874
00:52:52,000 --> 00:52:57,120
Speaker 1: a twenty three year old engineering student, continued on. There's

875
00:52:57,520 --> 00:53:01,759
Speaker 1: camera film personal diaries later found on the scene that

876
00:53:01,960 --> 00:53:05,160
Speaker 1: makes it clear they made camp on February onet, pitching

877
00:53:05,200 --> 00:53:08,960
Speaker 1: a large tent on the snowy slopes of a mountain.

878
00:53:09,000 --> 00:53:12,160
Speaker 1: I'm not gonna try to pronounce probably not on You're

879
00:53:12,600 --> 00:53:16,399
Speaker 1: intimately familiar with, but the name can be interpreted as

880
00:53:16,480 --> 00:53:20,719
Speaker 1: dead mountain in the language of the region's indigenous man

881
00:53:20,840 --> 00:53:25,359
Speaker 1: See people. Uh. So when a search team arrived a

882
00:53:25,400 --> 00:53:27,879
Speaker 1: few weeks later, because they just nobody ever heard from

883
00:53:27,920 --> 00:53:31,880
Speaker 1: them again. The other guy made it back and he

884
00:53:31,960 --> 00:53:35,040
Speaker 1: never heard from them again, Like none of the planned

885
00:53:35,480 --> 00:53:40,680
Speaker 1: you know, rendezvous happened. Uh. Their tent was found barely

886
00:53:40,680 --> 00:53:43,399
Speaker 1: sticking out of the snow. It appeared to be cut

887
00:53:43,440 --> 00:53:46,480
Speaker 1: open from the inside. Uh. The next day, the first

888
00:53:46,520 --> 00:53:49,080
Speaker 1: of the bodies were found near Cedar Tree. Over the

889
00:53:49,120 --> 00:53:53,319
Speaker 1: next few months, as the snow thawed, they gradually uncovered

890
00:53:53,719 --> 00:53:57,840
Speaker 1: just all sorts of creepy bodies. All nine of the

891
00:53:57,840 --> 00:54:01,080
Speaker 1: team members. Bodies were scattered around the mount slope, some

892
00:54:01,200 --> 00:54:03,840
Speaker 1: in a baffling state of undressed. They were half naked,

893
00:54:03,960 --> 00:54:06,200
Speaker 1: some of their skulls and chests have been smashed open.

894
00:54:07,120 --> 00:54:10,359
Speaker 1: Others had eyes and one lacked a tongue. Others had

895
00:54:10,360 --> 00:54:14,040
Speaker 1: eyes missing and one lacked a tongue. And yeah, people

896
00:54:14,120 --> 00:54:16,240
Speaker 1: were like, this doesn't make any sense. There was also

897
00:54:16,840 --> 00:54:20,440
Speaker 1: uh like radiation on one of the bodies. Uh. And

898
00:54:20,640 --> 00:54:26,439
Speaker 1: so people like the official explanation from the Soviet bureaucracy

899
00:54:26,640 --> 00:54:30,759
Speaker 1: was quote unknown natural force, and so that like it's

900
00:54:31,120 --> 00:54:34,800
Speaker 1: it's impossible not to be into this as like a

901
00:54:35,320 --> 00:54:40,000
Speaker 1: somebody who's open to conspiracy theories. Yeah, it could be

902
00:54:40,040 --> 00:54:44,880
Speaker 1: anything monsters yeties whatever. So back when we wrote about it,

903
00:54:44,920 --> 00:54:49,400
Speaker 1: we pointed out that it made sense as an avalanche, uh,

904
00:54:49,440 --> 00:54:53,080
Speaker 1: and just kind of some of the more inexplicable details,

905
00:54:53,120 --> 00:54:58,239
Speaker 1: like the radiation isn't actually that uncommon for anything that's

906
00:54:58,239 --> 00:55:01,759
Speaker 1: been laying in a snowy area that gets a lot

907
00:55:01,800 --> 00:55:05,120
Speaker 1: of sun because sun is a snow is a tremendous

908
00:55:05,120 --> 00:55:09,120
Speaker 1: reflector of the sun's energy. The missing eyes and tongue

909
00:55:09,400 --> 00:55:13,560
Speaker 1: are pretty common with bodies left in the wilderness for

910
00:55:13,560 --> 00:55:17,280
Speaker 1: a long time, since animals go for the soft parts first. Um.

911
00:55:17,360 --> 00:55:21,960
Speaker 1: And the various states of undressed are pretty common for

912
00:55:22,000 --> 00:55:26,560
Speaker 1: anybody suffering from hypothermia. And you know, the theory being

913
00:55:26,600 --> 00:55:29,400
Speaker 1: that they ran out of their tent when an avalanche

914
00:55:29,719 --> 00:55:32,640
Speaker 1: they heard an avalanche coming at night, uh, and the

915
00:55:32,680 --> 00:55:35,279
Speaker 1: tent and all their stuff was buried, and so they

916
00:55:35,920 --> 00:55:40,359
Speaker 1: died of hypothermia. People will as they're dying of hypothermia,

917
00:55:40,440 --> 00:55:45,640
Speaker 1: feel hot and like take off some of their clothes. Um. Yeah,

918
00:55:45,760 --> 00:55:53,319
Speaker 1: that's yeah, oh god, so the but people there there

919
00:55:53,400 --> 00:55:56,239
Speaker 1: was a lot of like pushback on that theory, not

920
00:55:56,400 --> 00:56:00,240
Speaker 1: just that that was like one of the more common explanations,

921
00:56:00,280 --> 00:56:01,920
Speaker 1: and we just pointed out that it made a lot

922
00:56:01,920 --> 00:56:07,040
Speaker 1: of sense, uh in ways that concy pushing back. Like

923
00:56:07,239 --> 00:56:09,759
Speaker 1: my first thing was like, yeah, probably like if they're

924
00:56:09,760 --> 00:56:12,600
Speaker 1: scattered everywhere to like who knows if even the avalanche

925
00:56:12,680 --> 00:56:16,400
Speaker 1: took some people. Yeah, so there was no snowfall on

926
00:56:16,440 --> 00:56:20,000
Speaker 1: the night that the avalanche would have happened, and usually

927
00:56:20,080 --> 00:56:23,600
Speaker 1: you need snowfall to add weight to the snow burden

928
00:56:23,840 --> 00:56:27,160
Speaker 1: that triggers the collapse. Most of the blunt forest trauma

929
00:56:27,200 --> 00:56:30,520
Speaker 1: like injuries and some of the soft tissue damage were

930
00:56:30,600 --> 00:56:36,440
Speaker 1: atypical of things caused by avalanches, um where usually people

931
00:56:36,640 --> 00:56:42,839
Speaker 1: mostly asphyxiate. And then if an avalanche had occurred, uh oh.

932
00:56:42,880 --> 00:56:45,360
Speaker 1: There there's this thing about like a gap of nine

933
00:56:45,360 --> 00:56:49,760
Speaker 1: hours between when they made camp and when the avalanche happened.

934
00:56:49,760 --> 00:56:51,840
Speaker 1: I think it assumed that they would have caused the

935
00:56:51,880 --> 00:56:55,120
Speaker 1: avalanche because they're like cutting into the side of the

936
00:56:55,160 --> 00:56:58,400
Speaker 1: mountain to make camp and that's usually how those things happen,

937
00:56:58,600 --> 00:57:02,560
Speaker 1: and yet it happened nine hours later. Um. So this

938
00:57:02,600 --> 00:57:07,160
Speaker 1: new study basically just takes those down point by point. Um.

939
00:57:07,440 --> 00:57:10,920
Speaker 1: I think the most interesting way it's taken down is

940
00:57:10,960 --> 00:57:14,560
Speaker 1: that the idea that there's dimension, the fact that people

941
00:57:14,560 --> 00:57:17,640
Speaker 1: say it wasn't a steep enough hill that they were

942
00:57:17,640 --> 00:57:20,080
Speaker 1: camping on. Oh so they're saying it was even too

943
00:57:20,080 --> 00:57:21,840
Speaker 1: flat for an aval It was too flat for an

944
00:57:21,840 --> 00:57:24,800
Speaker 1: avalanche is one of the things they're saying. But that's

945
00:57:24,920 --> 00:57:29,720
Speaker 1: actually a optical illusion that you see. Have you ever

946
00:57:29,880 --> 00:57:32,960
Speaker 1: heard of places where they say, like water flows uphill

947
00:57:33,120 --> 00:57:37,200
Speaker 1: and it's like this mystery of like a place where

948
00:57:37,240 --> 00:57:40,160
Speaker 1: gravity doesn't operator. Gravity is weird, Like you drop a

949
00:57:40,160 --> 00:57:43,440
Speaker 1: stone and it kind of falls to the side. Man,

950
00:57:45,080 --> 00:57:50,040
Speaker 1: So all of uh that that actually is a thing

951
00:57:50,120 --> 00:57:53,880
Speaker 1: that happens. Like basically, you're at a place that a

952
00:57:54,000 --> 00:57:58,680
Speaker 1: chunk of flat ground that is actually on a portion

953
00:57:58,720 --> 00:58:02,000
Speaker 1: of ground that's embedded with within a larger slope. So

954
00:58:02,120 --> 00:58:07,120
Speaker 1: even though all your sort of context clues point to

955
00:58:07,280 --> 00:58:10,080
Speaker 1: the fact that you're on flat ground, you're actually on

956
00:58:10,120 --> 00:58:13,240
Speaker 1: a slope. And that's what was happening here. They say

957
00:58:13,280 --> 00:58:17,440
Speaker 1: that this piece of land, when you go and study it,

958
00:58:17,480 --> 00:58:23,000
Speaker 1: has at least the grade needed to trigger avalanches. There

959
00:58:23,080 --> 00:58:26,160
Speaker 1: wasn't snowfall, but there was massive wind. But one of

960
00:58:26,160 --> 00:58:28,200
Speaker 1: the reasons this is like kind of a fun story

961
00:58:28,240 --> 00:58:32,400
Speaker 1: that I think might actually like become popular enough to

962
00:58:32,480 --> 00:58:35,160
Speaker 1: put an end to this is that they used Frozen,

963
00:58:35,480 --> 00:58:42,480
Speaker 1: the Disney movie to figure this out, like like clues

964
00:58:42,520 --> 00:58:48,760
Speaker 1: from the film or so. One of the scientists who

965
00:58:49,600 --> 00:58:55,000
Speaker 1: is basically created this paper with the official explanation back

966
00:58:55,040 --> 00:58:57,960
Speaker 1: when Frozen came out was like, holy shit, they have

967
00:58:58,200 --> 00:59:03,840
Speaker 1: absolutely nailed uh the way snowfalls and is blown away

968
00:59:03,840 --> 00:59:08,120
Speaker 1: and is like actually behaves. And so he actually traveled

969
00:59:08,160 --> 00:59:10,880
Speaker 1: to Hollywood and met with the three D modeling company

970
00:59:10,880 --> 00:59:14,320
Speaker 1: that created the snow for Frozen and like got a

971
00:59:14,320 --> 00:59:18,960
Speaker 1: better idea of how snow behaves and like used their

972
00:59:19,040 --> 00:59:22,720
Speaker 1: algorithms that they used on Frozen to better inform his

973
00:59:22,880 --> 00:59:27,200
Speaker 1: algorithms of just like how snow operates. And then they

974
00:59:27,280 --> 00:59:32,320
Speaker 1: also consulted these old GM studies that they had done

975
00:59:32,440 --> 00:59:36,439
Speaker 1: with a bunch of cadavers, like back when people weren't

976
00:59:36,440 --> 00:59:39,480
Speaker 1: paying as much attention. GM took a hundred dead bodies

977
00:59:39,520 --> 00:59:42,800
Speaker 1: and just like through heavy shit at them, uh, and

978
00:59:42,880 --> 00:59:45,800
Speaker 1: just like studied the way that the bodies were destroyed.

979
00:59:46,440 --> 00:59:51,080
Speaker 1: Um were fourteen year olds in charge of the Yeah,

980
00:59:51,080 --> 00:59:56,680
Speaker 1: I just smash him up, and we're like, oh and

981
00:59:56,720 --> 01:00:00,320
Speaker 1: so yeah, that's bad. It's bad. So, like of that

982
01:00:00,360 --> 01:00:04,800
Speaker 1: study data, they basically found that the injuries were pretty

983
01:00:04,840 --> 01:00:07,920
Speaker 1: consistent with what you would have seen if there was

984
01:00:08,160 --> 01:00:12,840
Speaker 1: a like suv style chunk of hard snow or ice

985
01:00:13,000 --> 01:00:16,120
Speaker 1: inside of the avalanche which is hitting you right. Yeah,

986
01:00:16,400 --> 01:00:19,840
Speaker 1: so they think the avalanche hit them. Uh, they were

987
01:00:19,920 --> 01:00:22,200
Speaker 1: camped at a pretty like when you look at where

988
01:00:22,200 --> 01:00:24,720
Speaker 1: their beds were, they were actually like dug into the

989
01:00:24,720 --> 01:00:28,160
Speaker 1: snow in a way that they were pretty well supported.

990
01:00:28,240 --> 01:00:32,560
Speaker 1: So when the thing hit them, they were pre demolished

991
01:00:32,560 --> 01:00:36,600
Speaker 1: but probably didn't die. And then their camp mates like

992
01:00:36,760 --> 01:00:40,520
Speaker 1: dragged them out to safety and then went downhill. Yeah,

993
01:00:40,600 --> 01:00:45,720
Speaker 1: all went downhill from there. Um, but it's a wild story. Uh.

994
01:00:45,760 --> 01:00:49,280
Speaker 1: And I don't know. I do think that this is

995
01:00:49,880 --> 01:00:55,080
Speaker 1: the explanation. I don't think there's been Yettie alien explanation.

996
01:00:55,240 --> 01:00:58,960
Speaker 1: Eddie Gang, where are you at come on this terrible study?

997
01:00:59,120 --> 01:01:03,520
Speaker 1: Yet they're going to uh they Yeah, I am making

998
01:01:03,880 --> 01:01:06,280
Speaker 1: a lot of enemies. As reasonable as this might sound,

999
01:01:06,800 --> 01:01:12,040
Speaker 1: I'm gonna be what in big community people like a sexier.

1000
01:01:12,160 --> 01:01:17,320
Speaker 1: YETI explains like, come on, what they're gonna be like, well,

1001
01:01:17,360 --> 01:01:20,360
Speaker 1: if they use Frozen, we will use the modeling of

1002
01:01:20,440 --> 01:01:25,200
Speaker 1: the child Child's film Abominable to then prove our yetti theory.

1003
01:01:25,200 --> 01:01:27,320
Speaker 1: It's like, well, no, that's not what they did. It

1004
01:01:27,440 --> 01:01:31,000
Speaker 1: wasn't just because there was a movie that that reinforced

1005
01:01:31,040 --> 01:01:34,120
Speaker 1: their their argument. But go at it YETI gang, I'd

1006
01:01:34,120 --> 01:01:36,240
Speaker 1: love to see it. Yeah, they probably will use the

1007
01:01:36,280 --> 01:01:40,680
Speaker 1: fact that, oh, because Frozen Okay, yeah, yeah, what a

1008
01:01:40,800 --> 01:01:44,560
Speaker 1: nice monster do it that Elsa had created? Right? That's

1009
01:01:44,560 --> 01:01:47,040
Speaker 1: where it's that's where you realize to like you don't

1010
01:01:47,080 --> 01:01:50,440
Speaker 1: get don't get in arguments with conspiracy theories. Yeah, because

1011
01:01:50,480 --> 01:01:53,640
Speaker 1: if you're not on the same page, then nothing you're

1012
01:01:53,680 --> 01:01:56,360
Speaker 1: saying is real and there will be It's like I

1013
01:01:56,400 --> 01:01:58,760
Speaker 1: don't even know how you reverse engineer common ground at

1014
01:01:58,800 --> 01:02:00,680
Speaker 1: that point, like all right, well we're can we agree

1015
01:02:01,160 --> 01:02:03,960
Speaker 1: what snow? Can we agree that that's frozen like a

1016
01:02:04,040 --> 01:02:07,280
Speaker 1: water that's very in a solid state? Okay? Okay, moving on,

1017
01:02:07,720 --> 01:02:10,880
Speaker 1: what's Russia? And then it all falls apart, like an

1018
01:02:10,880 --> 01:02:13,560
Speaker 1: experimental ground Russian government of the world to like create

1019
01:02:13,600 --> 01:02:16,480
Speaker 1: like new bio like bio organisms that are like like

1020
01:02:16,600 --> 01:02:20,600
Speaker 1: just terrorize the earth. Russia exists, right, and you're like,

1021
01:02:20,840 --> 01:02:30,200
Speaker 1: who's now, who's knie? Uh? Did you say rush up? What? Alright, Miles,

1022
01:02:30,960 --> 01:02:33,720
Speaker 1: it's been a pleasure talking to you today, as always,

1023
01:02:34,200 --> 01:02:36,440
Speaker 1: Where can people find you and follow you? And what's

1024
01:02:36,440 --> 01:02:40,800
Speaker 1: a tweet? You've been enjoying Twitter, Instagram at Miles of Gray.

1025
01:02:41,200 --> 01:02:44,080
Speaker 1: Also for fiance, you're doing a lot more twitchy stuff,

1026
01:02:44,120 --> 01:02:46,720
Speaker 1: So go to twitch dot tv slash for twenty Day

1027
01:02:46,760 --> 01:02:52,400
Speaker 1: Fiance for that kind of that snark. I guess all right,

1028
01:02:52,440 --> 01:02:55,680
Speaker 1: So I got a couple of tweets um actually not

1029
01:02:55,800 --> 01:02:58,920
Speaker 1: just this one from a reductress because it cut me

1030
01:02:59,200 --> 01:03:03,760
Speaker 1: straight in my heartbone as somebody who has been dabbling

1031
01:03:03,760 --> 01:03:06,120
Speaker 1: on TikTok and just seeing what what's going on there,

1032
01:03:06,120 --> 01:03:08,640
Speaker 1: And this is from at reductor saying are you ugly

1033
01:03:09,080 --> 01:03:10,960
Speaker 1: or did you just see a seventeen year old on

1034
01:03:11,000 --> 01:03:17,880
Speaker 1: TikTok um? And yeah, it's it's wild like not you know, no,

1035
01:03:18,000 --> 01:03:19,520
Speaker 1: you know, you can kind of keep track of like

1036
01:03:19,680 --> 01:03:21,840
Speaker 1: what the youth them are doing out there in the

1037
01:03:21,880 --> 01:03:24,080
Speaker 1: world because you're out there and you see the kids

1038
01:03:24,240 --> 01:03:25,920
Speaker 1: in their faces and you see what they're up to.

1039
01:03:26,240 --> 01:03:29,120
Speaker 1: But when you don't have that kind of data from

1040
01:03:29,200 --> 01:03:31,800
Speaker 1: you know, the world being shut down. TikTok is really

1041
01:03:31,840 --> 01:03:33,640
Speaker 1: where I'm seeing and I'm like, damn, these kids are

1042
01:03:33,680 --> 01:03:37,240
Speaker 1: like on some radical ship and also like and they're

1043
01:03:37,240 --> 01:03:41,240
Speaker 1: doing it with swag too. So look at y'all on

1044
01:03:41,320 --> 01:03:49,440
Speaker 1: that TikTok ship um tweet I've been enjoying. Uh, somebody

1045
01:03:49,520 --> 01:03:51,439
Speaker 1: brought it to my attention that a tweet I had

1046
01:03:51,640 --> 01:03:55,080
Speaker 1: liked and retweeted was actually very similar to an earlier tweet.

1047
01:03:55,120 --> 01:03:57,960
Speaker 1: I don't necessarily think that that means it was stolen.

1048
01:03:58,040 --> 01:04:00,640
Speaker 1: I just you know, parallel. Thank But I do like

1049
01:04:00,720 --> 01:04:05,360
Speaker 1: to give credit to s J. K. Salisbury who tweeted

1050
01:04:05,480 --> 01:04:08,800
Speaker 1: another wooden ball would kill the makers of avocados to

1051
01:04:08,880 --> 01:04:11,080
Speaker 1: include a different toy like a mood ring or a

1052
01:04:11,120 --> 01:04:16,439
Speaker 1: novelty racer um, and that was Kelly and Kanye at

1053
01:04:16,560 --> 01:04:20,880
Speaker 1: Jerry Roblado pointed out that that was the earlier tweets,

1054
01:04:20,920 --> 01:04:24,920
Speaker 1: So shout out to everyone involved in allowing me to

1055
01:04:25,040 --> 01:04:30,040
Speaker 1: say that I liked that tweet. Um. Also, your co

1056
01:04:30,160 --> 01:04:33,920
Speaker 1: host of four twenty Day Fiance The Sophia Sophia Alexandra

1057
01:04:34,040 --> 01:04:37,520
Speaker 1: tweeted her immunity is when those stampeding wild beasts got

1058
01:04:37,560 --> 01:04:43,760
Speaker 1: off for trampling mufasa. Uh. You can find me on

1059
01:04:43,800 --> 01:04:46,400
Speaker 1: Twitter at Jack Underscore. O'Brien, you can find us on

1060
01:04:46,400 --> 01:04:50,320
Speaker 1: Twitter at Daily Zeitgeist. We're at the Daily Zeitgeist on Instagram,

1061
01:04:50,320 --> 01:04:52,400
Speaker 1: we have a Facebook fan page and a website Daily

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01:04:52,520 --> 01:04:55,640
Speaker 1: zikeis dot com. Or post our episodes in our footnotes

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01:04:56,800 --> 01:04:58,600
Speaker 1: we link off to the information that we talked about

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01:04:58,600 --> 01:05:01,560
Speaker 1: in today's episode as well. The song we ride out

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Speaker 1: on miles what are we riding into this fine Tuesday palm.

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Speaker 1: This is from Taku, who's just just a lovely beat

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Speaker 1: maker from Shreia out in Australia, and this track is

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Speaker 1: called remember Me, and it's got like a great dreamy vibe, like, uh,

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Speaker 1: I don't know, it feels like a dream, but it's

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Speaker 1: this very Yeah, it's nice. It's got his major keytonality

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01:05:31,800 --> 01:05:34,320
Speaker 1: not to minors. It's not giving you any attention and

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01:05:34,360 --> 01:05:36,480
Speaker 1: it just feels like you're, you know, if you were

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01:05:36,480 --> 01:05:38,800
Speaker 1: coming back from like a hip hop dream back to

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01:05:38,920 --> 01:05:41,560
Speaker 1: the to the real world waking up. So yeah, remember

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Speaker 1: Me by tak al Right, well we're going to ride

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01:05:44,600 --> 01:05:47,360
Speaker 1: out on that. The Daily is like, guys, the production

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01:05:47,400 --> 01:05:49,840
Speaker 1: of My Heart Radio from more podcast from my Heart Radio,

1078
01:05:49,920 --> 01:05:52,400
Speaker 1: visit the I Heart Radio app, Apple podcast, or wherever

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01:05:52,400 --> 01:05:55,000
Speaker 1: you listen to your favorite shows. That is going to

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01:05:55,120 --> 01:05:57,400
Speaker 1: do it for this morning. We're back this afternoon to

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01:05:57,480 --> 01:05:59,080
Speaker 1: tell you what's trending and we'll talk to you all

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Speaker 1: that by to