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Speaker 1: Hey guys, So I've always wanted to do some sort

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Speaker 1: of like top Episodes of the Year rundown thingy. This year,

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Speaker 1: I had a little extra time before taking off for

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Speaker 1: the holiday, and so kind of threw something together just

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Speaker 1: based on what the episodes were that you listen to

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Speaker 1: the most. In future years, I'd like to open it

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Speaker 1: up for voting, get your input. But for this year,

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Speaker 1: we're just gonna be rerunning each of the top five

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Speaker 1: episodes while we're on holiday break. And yeah, here is

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Speaker 1: your number three month listened to episode of the year.

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Speaker 1: I hope you enjoy. Hello the Internet, and welcome to

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Speaker 1: Season three h three, Episode three of Daly's production of

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

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Speaker 1: a deep dive into America's air consciousness. And it is Friday,

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Speaker 1: September eighth, twenty twenty three. Never Forget, Never Forget, three

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Speaker 1: days away Baby from the Big One, nine to eleven.

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Speaker 1: But until then it's.

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Speaker 2: Star Trek Day, World a Day and National Pediatric Hematology

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Speaker 2: Oncology Nurses.

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Speaker 1: Day three days of nine to eleven. Yes, happy to

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Speaker 1: all who observe. My name is Jack O'Brian aka, I

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Speaker 1: am having stomach pain. Dukie flowing like champagne shit all

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Speaker 1: through this plane. That is courtesy of Christy Amagucci made

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Speaker 1: referencing the man who shipped a plane. I feel like

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Speaker 1: that's a good be like a good David Bowie, weird

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Speaker 1: Al Yankovic, man who shit a plane. And I'm thrilled

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Speaker 1: to be joined as always by my co host mister

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Speaker 1: Miles Grass Miles Gray in the boot of in Lea

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Speaker 1: Mouse Apple Katcha.

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Speaker 2: But let me get to the actual part of the

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Speaker 2: course here where it goes.

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Speaker 3: Miles in the boot again was in Italy.

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Speaker 2: Baby Feta Chinie Baby for Cotta Red Brain. Shout out

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Speaker 2: to tal Mary on the discord for giving us that

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Speaker 2: loser back Italy aka mashup.

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Speaker 1: Well that third voice that you hear, They're coming from poolside,

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Speaker 1: coming to you from poolside.

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Speaker 4: Yah is one.

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Speaker 1: Of our favorite guests, one of your favorite guests, a writer,

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Speaker 1: an actor, one of the funniest stand up comedians doing it.

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Speaker 1: She has one of the best comedy specials of the

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Speaker 1: year called Bitch Grow Up. You can go watch on Max.

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Speaker 1: You must go watch it on Max immediately.

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Speaker 2: We have to say it, you know, we have to

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Speaker 2: say it. No, you don't have to do the promo.

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Speaker 2: Let us do the promo for you.

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Speaker 1: Yes, it is the hilarious non Union Gigs Talented Marcella Are.

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Speaker 2: Thank god?

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Speaker 1: Is me Beyonce?

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Speaker 5: Thank you so much. I am the Maya headlining the

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Speaker 5: Hollywood Improv September fifteenth, La, come check me out.

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Speaker 3: Go it's my birthday, is it?

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Speaker 1: Yeah?

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Speaker 5: Come ruin it? Come watch me?

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Speaker 1: Yeah? Why not? Let me your birthday? Birthday? Girl in

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Speaker 1: the audienceday, birthday, my birthday.

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Speaker 5: My favorite thing to do. When people are coming shows

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Speaker 5: and they interrupt my going it's my birthday. I'm like,

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Speaker 5: bitches is an Applebee's? I don't give a that is

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Speaker 5: your birthday? Yeah, I'm like, I don't care.

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Speaker 1: Oh bitch.

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Speaker 2: Learned You're like, bitch. Oh, I got a candle right

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Speaker 2: here for you. Let me light it.

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Speaker 5: Oh here it is middle fingers everyone listening. Yes, thank you,

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Speaker 5: thank you so much for having me. Me Beyonce, I

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Speaker 5: am the Maya.

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Speaker 1: Did you go to the Beyonce show? I wish I.

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Speaker 5: Did my foot injury stop me from knowing. I could

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Speaker 5: commit to so much walking and standing If.

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Speaker 1: I had to guess what happened to your foot, It's

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Speaker 1: that you broke it off. In some full assy, some

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Speaker 1: full ass. I guess that's what I said. Yeah, I

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Speaker 1: don't know why I said that.

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Speaker 5: But my dis coming have a very unique, unique foot

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Speaker 5: injury because I heard it in two spots. He was like,

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Speaker 5: I have never seen anything like this, because what I

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Speaker 5: did was I sprained it and then I ignored the

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Speaker 5: signs that it was actually badly injured and I injured

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Speaker 5: it and in two other spots and as a torn

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Speaker 5: ligament that is almost fully healed, and then some shit

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Speaker 5: happening on the bottom. Yeah, it is a disaster and

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Speaker 5: it's been now almost a year of it.

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Speaker 1: So but I'm.

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Speaker 5: Getting I'm healing, I'm getting better. I couldn't go see

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Speaker 5: Beyonce because I saw her once before and it was

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Speaker 5: a lot of walking from the car to the stadium,

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Speaker 5: to your seats, to the bathroom to get a drink,

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Speaker 5: to by merch, like it's a lot. On top of

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Speaker 5: she did three hours, and that's what I was informed

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Speaker 5: of early on, and I was like, yeah, I can't

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Speaker 5: handle that so too much. I wish I could have.

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Speaker 5: But I was also very happy because throughout all of

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Speaker 5: her shows, especially in LA, I got a lot of

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Speaker 5: messages from people that were like every time she talked,

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Speaker 5: I thought of you every time she opened her mouth,

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Speaker 5: and I was like, that's how mar Marcella does that.

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Speaker 5: And it brought me so much, so much, Do I

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Speaker 5: really warm my spirit that I was with people at

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Speaker 5: the Beatle because it was it's such a beautiful like album,

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Speaker 5: you know, it's so loving and that's what she's all about.

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Speaker 5: And I love that I was there with everybody.

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Speaker 1: So it was cool.

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Speaker 3: Experience and your energy was channeled even there.

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Speaker 1: Yes, So that was cool. All right, Well, Marcella, we're

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

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Speaker 1: in a moment. First, a couple of things we're talking about.

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Speaker 1: We're just gonna do a quick check in on Donald Trump,

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Speaker 1: and we're gonna hear from him. You know, everybody's talking

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Speaker 1: about him. What's he have to say about what's going

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Speaker 1: on with him? Because that's got to be trustworthy, right.

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Speaker 5: Yeah, it's important to know what he thinks, right, right,

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Speaker 5: because it's valid and rational.

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Speaker 1: Yeah, we'll talk about the way that AI could be

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Speaker 1: used in healthcare and then how it's actually probably going

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Speaker 1: to be used and just be bad, be bad, It's

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Speaker 1: going to be bad. We'll talk about rotten tomatoes being

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Speaker 1: full of shit, like more than we even thought, I guess.

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Speaker 3: Because it's easy to rig the system they rigged. Yeah,

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Speaker 3: you can pay for reviews.

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Speaker 1: You can just like hire some low level reviewers to

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Speaker 1: boost your score up. So all of that plenty more.

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Speaker 1: But first, Marcela, we do like to ask our guest,

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Speaker 1: what is something from your search history?

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Speaker 5: Oh my god, my search history right now is filled

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Speaker 5: with questions about Tears of the Kingdom. So I'm playing

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Speaker 5: the new Zelda game and every time I try to

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Speaker 5: work on something, some side quester or some shrine and

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Speaker 5: I can't figure it out. I just google because I

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Speaker 5: could give a fuck, you know, I don't care. And

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Speaker 5: the last one was really funny because there's a character,

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Speaker 5: there's a monster character who's obsessed with monsters. Their name

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Speaker 5: is Kilton, and he's asking you to take pictures of

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Speaker 5: other monsters so he can create he can get like

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Speaker 5: statues of these monsters made on to his property. And

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Speaker 5: as I'm doing them, you know, you get like good rewards.

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Speaker 5: As I'm doing them, I'm like, wait a minute, are

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Speaker 5: all these monsters would come to life? And I'm gonna

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Speaker 5: have to kill them? And like this is actually creating

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Speaker 5: a problem and I probably shouldn't be doing this. So

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Speaker 5: the last Google search was me trying to figure out

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Speaker 5: if that's what happens, because I was like, I don't

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Speaker 5: want to finish this quest if I am just gonna

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Speaker 5: create more problems for myself. And I'm happy to say

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Speaker 5: that is not the case. Oh shit, I've been so

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Speaker 5: I was already on the last one, but the last

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Speaker 5: one was like a three headed monster, and I was like,

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Speaker 5: this is if he has to come to life, I'm

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Speaker 5: going to like lose so many materials.

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Speaker 1: I like, how you a hold up? Now?

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Speaker 2: Do I really help this motherfucker out just so I

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Speaker 2: could fight a fucking three headed Nah? Yeah, I like

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Speaker 2: that when you have those game instincts where you're like,

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Speaker 2: wait a tick, yeah, Am I.

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Speaker 3: The architect of my own fucking downfall right now?

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Speaker 6: Yeah?

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Speaker 1: Yeah?

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Speaker 5: Yeah. So it was really funny because I was like, what,

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Speaker 5: that's the last thing I know? I was like, wow,

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Speaker 5: I really do know. I'm like, I've spent so much

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Speaker 5: of this game trying to avoid a lot of fighting,

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Speaker 5: which is hilarious because that's it either fighting or fucking wandering, right,

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Speaker 5: And I've been really enjoying the wandering because I noticed

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Speaker 5: that my anxiety goes up when I got to fight

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Speaker 5: a gang of.

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Speaker 1: Fool Yeah for real, real, you know, just like real life.

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Speaker 5: So I've been trying to avoid as much fighting if

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Speaker 5: it's not necessary. So that's a little insight because I

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Speaker 5: feel like, you know, that's a good game. But also

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Speaker 5: there's hacks everybody knows.

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Speaker 3: Yeah, yeah, you like the idea of a monster who's

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Speaker 3: obsessed with monsters.

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Speaker 5: He's like so horny for monsters.

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Speaker 1: Yeah, I guess on one hand, that's like just a

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Speaker 1: people person, yeah, exactly, but with monsters.

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Speaker 5: He's a monster monster.

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Speaker 1: Yeah, he's a monster's monster. Really, you know, deep down, who's.

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Speaker 3: Your monster's favorite monster?

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Speaker 5: That everybody's monster, got a favorite monster?

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Speaker 1: Who's your favorite monster?

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Speaker 5: Who's my favorite monster? Oh that's a good question. Oh

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Speaker 5: like in real life.

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Speaker 3: Whatever, I mean, like what I want to ask that question?

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Speaker 1: What? What? What comes up to the surface.

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Speaker 5: For I think Gordon Ramsey is my favorite monster?

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Speaker 1: Oh shit, that's.

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Speaker 5: He's a monster, but like he's a good monster. He

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Speaker 5: really does want perfection off a sandwich and you're just like, bro,

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Speaker 5: what is your issue? But I get it because sandwiches

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Speaker 5: can be so delicious if they're.

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Speaker 2: Made right right on second that I used to be

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Speaker 2: a PA and like worked on Hell's kitchen and he

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Speaker 2: changed the craft services company and catering for the crew

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Speaker 2: because he said the food wasn't good enough and and

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Speaker 2: like that's wild when you have somebody like take up

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Speaker 2: for the crew to be like the fuck is this

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Speaker 2: grilled cheese?

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Speaker 5: Nah?

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Speaker 2: Then the next day a new fucking catering company was

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Speaker 2: in like making fucking like like Ribbis and ship.

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Speaker 1: Hell, he has a monster on behalf of the people

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Speaker 1: he can.

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Speaker 5: That's why that's why I like what he does absolutely, but.

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Speaker 2: Also he could be a monster of the crew also

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Speaker 2: he also he also made some motherfuckers feel tiny on

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Speaker 2: that that I'm not going.

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Speaker 1: To I believe that.

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Speaker 5: But that's the thing is, like no monster is going

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Speaker 5: to be perfect. No monsters cuts both ways. It definitely

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Speaker 5: cuts absolutely. So that's why he's my favorite monster because

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Speaker 5: he's still a monster at the end of the day.

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Speaker 5: But when he does some shit right, you're like, yeah, thank.

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Speaker 1: You for this. By thank you, yeah exactly.

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Speaker 5: Do you have a favorite monster?

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Speaker 1: Jack? Thank you for asking. Ellen DeGeneres. It's the it's

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Speaker 1: the toothpick thing is the creativity of the toothpick thing,

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Speaker 1: like she would hide toothpicks around just to see if

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Speaker 1: like her cleaning. It's just like such dedication to Minutia

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Speaker 1: to like having the terror of working for her Pervade

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Speaker 1: every like square inch of her Home's really like an

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Speaker 1: amazing level of dedication.

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Speaker 5: That's true. What about this new Jimmy Fallon?

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Speaker 1: Uh jim more because he's like kind of we're just

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Speaker 1: finding out. The details are a little bit sketchy, but

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Speaker 1: he's definitely mostress. Yeah, but it's just he seems a

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Speaker 1: little inconsistent.

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Speaker 7: Yes, it seems like like sometimes he's like they're like,

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Speaker 7: he's so fun, and then other times they're like, it's

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Speaker 7: a bad Jimmy day. Yeah, he's a drunk today, right,

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Speaker 7: might be a little bit drunk today.

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Speaker 1: Yeah. I just like that they had the phrase we're

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Speaker 1: up against it, that was the code word. I did

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Speaker 1: not appreciate that part. There's there's no article on Vulture

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Speaker 1: about how the Fallon is like a bad place to work,

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Speaker 1: like really bad and toxic and he is really rough,

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Speaker 1: and they talk about how the inside the code phrases

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Speaker 1: were really up against it today, and that meant like

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Speaker 1: Jimmy is being a asshole. Yeah, everybody steer clear. But

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Speaker 1: like that, now they can't use that phrase anymore. They

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Speaker 1: need to. He's probably like thinking back and being like

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Speaker 1: this motherfucker always said there, but that was like just

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Speaker 1: a thing they said. Yeah.

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Speaker 5: Now when people have like a food craving, like let's

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Speaker 5: go get cheese it, She's like, is that the new

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Speaker 5: code let's go get cheese it? The paranoia is gonna

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Speaker 5: go over?

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Speaker 1: Man, we got cheese its man? Okay, yeah, yeah, all right.

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Speaker 1: What is uh, Marcella, what is something that you think

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Speaker 1: is overrated?

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Speaker 5: You know, I couldn't think of something, maybe because I

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Speaker 5: got a little high, but.

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Speaker 1: Yeah, you get a little high, A little high this time,

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Speaker 1: ladies and gentlemen, introducing a little bit high Marcella for

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Speaker 1: the first time. She's never been a little bit high

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Speaker 1: on our podcast.

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Speaker 5: On anyone's podcast. Okay, I trust you, guys.

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Speaker 1: Yeah, I saw that.

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Speaker 5: I thought there was not going to be a guest host.

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Speaker 5: I was like, you know what, I can get a

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Speaker 5: little high. I read the comments. People get mad and

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Speaker 5: I'm so mean to Jack and I know when I'm

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Speaker 5: a little high, I'm a little more.

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Speaker 3: I like that Jackie like it's fun.

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Speaker 5: It's all Goodah, I loved Jack's like you nice to

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Speaker 5: Marslo when she's mean to me.

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Speaker 1: Guys, it's okay.

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Speaker 5: Yeah, I mean a lot of and you know what's

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Speaker 5: funny is a lot of guys don't understand it too.

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Speaker 5: There's a comic who's like, has a little crush on me,

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Speaker 5: and I'm just very mean to him. I'm very and

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Speaker 5: and I really give it to him more because i

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Speaker 5: know he really enjoys it. And I'm waiting for him.

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Speaker 5: I'm waiting for him to be like, Marcella, why are

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Speaker 5: you so mean? And I can't wait to be like,

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Speaker 5: stop asking me why I'm so mean? And ask yourself

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Speaker 5: why you like it?

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Speaker 1: You bitch.

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Speaker 5: Oh that's going to be a moment when he when

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Speaker 5: I have that exchange on waiting. I know it's gonna

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Speaker 5: happen one day because this is such a lot so mean.

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Speaker 1: Why do you like it?

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Speaker 5: Yeah, that's the real question. What don't your mom do

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Speaker 5: to you?

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Speaker 6: Bro?

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Speaker 1: That's what I want to know? What you little pain hug?

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Speaker 5: So yeah, I'm a little high. So I'm a little

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Speaker 5: I'm a little more fun and a little more rambly.

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Speaker 5: But it'll be worth it, guys. So like I just

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Speaker 5: showed you, but over under it. I couldn't think of something,

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Speaker 5: but I decided to google things like what did people

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Speaker 5: find overrated?

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Speaker 1: Because I was like, there you go.

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Speaker 5: This is because it's all it's all opinion. So the

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Speaker 5: top three things in the first three articles, the number

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Speaker 5: one thing one bitch wrote bacon overrated is overrated? Okay,

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Speaker 5: so I already know the bitch has no taste.

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Speaker 1: Like, I also feel like you're about to out a

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Speaker 1: bunch of our former guests because they're like, what if

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Speaker 1: you well, what if you like named a bunch of

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Speaker 1: like other people and underrated And they were just like googling,

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Speaker 1: like I don't have opinions are overrated?

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Speaker 5: Probably that's probably true. Damn to think about that. You're

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Speaker 5: totally right. But bacon, come on, simple me. It's so good.

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Speaker 2: I think we've said this in the in the past.

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Speaker 2: We're like, it's properly rated. I get like ten years

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Speaker 2: ago we were doing too much.

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Speaker 1: It was debate overrated by some people who are not

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Speaker 1: worth paying attention to a people. Yeah, but like who

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Speaker 1: gives a fuck about them? Like let them have their

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Speaker 1: weird little thing.

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Speaker 2: Yeah, go to the like, as they say, it's like

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Speaker 2: the fucking heart Attack Cafe or whatever the fuck it's called,

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Speaker 2: where like everything is bacon, Like I get it, we

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Speaker 2: get it, but like you don't, don't you gotta hate

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Speaker 2: on it?

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Speaker 5: Yeah, it's so tasty, it's so good good.

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Speaker 1: What else is when you find yourself being like, uh,

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Speaker 1: like I don't even really like this thing. That's fucking

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Speaker 1: delicious and like makes my body like involuntarily do things

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Speaker 1: like water just pour.

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Speaker 5: Out of my mouth, like oh my god, that's.

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Speaker 3: Wow.

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Speaker 1: But but when that's your take, like you're you're working

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Speaker 1: too hard to have a take exactly.

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Speaker 5: I also just you know, it's good when religions are

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Speaker 5: like you cannot have it, like you know that is good?

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Speaker 1: Man?

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Speaker 5: Got ready here? So another okay, another article. I'm not

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Speaker 5: going to name these people because they're all weirdos. But

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Speaker 5: another person they're article and her number one choice was clubbing, clubbing,

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Speaker 5: going on out clubbing, and it was just like, you loser, bitch, like,

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Speaker 5: stop imposing your introverted lifestyle on people who enjoy being outside.

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Speaker 1: When was the last time you went to the club. Though,

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Speaker 1: more so you go to the club.

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Speaker 5: Well, because of my foot injury, it has been few

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Speaker 5: and far between, but I will say like two weeks ago.

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Speaker 5: I think it was like my birthday party weekend, so

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Speaker 5: was what middle of August. So yeah, like a couple

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Speaker 5: of weeks ago without and I because my foot injury

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Speaker 5: couldn't really do much. But yeah, I took my boyfriend

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Speaker 5: to this place in Medesto called Crocodile. It's like this

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Speaker 5: old ass like it is a wedding reception without the family.

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Speaker 1: It is so fun.

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Speaker 5: There's like no drinks on the dance floor. They encourage

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Speaker 5: people to dress up. There is a strict dress code

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Speaker 5: and not like not on some like high racist dress

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Speaker 5: code on some like don't come in here, sloppy bro,

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Speaker 5: like guys have to have their shirts buttoned up. If

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Speaker 5: they wear a button up, like you cannot have a

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Speaker 5: little like tank top showing underneath, like the very much

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Speaker 5: like no, bro, We're here to dance and drink and

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Speaker 5: have a good time and respect everybody. And they're on it.

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Speaker 5: Like I saw him go up to some guy, some

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Speaker 5: young dude, because he thought he could get away with whatever.

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Speaker 5: He was dressed up really cute, but he unbuttoned his

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Speaker 5: shirt to like show off. And dudes came up and

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Speaker 5: were like, you need to button your shirt back up, bro,

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Speaker 5: And I loved it because I was like, yeah, I

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Speaker 5: got dressed up. You should be dressed up too. Couples,

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Speaker 5: like seventy year old couples come in there dressed matching

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Speaker 5: and dance the night away. Like I love going out dancing,

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Speaker 5: and that's maybe that doesn't fall under clubbing, but there

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Speaker 5: are places to go out and have a good time

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Speaker 5: and dance and enjoy yourself. And I hate when people

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Speaker 5: are like clubbing is so lame, Like, girl, you just

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Speaker 5: don't like to drink and be out and it's loud

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Speaker 5: and you're probably neurodivergent. You can't handle all the fucking

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Speaker 5: sensory overload.

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Speaker 1: Shit. That's you might like to a this This shit

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Speaker 1: sounds so fun. We had another guest talk about like

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Speaker 1: going to like there was like a chandelier room where

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Speaker 1: they were like singing Elvis or some shit like just

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Speaker 1: like go find a weird club to club.

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Speaker 5: Yes, I'm the club that works for you, But I

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Speaker 5: don't shit on clubbing. Come on, Jersey short shit, I

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Speaker 5: get like, you don't want to be around that. I understand, sure,

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Speaker 5: you know there's different.

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Speaker 3: What about Bottle Wars.

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Speaker 2: Did y'all see that clip over the weekend of the

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Speaker 2: bottle warship that was going on, I know, like a

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Speaker 2: Houston nightclub.

381
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Speaker 3: It was like New York versus Baltimore. Bottle It's like

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Speaker 3: this ship people.

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Speaker 1: Have been doing for a while.

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Speaker 2: You just start, you just start emptying bottles onto the

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Speaker 2: floor for the flex. Oh God, like dumping like six

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Speaker 2: thousand dollars with their liquor, like onto the floor. Yeah, frud,

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00:18:38,600 --> 00:18:40,960
Speaker 2: it's the flex, you know what I mean. It manifests

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Speaker 2: in different ways. But I'm at the Crocodile's website. I'm

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Speaker 2: looking at they have a whole dress code sub page,

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Speaker 2: like so women, no sports attire, no T shirts, no sweats,

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Speaker 2: no baseball caps, beanies, no overly revealing clothing.

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Speaker 1: I like that. They're like, we're modest.

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Speaker 2: The collar dress shirts required, no sports attire, no T

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Speaker 2: shirt or no longer short sleeve shirts, no Henley's, no

395
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Speaker 2: shallow V necks, oh Henley style of shallow V necks okay,

396
00:19:05,880 --> 00:19:09,800
Speaker 2: no hoodies, okay, shallow neck nice dress sweaters Okay.

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Speaker 1: I can't get away with my ship that I try

398
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Speaker 1: and pull off with my V neck deep that j

399
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Speaker 1: Lo deep f where I'm flirting with exposing navel. Yeah.

400
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Speaker 3: No sweats, no baggy loose clothing. No club colors. What

401
00:19:24,280 --> 00:19:25,200
Speaker 3: are club colors?

402
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Speaker 1: Are they trying to.

403
00:19:26,760 --> 00:19:28,119
Speaker 5: Club colors like gang colors?

404
00:19:28,160 --> 00:19:28,760
Speaker 1: Like red white?

405
00:19:28,800 --> 00:19:30,760
Speaker 5: Okay, I mean red, but I mean red blue?

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Speaker 1: Yeah? Whatever else?

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00:19:32,400 --> 00:19:33,920
Speaker 3: No sandals, slip flops, no fans.

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00:19:34,040 --> 00:19:36,200
Speaker 1: They can't wear red or blue, or you just can't

409
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Speaker 1: wear all red or.

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Speaker 5: It depends on the red and the blue. You can

411
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Speaker 5: usually tell.

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Speaker 2: Right right right, Yeah, if you don't have like a

413
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Speaker 2: flag coming out of your pocket with your cool suit on,

414
00:19:46,640 --> 00:19:49,639
Speaker 2: then maybe we have a problem. Wait, but you're saying

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Speaker 2: this isn't on some like racist ship, because it's very

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Speaker 2: much like they just want or they just want everybody

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Speaker 2: to come in, like it's like it's a nice place,

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Speaker 2: Like it's.

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Speaker 5: A nice place. I'm telling you that's not They're they're

420
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Speaker 5: totally like try me hood motherfuckers show up, but they

421
00:20:03,480 --> 00:20:06,440
Speaker 5: are there. They're in their player fit. Yeah, that's what

422
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Speaker 5: I'm saying.

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Speaker 1: It's like it is.

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00:20:08,680 --> 00:20:11,600
Speaker 5: It seems like it off top, like I get it,

425
00:20:11,640 --> 00:20:13,320
Speaker 5: but when you're reading it, but I'm telling you it's

426
00:20:13,359 --> 00:20:14,960
Speaker 5: not that they're like no, no, no, you can be hood

427
00:20:15,040 --> 00:20:16,960
Speaker 5: is fucking here, and you can like be annoying and

428
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Speaker 5: all that.

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Speaker 1: Shit, but just be cute.

430
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Speaker 5: Don't be a messy on the dance floor. You can't.

431
00:20:22,480 --> 00:20:24,160
Speaker 5: Like they really are just like no, we're just here.

432
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Speaker 5: It's like family. Like it's like a family function.

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Speaker 1: I love it.

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Speaker 3: Wow, interior does look like a time capsule.

435
00:20:31,600 --> 00:20:33,919
Speaker 5: It is, dude. It's so fun if you're ever in

436
00:20:33,960 --> 00:20:35,480
Speaker 5: the Central Valle and you want to go out, especially

437
00:20:35,480 --> 00:20:37,639
Speaker 5: on Saturday night. Friday nights are pretty empty, but Saturay

438
00:20:37,680 --> 00:20:39,720
Speaker 5: nights are always popping. They're so and it's the music

439
00:20:39,800 --> 00:20:44,240
Speaker 5: is insane. Yeah, They're music is insane.

440
00:20:44,920 --> 00:20:48,320
Speaker 3: Like it looks like it's like a fucking scene in Carlito's.

441
00:20:47,800 --> 00:20:50,600
Speaker 5: Way or yes, dude, yeah, so what I'm saying when

442
00:20:50,640 --> 00:20:53,200
Speaker 5: you walk in there, you want to look cute, right

443
00:20:53,320 --> 00:20:55,240
Speaker 5: right right, they want you to match the vibe.

444
00:20:55,400 --> 00:21:00,280
Speaker 1: It's like Carcado's Way mixed with a laser tag. Yeah.

445
00:21:00,359 --> 00:21:03,679
Speaker 5: Yeah, it's so the music Like the other day, it

446
00:21:03,760 --> 00:21:09,040
Speaker 5: was like it went from like suave mee to like eminem.

447
00:21:08,880 --> 00:21:11,200
Speaker 5: I'm like, the music is insane.

448
00:21:11,640 --> 00:21:12,160
Speaker 1: It's silly.

449
00:21:12,200 --> 00:21:14,560
Speaker 5: It's just you're there for a silly time right now,

450
00:21:14,880 --> 00:21:16,920
Speaker 5: and that's the other thing. That's why it's like even

451
00:21:16,960 --> 00:21:19,000
Speaker 5: the music is like kind of corny, but like in

452
00:21:19,080 --> 00:21:21,479
Speaker 5: that fun way where we're like we're never in high school.

453
00:21:21,680 --> 00:21:23,040
Speaker 3: Yeah yeah, you know, you're at a party.

454
00:21:23,040 --> 00:21:25,040
Speaker 5: You're just like, you know, you're just gonna be kissing tonight.

455
00:21:25,119 --> 00:21:25,520
Speaker 5: That's it.

456
00:21:25,760 --> 00:21:27,600
Speaker 3: Wow, shout out crocodile.

457
00:21:27,920 --> 00:21:29,920
Speaker 5: I love crocodiles. It's the funnest all right.

458
00:21:30,000 --> 00:21:32,400
Speaker 1: So finding a weird club is underrated?

459
00:21:33,320 --> 00:21:36,520
Speaker 5: Like cool, Yeah, she was saying it was overrated, but

460
00:21:36,560 --> 00:21:38,640
Speaker 5: it's like, no, man, find the clubs that worked for you.

461
00:21:39,280 --> 00:21:42,280
Speaker 5: And then the final article that I saw that the

462
00:21:43,080 --> 00:21:46,120
Speaker 5: I I've only gave the first one for the other

463
00:21:46,119 --> 00:21:47,919
Speaker 5: two articles. I got to give the two on this

464
00:21:47,960 --> 00:21:51,040
Speaker 5: one because it's really funny how they go hand in hand.

465
00:21:51,320 --> 00:21:54,199
Speaker 5: This guy's idea of what's overrated. The first one is

466
00:21:54,200 --> 00:21:57,640
Speaker 5: a formal education, which I agree, But then the second

467
00:21:57,640 --> 00:21:58,760
Speaker 5: one is science.

468
00:22:01,119 --> 00:22:06,720
Speaker 1: This guy's got some opinions on he's got he's done

469
00:22:06,720 --> 00:22:09,720
Speaker 1: some research, some of his own research on the vaccine.

470
00:22:09,880 --> 00:22:12,119
Speaker 1: I don't think he's a fan of Fauci Yeah.

471
00:22:12,000 --> 00:22:14,119
Speaker 5: Because it's like, oh yeah, formal education. I agree. Not

472
00:22:14,200 --> 00:22:17,400
Speaker 5: everybody should be required to fucking for any fucking job

473
00:22:17,480 --> 00:22:20,840
Speaker 5: to have h a fucking aa ba any of that ship.

474
00:22:20,880 --> 00:22:23,199
Speaker 5: There's plenty of jobs of don't eat that ship. And

475
00:22:23,200 --> 00:22:24,760
Speaker 5: then he said science, and I was like, bro, I'm

476
00:22:24,760 --> 00:22:28,280
Speaker 5: tapping out. We are not agreeing on the same on

477
00:22:28,320 --> 00:22:29,160
Speaker 5: the same number one.

478
00:22:29,440 --> 00:22:31,800
Speaker 2: He's like, yeah, well I got my master's degree on YouTube,

479
00:22:31,920 --> 00:22:34,159
Speaker 2: you know what I mean. That's why I can go

480
00:22:34,320 --> 00:22:36,040
Speaker 2: toe to toe with any person who went to a

481
00:22:36,119 --> 00:22:37,480
Speaker 2: brick and mortar college.

482
00:22:37,359 --> 00:22:41,400
Speaker 1: Like Okay, sure, okay, actually got a Bachelor of Science

483
00:22:41,640 --> 00:22:47,400
Speaker 1: from Praguer University, from which that's not usually there special.

484
00:22:47,200 --> 00:22:49,080
Speaker 3: On price based it's all faith based science.

485
00:22:49,640 --> 00:22:55,640
Speaker 5: I got mine at ragu University, all.

486
00:22:56,040 --> 00:23:00,600
Speaker 2: Sauce prego rag Old world style degree.

487
00:23:02,520 --> 00:23:06,560
Speaker 1: All right, Uh, let's take a quick break and we'll

488
00:23:06,560 --> 00:23:09,080
Speaker 1: come back and we'll check in with Donald Trump. We

489
00:23:09,200 --> 00:23:10,640
Speaker 1: like to do it once a week, just make sure

490
00:23:10,640 --> 00:23:12,520
Speaker 1: he's doing all right. We'll be right back.

491
00:23:23,119 --> 00:23:24,280
Speaker 4: And we're back.

492
00:23:25,400 --> 00:23:27,920
Speaker 1: And we like just checking with Donald Trump every once

493
00:23:27,920 --> 00:23:33,600
Speaker 1: in a while. Yet about him, But what's he have

494
00:23:33,760 --> 00:23:36,840
Speaker 1: to say about what he's up two miles just for

495
00:23:37,080 --> 00:23:40,480
Speaker 1: just quick checking for starters. The judge in his second

496
00:23:40,680 --> 00:23:44,760
Speaker 1: Eging Carrol defamation case, he's already been like, yo, man,

497
00:23:45,000 --> 00:23:48,120
Speaker 1: you're you're liable, Okay, So the only thing lal left

498
00:23:48,160 --> 00:23:50,639
Speaker 1: to do is for the jury to just get together

499
00:23:50,680 --> 00:23:52,480
Speaker 1: in January and just figure out how.

500
00:23:52,440 --> 00:23:55,480
Speaker 2: Much you owe eging Carrol. This isn't about whether or

501
00:23:55,520 --> 00:23:58,320
Speaker 2: not you are guilty or not, like we've we've determined that.

502
00:23:58,800 --> 00:24:01,800
Speaker 2: So this jury we'll get together just to figure out

503
00:24:01,840 --> 00:24:03,960
Speaker 2: to put a price tag on the fuckery. Mind you

504
00:24:04,000 --> 00:24:05,879
Speaker 2: that he already has to pay her five million dollars

505
00:24:05,920 --> 00:24:08,680
Speaker 2: from the previous judgment, and Trump was trying to get

506
00:24:08,680 --> 00:24:10,840
Speaker 2: the judge to put a cap on the damages and

507
00:24:10,880 --> 00:24:12,879
Speaker 2: be like, well, I've already paid five The judge.

508
00:24:12,640 --> 00:24:15,720
Speaker 1: Like nah, nah, seems like you're pretty rich. You keep

509
00:24:15,800 --> 00:24:18,280
Speaker 1: saying that it's kind of your whole thing, right.

510
00:24:18,359 --> 00:24:20,200
Speaker 2: Yeah, you got it, You got it like that right here,

511
00:24:20,280 --> 00:24:23,240
Speaker 2: there's another ten million, right, that's fine, Just sell a

512
00:24:23,240 --> 00:24:25,320
Speaker 2: couple more mugs with your with your muckshot on it.

513
00:24:26,040 --> 00:24:29,679
Speaker 5: But anyway, that's like that classic like a young rapper

514
00:24:30,000 --> 00:24:32,760
Speaker 5: has way too much shit on his ig and then

515
00:24:32,880 --> 00:24:35,040
Speaker 5: he fucking gets guess has to go to court for

516
00:24:35,080 --> 00:24:37,200
Speaker 5: some ship and they just pull up his ig, like, bro,

517
00:24:37,359 --> 00:24:40,400
Speaker 5: look at all this money and drugs like guns, and shiit,

518
00:24:40,480 --> 00:24:43,000
Speaker 5: bro what are you doing? Stop putting your own shit

519
00:24:43,080 --> 00:24:43,600
Speaker 5: on blasts?

520
00:24:44,560 --> 00:24:46,560
Speaker 1: I have nothing you're on her? Please?

521
00:24:46,640 --> 00:24:49,520
Speaker 2: Yeah, they're like you were just you just bought a

522
00:24:49,680 --> 00:24:52,640
Speaker 2: fucking three hundred thousand dollars chain with your face made

523
00:24:52,680 --> 00:24:53,840
Speaker 2: of diamonds.

524
00:24:53,680 --> 00:24:56,040
Speaker 5: Right right, just about a new weave for sure.

525
00:24:57,240 --> 00:25:01,960
Speaker 1: That's my character has done tried the that's a characters

526
00:25:04,200 --> 00:25:08,600
Speaker 1: elected president. So who is stupid? Now? You elected a

527
00:25:08,760 --> 00:25:13,920
Speaker 1: w character president. Dumbass actually broke.

528
00:25:13,760 --> 00:25:17,160
Speaker 2: That's yeah, that's too wearing his wife's purse in the airport.

529
00:25:17,560 --> 00:25:21,119
Speaker 2: You know, they're like, that's a character, sir. Okay, although

530
00:25:21,160 --> 00:25:23,200
Speaker 2: Trump he probably looked good with a lace front, though

531
00:25:23,560 --> 00:25:25,360
Speaker 2: he probably I'm surprised he.

532
00:25:25,280 --> 00:25:27,680
Speaker 5: Does look good with the lace front. That's why he's wearing, right, I.

533
00:25:27,600 --> 00:25:31,600
Speaker 2: Mean, I mean he could go harder, but hey, I

534
00:25:31,600 --> 00:25:35,000
Speaker 2: get he only trusts him to apply it. But so anyway,

535
00:25:35,200 --> 00:25:38,040
Speaker 2: when the next thing that happened was his documents case, right,

536
00:25:38,359 --> 00:25:40,280
Speaker 2: Trump seems to be at the end of his rope

537
00:25:40,280 --> 00:25:42,800
Speaker 2: here in terms of like what his possible defense could

538
00:25:42,800 --> 00:25:46,199
Speaker 2: be because obviously this case isn't the most significant for

539
00:25:46,280 --> 00:25:48,240
Speaker 2: him from like a legal standpoint, like not like the

540
00:25:48,320 --> 00:25:51,000
Speaker 2: Rico case. And ship, you know, or the other shit

541
00:25:51,040 --> 00:25:54,760
Speaker 2: happening in DC. But this, like it's clear now he

542
00:25:54,800 --> 00:25:58,400
Speaker 2: doesn't really have like an articulated defense because he keeps

543
00:25:58,400 --> 00:26:01,359
Speaker 2: saying the same thing over and over. He was on

544
00:26:01,400 --> 00:26:03,679
Speaker 2: a Hugh Hewitt show and he was talking about how

545
00:26:03,800 --> 00:26:06,680
Speaker 2: like I'll take the stand, I'll take the stand, and

546
00:26:07,240 --> 00:26:11,480
Speaker 2: also about how he's protected under the Presidential Records Act.

547
00:26:11,560 --> 00:26:13,080
Speaker 1: Spoiler alert, he is not.

548
00:26:13,320 --> 00:26:16,640
Speaker 2: But I just just listen to this now of him

549
00:26:16,680 --> 00:26:19,080
Speaker 2: just going yammering on about how he's all good in

550
00:26:19,119 --> 00:26:20,520
Speaker 2: this case because it doesn't matter.

551
00:26:20,880 --> 00:26:23,440
Speaker 5: Did you direct anyone to move the boxes, mister p

552
00:26:23,720 --> 00:26:25,960
Speaker 5: did you tell anyone to move the boxes.

553
00:26:25,480 --> 00:26:27,720
Speaker 6: Out about anything? You know why?

554
00:26:28,040 --> 00:26:28,760
Speaker 1: I don't talk about it.

555
00:26:28,880 --> 00:26:31,120
Speaker 6: I'm allowed to do whatever I want. I come under

556
00:26:31,160 --> 00:26:34,359
Speaker 6: the Presidential Records Act. I'm not telling you. And every

557
00:26:34,359 --> 00:26:36,520
Speaker 6: time I told to you, oh, I have a breaking story,

558
00:26:36,600 --> 00:26:39,560
Speaker 6: you don't have any story. I come under the Presidential

559
00:26:39,640 --> 00:26:44,240
Speaker 6: Records Act. I'm allowed to do everything I did.

560
00:26:44,680 --> 00:26:46,439
Speaker 1: So who is Hugh Hewitt?

561
00:26:46,960 --> 00:26:49,760
Speaker 2: Hugh Hewitt is like one of these like goony right

562
00:26:49,800 --> 00:26:52,480
Speaker 2: wing like radio dudes, like commentator guys.

563
00:26:52,640 --> 00:26:55,480
Speaker 1: He's not he's not a he's he's but he's like

564
00:26:55,600 --> 00:26:58,840
Speaker 1: right wing. He's the right wing that Trump thinks is

565
00:26:58,880 --> 00:26:59,879
Speaker 1: against him now.

566
00:27:00,160 --> 00:27:02,800
Speaker 2: But he'll also you know, he'll also you know, like

567
00:27:02,920 --> 00:27:06,760
Speaker 2: vacillate between reasonable conservative and then just you know, full

568
00:27:06,800 --> 00:27:08,240
Speaker 2: throat licking.

569
00:27:08,400 --> 00:27:11,720
Speaker 1: So you know he loves this ship so much. Trump

570
00:27:11,920 --> 00:27:14,199
Speaker 1: like he loves going on a show and being like,

571
00:27:14,280 --> 00:27:17,359
Speaker 1: you don't got ship, You're fucking stupid.

572
00:27:18,040 --> 00:27:20,880
Speaker 5: I just look at you, like, yes, everything I did,

573
00:27:21,000 --> 00:27:23,159
Speaker 5: I did it because I could do it. But I

574
00:27:23,240 --> 00:27:24,520
Speaker 5: definitely did it.

575
00:27:25,160 --> 00:27:27,400
Speaker 1: Shut the fuck up. But then if I have this

576
00:27:27,920 --> 00:27:32,320
Speaker 1: interpretation of the law incorrect, I'm totally fucked. But yeah,

577
00:27:32,359 --> 00:27:34,639
Speaker 1: I did it, and there's nothing you can do because

578
00:27:34,680 --> 00:27:37,200
Speaker 1: I'm bulletproof. Baby.

579
00:27:37,760 --> 00:27:41,160
Speaker 5: It's really funny, like you have nothing, but I did it.

580
00:27:41,320 --> 00:27:45,040
Speaker 1: You have, but I do that. But I can do

581
00:27:45,080 --> 00:27:47,920
Speaker 1: that is the thing. But I'm not ship. But I'm

582
00:27:47,960 --> 00:27:48,800
Speaker 1: not telling you.

583
00:27:49,040 --> 00:27:52,600
Speaker 5: That's how you also say, but here is my confession.

584
00:27:52,680 --> 00:27:53,280
Speaker 1: But I did that.

585
00:27:53,640 --> 00:27:54,920
Speaker 5: I'm my confession.

586
00:27:55,359 --> 00:27:58,800
Speaker 1: Thank you that because it would be good for your ratings.

587
00:27:59,000 --> 00:28:02,520
Speaker 2: Well then he goes on again, and then Hugh Hewitt's like,

588
00:28:02,720 --> 00:28:04,640
Speaker 2: all right, hypothetical, you're on the stand.

589
00:28:04,640 --> 00:28:06,160
Speaker 1: What you gonna say?

590
00:28:06,840 --> 00:28:08,080
Speaker 5: Oh my god, are you serious?

591
00:28:08,400 --> 00:28:09,800
Speaker 6: I think that.

592
00:28:10,200 --> 00:28:13,080
Speaker 5: Okay, if you do and they ask you on the stand,

593
00:28:13,160 --> 00:28:15,080
Speaker 5: did you order anyone to move boxes?

594
00:28:15,160 --> 00:28:16,119
Speaker 1: How will you answer?

595
00:28:16,960 --> 00:28:20,000
Speaker 6: I'm not answering that question for you. But I'm totally

596
00:28:20,000 --> 00:28:25,480
Speaker 6: covered under the law. Okay, Presidential Records Act. Just read it,

597
00:28:25,920 --> 00:28:28,440
Speaker 6: you take a look at it. I'm totally covered under

598
00:28:28,480 --> 00:28:28,840
Speaker 6: the law.

599
00:28:29,200 --> 00:28:33,080
Speaker 2: Spoiler alert, he is, in fact, not fucking covered under

600
00:28:33,119 --> 00:28:35,000
Speaker 2: the Presidential Records Act.

601
00:28:35,240 --> 00:28:35,600
Speaker 1: Just read it.

602
00:28:36,240 --> 00:28:37,919
Speaker 3: Just read it.

603
00:28:38,840 --> 00:28:41,920
Speaker 2: Any asshole with Google, including myself, can look up the

604
00:28:41,920 --> 00:28:45,120
Speaker 2: Presidential Records Act, and it states that all White House

605
00:28:45,240 --> 00:28:49,920
Speaker 2: records related to government business are public property, and that

606
00:28:50,000 --> 00:28:54,240
Speaker 2: when the president and vice president leave office, those documents

607
00:28:54,480 --> 00:28:58,200
Speaker 2: must be transferred to the National Archives for preservation.

608
00:28:58,800 --> 00:29:01,400
Speaker 3: So there's not a single thing under there that is

609
00:29:01,440 --> 00:29:02,640
Speaker 3: being like, just read it.

610
00:29:02,720 --> 00:29:03,240
Speaker 1: I'm covered.

611
00:29:03,280 --> 00:29:04,920
Speaker 2: It's like you're not, but you're just trying to do

612
00:29:04,960 --> 00:29:06,600
Speaker 2: that thing where you think if you say that shit

613
00:29:06,680 --> 00:29:09,120
Speaker 2: over and over and over again that I'm sure maybe

614
00:29:09,120 --> 00:29:11,200
Speaker 2: some of the people listening to that interview might believe that,

615
00:29:11,640 --> 00:29:14,480
Speaker 2: but when it goes to when it's actually presented, they're

616
00:29:14,480 --> 00:29:16,719
Speaker 2: gonna be like, sir, this is it doesn't say anything

617
00:29:16,760 --> 00:29:18,560
Speaker 2: about that ship here at all.

618
00:29:19,320 --> 00:29:22,520
Speaker 1: And he's he's surrounded by lawyers who are on his payroll,

619
00:29:22,840 --> 00:29:25,320
Speaker 1: and he's fired all the ones who tell him the

620
00:29:25,320 --> 00:29:28,600
Speaker 1: stuff that he doesn't want to hear. Yeah, so he's

621
00:29:28,720 --> 00:29:32,160
Speaker 1: just strong and wrong, baby, strong and wrong.

622
00:29:32,560 --> 00:29:34,560
Speaker 2: I bet his lawyers, like, we're up against it today,

623
00:29:34,600 --> 00:29:38,080
Speaker 2: folks were against it. Did you hear that fucking interview?

624
00:29:38,520 --> 00:29:39,800
Speaker 1: I'm covered. I'm covered.

625
00:29:39,840 --> 00:29:41,600
Speaker 3: It's like, I don't know what is he even talking about.

626
00:29:41,720 --> 00:29:43,560
Speaker 5: So if he did it, it doesn't matter. He did

627
00:29:43,560 --> 00:29:44,360
Speaker 5: it and he's covered.

628
00:29:45,600 --> 00:29:48,600
Speaker 2: And also, the thing is this guy, he always he's

629
00:29:48,640 --> 00:29:51,040
Speaker 2: always pump faking like he'll take the stand. He says

630
00:29:51,080 --> 00:29:53,960
Speaker 2: that all the time. He's not, I'll take the stand,

631
00:29:53,960 --> 00:29:58,160
Speaker 2: it doesn't matter, but he never does. So again it's

632
00:29:58,240 --> 00:30:00,320
Speaker 2: this is just where he's at. He's only got a

633
00:30:00,400 --> 00:30:02,520
Speaker 2: bunch of pump faking left and I don't know where

634
00:30:02,520 --> 00:30:03,320
Speaker 2: it's gonna end up.

635
00:30:03,360 --> 00:30:06,080
Speaker 1: But hey, you wish i'd take the stand? Be good

636
00:30:06,080 --> 00:30:07,840
Speaker 1: for your ratings? What would you say?

637
00:30:08,080 --> 00:30:10,320
Speaker 2: I'm not telling you, but I'm good under the law,

638
00:30:10,360 --> 00:30:11,360
Speaker 2: I could do whatever I want.

639
00:30:11,440 --> 00:30:12,760
Speaker 1: That's essentially your answer.

640
00:30:13,000 --> 00:30:16,840
Speaker 5: Yeah, right, yes i did it, but I'm protected, right, judge,

641
00:30:16,920 --> 00:30:18,120
Speaker 5: I'm protected.

642
00:30:17,920 --> 00:30:21,240
Speaker 2: Right, I'll protect your good That I could do anything.

643
00:30:21,280 --> 00:30:24,680
Speaker 1: Were good, Here we good. He's gonna do that. That's

644
00:30:24,680 --> 00:30:28,320
Speaker 1: why he's gonna sit down and be like presidential record, ACKed,

645
00:30:28,680 --> 00:30:29,640
Speaker 1: we we good.

646
00:30:29,640 --> 00:30:32,040
Speaker 3: Here, we get here, I'm covered and got a tea time.

647
00:30:32,200 --> 00:30:33,960
Speaker 5: And I think that if he took the stand, he

648
00:30:34,000 --> 00:30:36,680
Speaker 5: would because he either talks one in two ways. He

649
00:30:37,040 --> 00:30:39,640
Speaker 5: confidently speaks with a lot of hand gestures, or he

650
00:30:39,720 --> 00:30:42,440
Speaker 5: has both hands planted. What do you think his strategy

651
00:30:42,480 --> 00:30:42,840
Speaker 5: would be.

652
00:30:43,240 --> 00:30:45,440
Speaker 2: I don't know if he's that, I don't know. With

653
00:30:45,560 --> 00:30:47,520
Speaker 2: his back against the wall, I feel like he might

654
00:30:47,520 --> 00:30:50,520
Speaker 2: be energetic. But you think about remember that time, like

655
00:30:50,560 --> 00:30:53,920
Speaker 2: in the Egen Carroll depositions, when he's like, I don't

656
00:30:53,960 --> 00:30:56,560
Speaker 2: know her, and then he mistook her for his wife, and.

657
00:30:56,480 --> 00:30:59,840
Speaker 1: Oh my god, it's like, you never want this dude.

658
00:31:00,040 --> 00:31:02,640
Speaker 2: And if he even if he is behaving, he's gonna

659
00:31:02,680 --> 00:31:04,800
Speaker 2: fucking self own somehow up there?

660
00:31:05,320 --> 00:31:06,360
Speaker 5: It's so wild.

661
00:31:07,520 --> 00:31:08,760
Speaker 1: Who's that wife?

662
00:31:09,040 --> 00:31:09,720
Speaker 3: Before there?

663
00:31:09,760 --> 00:31:16,160
Speaker 1: I am with my beautiful wife, looks beautiutiful, beautiful.

664
00:31:15,680 --> 00:31:18,520
Speaker 5: Wife that I went with. Sal Any chance I got

665
00:31:19,520 --> 00:31:20,360
Speaker 5: that woman? I don't know her.

666
00:31:20,400 --> 00:31:21,760
Speaker 1: She's ugly girl.

667
00:31:23,120 --> 00:31:26,520
Speaker 3: Okay, so I'm no further questions, Yeah, thank you.

668
00:31:26,360 --> 00:31:27,640
Speaker 5: No further answers. Please.

669
00:31:29,080 --> 00:31:34,080
Speaker 1: That's just what's gonna be for the answers, for the answers,

670
00:31:34,080 --> 00:31:37,880
Speaker 1: your honor, What is that? No further answer for the answers?

671
00:31:38,000 --> 00:31:39,520
Speaker 3: So what the fuck does that mean?

672
00:31:40,440 --> 00:31:42,520
Speaker 5: Yeah, you got to name that the pod.

673
00:31:42,760 --> 00:31:44,040
Speaker 1: No further answer.

674
00:31:43,920 --> 00:31:49,000
Speaker 3: Further you the trials of Donald J. Trump, further answers.

675
00:31:49,040 --> 00:31:52,880
Speaker 1: That's pretty good, all right. In other news, that's gonna

676
00:31:52,880 --> 00:31:56,239
Speaker 1: be my new transition. There's been there's been a lot

677
00:31:56,280 --> 00:32:02,160
Speaker 1: of discussion lately about AI replacing writers, taxi drivers. There's

678
00:32:02,160 --> 00:32:04,440
Speaker 1: also a lot of concern regarding whether or not AI

679
00:32:04,520 --> 00:32:11,080
Speaker 1: should replace doctors. And so there's been articles claiming that

680
00:32:11,240 --> 00:32:14,560
Speaker 1: I can help the health care industry, and that is

681
00:32:14,720 --> 00:32:19,080
Speaker 1: absolutely true. Like there's been articles that claim that like

682
00:32:19,200 --> 00:32:24,000
Speaker 1: AI chatbots can help healthcare like doctors with their bedside manner.

683
00:32:24,640 --> 00:32:28,240
Speaker 1: There's ways that it helps cut time with like finding

684
00:32:28,680 --> 00:32:34,000
Speaker 1: data retrieval and like big diagnostic things. Those are all true,

685
00:32:34,080 --> 00:32:39,680
Speaker 1: but it's always being framed as like replacing the doctors,

686
00:32:39,760 --> 00:32:43,880
Speaker 1: better than better than doctor, just like they want it

687
00:32:43,920 --> 00:32:46,760
Speaker 1: to be something that it's not. They want it to

688
00:32:46,760 --> 00:32:51,600
Speaker 1: be robot doctor, who's like, you know, just that's the

689
00:32:51,640 --> 00:32:54,880
Speaker 1: only person you deal with and it's like definitely does

690
00:32:54,920 --> 00:32:58,600
Speaker 1: not work that well. Like so, for instance, this one

691
00:32:58,640 --> 00:33:02,440
Speaker 1: of the studies, by the way, the methodology is comparing

692
00:33:02,600 --> 00:33:08,040
Speaker 1: chat GPT with like chat GPT responses to questions that

693
00:33:08,160 --> 00:33:13,880
Speaker 1: people posed in a ask docs subreddit with like what

694
00:33:13,920 --> 00:33:17,840
Speaker 1: the actual doctors in a subreddit said, and then being

695
00:33:17,920 --> 00:33:20,520
Speaker 1: like they actually like did better than the doctors and

696
00:33:20,960 --> 00:33:25,480
Speaker 1: on reddit. So I think they could replace them like.

697
00:33:25,480 --> 00:33:29,160
Speaker 2: That, Wow, that's the low hanging ass fruit right there

698
00:33:29,160 --> 00:33:30,240
Speaker 2: to try and make your point.

699
00:33:30,800 --> 00:33:34,880
Speaker 1: Yeah, yeah, I mean I think that they're like chat

700
00:33:34,880 --> 00:33:39,400
Speaker 1: GPT is a good tool, Like it's a cool tool.

701
00:33:39,480 --> 00:33:44,200
Speaker 1: It's it's like they're trying to replace their like when Google,

702
00:33:44,320 --> 00:33:47,520
Speaker 1: if Google was invented and they were like, well, you

703
00:33:47,560 --> 00:33:51,560
Speaker 1: don't need lawyers anymore, you don't need doctors anymore because

704
00:33:51,600 --> 00:33:53,240
Speaker 1: all you need to do is Google this.

705
00:33:53,240 --> 00:33:54,120
Speaker 3: Shit ask a question.

706
00:33:54,120 --> 00:33:57,520
Speaker 1: It's like, no, this is a good tool for doctors.

707
00:33:57,640 --> 00:34:00,320
Speaker 1: I want my doctor to google what I have, but

708
00:34:00,400 --> 00:34:02,160
Speaker 1: I don't want my doctor.

709
00:34:01,880 --> 00:34:05,920
Speaker 3: To be Google, to be Google exactly.

710
00:34:05,520 --> 00:34:09,160
Speaker 1: And I want my doctor to have some of their

711
00:34:09,320 --> 00:34:12,480
Speaker 1: energy saved up by using this language model to help them,

712
00:34:12,520 --> 00:34:16,400
Speaker 1: like write emails that are clear and concise and like

713
00:34:16,520 --> 00:34:19,440
Speaker 1: convey the information that needs to be conveyed. I don't

714
00:34:19,560 --> 00:34:23,680
Speaker 1: need them to use this language model to replace the doctor,

715
00:34:24,160 --> 00:34:28,439
Speaker 1: which is just going to be a worse thing. It's

716
00:34:28,480 --> 00:34:31,439
Speaker 1: just going to create a thing, like a thing that's

717
00:34:31,520 --> 00:34:35,360
Speaker 1: harder to tell that you're being fucked over because they

718
00:34:35,520 --> 00:34:38,440
Speaker 1: are able to like string together sentences that make it

719
00:34:38,560 --> 00:34:42,239
Speaker 1: sound like you're dealing with an actual healthcare professional, but

720
00:34:42,360 --> 00:34:46,640
Speaker 1: it's actually like a medical chatbot. Like there are so

721
00:34:46,680 --> 00:34:50,239
Speaker 1: they've already been testing this med Palm two at the

722
00:34:50,320 --> 00:34:53,879
Speaker 1: Mayo Clinic, and like the way they write about it

723
00:34:53,920 --> 00:34:57,720
Speaker 1: is like in almost every metric such as showing evidence

724
00:34:57,760 --> 00:35:01,960
Speaker 1: of reasoning, consensus supported answers, the med Palm two performed

725
00:35:02,040 --> 00:35:05,040
Speaker 1: more or less as well as the actual doctors. It's like,

726
00:35:05,680 --> 00:35:10,160
Speaker 1: why it shouldn't be a fucking competition. Yeah, it's insane

727
00:35:10,480 --> 00:35:13,320
Speaker 1: to like think of it as a competition. It's not.

728
00:35:13,600 --> 00:35:15,680
Speaker 1: It's not supposed to be replacing them. It should be

729
00:35:16,400 --> 00:35:20,360
Speaker 1: improving the quality of care that people get, which is

730
00:35:20,400 --> 00:35:23,960
Speaker 1: an actual problem that everybody like seems to acknowledge. In

731
00:35:23,960 --> 00:35:27,760
Speaker 1: the United States. It's like really hard to get good

732
00:35:27,880 --> 00:35:30,640
Speaker 1: medical care, but it really feels like it's because of

733
00:35:30,680 --> 00:35:34,800
Speaker 1: the private equity ification of the us like the fact

734
00:35:34,880 --> 00:35:38,160
Speaker 1: that the only thing that excites the people who make

735
00:35:38,360 --> 00:35:42,239
Speaker 1: the decisions at a high level is cutting costs and

736
00:35:42,280 --> 00:35:46,480
Speaker 1: like improving profit, and so their interpretation of AI is

737
00:35:46,520 --> 00:35:50,080
Speaker 1: not like, here's a good tool that will improve the product,

738
00:35:50,160 --> 00:35:52,200
Speaker 1: because they don't give a fuck about the product. All

739
00:35:52,239 --> 00:35:56,839
Speaker 1: they care about is finding ways to cut costs. And

740
00:35:56,880 --> 00:35:59,880
Speaker 1: so that's why it's always being framed as well, this

741
00:36:00,160 --> 00:36:04,399
Speaker 1: can replace people, right, It's like, no, it can make

742
00:36:04,840 --> 00:36:07,759
Speaker 1: people better at their job, baby, if you like, give

743
00:36:07,800 --> 00:36:09,920
Speaker 1: them a chance to learn how to use it, and

744
00:36:09,920 --> 00:36:11,960
Speaker 1: if you like, think about it in a way that's

745
00:36:12,000 --> 00:36:15,640
Speaker 1: like patient and thoughtful. But instead the only thing that

746
00:36:15,640 --> 00:36:18,600
Speaker 1: they care about is cutting how many jobs they can cut,

747
00:36:18,600 --> 00:36:22,960
Speaker 1: which is it's crazy that like we are allowing that

748
00:36:23,160 --> 00:36:26,360
Speaker 1: to be the way that this technology is being written

749
00:36:26,400 --> 00:36:28,520
Speaker 1: about and like thought about at a broad.

750
00:36:28,320 --> 00:36:30,480
Speaker 2: Level, right, you know, a lot of it is looking

751
00:36:30,520 --> 00:36:33,839
Speaker 2: as like, oh AI is here to make everything like obsolete,

752
00:36:34,000 --> 00:36:37,040
Speaker 2: or it's like the be all end all of certain industries.

753
00:36:37,280 --> 00:36:40,280
Speaker 2: Like that's sort of the dominant narrative which has everyone

754
00:36:40,320 --> 00:36:43,120
Speaker 2: being like, so it's smarter than a doctor, so it's

755
00:36:43,160 --> 00:36:46,000
Speaker 2: better than a doctor. So it's better than a writer.

756
00:36:46,280 --> 00:36:49,040
Speaker 2: It's like rather than well no, and even like when

757
00:36:49,040 --> 00:36:52,920
Speaker 2: they're talking about when they're like analyzing the performance of

758
00:36:52,960 --> 00:36:56,040
Speaker 2: that one AI, it's like performed more or less as

759
00:36:56,080 --> 00:37:00,440
Speaker 2: well as the actual doctors. Well what's less? Yeah, like

760
00:37:00,520 --> 00:37:03,440
Speaker 2: how that is less? Like that's kind of a big

761
00:37:03,480 --> 00:37:07,440
Speaker 2: fucking deal if you completely misdiagnosed shit, because like with

762
00:37:07,560 --> 00:37:09,960
Speaker 2: all AI, like you know, you can already see how

763
00:37:10,000 --> 00:37:12,360
Speaker 2: there's gonna be, like the kind of care can be

764
00:37:12,400 --> 00:37:15,239
Speaker 2: all fucked up based on like racial biases and shit

765
00:37:15,320 --> 00:37:17,399
Speaker 2: like that, because even when you look at right, even

766
00:37:17,440 --> 00:37:20,719
Speaker 2: if it is going through like actual data, if you

767
00:37:20,760 --> 00:37:24,359
Speaker 2: look at clinical trials for certain medications, if sometimes a

768
00:37:24,360 --> 00:37:27,480
Speaker 2: certain ethnic group isn't responding as well as another, they

769
00:37:27,480 --> 00:37:30,799
Speaker 2: will just preclude them from participating in the study to

770
00:37:30,880 --> 00:37:33,560
Speaker 2: make the to make the results look better. Where it's like, yeah,

771
00:37:33,600 --> 00:37:36,360
Speaker 2: well this shit actually works better for white Alzheimer's patients

772
00:37:36,360 --> 00:37:39,080
Speaker 2: and not black or Latino Alzheimer's patients, So like let's

773
00:37:39,120 --> 00:37:41,360
Speaker 2: have a couple of them in there, but mostly the

774
00:37:41,400 --> 00:37:44,520
Speaker 2: white ones, because those are showing us the best results.

775
00:37:44,640 --> 00:37:47,120
Speaker 3: And we're all we already have a deficit in even

776
00:37:47,120 --> 00:37:49,200
Speaker 3: how we even are testing certain things.

777
00:37:49,200 --> 00:37:52,440
Speaker 1: So and I met with like a really good doctor

778
00:37:52,800 --> 00:37:56,759
Speaker 1: like that and somebody who's like really smart about how

779
00:37:56,800 --> 00:38:00,279
Speaker 1: they use AI. AI could actually be used to like

780
00:38:00,360 --> 00:38:04,279
Speaker 1: fight against the biases that do exist with doctors. Like

781
00:38:04,360 --> 00:38:07,600
Speaker 1: doctors are not above that shit by any means. Like

782
00:38:07,640 --> 00:38:11,600
Speaker 1: they're some of the most sexist, racist motherfuckers and you

783
00:38:11,640 --> 00:38:15,440
Speaker 1: know out here because they're like sitting there getting told

784
00:38:15,520 --> 00:38:17,640
Speaker 1: they're like incredibly smart and they have like a lot

785
00:38:17,640 --> 00:38:20,239
Speaker 1: of authority. And so whenever there's a lot of authority,

786
00:38:20,320 --> 00:38:23,120
Speaker 1: you see a lot of biases and shit like just

787
00:38:23,400 --> 00:38:27,359
Speaker 1: go unchecked. So there's a problem, there's an issue there.

788
00:38:27,880 --> 00:38:31,200
Speaker 1: And I don't think that, Like I think in a

789
00:38:31,239 --> 00:38:35,160
Speaker 1: different world where we don't approach a technology and say

790
00:38:35,160 --> 00:38:38,880
Speaker 1: how many jobs could this cut like as the first question,

791
00:38:39,560 --> 00:38:41,239
Speaker 1: we would be approaching this and being like, oh, this

792
00:38:41,280 --> 00:38:44,200
Speaker 1: is a great opportunity to fucking like improve medical care

793
00:38:44,239 --> 00:38:47,319
Speaker 1: and like cut out all those bias, all these biases

794
00:38:47,360 --> 00:38:51,960
Speaker 1: by like you know, doing like approaching data differently or

795
00:38:52,120 --> 00:38:55,920
Speaker 1: like using like approaching people, you know, an just doing

796
00:38:55,960 --> 00:38:59,120
Speaker 1: a better job like parsing through massive amounts of data

797
00:38:59,160 --> 00:39:03,560
Speaker 1: without racism goggles on, you know. But yeah, I always find.

798
00:39:03,400 --> 00:39:06,400
Speaker 5: It interesting when you actually stumble upon a really good doctor,

799
00:39:06,520 --> 00:39:09,000
Speaker 5: and like the thing that you notice from a good

800
00:39:09,000 --> 00:39:11,600
Speaker 5: doctor to a not good doctor is that they actually

801
00:39:11,600 --> 00:39:14,000
Speaker 5: like take their time and they want to know the

802
00:39:14,400 --> 00:39:17,160
Speaker 5: real personal route of like where the fuck this shit

803
00:39:17,239 --> 00:39:19,600
Speaker 5: is coming from, you know, and you're not just feeling

804
00:39:19,640 --> 00:39:21,359
Speaker 5: out a form that says like, yes, that's my father

805
00:39:21,480 --> 00:39:24,160
Speaker 5: had cancer and died, like you're you know, you're actually

806
00:39:24,239 --> 00:39:27,239
Speaker 5: engaging with like okay, like what does this mean? And

807
00:39:27,280 --> 00:39:28,879
Speaker 5: what does that mean? And where is this and where

808
00:39:28,920 --> 00:39:30,759
Speaker 5: do you work? And what's your stress levels? And like

809
00:39:30,760 --> 00:39:32,560
Speaker 5: when you have a doctor that really cares, it's such

810
00:39:32,600 --> 00:39:35,919
Speaker 5: a fucking huge difference. And if they did have those

811
00:39:36,000 --> 00:39:39,160
Speaker 5: tools and they could like you know, precision focus on

812
00:39:39,200 --> 00:39:41,400
Speaker 5: whatever it is that's bothering you, it would be so

813
00:39:41,520 --> 00:39:44,400
Speaker 5: nice in an ideal way. And it's also going back

814
00:39:44,440 --> 00:39:46,120
Speaker 5: to what you were saying, Jack about how like it's

815
00:39:46,160 --> 00:39:49,000
Speaker 5: crazy that like when we get some technology, it's like

816
00:39:49,040 --> 00:39:52,680
Speaker 5: how do we cut jobs? Yeah, And it's it's wild

817
00:39:52,800 --> 00:39:56,640
Speaker 5: that right now the AI conversation is like that, But

818
00:39:56,680 --> 00:39:59,319
Speaker 5: it's also not because when you think about the way

819
00:39:59,320 --> 00:40:01,840
Speaker 5: people talk about it immigrants and immigrants taking people's jobs,

820
00:40:01,840 --> 00:40:04,760
Speaker 5: it's like there's so much disconnect of like what's really

821
00:40:04,800 --> 00:40:08,080
Speaker 5: happening in all these industries, and AI is just about

822
00:40:08,120 --> 00:40:11,880
Speaker 5: to make that shit so much worse. And that's of

823
00:40:11,920 --> 00:40:14,160
Speaker 5: course why they're doing the double strike with this with

824
00:40:14,200 --> 00:40:17,440
Speaker 5: my industry. And it's really fascinating to be in it

825
00:40:17,560 --> 00:40:20,960
Speaker 5: so deep and in every seems to be like every

826
00:40:20,960 --> 00:40:23,680
Speaker 5: industry is getting affected by the shit. It's really wild,

827
00:40:23,840 --> 00:40:25,440
Speaker 5: Like where is this going?

828
00:40:25,480 --> 00:40:27,879
Speaker 2: I don't know, yeah, because it starts off as being

829
00:40:27,880 --> 00:40:30,319
Speaker 2: like this will help people's efficiency, and then again, like

830
00:40:30,360 --> 00:40:32,600
Speaker 2: when you start, if you have like a company that

831
00:40:32,640 --> 00:40:36,000
Speaker 2: has to answer at a Wall Street, then you can

832
00:40:36,040 --> 00:40:37,959
Speaker 2: see how that pressure can turn into just like well,

833
00:40:38,520 --> 00:40:39,839
Speaker 2: why don't we just make them all.

834
00:40:39,760 --> 00:40:41,840
Speaker 3: A bunch of chatbots or at least a lot of

835
00:40:41,880 --> 00:40:43,000
Speaker 3: them could be you know, for.

836
00:40:42,960 --> 00:40:45,640
Speaker 2: A lot of this shit, And it is a slippery slope,

837
00:40:45,640 --> 00:40:48,000
Speaker 2: and like to the point of like I get too

838
00:40:48,400 --> 00:40:51,560
Speaker 2: because some like medical systems are so overburdened, like I

839
00:40:51,560 --> 00:40:54,120
Speaker 2: feel like factors own they're like I have to see

840
00:40:54,120 --> 00:40:57,000
Speaker 2: like nine hundred fucking people in a day because I'm

841
00:40:57,040 --> 00:40:59,960
Speaker 2: already they're already cutting costs that they're there, I can't

842
00:41:00,120 --> 00:41:03,000
Speaker 2: give the quality of care. So shit is slipping through

843
00:41:03,040 --> 00:41:06,120
Speaker 2: the cracks, and that happened tragically with my cousin, you know,

844
00:41:06,239 --> 00:41:10,640
Speaker 2: like having something completely misdiagnosed, go completely under the fucking radar,

845
00:41:10,719 --> 00:41:12,640
Speaker 2: and then oh, sorry, you have.

846
00:41:12,680 --> 00:41:16,360
Speaker 3: Stage four cancer. Sure, I mean, and that's what happens.

847
00:41:16,400 --> 00:41:19,520
Speaker 2: And you think about how frequent that kind of shit happened,

848
00:41:19,560 --> 00:41:22,040
Speaker 2: Like how frequently that kind of thing happens, and you're like,

849
00:41:23,000 --> 00:41:26,520
Speaker 2: isn't there a way to help that for somebody who unfortunate,

850
00:41:26,600 --> 00:41:28,480
Speaker 2: like you know, I guess I won't say that a

851
00:41:28,520 --> 00:41:30,439
Speaker 2: doctor is negligent because I don't know what the fuck

852
00:41:30,480 --> 00:41:33,200
Speaker 2: their work environment is like, but those are the instances

853
00:41:33,200 --> 00:41:36,200
Speaker 2: where you feel like, couldn't that help, like, like more

854
00:41:36,400 --> 00:41:38,879
Speaker 2: or less they have fall through the Yeah, fall through

855
00:41:38,880 --> 00:41:41,680
Speaker 2: the fucking cracks of being like, no, you're fine, it's fibroids,

856
00:41:41,680 --> 00:41:43,279
Speaker 2: don't worry about it, don't worry about it, don't worry

857
00:41:43,280 --> 00:41:43,640
Speaker 2: about it.

858
00:41:43,640 --> 00:41:46,120
Speaker 3: And then it's, hey, we totally fucked that up.

859
00:41:46,239 --> 00:41:49,719
Speaker 2: Sorry, Yeah, that's just I think those are Sorry, Yeah,

860
00:41:50,400 --> 00:41:51,640
Speaker 2: that's what's fucked up.

861
00:41:51,719 --> 00:41:52,719
Speaker 1: Yeah, that's that.

862
00:41:52,719 --> 00:41:55,560
Speaker 3: That's where you're hoping that is there a way to

863
00:41:55,600 --> 00:41:56,839
Speaker 3: improve that, you know what I mean?

864
00:41:56,880 --> 00:41:59,000
Speaker 2: But not just wholesale being like and the ship is

865
00:41:59,040 --> 00:42:02,759
Speaker 2: more popping than doctor end up moving on yeah, yeah,

866
00:42:02,800 --> 00:42:03,239
Speaker 2: and then and.

867
00:42:03,200 --> 00:42:05,880
Speaker 5: Then you get into the deeper conversation of like medical

868
00:42:05,920 --> 00:42:08,560
Speaker 5: school being so fucking expensive and only certain types of

869
00:42:08,560 --> 00:42:10,640
Speaker 5: people have access to And I mean that that's what's

870
00:42:10,640 --> 00:42:13,080
Speaker 5: fucked is there's so many layers that's happening in this

871
00:42:13,120 --> 00:42:15,279
Speaker 5: conversation that we're trying to happen, right, But it's like

872
00:42:15,760 --> 00:42:18,359
Speaker 5: you feel fucking crazy because you're just like I just

873
00:42:18,400 --> 00:42:20,839
Speaker 5: want everyone I love to get good health care. Why

874
00:42:20,880 --> 00:42:21,800
Speaker 5: is that so hard?

875
00:42:22,120 --> 00:42:24,960
Speaker 1: Right? Why do you have to like, yeah, well there

876
00:42:24,960 --> 00:42:27,200
Speaker 1: has to be like a shortage of doctors and they

877
00:42:27,239 --> 00:42:30,480
Speaker 1: have to be like treated like shit, like professionally hazed

878
00:42:30,800 --> 00:42:33,359
Speaker 1: to like like there's like stories when you like people

879
00:42:33,400 --> 00:42:36,359
Speaker 1: are going through medical school. They're like these stories where

880
00:42:36,400 --> 00:42:38,840
Speaker 1: it's like, yeah, so, like I would be driving to

881
00:42:39,040 --> 00:42:43,000
Speaker 1: work and like part of my routine was I would

882
00:42:43,000 --> 00:42:45,680
Speaker 1: fall asleep at the red light and then people would

883
00:42:45,680 --> 00:42:47,600
Speaker 1: wake me up by honking at me when the light

884
00:42:47,680 --> 00:42:49,879
Speaker 1: turned green. And that's how I would like sleep during

885
00:42:49,920 --> 00:42:53,120
Speaker 1: medical school. It's like Jesus, Jesus, well why do you

886
00:42:53,160 --> 00:42:55,240
Speaker 1: why no other professional?

887
00:42:55,320 --> 00:42:55,800
Speaker 5: Crazy?

888
00:42:55,960 --> 00:42:59,680
Speaker 1: Yeah, but there is this interesting case study of like

889
00:43:00,840 --> 00:43:04,319
Speaker 1: the whole AI thing, and like everybody going crazy about

890
00:43:04,320 --> 00:43:07,759
Speaker 1: AI right now is really like a media bubble, and

891
00:43:07,840 --> 00:43:10,560
Speaker 1: like this technology has been around. It's just like once

892
00:43:10,640 --> 00:43:13,920
Speaker 1: chad GPT happened and a lot of people were like, whoa,

893
00:43:14,040 --> 00:43:17,120
Speaker 1: it feels like I'm talking to person right when I

894
00:43:17,200 --> 00:43:19,839
Speaker 1: talk to this thing and they give me answers. Yes,

895
00:43:19,920 --> 00:43:23,040
Speaker 1: the answers aren't correct, but they get a lot of

896
00:43:23,080 --> 00:43:25,840
Speaker 1: shit wrong. But it's interesting that it can type answers,

897
00:43:26,040 --> 00:43:29,040
Speaker 1: So I'm impressed. But like there's been AI for a while.

898
00:43:29,080 --> 00:43:33,160
Speaker 1: And there was this company called Babylon Health, a startup

899
00:43:33,239 --> 00:43:37,160
Speaker 1: in twenty eighteen that they had an AI chatbot that

900
00:43:37,360 --> 00:43:41,359
Speaker 1: was reportedly able to diagnose medical conditions as accurately as

901
00:43:41,400 --> 00:43:44,600
Speaker 1: a doctor, and the claim that claim was based on

902
00:43:44,640 --> 00:43:47,480
Speaker 1: a study that, as noted in a follow up paper

903
00:43:47,520 --> 00:43:51,560
Speaker 1: by researchers, did not offer convincing evidence that Babylon can

904
00:43:51,600 --> 00:43:56,280
Speaker 1: perform better than doctors in any realistic situation, and there

905
00:43:56,400 --> 00:44:01,919
Speaker 1: is a possibility that it might perform significantly worse. And yeah,

906
00:44:01,960 --> 00:44:05,680
Speaker 1: it's just like the over promising of the technology, like

907
00:44:05,880 --> 00:44:10,640
Speaker 1: just immediately, like starts immediately, because the second you have

908
00:44:10,840 --> 00:44:14,120
Speaker 1: something like this, it gets put in a room with

909
00:44:14,239 --> 00:44:17,800
Speaker 1: people who are like, how do we at the highest level,

910
00:44:17,880 --> 00:44:20,239
Speaker 1: as people who aren't involved in this at all, We're

911
00:44:20,239 --> 00:44:24,520
Speaker 1: not doctors, how do we make the most money off

912
00:44:24,560 --> 00:44:26,080
Speaker 1: of that? And the way to make the most money

913
00:44:26,080 --> 00:44:31,560
Speaker 1: off of that is to just give unrealistic prognostications of

914
00:44:31,600 --> 00:44:33,359
Speaker 1: like what the thing's going to be able to do,

915
00:44:33,560 --> 00:44:36,719
Speaker 1: and like not work with it at all. And I

916
00:44:36,719 --> 00:44:39,960
Speaker 1: feel like that's what AI is to a large degree.

917
00:44:40,000 --> 00:44:43,480
Speaker 1: It's like a word for tech advances that we already

918
00:44:43,560 --> 00:44:46,360
Speaker 1: have or that have been happening for a while. But

919
00:44:46,600 --> 00:44:49,920
Speaker 1: the media bubble is like an opportunity for these like

920
00:44:50,000 --> 00:44:53,399
Speaker 1: financial powers to like dig in and try to make

921
00:44:53,480 --> 00:44:57,520
Speaker 1: money by like claiming that they can cut jobs. But anyways,

922
00:44:57,600 --> 00:45:02,920
Speaker 1: this Babylon company, the NHS, the British like healthcare system,

923
00:45:03,320 --> 00:45:06,600
Speaker 1: signed a big contract with them to like take over

924
00:45:06,920 --> 00:45:11,439
Speaker 1: signed three projects with the UK's NHS hospital trusts, and

925
00:45:12,320 --> 00:45:15,279
Speaker 1: they ended up canceling almost all of them like eight

926
00:45:15,360 --> 00:45:17,920
Speaker 1: years before they were supposed to.

927
00:45:18,640 --> 00:45:22,880
Speaker 2: And yeah, also, I like I wonder if the person

928
00:45:22,880 --> 00:45:25,400
Speaker 2: who named it was ros was like a Rastafarian was

929
00:45:25,600 --> 00:45:28,080
Speaker 2: looking at it like a Babylon type thing, you know,

930
00:45:28,280 --> 00:45:31,439
Speaker 2: like real, like the oppressive systems that we live under

931
00:45:31,440 --> 00:45:35,480
Speaker 2: according to the Rastafarian version of Babylon, Like this is bullshit.

932
00:45:35,880 --> 00:45:38,520
Speaker 2: This is hyper capitalist, oppressive bullshit.

933
00:45:39,320 --> 00:45:42,120
Speaker 1: They invented it and were like, uh oh oh this

934
00:45:42,200 --> 00:45:45,240
Speaker 1: is bad Babylon. Yeah. Yeah, I have two thoughts.

935
00:45:45,600 --> 00:45:49,680
Speaker 5: First one is I think it's also amusing because at

936
00:45:49,719 --> 00:45:51,879
Speaker 5: some point in the future, like yeah, they'd get these

937
00:45:51,880 --> 00:45:54,840
Speaker 5: AI whatever chat bought to come fucking take over the

938
00:45:54,880 --> 00:45:57,360
Speaker 5: doctor's job. But then there's gonna be the next level

939
00:45:57,360 --> 00:46:00,799
Speaker 5: of exploitation after that, where now like you're privacy is

940
00:46:00,800 --> 00:46:03,520
Speaker 5: at risk, like literal pictures of your body are now

941
00:46:03,719 --> 00:46:07,440
Speaker 5: and like hidden within these ai chat you know, men

942
00:46:07,440 --> 00:46:09,920
Speaker 5: are gonna fucking hack that ship. Now they got pictures

943
00:46:09,920 --> 00:46:12,920
Speaker 5: of my pussy because my gynecologists had to like you know,

944
00:46:13,200 --> 00:46:15,360
Speaker 5: ship with you know taking pictures and check if I

945
00:46:15,360 --> 00:46:17,839
Speaker 5: got fibroids, you know know whatever. It's like, you know,

946
00:46:17,920 --> 00:46:20,520
Speaker 5: that's the next level once they introduced the shit. I mean,

947
00:46:20,520 --> 00:46:25,200
Speaker 5: that's what's happening with fucking cars. Article about car Yeah,

948
00:46:25,640 --> 00:46:29,239
Speaker 5: whether they's all they say, like yeah, they're keeping taps

949
00:46:29,280 --> 00:46:33,319
Speaker 5: on your sex life on everything like that. You get

950
00:46:33,320 --> 00:46:35,680
Speaker 5: that smart ship in anything and you're gonna get to

951
00:46:35,719 --> 00:46:37,400
Speaker 5: that point where it's like it just gets nasty.

952
00:46:37,600 --> 00:46:40,640
Speaker 2: It was, yeah, like that article ended with like they're

953
00:46:40,680 --> 00:46:43,080
Speaker 2: in a damn company that isn't fucking with your data,

954
00:46:43,160 --> 00:46:45,440
Speaker 2: they said, except for two and they were like based

955
00:46:45,440 --> 00:46:48,000
Speaker 2: in Europe because they have to actually abide by their

956
00:46:48,040 --> 00:46:48,800
Speaker 2: privacy laws.

957
00:46:48,800 --> 00:46:51,959
Speaker 3: Everybody else they're hoovering up data.

958
00:46:52,000 --> 00:46:53,120
Speaker 1: They're watching you. Fuck.

959
00:46:53,840 --> 00:46:57,800
Speaker 5: Oh yeah, they're watching the slogan you thought.

960
00:46:57,600 --> 00:47:00,759
Speaker 3: When the car was off, its headlights were watching you.

961
00:47:01,200 --> 00:47:01,399
Speaker 1: Wait.

962
00:47:02,320 --> 00:47:05,680
Speaker 5: Yeah nasty. And then the second thought I had was

963
00:47:06,200 --> 00:47:11,080
Speaker 5: it's interesting how I mean, I think naturally all technology

964
00:47:11,239 --> 00:47:14,120
Speaker 5: there's a dark side to it, right, any any invention

965
00:47:14,239 --> 00:47:15,520
Speaker 5: it is going to have a dark side to it.

966
00:47:15,520 --> 00:47:18,120
Speaker 5: You know, you invented whatever the car and it's made

967
00:47:18,120 --> 00:47:20,200
Speaker 5: to transport, but you can kill somebody with your car.

968
00:47:20,440 --> 00:47:22,520
Speaker 5: So made me think about how like I wonder if

969
00:47:22,520 --> 00:47:25,279
Speaker 5: when pins were invented, like if like who was the

970
00:47:25,360 --> 00:47:28,560
Speaker 5: first person to get to die getting stabbed by a pin?

971
00:47:29,160 --> 00:47:31,120
Speaker 5: And they were like, this is out of control. This

972
00:47:31,160 --> 00:47:33,080
Speaker 5: is why we can have pins in our household or

973
00:47:33,120 --> 00:47:36,240
Speaker 5: pencils or whatever. The fud there's literally it's just always

974
00:47:36,400 --> 00:47:38,680
Speaker 5: this is human nature, it seems or it's like you

975
00:47:38,719 --> 00:47:40,960
Speaker 5: invent something and it's going to be used for evil

976
00:47:41,000 --> 00:47:42,640
Speaker 5: because that's the way humans work.

977
00:47:42,960 --> 00:47:46,280
Speaker 1: I mean, we already have this one example that's pretty creepy,

978
00:47:46,600 --> 00:47:51,320
Speaker 1: like so like that. There's this thing called Deepscribe, which

979
00:47:51,360 --> 00:47:55,160
Speaker 1: is it's like your doctor will use it to record

980
00:47:55,800 --> 00:48:00,279
Speaker 1: your conversation with your doctor, and it captures the incomes

981
00:48:00,320 --> 00:48:04,719
Speaker 1: in real time and automatically extracts medical information throughout the conversation.

982
00:48:06,120 --> 00:48:10,200
Speaker 1: Which sounds like a good idea good use of the technology,

983
00:48:10,480 --> 00:48:12,880
Speaker 1: except the software was only able to catch eighty percent

984
00:48:12,920 --> 00:48:16,240
Speaker 1: of what was recorded. Okay, their AI would have quote

985
00:48:16,239 --> 00:48:21,360
Speaker 1: hallucinations and provide quote false information about patients, including errors

986
00:48:21,480 --> 00:48:26,280
Speaker 1: enlisting medications, which seems pretty bad. So to fix this issue,

987
00:48:26,320 --> 00:48:29,200
Speaker 1: the company hired a team of two hundred random people

988
00:48:29,600 --> 00:48:32,680
Speaker 1: who would fix the notes by listening into these supposedly

989
00:48:32,760 --> 00:48:38,040
Speaker 1: private recordings of medical appointments. So problem solved. We just

990
00:48:38,080 --> 00:48:41,680
Speaker 1: got random you know, a random.

991
00:48:42,320 --> 00:48:48,280
Speaker 5: All I'm hearing job creation may come on, bro, you're.

992
00:48:49,080 --> 00:48:52,000
Speaker 3: That's true to those two humans?

993
00:48:52,480 --> 00:48:54,760
Speaker 1: Yes, that's so crazy.

994
00:48:55,440 --> 00:48:56,000
Speaker 5: I hate it.

995
00:48:56,520 --> 00:48:58,680
Speaker 1: Yeah, and then they're also using it to screw you

996
00:48:58,680 --> 00:49:02,359
Speaker 1: over on insurance claim. So oh, we didn't even get

997
00:49:02,400 --> 00:49:03,000
Speaker 1: to the good part.

998
00:49:03,120 --> 00:49:05,120
Speaker 2: Yeah, where they're like, yeah, yeah, this will just go

999
00:49:05,200 --> 00:49:08,000
Speaker 2: through insurance claims to help a doctor deny all of them.

1000
00:49:08,160 --> 00:49:10,960
Speaker 5: Yeah, yeah, crazier to deny.

1001
00:49:11,120 --> 00:49:13,839
Speaker 1: The insurance company's Sigma is being sued for using an

1002
00:49:13,840 --> 00:49:18,120
Speaker 1: AI algorithm instead of doctors to review customer claims, allowing

1003
00:49:18,160 --> 00:49:21,960
Speaker 1: their doctors to deny claims without ever opening patient files.

1004
00:49:22,440 --> 00:49:23,680
Speaker 1: That's great, that's great.

1005
00:49:24,680 --> 00:49:27,400
Speaker 2: Yeah, I like how Yeah, there's like one example of

1006
00:49:27,440 --> 00:49:30,360
Speaker 2: this woman who had like, was getting an ultrasound because

1007
00:49:30,400 --> 00:49:33,319
Speaker 2: they thought she may have ovarian cancer, and they found

1008
00:49:33,400 --> 00:49:37,319
Speaker 2: a cyst and then just denied her claim for the

1009
00:49:37,400 --> 00:49:39,560
Speaker 2: ultrasound and a follow up procedure.

1010
00:49:39,160 --> 00:49:42,120
Speaker 1: Claiming neither were medically necessary and leaving her on the

1011
00:49:42,160 --> 00:49:45,440
Speaker 1: hook for seven hundred and twenty three dollars, which is.

1012
00:49:45,440 --> 00:49:47,600
Speaker 2: Wild because they could have just done that without the

1013
00:49:47,640 --> 00:49:52,000
Speaker 2: AI read you know what I mean. So Okay, sure,

1014
00:49:52,080 --> 00:49:54,560
Speaker 2: I guess it's more efficient or just as fucked up.

1015
00:49:55,040 --> 00:49:57,799
Speaker 1: Yeah, all right. Should we take one more break and

1016
00:49:57,840 --> 00:50:01,480
Speaker 1: come back and talk about the real scandal that rotten

1017
00:50:01,520 --> 00:50:05,120
Speaker 1: Tomatoes might not be as trustworthy as we all thought?

1018
00:50:05,280 --> 00:50:19,360
Speaker 4: Oh no, and we're back.

1019
00:50:19,560 --> 00:50:21,759
Speaker 1: Or do you want to bring us back Marcella, and

1020
00:50:21,800 --> 00:50:25,960
Speaker 1: we're back there we go with Gusto, one of the

1021
00:50:25,960 --> 00:50:30,799
Speaker 1: best in the business. All right, so Rotten Tomatoes, I've

1022
00:50:30,800 --> 00:50:34,080
Speaker 1: been I've been a Rotten Tomatoes skeptic for a while.

1023
00:50:34,640 --> 00:50:39,319
Speaker 5: You are that kind of white guy, had.

1024
00:50:39,280 --> 00:50:44,799
Speaker 1: Some I have some thoughts about Rotten Tomatoes. Pull Marcella.

1025
00:50:45,640 --> 00:50:46,760
Speaker 5: I just closed my laptop.

1026
00:50:47,200 --> 00:50:50,160
Speaker 2: Yeah, that would be in your online dating profile. It's like,

1027
00:50:50,200 --> 00:50:52,920
Speaker 2: here's some things about me, Rotten Tomatoes skeptic.

1028
00:50:53,440 --> 00:50:55,440
Speaker 1: Yeah, okay, follow.

1029
00:50:58,160 --> 00:51:00,200
Speaker 3: I prefer Metacritic person.

1030
00:51:00,440 --> 00:51:03,400
Speaker 1: Oh my god, because they have a little bit more

1031
00:51:03,280 --> 00:51:06,279
Speaker 1: a little bit more specific with the inputs that they

1032
00:51:06,320 --> 00:51:09,520
Speaker 1: put in, so like a five star review, not just

1033
00:51:09,719 --> 00:51:12,799
Speaker 1: fresh it is, you know, input as a ten out

1034
00:51:12,800 --> 00:51:13,120
Speaker 1: of ten.

1035
00:51:13,640 --> 00:51:16,759
Speaker 5: Heay, but wait, backet up a second. Who is submitting

1036
00:51:16,880 --> 00:51:19,120
Speaker 5: to Rotten Tomatoes For those that don't know, like me,

1037
00:51:19,280 --> 00:51:23,200
Speaker 5: for example, movie critics. Okay, but what who defines that?

1038
00:51:23,920 --> 00:51:26,239
Speaker 1: Who defines the movie cras That's a great that's a

1039
00:51:26,239 --> 00:51:29,960
Speaker 1: great point. Yeah, and a great question. And it is

1040
00:51:30,080 --> 00:51:34,200
Speaker 1: the crux of the issue, right because Rotten Tomatoes determines

1041
00:51:34,239 --> 00:51:37,279
Speaker 1: it and the owners of Rotten Tomatoes have some say

1042
00:51:37,400 --> 00:51:41,120
Speaker 1: into who gets allowed in there. And you'll never believe

1043
00:51:41,160 --> 00:51:46,719
Speaker 1: this Rotten Tomatoes is owned by a movie studio. They're

1044
00:51:46,800 --> 00:51:51,120
Speaker 1: owned by Universal Pictures and Warner Brothers. Or sorry, they're

1045
00:51:51,120 --> 00:51:54,719
Speaker 1: owned by Warner Brothers and fan Dango, which shares a

1046
00:51:54,719 --> 00:51:59,360
Speaker 1: parent company with Universal Pictures. So yeah, they so it

1047
00:51:59,440 --> 00:52:03,480
Speaker 1: was just reviewed that a PR company called Bunker fifteen,

1048
00:52:04,080 --> 00:52:08,400
Speaker 1: which is just like, ugh, you know, that's a shit job.

1049
00:52:08,680 --> 00:52:10,920
Speaker 2: Like if something like where do you work Bunker fifteen,

1050
00:52:10,960 --> 00:52:13,040
Speaker 2: They're like, oh, I don't know what that is, but

1051
00:52:13,160 --> 00:52:13,919
Speaker 2: I'm sorry, your.

1052
00:52:13,840 --> 00:52:16,759
Speaker 5: Boss, Pepe the Frog Jesus really.

1053
00:52:18,120 --> 00:52:20,600
Speaker 3: Bunker fifteen, it's the woajack memea.

1054
00:52:21,200 --> 00:52:25,280
Speaker 1: Bunker fifteen juiced the rotten Tomato scores of the movie

1055
00:52:25,480 --> 00:52:29,920
Speaker 1: Ophelia because when the reviews came out, it was so,

1056
00:52:30,000 --> 00:52:34,520
Speaker 1: this is a feminist retelling of Hamlet starring Opheliate Art,

1057
00:52:35,360 --> 00:52:39,080
Speaker 1: And the reviews came out and it was coming in

1058
00:52:39,080 --> 00:52:42,520
Speaker 1: at a forty six percent, which he classifies that is rotten.

1059
00:52:42,960 --> 00:52:48,080
Speaker 1: And then Bunker fifteen offered critics in quotes fifty dollars

1060
00:52:48,200 --> 00:52:52,200
Speaker 1: or more to post positive reviews, which bumped the rating

1061
00:52:52,400 --> 00:52:55,440
Speaker 1: up too fresh, And the next month the movie was

1062
00:52:55,480 --> 00:52:57,560
Speaker 1: acquired for distribution, So.

1063
00:52:58,320 --> 00:53:00,000
Speaker 3: Damn for fifty bucks starting for fifty.

1064
00:53:00,400 --> 00:53:03,600
Speaker 1: It's so low, Like that's what. It's so fucking cheap, Like,

1065
00:53:03,680 --> 00:53:04,560
Speaker 1: what y'all.

1066
00:53:04,480 --> 00:53:07,640
Speaker 2: If you're gonna, like, if you're gonna sell out your

1067
00:53:08,000 --> 00:53:12,320
Speaker 2: little rotten tomatoes, cred, go get some more fucking coin

1068
00:53:12,440 --> 00:53:12,719
Speaker 2: than that.

1069
00:53:13,360 --> 00:53:15,440
Speaker 5: Yeah, it just shows you how much people are fucking

1070
00:53:15,520 --> 00:53:18,719
Speaker 5: whack as fucking sellouts and have no morals or ethics.

1071
00:53:19,120 --> 00:53:21,640
Speaker 2: Yeah, but they're also like, here's the thing, you don't know.

1072
00:53:21,920 --> 00:53:25,719
Speaker 2: I do about forty of these a day, right when

1073
00:53:25,719 --> 00:53:27,480
Speaker 2: I kept figuring I'm making two grand a day.

1074
00:53:27,480 --> 00:53:28,680
Speaker 1: Actually I'm doing all right.

1075
00:53:29,200 --> 00:53:31,200
Speaker 5: Yeah, I mean that is how they think, though, I.

1076
00:53:31,200 --> 00:53:36,200
Speaker 1: Bet I'm sure so. Bunker fifteen denied the allegations, but

1077
00:53:36,360 --> 00:53:39,800
Speaker 1: also admitted they have a quote small handful of writers

1078
00:53:39,840 --> 00:53:43,200
Speaker 1: that are set up that are set up a specific

1079
00:53:43,239 --> 00:53:46,399
Speaker 1: system where filmmakers can sponsor or pay to have them

1080
00:53:46,440 --> 00:53:50,040
Speaker 1: review a film, which doesn't sound like a denial of

1081
00:53:50,080 --> 00:53:50,960
Speaker 1: the allegation, Like.

1082
00:53:50,880 --> 00:53:54,239
Speaker 2: How they go, Hey, man, are you paying critics to to, like,

1083
00:53:54,440 --> 00:53:57,000
Speaker 2: you know, pad the reviews of films that are your

1084
00:53:57,000 --> 00:53:58,280
Speaker 2: clients at your PR company.

1085
00:53:58,360 --> 00:54:01,439
Speaker 1: We absolutely deny that much. No, what we do do

1086
00:54:01,560 --> 00:54:04,440
Speaker 1: have some critics who we can pay be paid.

1087
00:54:04,520 --> 00:54:07,080
Speaker 5: They got the same pr company as Trump. Huh, I know,

1088
00:54:07,880 --> 00:54:08,880
Speaker 5: but I protected.

1089
00:54:09,000 --> 00:54:11,799
Speaker 2: So yeah, right, they're saying we're covered on the Presidential

1090
00:54:11,800 --> 00:54:14,000
Speaker 2: Records Act. So next question, my honor.

1091
00:54:15,440 --> 00:54:18,279
Speaker 1: And since the Vulture piece went up alleging all of this,

1092
00:54:18,680 --> 00:54:22,520
Speaker 1: the Ophelia page has disappeared from Real.

1093
00:54:22,520 --> 00:54:26,440
Speaker 2: Tomatoes and waits and they use this to secure distribution.

1094
00:54:27,120 --> 00:54:31,400
Speaker 1: Yeah, so that's that's why there that's there's gonna that.

1095
00:54:31,640 --> 00:54:34,480
Speaker 1: Like that feels like a lawsuit from whoever bought the

1096
00:54:34,480 --> 00:54:39,000
Speaker 1: distribution rights for that movie. Yeah, oh yeah, it's really

1097
00:54:39,080 --> 00:54:42,000
Speaker 1: Oh wait, I don't think that thing made much money.

1098
00:54:42,200 --> 00:54:45,080
Speaker 2: Oh shit whenever, So if you google it, it'll give

1099
00:54:45,120 --> 00:54:47,000
Speaker 2: you out like a result on Google. Then you go

1100
00:54:47,120 --> 00:54:51,479
Speaker 2: and guess what homie is? Four h four baby not here?

1101
00:54:51,680 --> 00:54:53,839
Speaker 2: That the droids you are looking for are not here.

1102
00:54:53,880 --> 00:54:55,279
Speaker 2: These are not the droids you're looking for.

1103
00:54:56,080 --> 00:54:58,600
Speaker 1: That movie ended up making two hundred and forty two

1104
00:54:58,800 --> 00:55:03,080
Speaker 1: thousand dollars, which I think is probably more than anybody

1105
00:55:03,120 --> 00:55:10,359
Speaker 1: paid to for Damn. It's uh, it's not good. But

1106
00:55:10,440 --> 00:55:14,640
Speaker 1: I yeah, like Rotten Tomatoes just has always seemed completely

1107
00:55:14,680 --> 00:55:17,680
Speaker 1: suspect and like arbitrary. You'd go, you'd see like a

1108
00:55:17,719 --> 00:55:20,600
Speaker 1: movie that like doesn't have good reviews and you go

1109
00:55:20,640 --> 00:55:22,680
Speaker 1: on Rotten Tomato and it would be like seventy six

1110
00:55:22,760 --> 00:55:27,200
Speaker 1: percent like fresh. It's like whoa, whoa, what's anybody? We're

1111
00:55:27,239 --> 00:55:30,319
Speaker 1: just we're just taking their word for it. And they've

1112
00:55:30,360 --> 00:55:34,840
Speaker 1: also like there was they delayed the posting of the

1113
00:55:34,920 --> 00:55:38,280
Speaker 1: reviews of Justice League when that came out in twenty seventeen,

1114
00:55:38,760 --> 00:55:41,400
Speaker 1: Like it got real bad reviews, but they like wouldn't

1115
00:55:41,440 --> 00:55:44,319
Speaker 1: post the reviews for a while. They would be people

1116
00:55:44,320 --> 00:55:46,719
Speaker 1: to buy tickets that they wanted to find out the

1117
00:55:46,719 --> 00:55:47,799
Speaker 1: movie was bad. Yeah.

1118
00:55:47,880 --> 00:55:51,120
Speaker 2: One of our earliest episodes was I remember us talking

1119
00:55:51,160 --> 00:55:53,319
Speaker 2: about that and we're like, what they can do that?

1120
00:55:53,400 --> 00:55:55,960
Speaker 3: And then we're like, oh, Warner Brothers owns it.

1121
00:55:55,920 --> 00:55:58,440
Speaker 1: Okay, I guess they was Rotten Tomatoes.

1122
00:55:58,560 --> 00:56:00,640
Speaker 5: It's so wild that, like this country is so like

1123
00:56:00,719 --> 00:56:04,000
Speaker 5: freedom of speech, freedom of speech, and any chance anyone gets, like,

1124
00:56:04,040 --> 00:56:06,719
Speaker 5: but how much does it cost to get you to

1125
00:56:06,840 --> 00:56:07,920
Speaker 5: change that speech?

1126
00:56:08,360 --> 00:56:08,440
Speaker 6: Right?

1127
00:56:08,680 --> 00:56:11,000
Speaker 1: Right? Exactly? How can I buy?

1128
00:56:11,120 --> 00:56:12,000
Speaker 5: Cost of speech?

1129
00:56:12,080 --> 00:56:12,319
Speaker 1: Yeah?

1130
00:56:12,440 --> 00:56:14,840
Speaker 5: Cost of speech? Like god, that's so wild.

1131
00:56:14,880 --> 00:56:16,879
Speaker 3: I mean, you got discounted speech over here if you're

1132
00:56:16,920 --> 00:56:17,560
Speaker 3: looking for it.

1133
00:56:17,680 --> 00:56:20,440
Speaker 5: Shit. It's also like I know that when I use

1134
00:56:20,480 --> 00:56:22,160
Speaker 5: like YELP, because I don't. I don't use wrote Tomatos.

1135
00:56:22,160 --> 00:56:24,040
Speaker 5: I don't give a shit about movies, but when I

1136
00:56:24,120 --> 00:56:27,080
Speaker 5: use Yelp, I'm definitely always like I can't trust you.

1137
00:56:27,160 --> 00:56:29,959
Speaker 5: Like I hate. I hate when like you read something

1138
00:56:30,000 --> 00:56:32,160
Speaker 5: and you're like, this has nothing to do with the food.

1139
00:56:32,440 --> 00:56:35,800
Speaker 2: Right, well, you know right, because there's there's Yelp reviews

1140
00:56:35,800 --> 00:56:38,920
Speaker 2: where you go, oh, you fucked up on your anniversary dinner. Bro,

1141
00:56:39,239 --> 00:56:41,399
Speaker 2: you're taking it out on this restaurant that they didn't

1142
00:56:41,440 --> 00:56:43,480
Speaker 2: have X, Y and Z because it's like I made

1143
00:56:43,680 --> 00:56:46,080
Speaker 2: I made a reservation that day and then when we

1144
00:56:46,120 --> 00:56:46,880
Speaker 2: got there it's.

1145
00:56:46,719 --> 00:56:47,520
Speaker 1: Like hold, no, no, no, no no.

1146
00:56:47,800 --> 00:56:49,840
Speaker 5: My favorite, of course is the I eat here a

1147
00:56:49,840 --> 00:56:52,440
Speaker 5: million times, but today it's like, rich, why didn't you

1148
00:56:52,480 --> 00:56:55,719
Speaker 5: review the other million times when he killed it? That's

1149
00:56:55,760 --> 00:56:59,120
Speaker 5: the actual review I want. But anyways, so I'm curious

1150
00:56:59,160 --> 00:57:01,640
Speaker 5: when you guys use run Tomatoes, like, is there ever

1151
00:57:01,760 --> 00:57:05,600
Speaker 5: like you feel like this person is insincere or this

1152
00:57:05,680 --> 00:57:08,279
Speaker 5: is a suspect, Like can you can you read that

1153
00:57:08,360 --> 00:57:09,839
Speaker 5: the way that you can kind of see it in Yelp.

1154
00:57:10,640 --> 00:57:15,720
Speaker 1: The other thing that's really weird, it's totally subjective whether

1155
00:57:15,760 --> 00:57:19,640
Speaker 1: a thing, whether a review is rotten or fresh, you know,

1156
00:57:20,080 --> 00:57:24,400
Speaker 1: like if they give it like two stars, sometimes they'll

1157
00:57:24,440 --> 00:57:29,439
Speaker 1: put that as rotten as fresh, like it totally. It's yeah,

1158
00:57:29,440 --> 00:57:36,720
Speaker 1: it's really, there's a there's a lot of like shape, consistency, consistency,

1159
00:57:36,880 --> 00:57:39,439
Speaker 1: thank you, which is why if you'll, if you'll check

1160
00:57:39,480 --> 00:57:42,880
Speaker 1: your inbox, I've just sent you a fifteen page slide show.

1161
00:57:44,840 --> 00:57:47,080
Speaker 3: Metacritic is the superior website.

1162
00:57:47,440 --> 00:57:52,280
Speaker 1: Metacritic, the superior website. All right, Marcella, such a pleasure

1163
00:57:52,320 --> 00:57:52,680
Speaker 1: having you.

1164
00:57:55,160 --> 00:58:00,520
Speaker 2: Were so wonderful, kind of bummed out, you go bring

1165
00:58:00,560 --> 00:58:00,840
Speaker 2: it back?

1166
00:58:00,960 --> 00:58:02,680
Speaker 1: Where's that dial of smoke up? Real quick?

1167
00:58:04,800 --> 00:58:06,680
Speaker 5: I'll come back, and I got promote. I guess I'll

1168
00:58:06,680 --> 00:58:08,080
Speaker 5: take this to the show. So I was like, let

1169
00:58:08,080 --> 00:58:10,720
Speaker 5: me be nice, even though I don't like being nice.

1170
00:58:11,080 --> 00:58:14,160
Speaker 2: Nah, I mean if I don't think, I think it's

1171
00:58:14,200 --> 00:58:18,480
Speaker 2: a misnomer to even to categorize you are no miles.

1172
00:58:18,480 --> 00:58:20,720
Speaker 5: We agree on that shit, Yeah, no, we agree. I

1173
00:58:20,720 --> 00:58:23,600
Speaker 5: want to work on a bit about character versus demeanor. Yeah,

1174
00:58:23,640 --> 00:58:26,480
Speaker 5: because people confuse your demeanor with your character and this

1175
00:58:26,600 --> 00:58:29,000
Speaker 5: is not how it should be. But ye know people

1176
00:58:29,040 --> 00:58:31,560
Speaker 5: are ready for all that shit. Yeah, oh my god,

1177
00:58:31,640 --> 00:58:35,520
Speaker 5: she said, please shut the fuck up. She told she

1178
00:58:35,600 --> 00:58:41,920
Speaker 5: stole your husband when she said please bitch so dumb. Anyways,

1179
00:58:42,000 --> 00:58:43,840
Speaker 5: that was my final rant. Thank you so much. You

1180
00:58:43,880 --> 00:58:47,000
Speaker 5: can find me at Marcella Comedy and now reminder, I

1181
00:58:47,120 --> 00:58:49,840
Speaker 5: will be headlining the Hollywood Improv September fifteenth. I'm coming

1182
00:58:49,880 --> 00:58:52,480
Speaker 5: to Chandler, Arizona in September twenty second. I'll be in

1183
00:58:52,560 --> 00:58:54,800
Speaker 5: Portland headlining High Note Comedy on the twenty eighth. I'll

1184
00:58:54,800 --> 00:58:57,280
Speaker 5: also be opening for Michelle Buteau, so if you got tickets,

1185
00:58:57,320 --> 00:58:59,960
Speaker 5: I think there's still one more show with her. Excited

1186
00:59:00,120 --> 00:59:02,160
Speaker 5: about that. I'm also coming to the Santa Cruz Festival

1187
00:59:03,080 --> 00:59:05,680
Speaker 5: in October, and I'm doing Vegas in November, but I

1188
00:59:05,680 --> 00:59:07,840
Speaker 5: think I'll be back to kick it with you.

1189
00:59:08,000 --> 00:59:10,800
Speaker 1: Yeah you guys, tell you, thank you amazing. Do you

1190
00:59:10,880 --> 00:59:12,360
Speaker 1: like doing Vegas? You like doing Vegas?

1191
00:59:12,400 --> 00:59:14,680
Speaker 5: For my first time doing Vegas, oh wow, it's the

1192
00:59:14,720 --> 00:59:16,560
Speaker 5: middle of the week show, So I'm excited about that

1193
00:59:16,600 --> 00:59:19,760
Speaker 5: because I really feel like the weekend crowd is fucking

1194
00:59:19,920 --> 00:59:20,600
Speaker 5: too insane.

1195
00:59:20,880 --> 00:59:25,360
Speaker 1: Yeah bonkers, people who've already like lost their mortgage.

1196
00:59:25,400 --> 00:59:26,840
Speaker 5: Oh my god. I mean you're gonna have that any

1197
00:59:26,880 --> 00:59:28,960
Speaker 5: day of the week, but on the weekend there are

1198
00:59:28,960 --> 00:59:32,320
Speaker 5: like people coming to have a week and to really lose.

1199
00:59:32,080 --> 00:59:35,280
Speaker 2: Big exactly somebody who like already legally died at a

1200
00:59:35,320 --> 00:59:38,440
Speaker 2: hospital and then came back drunk for like your say.

1201
00:59:38,640 --> 00:59:40,080
Speaker 5: Yeah, during the week, you got all the people that

1202
00:59:40,160 --> 00:59:43,160
Speaker 5: have like their little vouchers and their discounted rooms and

1203
00:59:43,480 --> 00:59:46,840
Speaker 5: the pros the pros that I am excited about that,

1204
00:59:46,880 --> 00:59:50,280
Speaker 5: but I don't know how that's gonna be. I've never

1205
00:59:50,320 --> 00:59:50,960
Speaker 5: done Vegas.

1206
00:59:51,400 --> 00:59:52,040
Speaker 1: I'm excited.

1207
00:59:52,560 --> 00:59:56,360
Speaker 5: Piece of media I'm enjoying is my friend Coreyla Kasik

1208
00:59:56,480 --> 00:59:59,960
Speaker 5: has this. Uh, you guys would appreciate this. I have

1209
01:00:00,120 --> 01:00:01,400
Speaker 5: written down in the room and I had to come

1210
01:00:01,440 --> 01:00:05,120
Speaker 5: down here. But I think it's like Molly bro on Instagram.

1211
01:00:05,440 --> 01:00:07,560
Speaker 5: He's going viral right now because he was at Burning

1212
01:00:07,600 --> 01:00:10,439
Speaker 5: Man and so he just is like doing like a Hey, guys,

1213
01:00:10,480 --> 01:00:12,040
Speaker 5: do you need a spond If you want to sponsor

1214
01:00:12,160 --> 01:00:15,040
Speaker 5: us right here stranded, just one dollar, we'll get us.

1215
01:00:15,280 --> 01:00:18,760
Speaker 5: If a million of you donate, one dollar can get us.

1216
01:00:18,880 --> 01:00:23,760
Speaker 5: Hella Molly. It's just like the funniest, stupidest shit. He's

1217
01:00:23,800 --> 01:00:25,320
Speaker 5: part of a crew called the Dress Up Gang, which

1218
01:00:25,320 --> 01:00:28,160
Speaker 5: Frankieknionaz is also in which you guys, ah yes from

1219
01:00:28,560 --> 01:00:32,680
Speaker 5: Fit but they it's just such a funny character. His

1220
01:00:32,800 --> 01:00:35,960
Speaker 5: name is Molly bro on Instagram or fuck I wish

1221
01:00:36,000 --> 01:00:37,160
Speaker 5: I think I have it wrong. Let me see if

1222
01:00:37,200 --> 01:00:39,760
Speaker 5: I can't pull it up. Official Molly bro is his

1223
01:00:39,880 --> 01:00:46,800
Speaker 5: Instagram handle and he has really silly Molly content and

1224
01:00:46,840 --> 01:00:49,160
Speaker 5: it's actually very funny. I don't do Molly, but I

1225
01:00:49,680 --> 01:00:51,280
Speaker 5: you know, I can appreciate a good character.

1226
01:00:51,600 --> 01:00:56,320
Speaker 1: Yeah, why not? And Miles, where can people find you?

1227
01:00:56,440 --> 01:00:58,240
Speaker 1: Is there a working media you've been in drunk? Yeah?

1228
01:00:58,320 --> 01:01:01,760
Speaker 2: Find me on Twitter, instaut Graham, all them at based

1229
01:01:02,000 --> 01:01:04,480
Speaker 2: life forums, at Miles of Gray. You know where to

1230
01:01:04,480 --> 01:01:06,640
Speaker 2: find us with our basketball takes. That's Miles and Jack

1231
01:01:06,680 --> 01:01:10,040
Speaker 2: got mad boosties. You like ninety Day Fiance, Check Sophia,

1232
01:01:10,080 --> 01:01:12,600
Speaker 2: Alexander and I out on four to twenty day Fiance

1233
01:01:13,080 --> 01:01:16,160
Speaker 2: and True The True Crime Show. Check it out, The

1234
01:01:16,200 --> 01:01:19,880
Speaker 2: Good Thief please check it out. And let's see tweet

1235
01:01:19,920 --> 01:01:23,760
Speaker 2: I like from Brittany Nichols at Bee is hilarious tweeted

1236
01:01:23,960 --> 01:01:24,960
Speaker 2: strike knees to end.

1237
01:01:25,440 --> 01:01:27,919
Speaker 3: You know how many birds I can name? Now it's

1238
01:01:28,000 --> 01:01:28,600
Speaker 3: too much?

1239
01:01:29,640 --> 01:01:33,160
Speaker 2: And if that's the metric, using like, oh no, I've

1240
01:01:33,160 --> 01:01:36,040
Speaker 2: become a bird watching fool. And now I can I'm

1241
01:01:36,080 --> 01:01:39,160
Speaker 2: having arguments over what kind of hawks there are? I

1242
01:01:39,240 --> 01:01:40,840
Speaker 2: get it, maybe outside a little too.

1243
01:01:41,040 --> 01:01:45,400
Speaker 5: Wait, I always saw her at was bish hilarious. Wow,

1244
01:01:45,400 --> 01:01:46,760
Speaker 5: I've been reading it wrong this whole time.

1245
01:01:47,240 --> 01:01:48,960
Speaker 2: I mean, I think I feel like it could be

1246
01:01:49,000 --> 01:01:51,000
Speaker 2: both because I used to read it like that too,

1247
01:01:51,040 --> 01:01:52,000
Speaker 2: and then I was like, well.

1248
01:01:51,880 --> 01:01:54,760
Speaker 3: This bee is but it's also Look it's it's up

1249
01:01:54,800 --> 01:01:55,280
Speaker 3: to the reader.

1250
01:01:55,560 --> 01:01:56,800
Speaker 5: She's a great she's a great writer.

1251
01:01:56,840 --> 01:01:59,320
Speaker 3: I think we can Yeah, oh, we can all agree

1252
01:01:59,360 --> 01:01:59,600
Speaker 3: on that.

1253
01:02:00,040 --> 01:02:02,440
Speaker 5: Where can people find you? What enjoying?

1254
01:02:02,720 --> 01:02:06,680
Speaker 1: Asking? You can find me a Jack underscore Olbrian. Uh.

1255
01:02:07,120 --> 01:02:11,840
Speaker 1: Tweet I'm enjoying is from Brian Jordan Alvrez, who tweeted

1256
01:02:12,400 --> 01:02:19,680
Speaker 1: Courtyard Marriott Marion Cotty Yard really makes you think, and

1257
01:02:19,800 --> 01:02:23,040
Speaker 1: it did all night. Couldn't stop thinking about it.

1258
01:02:24,560 --> 01:02:27,560
Speaker 5: Sorry, you guys should be saying an X that I like,

1259
01:02:27,680 --> 01:02:28,680
Speaker 5: not a tweet that I like.

1260
01:02:28,800 --> 01:02:31,120
Speaker 3: Fuck that absolutely.

1261
01:02:32,280 --> 01:02:39,520
Speaker 1: The diok I will not formerly known as Twitter you

1262
01:02:40,040 --> 01:02:42,720
Speaker 1: every publication in America.

1263
01:02:43,240 --> 01:02:44,800
Speaker 5: I'm sorry, Jack. Where people can find you?

1264
01:02:45,520 --> 01:02:47,760
Speaker 1: Uh? You can find me a Jack underscore? Brian on

1265
01:02:47,840 --> 01:02:53,280
Speaker 1: Twitter Twitter? Find us on Twitter at Daily Zei Geist

1266
01:02:53,520 --> 01:02:56,280
Speaker 1: d D Daily Zay Geist on Instagram We have Facebook fan

1267
01:02:56,360 --> 01:02:58,720
Speaker 1: page and a website daily zeikeuis dot com. We post

1268
01:02:58,800 --> 01:03:01,720
Speaker 1: our episodes on our foot no good notes where we

1269
01:03:01,760 --> 01:03:04,080
Speaker 1: link off the information that we talked about today's episode

1270
01:03:04,120 --> 01:03:07,360
Speaker 1: as well as a song that we think you might enjoy. Miles,

1271
01:03:07,400 --> 01:03:09,520
Speaker 1: what is the song that you think people might enjoy?

1272
01:03:09,680 --> 01:03:12,280
Speaker 2: I think we're gonna go out on a little bit

1273
01:03:12,320 --> 01:03:15,680
Speaker 2: of some UK rap here some drill from quang Face

1274
01:03:15,880 --> 01:03:21,000
Speaker 2: k W E n G Face. This is the remix

1275
01:03:21,160 --> 01:03:25,040
Speaker 2: to the track Freedom. It's called Freedom Too with Joy

1276
01:03:25,480 --> 01:03:28,840
Speaker 2: Orbison and over Mono. I think are the beat makers

1277
01:03:28,680 --> 01:03:31,120
Speaker 2: that flipped the beat. But anyway, Freedom with the number

1278
01:03:31,120 --> 01:03:32,880
Speaker 2: two by quang Face.

1279
01:03:33,360 --> 01:03:35,720
Speaker 1: Get into it? Do it all right? Well, we'll link

1280
01:03:35,760 --> 01:03:37,600
Speaker 1: off to that in the footnotes of the Daily is

1281
01:03:37,600 --> 01:03:40,040
Speaker 1: the production of iHeartRadio. For more podcasts. My Heart Radio

1282
01:03:40,160 --> 01:03:42,480
Speaker 1: is the iHeartRadio Wrap, Apple podcast or wherever you listen

1283
01:03:42,480 --> 01:03:44,640
Speaker 1: to your favorite shows. That is going to do it

1284
01:03:44,720 --> 01:03:48,600
Speaker 1: for us. This morning. We are back on Monday with

1285
01:03:48,720 --> 01:03:51,080
Speaker 1: an episode where Miles and I are gonna tell you

1286
01:03:51,120 --> 01:03:52,040
Speaker 1: what was trending.

1287
01:03:51,760 --> 01:03:52,240
Speaker 3: Over the weekend.

1288
01:03:52,320 --> 01:03:55,680
Speaker 1: We'll tell you stuff we think is overrated underrated Monday

1289
01:03:56,120 --> 01:03:59,480
Speaker 1: like early afternoon ish and then Tuesday with a special

1290
01:03:59,560 --> 01:04:02,080
Speaker 1: episode Thode and we'll talk to you all then have

1291
01:04:02,120 --> 01:04:03,840
Speaker 1: a great oh and there there will be a highlight

1292
01:04:03,920 --> 01:04:07,040
Speaker 1: reel of the best of this week over the weekend

1293
01:04:07,400 --> 01:04:11,720
Speaker 1: the weekly like guys, so much ship for you guys

1294
01:04:11,720 --> 01:04:12,440
Speaker 1: to pay attention to.

1295
01:04:12,560 --> 01:04:15,000
Speaker 5: Did you guys do like a v H one countdown style.

1296
01:04:15,920 --> 01:04:20,320
Speaker 1: Of our greatest hits of number five this week number.

1297
01:04:21,280 --> 01:04:23,160
Speaker 5: Brought to you by Coca Cola Classic.

1298
01:04:23,560 --> 01:04:27,040
Speaker 1: That's right, that's what we're leaving money on the table

1299
01:04:27,040 --> 01:04:32,360
Speaker 1: by Yeah. Any right, we'll talk to y'all on Monday. Bye,

1300
01:04:32,800 --> 01:04:33,360
Speaker 1: thank you,