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Speaker 1: Hey, guys, ready or not, twenty twenty four is here

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Speaker 1: and we here at Breaking Points, are already thinking of

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Speaker 1: ways we can up our game for this critical election.

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Speaker 2: We rely on our premium subs to expand coverage, upgrade

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Speaker 2: the studio ad staff give you, guys, the best independent coverage.

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Speaker 3: That is possible.

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Speaker 2: If you like what we're all about, it just means

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Speaker 2: the absolute world to have your support.

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Speaker 3: But enough with that, let's get to the show.

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Speaker 2: Good morning, everybody, Welcome to Spooky Points with Crystal and Sager.

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Speaker 2: I am the hidden Indian passenger of the Ghost of

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Speaker 2: the Titanic. Sager and Jenny and I have a co

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Speaker 2: host today here from Australia.

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Speaker 4: What up, everybody.

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Speaker 1: Australian break dancing Phenom.

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Speaker 4: Raygun right here, right here.

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Speaker 1: That's right, took the world by storm with my iconic

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Speaker 1: moves and I thought I would join everybody this morning

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Speaker 1: to show you a few of those.

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Speaker 4: You ready, Are you ready?

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Speaker 3: Absolutely?

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Speaker 4: Here we go, world, Here we go, Here we go.

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Speaker 4: What up, everybody? That's right? I got a little sprinkler

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Speaker 4: for you. I got a little sprinkler for you. I

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Speaker 4: got a little kangaroo hot for you. I got a

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Speaker 4: little Kangaroo Hot for you ready for this one?

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Speaker 3: Whoa?

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Speaker 1: You were not ready for that? You were not ready

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Speaker 1: for that? How about this one?

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Speaker 4: You ready? You ready watch this?

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Speaker 1: That's right, that's right, that's right, that's right.

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Speaker 4: You know what.

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Speaker 1: You know what, I got another one for you. Bam,

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Speaker 1: that's right, that's right. Give me the gold medal right now?

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Speaker 3: Well, I certainly didn't have that at my time at

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Speaker 3: the Olympics.

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Speaker 4: I mean, she's a superstar, right. I had to go

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Speaker 4: with Raygun because.

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Speaker 1: She is such an inspiration that literally anyone could make

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Speaker 1: it to the Olympics.

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Speaker 3: You're absolutely right. For my mind is much less complicated.

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Speaker 2: I got this for my wedding, and I just said,

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Speaker 2: the only way I can justify this expense is if

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Speaker 2: I wear it for Halloween every single day.

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Speaker 3: So there we go. That was fun. I enjoyed it.

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Speaker 4: Tents around here, so we wanted the lights mood.

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Speaker 3: You've got a light in the mood. So what has

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Speaker 3: even in the show?

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Speaker 1: I've been too Genuinely, I was a little too focused

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Speaker 1: on my Kangaroo hops to really think it through.

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Speaker 4: But I know we got polls.

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Speaker 1: We got Injermentum coming in to talk to us about

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Speaker 1: why he thinks the polls could be under estimating Kamala.

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Speaker 1: He was right about things in the past that we'll

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Speaker 1: talk to him about.

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Speaker 4: That Trump was in a garbage truck.

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Speaker 3: Trump's in a garbage truck.

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Speaker 1: Elon is threatening a severe depression if Trump gets elected,

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Speaker 1: as if that's like a positive thing. We got Bill

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Speaker 1: Clinton running around really not being helpful in Michigan with

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Speaker 1: air of American voters. We got to stop the steal nonsense.

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Speaker 1: So anyway, happy Halloween, everybody, hope happen very well.

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Speaker 2: I hope that we can raise your spirit, inspire the holidays.

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Speaker 2: It's you know, it doesn't always have to be tense

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Speaker 2: and can we can still have a good time even

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Speaker 2: what is it five.

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Speaker 3: Days before the election a something like that. Yeah, it's

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Speaker 3: five days before So there we go. We're excited.

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Speaker 2: Thank you to all of our premusubscribers breakingpoints dot com.

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Speaker 3: You can go ahead and sign up there.

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Speaker 2: I guess we should have done to give away like

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Speaker 2: a first reveal right in terms of our Yeah, that

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Speaker 2: would have been a smart business level, but instead we're

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Speaker 2: just over here having fun. So to make up for it,

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Speaker 2: go ahead and sign up Breaking Points. We've got a

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Speaker 2: great election night show planned for everybody. We're genuinely, really

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Speaker 2: really excited for it. So that's right, all right, let's

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Speaker 2: go ahead and start.

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Speaker 4: Let's do it.

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Speaker 1: Actually, one of our premium subscribers sort of inspired this

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Speaker 1: whole month.

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Speaker 4: That's true that we got a question Ama about like, hey,

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Speaker 4: will you guys dress up?

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Speaker 3: Yeah, and I was she didn't want to do it.

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Speaker 1: I don't know because if there's some I mean, we

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Speaker 1: are coming Israel in the show, so it's not like

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Speaker 1: there's not serious things in the potential sever your depression.

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Speaker 1: So it's not like there aren't serious things in the show,

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Speaker 1: and so you can feel sort of like, I mean,

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Speaker 1: I do feel sort of ridiculous right at this moment, but.

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Speaker 3: What the hell?

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Speaker 2: I felt most ridiculous in what the heck because the

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Speaker 2: guy looked over at me and started giggling.

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Speaker 4: Yeah.

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Speaker 1: When I was just walking over here in the street,

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Speaker 1: I was like, Oh, this is.

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Speaker 2: Embarrassing, conceivably something you could ever wear Civilion.

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Speaker 4: That's yeah. Actually that's actually what makes.

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Speaker 1: It more embarrassing, Yeah, because you, oh, people think I'm

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Speaker 1: actually just wearing them, like for real, just wearing them.

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Speaker 4: So anyway, we all here.

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Speaker 3: We are our spirit here here we are point.

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Speaker 1: All right, let's get to what is going on in

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Speaker 1: the world. Start with some polling.

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Speaker 3: Here.

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Speaker 1: We had a little snippet from Harry Enton over at

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Speaker 1: CNN talking about how if Trump does ultimately win, many

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Speaker 1: signs were there indicating that that could possibly be the case.

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Speaker 5: Let's take a listen to that percentage of the public

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Speaker 5: that thinks that the country is on the right track

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Speaker 5: when the incumbent party loses. It's twenty five percent. That

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Speaker 5: twenty five percent looks an awful bit luck like that

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Speaker 5: twenty eight percent up there, It doesn't look anything anything

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Speaker 5: like this. Forty two percent doesn't look anything like this

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Speaker 5: twenty eight percent. So the bottom line is very few

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Speaker 5: Americans think the country is on the right track at

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Speaker 5: this particular point. It tracks much more with when the

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Speaker 5: incumbent party loses than when it wins. In fact, I

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Speaker 5: went back through history. There isn't a single time in

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Speaker 5: which twenty eight percent of the American public thinks the

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Speaker 5: country is going on the right track in which the

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Speaker 5: incumbent party actually won. They always lose when just twenty

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Speaker 5: eight percent of the country believes that the country is

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Speaker 5: on the right track. Now, we don't know if Kamala

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Speaker 5: Harris is going to succeed Joe Biden. But we know

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Speaker 5: back in two thousand and eight George W. Bush, his

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Speaker 5: approval rating was down in the twenties. Did a Republican

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Speaker 5: succeed George W.

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Speaker 4: Bush?

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Speaker 6: No?

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Speaker 5: How about in nineteen sixty eight Lyndon Baines Johnson, his

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Speaker 5: net approval rating was negative. Did a Democrats succeed Lyndon

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Speaker 5: Baines Johnson?

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Speaker 7: No?

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Speaker 5: How about in fifty two Harry S. Truman. His approval

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Speaker 5: rating was in the twenties, if not the upper teens.

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Speaker 5: Did a Democrat succeed Harry AS Truman in fifty two?

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Speaker 5: My memory? No? No is Kate Balwins says Diana. Diana's

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Speaker 5: Republicans have been registered voters in big, huge numbers. They

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Speaker 5: have been gaining in party registration versus the Democrats in

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Speaker 5: the swing states with party registration. We're talking Arizona. I

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Speaker 5: think it's a five point they've expanded their lead from

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Speaker 5: five points from where it was back in twenty twenty.

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Speaker 5: How about Nevada, big Republican registrations there, they liked the

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Speaker 5: early vote. How about North Carolina, big Republican registration games.

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Speaker 5: How about Pennsylvania. We spoke about it before a few

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Speaker 5: months ago, big Republican Party registration gains versus from where

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Speaker 5: they were.

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Speaker 1: So we're definitely at the point in the election saga

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Speaker 1: where there's something for everyone. Absolutely, if you want to

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Speaker 1: look at those historical trends, totally legitimate wrong track numbers

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Speaker 1: are horrific for Democrats. You've got a very unpopular Democratic incomment.

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Speaker 1: And to be honest with you, I do think if

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Speaker 1: Kama will lose this as much as these factors that

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Speaker 1: we talk about in terms of their you know, totally

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Speaker 1: losing the air of American Muslim American vote, turning off

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Speaker 1: young voters, like running around with Liz Cheney, all of

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Speaker 1: these things are going to matter. But I honestly do

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Speaker 1: think that the unpopularity of Joe Biden is the biggest

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Speaker 1: weight around her neck.

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Speaker 2: Yeah, sometimes the fundamentals are the most important, and we've

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Speaker 2: been looking at global trends. You've seen post incumbent parties.

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Speaker 2: Mattic Lacius recently wrote about this. Nate Silver also identified

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Speaker 2: the trend. But they've lost elections across the entire globe.

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Speaker 2: So sometimes that is just a precursor and a signal

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Speaker 2: of what is to come now, as you said, there

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Speaker 2: are signs for everybody. Let's put this up there on

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Speaker 2: the screen, please, Well you can see in this very

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Speaker 2: large pole sample we talked a little bit about this

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Speaker 2: last time around, some seventy thousand American adults. So that's

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Speaker 2: why you should pay attention. This is national likely voters,

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Speaker 2: and they have Arizona. I'm gonna read each state. Arizona

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Speaker 2: they have Trump at fifty one forty seven, Georgia fifty

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Speaker 2: one forty six for Trump. Now in Michigan it actually

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Speaker 2: is flipped. They have Harris fifty one Trump, forty six. Nevada,

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Speaker 2: this was the biggest surprise for me. Harris fifty one

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Speaker 2: Trump forty seven, North Carolina fifty forty eight for Trump,

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Speaker 2: Pennsylvania Razor tied forty nine, forty eight for Kamala Harris,

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Speaker 2: Texas fifty one to forty seven, and then Wisconsin at

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Speaker 2: fifty and forty seven. So clearly what you can see

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Speaker 2: is a slight edge for Kamala Harris in the Blue

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Speaker 2: Wall States, with the Sun Belt states really moving away

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Speaker 2: from her, which would put her around a two seventy

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Speaker 2: two sixty eight. In fact, though Nate Silver's new analysis

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Speaker 2: has North Carolina as the most likely tipping point state

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Speaker 2: at number two. If you do see a shock on

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Speaker 2: election night, it could certainly come from there, And that'd

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Speaker 2: be a very interesting map, It really would where we

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Speaker 2: could see if Kamala was able to flip North Carolina

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Speaker 2: but then lose all of the Sunbell states, you would

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Speaker 2: have a full on, quote unquote realignment phenomenon where you

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Speaker 2: just have this coalition of white, suburban, college educated voters

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Speaker 2: who deliver the presidency to her across these four states,

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Speaker 2: even though Trump is able to run up his margins

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Speaker 2: in all of the Sunbelt and particularly with black and

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Speaker 2: Latino men.

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Speaker 1: Yeah, Democrats like the gender divide that we've seen in

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Speaker 1: the early voting results, and I've even seen some Republicans

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Speaker 1: like Cernovich and Charlie Kirk now saying that they're concerned

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Speaker 1: about that. Now they may just be saying that to

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Speaker 1: try to make sure they're re juicing their voters and

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Speaker 1: not giving them a sense of overconfidence, because overconfidence is

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Speaker 1: like all that the Trump campaign has been selling. So

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Speaker 1: I don't know how genuinely concerned they are, But North

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Speaker 1: Carolina is one of those states where there's been a

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Speaker 1: significant gender gap in terms of turnout where there are

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Speaker 1: significantly more women turning out in the early vote than men. Now,

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Speaker 1: early vote, you really shouldn't make that much of it,

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Speaker 1: because who knows who's going to sho go up on

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Speaker 1: election day and that's all that counts. Like voting early

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Speaker 1: versus voting on election day, your vote still only counts once.

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Speaker 1: But if you're looking to try to read the tea

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Speaker 1: leaves about who's excited, who's turning out their coalition, et cetera,

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Speaker 1: that's a sign that's positive in favor of Democrats that

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Speaker 1: at least some Republicans are voicing some concern about, Yeah, I.

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Speaker 2: Have the early vote female number actually right in front

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Speaker 2: of me. So in Georgia the early vote female share

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Speaker 2: was fifty six, and twenty twenty it's fifty five percent today.

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Speaker 3: In Pennsylvania, this is where it's the most interesting.

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Speaker 2: In twenty twenty it was fifty seven, twenty twenty four,

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Speaker 2: it's fifty six. Arizona it was twenty twenty fifty four,

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Speaker 2: twenty twenty four fifty two percent. So you have a

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Speaker 2: majority female early vote all across the board. There's been

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Speaker 2: some talk of this whole so co called like cannibalizing

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Speaker 2: vote phenomenon. But I'm just not sure how much to

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Speaker 2: buy that. It's very clear to me that the country

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Speaker 2: just loves early voting.

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Speaker 3: Now.

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Speaker 2: I mean, you have almost fifty million ballots that have

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Speaker 2: been cast across I mean, that's almost one third of

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Speaker 2: the entire electorate. You'll have even more in the coming days,

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Speaker 2: and then obviously on election day itself, probably what'll be

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Speaker 2: going to see maybe not record turnout, but it will

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Speaker 2: not be the same as it was previously. So in Nevada,

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Speaker 2: I believe some sixty percent of the overall vote has

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Speaker 2: already been cast projected vote, so you can really start

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Speaker 2: to play with what you want for what the results are,

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Speaker 2: which is what John Rawlston has been looking at. But

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Speaker 2: there are signs for anybody who wants the tea leaves.

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Speaker 2: And I just think the main takeaway is you can't

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Speaker 2: be shocked at any outcomings.

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Speaker 1: Yeah, No, that's absolutely right, including like a significant blowout

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Speaker 1: for either side. Absolutely is in some ways almost more

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Speaker 1: likely than the you know, razor thin like they win

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Speaker 1: to seventy to do sixty eight or whatever. It's probably

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Speaker 1: more likely that all or most of the battleground states

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Speaker 1: go in one or the other direction. If you assume

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Speaker 1: that there's like let's say it's the you know, twenty

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Speaker 1: sixteen or twenty twenty polling this, then you end up

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Speaker 1: with Trump sweeping all of the battleground states. If it's

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Speaker 1: the twenty twenty two polling this, then you end up

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Speaker 1: with comelessly sweeping all but one of the battle ground states.

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Speaker 1: So who knows what you're really looking at. Speaking of Pennsylvania,

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Speaker 1: could put this up on the screen. This is the

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Speaker 1: latest from momth I surprise, they've got it really close.

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Speaker 4: I was sorry. This is the ann pole.

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Speaker 3: This is actually interesting because it's not tough.

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Speaker 1: It's not tied like so many of these polsters. I

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Speaker 1: really do buy into the view that many of these

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Speaker 1: polsters are very nervous about getting it blatantly wrong, and

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Speaker 1: so they're whether intentionally or not, somewhat putting their thumb

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Speaker 1: on the scales to make sure that the race is close.

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Speaker 1: And so so many of the poles are forty eight,

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Speaker 1: forty seven, forty nine, forty nine, et cetera. CNN put

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Speaker 1: on a pole that shows, you know, some significant separation

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Speaker 1: between Harris and Trump. You've got in Michigan, Kamala up

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Speaker 1: forty eight forty three. In Wisconsin, Kamala up fifty one

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Speaker 1: forty five. That's one of the states that has seen

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Speaker 1: to and some of the polling averages move against her

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Speaker 1: a bit, so for them to find her up by

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Speaker 1: six year outside of the margin of error is significant.

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Speaker 1: But then you've got Pennsylvania tied forty eight forty eight,

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Speaker 1: and obviously she's.

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Speaker 4: Going to take that blue wall path.

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Speaker 1: She has to sweep all of those states, you know,

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Speaker 1: assuming she doesn't win the sun melt states, which we

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Speaker 1: don't really know.

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Speaker 4: Those are all so are very close.

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Speaker 1: But these are the states that she's been performing the

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Speaker 1: best in buy and large, and they've got Pennsylvania tied

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Speaker 1: so incredibly close.

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Speaker 3: Oh my gosh.

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Speaker 2: Yeah, especially I mean even with Pa and that tie figure.

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Speaker 2: I'm just getting really sick of looking at it just

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Speaker 2: because there is no indication. I mean, look, last time around,

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Speaker 2: obviously everything's been within one point of each other with

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Speaker 2: Joe Biden. Same thing in twenty sixteen when Trump eked

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Speaker 2: out a win over there. Mamath gives us some similar

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Speaker 2: stuff to look at. Let's put this up there on

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Speaker 2: the screen. This is specifically the Pennsylvania voter poll So

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Speaker 2: what they find here is presidential support, and this is

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Speaker 2: included with third party Harris and Trump. So among registered

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Speaker 2: voters it's forty six forty seven. Amongst quote extremely motivated voters,

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Speaker 2: they have it as a tie. Amongst twenty twenty voters,

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Speaker 2: they actually have Trump up by one percent. Amongst twenty

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Speaker 2: twenty two, they have Kamala up by two percent. And

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Speaker 2: I don't know exactly what this means high moderate propensity voters.

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Speaker 2: Whatever the hell that is forty eight forty seven for commas.

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Speaker 2: I mean, basically, it's like in that tie tie in

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Speaker 2: all of this. I mean, and again, you know, when

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Speaker 2: you go early vote diving, you can find whatever you want.

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Speaker 2: You can terms look at the female vote, you can

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Speaker 2: you can look at rural versus urban. But I think

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Speaker 2: one of the main reasons why comparing it to twenty

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Speaker 2: twenty is just a real crapshoot is that Republicans have

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Speaker 2: dramatically changed their tune on early voting. They have dramatically

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Speaker 2: changed the way that they themselves are even looking and

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Speaker 2: about turnout and all of that. So, I mean, just

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Speaker 2: for example, this morning, I was looking at Georgia. In Georgia,

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Speaker 2: the highest percentage of early vote totals is in the

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Speaker 2: rural counties, it's not in Fulton, and in the suburban counties.

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Speaker 2: It's a lot of elderly whites who live in rural

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Speaker 2: districts who have been taking advantage of early vote because

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Speaker 2: that's what they were told to do by the Trump campaign.

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Speaker 2: Now that really much could put him over the edge,

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Speaker 2: considering that they lost by such a razor thin margin

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Speaker 2: back in twenty twenty, so it could very be a

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Speaker 2: smart strategy. But that's exactly my point is you could

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Speaker 2: still turn out quite close and even have some sort

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Speaker 2: of democratic surge with early voting or even late later

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Speaker 2: early voting and or election day voting. That really comes home.

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Speaker 2: That's a big question too about the gender gap. When

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Speaker 2: you've got majority female of the electorate in the early vote,

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Speaker 2: does that mean that men are going to wait until

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Speaker 2: day of? That is problematic just for in general, because

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Speaker 2: a you want to bank votes, but stuff happens sometimes

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Speaker 2: on election day, well, line is long, whatever, people don't

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Speaker 2: want to do it and they just died to go home.

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Speaker 2: So you know, all of these things certainly matter on

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Speaker 2: the margins.

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Speaker 4: Yeah.

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Speaker 1: Ryan made a point on Twitter yesterday that it's funny,

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Speaker 1: but also like not without any not without no backing.

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Speaker 1: He said, I love my dudes. But if there's a

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Speaker 1: nationwide contest between men and women that goes to the

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Speaker 1: group that manages to show up at the right place

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Speaker 1: and fill out their forms on time and do all

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Speaker 1: those things, the men would need to start with an

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Speaker 1: advantage of several millions to be competitive. So that's Ryan

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Speaker 1: Grimm's take on the gender battle here. But yeah, I mean,

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Speaker 1: I think that when we look at all these states

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Speaker 1: in the early vote in particular number one, you have

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Speaker 1: to keep in mind that in every single state the

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Speaker 1: rules are completely different. Some states have early in person.

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Speaker 1: Some states don't have early in person, some states have.

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Speaker 1: Pennsylvania sort of has early in person. But then it's

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Speaker 1: this weird like you have to go and pick up

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Speaker 1: an absentee ballot and then like come back.

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Speaker 2: And it's not like in person at the same time, right,

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Speaker 2: get it and then give it back.

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Speaker 1: It's not the same as like you just wait in

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Speaker 1: line and like do the thing like you would do

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Speaker 1: on election day. Like in Virginia, I already voted, And

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Speaker 1: that's how it works. You go in like it's election day,

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Speaker 1: and you you know, put in your in the machine,

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Speaker 1: and that's that's very simple. It's very simple and easy.

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Speaker 1: Pennsylvania is apparently more cumbersome. Some states have more of

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Speaker 1: a tradition of mail in balloting. A lot of the

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Speaker 1: Western states seem to have more of a tradition of

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Speaker 1: mail in balloting, even pre dating the pandemic. And then

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Speaker 1: you do still have some partisan differences in people's voting preferences.

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Speaker 1: So even though Republicans have been consistently urging early vote,

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Speaker 1: the emphasis has been on early in person. So for example,

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Speaker 1: in Nevada, you know, which is a state that looks

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Speaker 1: the best for Republicans in terms of the early vote.

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Speaker 1: The preponderance of the Republican vote is that early in person,

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Speaker 1: where Democrats continue to have somewhat of an edge in

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Speaker 1: the mail in balloting, and.

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Speaker 4: That seems to be pretty consistent.

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Speaker 1: You've got Elon Musk and you've got Trump still out

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Speaker 1: there swing like you know, concern about mail in ballots.

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Speaker 1: So that's been the preference for a lot of Republican voters.

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Speaker 1: And then on the Democratic side, I mean remember twenty twenty,

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Speaker 1: that was a pandemic, So there were people who turned

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Speaker 1: in a mail in ballot then, who normally are in

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Speaker 1: person voters who are reverting to back to what their

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Speaker 1: previous habits are because they're no longer fearful of being

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Speaker 1: around other human beings. So you would expect to have

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Speaker 1: some erosion in terms of the Democratic early vote.

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Speaker 4: That's why it.

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Speaker 1: Really is apples to oranges in terms of comparing the

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Speaker 1: twenty twenty four early vote numbers to the twenty twenty numbers.

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Speaker 1: So anyway, that's that's what we can say about it.

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Speaker 1: I think we have one more like tide pole that

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Speaker 1: we could show everybody for what it's worth. This is

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Speaker 1: a Marquette Law pole of likely Wisconsin voters. This is

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Speaker 1: a five and put this up on the screen, Harris

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Speaker 1: a fifty Trump at forty nine within the margin of error,

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Speaker 1: so effectively tied. And you also show here a very

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Speaker 1: close Wisconsin Senate race, which there have been some indications

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Speaker 1: that some the Senate races previously the Democrats outside of

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Speaker 1: Montana have been pretty consistently, you know, with a wide margin.

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Speaker 1: Wisconsin is one. In Pennsylvania is another where that gap

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Speaker 1: seems to have been closing in the final days. So

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Speaker 1: we'll see if that ultimately pans out.

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Speaker 4: I've also seen.

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Speaker 1: Some more positive indicators for John Tester in the state

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Speaker 1: of Montana. I've seen a couple polls that have him

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Speaker 1: tied with his opponent she he so perhaps giving Democrats

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Speaker 1: some bit of hope that they may actually be able

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Speaker 1: to hold I.

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Speaker 3: Saw that too.

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Speaker 2: Yeah, I mean, look that the Senate ones there's actually

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Speaker 2: not nearly enough high quality polling. I Montana in particular,

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Speaker 2: right very notoriously difficult pull a state poll. They also

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Speaker 2: usually don't do it at a state level or at

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Speaker 2: a national media level because they mostly leave it to

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Speaker 2: the campaigns. And then in terms of Ohio, I've seen

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Speaker 2: similar stuff where I've seen Shared Brown up by like

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Speaker 2: seven points, and then I've also seen it completely within

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Speaker 2: the margin of error, and then Bernie Morino's campaign cleaning

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Speaker 2: that he is ahead in their internal polls which they released.

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Speaker 2: So it's one of those where truly choose your own adventure.

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Speaker 2: We have one more flavor of a bunch of tied

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Speaker 2: states new Fox News battleground polls.

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Speaker 3: Can we put that one up there? Please? What do

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Speaker 3: we have?

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Speaker 2: Pennsylvania fifty forty nine for Trump, Michigan forty nine, forty nine,

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Speaker 2: North Carolina fifty forty nine. So the only one that's

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Speaker 2: actually interesting to me there is North Carolina because that

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Speaker 2: it was the closest one where they had Trump only

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Speaker 2: up by one. We're previously up by almost two to three,

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Speaker 2: and every other one that I looked at, considering that

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Speaker 2: Joe Biden only or lost it by about a pot

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Speaker 2: or point five percent last time, that means that Harris

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Speaker 2: very much could be put over the edge in some

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Speaker 2: sort of shock victory. Like I said, Nate Silver's new

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Speaker 2: election model now has North Carolina has the number two

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Speaker 2: tipping point state behind penncyl. In some ways, this is

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Speaker 2: not bad for those who are going to watch with us,

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Speaker 2: and because we're on East coast time, because we will

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Speaker 2: get the returns in pretty soon from Georgia, from North

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Speaker 2: Carolina and Pennsylvania, and we won't have to wait forever

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Speaker 2: for Arizona. For Arizona, and you know, the Nevada votes

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Speaker 2: to come in. If we get, for example, a Trump

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Speaker 2: sweep or a likely Trump sweep in all three of

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Speaker 2: those states, I you can pretty reliably say, like, what's

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Speaker 2: going to happen, although of course you know a lot

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Speaker 2: can but in terms. But and then other way, if

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Speaker 2: we see major Democratic overperformance. Back in twenty twenty two,

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Speaker 2: for example, early night, things didn't look good for Democrats

442
00:19:38,320 --> 00:19:40,879
Speaker 2: because Florida vote came in and it was a red

443
00:19:41,080 --> 00:19:45,320
Speaker 2: blowout for Ron Santis was looking like that way, that's right,

444
00:19:45,359 --> 00:19:48,480
Speaker 2: and then Pennsylvania returns and everyone it was a complete shock.

445
00:19:48,800 --> 00:19:51,400
Speaker 2: Of course, Fetterman ends up winning by five points, which

446
00:19:51,440 --> 00:19:54,800
Speaker 2: we knew relatively quickly on election night there, So keep

447
00:19:54,840 --> 00:19:58,520
Speaker 2: that in mind for you know, don't get don't base

448
00:19:58,640 --> 00:20:01,240
Speaker 2: your Florida conclusion off what happened, or is our Pensylvania

449
00:20:01,240 --> 00:20:04,120
Speaker 2: conclusion off what happens in Florida. My eyes are really

450
00:20:04,119 --> 00:20:07,880
Speaker 2: on North Carolina, Georgia and Pennsylvania in the East coast

451
00:20:07,960 --> 00:20:10,320
Speaker 2: time By around nine thirty ten PM, we're going to

452
00:20:10,359 --> 00:20:12,960
Speaker 2: have a pretty good idea about what's going on there

453
00:20:12,960 --> 00:20:15,240
Speaker 2: and over performance and stuff. And by that time the

454
00:20:15,280 --> 00:20:17,520
Speaker 2: Central time zone will also be come to come in

455
00:20:17,560 --> 00:20:20,280
Speaker 2: as well, and then we'll really see what the phenomenon

456
00:20:20,359 --> 00:20:21,320
Speaker 2: is there in the Blue states.

457
00:20:21,440 --> 00:20:22,200
Speaker 4: Yeah, that's greatol.

458
00:20:22,680 --> 00:20:25,639
Speaker 1: Also just a note for everybody programming note, So of

459
00:20:25,680 --> 00:20:27,640
Speaker 1: course we're going to do live election that coverage, which

460
00:20:27,640 --> 00:20:29,560
Speaker 1: were super excited about. We'll have Ryan and I'm only

461
00:20:29,640 --> 00:20:31,000
Speaker 1: here we'll do the whole thing. We're going to have

462
00:20:31,080 --> 00:20:33,640
Speaker 1: Logan in studio breaking down the numbers. That was one

463
00:20:33,640 --> 00:20:35,960
Speaker 1: of the areas that we were trying to improve upon

464
00:20:36,000 --> 00:20:38,160
Speaker 1: from some of the last elections, making sure because it's

465
00:20:38,160 --> 00:20:40,080
Speaker 1: hard for us to like talk and also really stay

466
00:20:40,160 --> 00:20:42,280
Speaker 1: on top of all the data that's coming in. So

467
00:20:42,320 --> 00:20:46,720
Speaker 1: we've got Logan coming into and partnering with Decision Desk HQ.

468
00:20:47,359 --> 00:20:49,040
Speaker 4: They are always very quick in.

469
00:20:49,000 --> 00:20:52,040
Speaker 1: Terms of getting the most updated results, so he's going

470
00:20:52,080 --> 00:20:54,600
Speaker 1: to be manning that and updating us throughout the night,

471
00:20:54,600 --> 00:20:56,639
Speaker 1: which we're very excited about. We're also going to do

472
00:20:57,520 --> 00:21:01,120
Speaker 1: election previews tomorrow with Ryan and Emily, so you can

473
00:21:01,560 --> 00:21:04,000
Speaker 1: take a look for that. And next week we're basically like,

474
00:21:04,440 --> 00:21:06,919
Speaker 1: you know, it may be that we know election night

475
00:21:07,000 --> 00:21:08,360
Speaker 1: what happens, and it's really.

476
00:21:08,119 --> 00:21:09,760
Speaker 4: Clear that is entirely possible.

477
00:21:09,800 --> 00:21:11,920
Speaker 1: It's also entirely possible that we go for a week

478
00:21:12,080 --> 00:21:15,280
Speaker 1: still looking like, oh, they're still counting in Philadelphia, they're

479
00:21:15,280 --> 00:21:18,439
Speaker 1: still counting in Phoenix or whatever. So we're kind of

480
00:21:18,440 --> 00:21:21,760
Speaker 1: all on standby next week to make sure that we're

481
00:21:21,800 --> 00:21:24,320
Speaker 1: able to continuously update you guys and stay on top

482
00:21:24,359 --> 00:21:27,359
Speaker 1: of whatever the news so can lever follow that in

483
00:21:27,400 --> 00:21:29,879
Speaker 1: the meantime. As I mentioned before, I don't know if

484
00:21:29,920 --> 00:21:33,359
Speaker 1: you guys have seen Edin Germentum on Twitter or on substack,

485
00:21:33,560 --> 00:21:37,239
Speaker 1: but his actual name is Josh. He was correct in

486
00:21:37,280 --> 00:21:39,840
Speaker 1: twenty twenty two about saying, hey, guys, this does soundok

487
00:21:39,920 --> 00:21:42,600
Speaker 1: like a red wave to me, when we and many

488
00:21:42,600 --> 00:21:45,760
Speaker 1: others were wrong. He also was correct in calling the

489
00:21:46,320 --> 00:21:50,560
Speaker 1: Georgia Warnock election another one that many analysts and polsters

490
00:21:50,560 --> 00:21:53,320
Speaker 1: got wrong. Now Josh it wants Colma to win. He

491
00:21:53,400 --> 00:21:56,199
Speaker 1: is a partisan, so factor that in. But you know,

492
00:21:56,280 --> 00:21:59,040
Speaker 1: he's done some really thoughtful analysis of why it could

493
00:21:59,080 --> 00:22:03,520
Speaker 1: be that the polls are underestimating the Democrats this time.

494
00:22:03,560 --> 00:22:06,440
Speaker 1: Went previously, in twenty twenty and twenty sixteen, they were

495
00:22:06,520 --> 00:22:09,359
Speaker 1: underestimating the Republicans. So we wanted to have Josh and

496
00:22:09,400 --> 00:22:11,479
Speaker 1: to break all of those insights down for us.

497
00:22:11,560 --> 00:22:12,200
Speaker 4: Let's get to it.

498
00:22:14,880 --> 00:22:17,880
Speaker 1: Joining us now by phone, we have Josh aka Eden Germentum.

499
00:22:18,000 --> 00:22:20,199
Speaker 3: Great to have you, sar, Yeah, it's great to be on.

500
00:22:20,520 --> 00:22:21,879
Speaker 1: Yeah, of course, so I just built you up a

501
00:22:21,920 --> 00:22:23,160
Speaker 1: little bit. We can throw this up.

502
00:22:23,040 --> 00:22:24,080
Speaker 4: On the screen.

503
00:22:24,200 --> 00:22:27,159
Speaker 1: A eight guys, A eight, Oh well, we will get

504
00:22:27,200 --> 00:22:28,960
Speaker 1: to this in a second, but the very last element

505
00:22:29,000 --> 00:22:30,560
Speaker 1: of this block is just to give you some cred.

506
00:22:30,880 --> 00:22:33,040
Speaker 1: You called twenty twenty two, right, you said, hey, I

507
00:22:33,040 --> 00:22:35,359
Speaker 1: don't think this looks like a red wave. You also

508
00:22:35,440 --> 00:22:38,920
Speaker 1: called the Warnock race correctly. You've since started a substack

509
00:22:38,960 --> 00:22:41,280
Speaker 1: where you've been delving into how you're looking at all

510
00:22:41,280 --> 00:22:46,119
Speaker 1: of these races. But you've been saying, hey, it's possible

511
00:22:46,400 --> 00:22:49,440
Speaker 1: that actually this time, unlike twenty sixteen and twenty twenty,

512
00:22:49,840 --> 00:22:53,800
Speaker 1: the polls could be systematically underestimating Democrats, which is what

513
00:22:54,000 --> 00:22:56,800
Speaker 1: happened in twenty twenty two. Now I laid out for

514
00:22:56,840 --> 00:22:59,000
Speaker 1: everybody like you're you know, you're a Partison, you want

515
00:22:59,040 --> 00:23:01,439
Speaker 1: Kamla to win. It's so people can factor that in

516
00:23:01,480 --> 00:23:04,480
Speaker 1: as they will. But what is your analysis of why

517
00:23:04,560 --> 00:23:07,200
Speaker 1: you think the polls could be looking more like a

518
00:23:07,240 --> 00:23:11,080
Speaker 1: twenty twenty two mid term miss that underestimated the Democrats

519
00:23:11,400 --> 00:23:13,000
Speaker 1: versus the last times that Trump.

520
00:23:12,800 --> 00:23:13,399
Speaker 4: Was on the ballot.

521
00:23:13,920 --> 00:23:14,160
Speaker 6: Yeah.

522
00:23:14,200 --> 00:23:17,320
Speaker 8: Well, this chart that you're seeing up here, this actually

523
00:23:17,359 --> 00:23:20,240
Speaker 8: isn't mine. This is by my friend Josh Taft, who

524
00:23:20,280 --> 00:23:22,960
Speaker 8: created this model. I just highlighted for him yesterday. But

525
00:23:23,040 --> 00:23:26,120
Speaker 8: it does illustrate something that I think is an important

526
00:23:26,119 --> 00:23:29,600
Speaker 8: consideration here. So the way this model works you can

527
00:23:29,640 --> 00:23:32,960
Speaker 8: see its past track record in its current projection, is

528
00:23:33,000 --> 00:23:36,720
Speaker 8: that it large it's partially based on polling, but it

529
00:23:36,760 --> 00:23:39,359
Speaker 8: also adjusts that the factor in things like pre election

530
00:23:39,480 --> 00:23:44,840
Speaker 8: indicators from actual voters, specifically special elections and primaries, And

531
00:23:44,880 --> 00:23:47,240
Speaker 8: what we've seen from this model is that that has

532
00:23:47,320 --> 00:23:51,240
Speaker 8: been relatively pretty predictive of whether or not they're the

533
00:23:51,280 --> 00:23:55,720
Speaker 8: polls are massively off. This model, when applied, that the

534
00:23:55,800 --> 00:23:59,399
Speaker 8: data we had in twenty twenty would have correctly like

535
00:23:59,720 --> 00:24:02,160
Speaker 8: in the CAD, that there was something wrong with the polling,

536
00:24:02,600 --> 00:24:06,800
Speaker 8: that pre election indicators like primaries and special elections weren't

537
00:24:06,840 --> 00:24:08,919
Speaker 8: picking up, that the polls that should Biden up by

538
00:24:08,920 --> 00:24:11,280
Speaker 8: eight or seven points were probably off. It would have

539
00:24:11,320 --> 00:24:13,639
Speaker 8: been correct then. It also would have been correct in

540
00:24:13,680 --> 00:24:17,000
Speaker 8: twenty twelve in saying that the very tide polls they

541
00:24:17,000 --> 00:24:21,520
Speaker 8: saw then that underestimated Democrats were not factoring in how

542
00:24:21,600 --> 00:24:24,399
Speaker 8: voters were voting on the ground through that year. And

543
00:24:24,480 --> 00:24:27,960
Speaker 8: what that model says now is that there's currently no

544
00:24:28,080 --> 00:24:33,200
Speaker 8: indication from how voters have been voting in primary elections

545
00:24:33,200 --> 00:24:36,199
Speaker 8: like the Washington primary, which is historically predictive, and in

546
00:24:36,240 --> 00:24:40,760
Speaker 8: special elections, that the polling we've seen with a slight

547
00:24:40,920 --> 00:24:44,439
Speaker 8: Kamala national lead is off in the way that it

548
00:24:44,480 --> 00:24:47,560
Speaker 8: was in twenty twenty. If anything, it probably maybe underestimating

549
00:24:47,600 --> 00:24:49,280
Speaker 8: her by a point or a point and a half.

550
00:24:49,520 --> 00:24:52,760
Speaker 8: So that's just one sign that like these kind of

551
00:24:52,760 --> 00:24:56,080
Speaker 8: canaries in the coal mine for major pro democratic polling

552
00:24:56,200 --> 00:24:58,399
Speaker 8: errors at least on the national level, are not showing

553
00:24:58,480 --> 00:24:59,240
Speaker 8: up right now.

554
00:24:59,119 --> 00:24:59,399
Speaker 3: Got it?

555
00:24:59,440 --> 00:25:02,440
Speaker 2: So, Josh, can you wrote a piece for your substack

556
00:25:02,520 --> 00:25:05,680
Speaker 2: specifically about all of the signs. Could you lay out

557
00:25:05,760 --> 00:25:07,879
Speaker 2: beyond the model, what some of the other things that

558
00:25:07,880 --> 00:25:11,159
Speaker 2: people can look at for the case for underestimating Democrats.

559
00:25:11,560 --> 00:25:15,919
Speaker 8: Yeah, of course so we I think Crystal mentioned that this,

560
00:25:16,080 --> 00:25:18,760
Speaker 8: and I said in the tagline of the article that

561
00:25:18,760 --> 00:25:20,800
Speaker 8: it would be something of a twenty twenty two reducts

562
00:25:21,240 --> 00:25:23,359
Speaker 8: and the thing to look at there isn't national polling,

563
00:25:23,359 --> 00:25:26,320
Speaker 8: which was actually pretty accurate at twenty twenty two. It

564
00:25:26,440 --> 00:25:30,199
Speaker 8: was state polling. Where the argument here from some people,

565
00:25:30,400 --> 00:25:33,159
Speaker 8: some very very pro Democratic people who essentially say the

566
00:25:33,160 --> 00:25:35,760
Speaker 8: party can do no wrong, is that this was because

567
00:25:35,800 --> 00:25:38,840
Speaker 8: there was a flood of red wave Republican polsters who

568
00:25:38,840 --> 00:25:41,120
Speaker 8: were just showing the party up no matter what, and

569
00:25:41,160 --> 00:25:43,639
Speaker 8: the aggregators were too credulous to keep them out of

570
00:25:43,640 --> 00:25:45,840
Speaker 8: their model. Some of these were run by like these

571
00:25:45,880 --> 00:25:49,720
Speaker 8: firms or run by actual high schoolers, and like they

572
00:25:49,840 --> 00:25:51,840
Speaker 8: just included them, and they always showed Republicans had and

573
00:25:51,880 --> 00:25:54,720
Speaker 8: they were wrong. And that happened to a degree. But

574
00:25:54,800 --> 00:25:57,359
Speaker 8: the larger thing that happened that year is that you

575
00:25:57,480 --> 00:26:00,960
Speaker 8: had non partisan polsters with no collection connect the Republicans,

576
00:26:01,240 --> 00:26:04,520
Speaker 8: often responding to the narratives put out by the right

577
00:26:05,080 --> 00:26:09,240
Speaker 8: and in some cases showing massive swings to the right

578
00:26:09,280 --> 00:26:11,639
Speaker 8: at the end of the race, and response to what

579
00:26:11,800 --> 00:26:14,480
Speaker 8: seems to have been just narratives and not what was

580
00:26:14,520 --> 00:26:17,360
Speaker 8: actually going on in the ground. You had in Washington,

581
00:26:17,440 --> 00:26:21,240
Speaker 8: the polls prior to October that year, we're showing the

582
00:26:21,320 --> 00:26:23,400
Speaker 8: race largely what it turned out to be. The Democrat

583
00:26:23,480 --> 00:26:25,800
Speaker 8: they were running for Senate won by about fifteen points.

584
00:26:26,119 --> 00:26:29,080
Speaker 8: But after Republican polsters started showing a tied race and

585
00:26:29,160 --> 00:26:32,560
Speaker 8: there was some chatter about it being a sleeper flip,

586
00:26:32,920 --> 00:26:35,600
Speaker 8: the non partisan posters followed their lead. They didn't find

587
00:26:35,680 --> 00:26:39,359
Speaker 8: leads like as close as what the Republican polsters found,

588
00:26:39,480 --> 00:26:41,640
Speaker 8: but they showed a pretty significant shift to the right,

589
00:26:41,960 --> 00:26:45,159
Speaker 8: and one that didn't happen. In reality, there was no

590
00:26:45,680 --> 00:26:48,760
Speaker 8: movement from the primary that year to the general election.

591
00:26:48,840 --> 00:26:52,000
Speaker 8: The Democrats won by fifteen points both times. And this

592
00:26:52,040 --> 00:26:55,160
Speaker 8: gets into a larger point that I think Nate Cone

593
00:26:55,200 --> 00:26:59,119
Speaker 8: and The New York Times kind of proved to an

594
00:26:59,160 --> 00:27:01,840
Speaker 8: extent when he talked about how posters have changed, is

595
00:27:01,840 --> 00:27:04,960
Speaker 8: that posters are terrified of getting a miss like twenty

596
00:27:05,000 --> 00:27:07,280
Speaker 8: twenty again. They don't think that's something they can afford

597
00:27:07,280 --> 00:27:10,399
Speaker 8: it all reputationally. Con even said like it has mental

598
00:27:10,440 --> 00:27:13,520
Speaker 8: health like problems for them, where they get really upset

599
00:27:13,560 --> 00:27:16,639
Speaker 8: if they miss it that badly, And they are pulling

600
00:27:16,680 --> 00:27:19,080
Speaker 8: out all the stops in a lot of ways in

601
00:27:19,119 --> 00:27:22,439
Speaker 8: a way in some ways that are reminiscent of pastimes

602
00:27:22,440 --> 00:27:24,679
Speaker 8: when they've gotten two in their heads about a polling

603
00:27:24,720 --> 00:27:26,680
Speaker 8: miss in a way that could end up getting them

604
00:27:26,680 --> 00:27:28,720
Speaker 8: on the wrong side of things on the opposite way.

605
00:27:29,400 --> 00:27:31,879
Speaker 1: Yeah, I think to me, one of the most compelling

606
00:27:32,200 --> 00:27:35,560
Speaker 1: data points in this direction is what you just laid out,

607
00:27:35,760 --> 00:27:38,359
Speaker 1: which is that we know that posters have actually changed

608
00:27:38,359 --> 00:27:43,120
Speaker 1: their methodology because they're so fearful and they've effectively used

609
00:27:43,119 --> 00:27:47,600
Speaker 1: this methodology of asking people to self recall how they voted,

610
00:27:48,160 --> 00:27:51,880
Speaker 1: which has in the past led to less accurate results,

611
00:27:51,880 --> 00:27:56,840
Speaker 1: consistently less accurate results. But their theory is, listen, there

612
00:27:56,880 --> 00:28:01,600
Speaker 1: could be this group of infrequent and less like sort

613
00:28:01,640 --> 00:28:05,119
Speaker 1: of politically engaged voters that tend to go for Donald

614
00:28:05,119 --> 00:28:07,800
Speaker 1: Trump that we just cannot get in touch with, like

615
00:28:07,840 --> 00:28:11,720
Speaker 1: we just cannot reflect them accurately within our polls. They

616
00:28:11,760 --> 00:28:15,040
Speaker 1: attribute that, you know, potentially that was the twenty sixteen miss.

617
00:28:15,080 --> 00:28:18,000
Speaker 1: Potentially that was again the twenty twenty miss. And that's

618
00:28:18,040 --> 00:28:21,840
Speaker 1: a plausible theoretical possibility. And so they're basically saying, we're

619
00:28:21,880 --> 00:28:25,080
Speaker 1: just going to use this method to juice Trump's numbers,

620
00:28:25,119 --> 00:28:28,480
Speaker 1: to try to Jerry rigg some way to account for

621
00:28:28,640 --> 00:28:31,719
Speaker 1: this vote, this like infrequent voter that we just cannot

622
00:28:31,760 --> 00:28:35,199
Speaker 1: reach and cannot model. The other possibility, and this was

623
00:28:35,200 --> 00:28:39,320
Speaker 1: also laid out by Nate Cohne, is that in twenty twenty,

624
00:28:39,440 --> 00:28:42,160
Speaker 1: In twenty sixteen, the miss was because posters weren't waiting

625
00:28:42,160 --> 00:28:45,320
Speaker 1: by education like college education versus non college. Okay, they

626
00:28:45,320 --> 00:28:47,840
Speaker 1: fix that in twenty twenty, they haven't even bigger miss.

627
00:28:48,280 --> 00:28:52,600
Speaker 1: The other theory is that twenty twenty was obviously during

628
00:28:52,600 --> 00:28:56,560
Speaker 1: the pandemic, and you had a partisan difference in the

629
00:28:56,600 --> 00:28:59,320
Speaker 1: way people responded to the pandemic. So you add a

630
00:28:59,320 --> 00:29:04,040
Speaker 1: lot of liberal, white collar professionals at home dying to

631
00:29:04,080 --> 00:29:06,040
Speaker 1: tell polster is how excited they were to vote for

632
00:29:06,080 --> 00:29:10,680
Speaker 1: Democrats and that that one off phenomenon is what led

633
00:29:10,720 --> 00:29:12,120
Speaker 1: to the miss in twenty twenty.

634
00:29:12,480 --> 00:29:14,680
Speaker 4: So if that's the explanation.

635
00:29:14,760 --> 00:29:18,320
Speaker 1: Then this attempt by polsters to sort of artificially juice

636
00:29:18,400 --> 00:29:22,160
Speaker 1: Trump's numbers may be, you know, correcting for something that

637
00:29:22,200 --> 00:29:26,160
Speaker 1: doesn't need correcting since the pandemic was such a unique

638
00:29:26,200 --> 00:29:29,160
Speaker 1: time period and had this, you know, unique partisan difference

639
00:29:29,160 --> 00:29:30,560
Speaker 1: in terms of how people responded to it.

640
00:29:30,920 --> 00:29:32,560
Speaker 4: You dug into the evidence.

641
00:29:32,720 --> 00:29:35,680
Speaker 1: For each of these possibilities, the just like you know,

642
00:29:35,760 --> 00:29:38,800
Speaker 1: infrequent voter that you just can't model and is more

643
00:29:38,880 --> 00:29:43,520
Speaker 1: overwhelmingly pro Trump than pro Democrat versus the different pandemic

644
00:29:43,600 --> 00:29:47,400
Speaker 1: response bias. What evidence did you find in favor or

645
00:29:47,600 --> 00:29:49,240
Speaker 1: pose to each of these theories.

646
00:29:49,800 --> 00:29:52,360
Speaker 8: Yeah, well, I think it's basically what you said, where

647
00:29:52,520 --> 00:29:55,720
Speaker 8: there's the grand unified theory as as Cone calls it,

648
00:29:56,000 --> 00:29:58,400
Speaker 8: which is that like there's just something about Trump voters

649
00:29:58,560 --> 00:30:01,840
Speaker 8: or just impossible to contact to low social trust you'll

650
00:30:01,880 --> 00:30:03,920
Speaker 8: never be able to reach them through traditional methods, and

651
00:30:03,960 --> 00:30:05,840
Speaker 8: you have to find some way to represent them when

652
00:30:05,840 --> 00:30:09,240
Speaker 8: you're polling. And like, I get that idea in theory,

653
00:30:09,320 --> 00:30:11,560
Speaker 8: I think in a sense it is kind of like

654
00:30:11,840 --> 00:30:17,640
Speaker 8: a lose state understanding that kind of exoticizes Trump voters

655
00:30:17,640 --> 00:30:20,440
Speaker 8: to a degree. I live in a purple state Georgia.

656
00:30:20,880 --> 00:30:23,400
Speaker 8: Trust me when I say that Trump voters are not

657
00:30:23,680 --> 00:30:26,120
Speaker 8: very quiet about who they support is essentially not now

658
00:30:27,160 --> 00:30:29,920
Speaker 8: articularly ashamed of in the way they might have been

659
00:30:29,960 --> 00:30:35,520
Speaker 8: eight years ago. And I just it's He calls it

660
00:30:35,560 --> 00:30:37,239
Speaker 8: the patchwork theory, which I think does it a bit

661
00:30:37,280 --> 00:30:39,520
Speaker 8: of a service. But that's more compelling to me because

662
00:30:39,600 --> 00:30:42,840
Speaker 8: it makes sense. We know that pollsters and in twenty

663
00:30:42,880 --> 00:30:44,920
Speaker 8: sixteen did not wit by education. We know that made

664
00:30:45,000 --> 00:30:46,920
Speaker 8: up for most of the missess that they made. We

665
00:30:47,000 --> 00:30:49,960
Speaker 8: know in twenty twenty there was a major non response

666
00:30:50,000 --> 00:30:53,800
Speaker 8: bias in favor of Democrats, something we're not necessarily seeing now.

667
00:30:53,840 --> 00:30:55,720
Speaker 8: One of the most interesting data points, and I wish

668
00:30:55,760 --> 00:30:57,280
Speaker 8: they did follow up on this because it could be

669
00:30:57,280 --> 00:31:00,240
Speaker 8: a huge deal, was a chart by people from the

670
00:31:00,240 --> 00:31:05,840
Speaker 8: Polarization Research Study looking at partisan response rates to you

671
00:31:06,000 --> 00:31:09,240
Speaker 8: gov polls, and they've found that since about like the

672
00:31:09,280 --> 00:31:12,640
Speaker 8: beginning of this year, the rate of Democrats responding to

673
00:31:12,680 --> 00:31:15,760
Speaker 8: their polls has gone down and the rate of republicansponding

674
00:31:16,040 --> 00:31:18,600
Speaker 8: responding has gone up to the extent that they've had

675
00:31:18,640 --> 00:31:23,440
Speaker 8: to actually await Democratic respondents higher than Republican respondents. So

676
00:31:24,640 --> 00:31:26,960
Speaker 8: this phenomenon, like to the extent that it might have

677
00:31:27,000 --> 00:31:29,320
Speaker 8: happened before, we may not have any proof that it's

678
00:31:29,400 --> 00:31:32,160
Speaker 8: happening now. And we know that you gov has like

679
00:31:32,200 --> 00:31:35,600
Speaker 8: they've shown that they've made changes to account for this

680
00:31:35,720 --> 00:31:40,280
Speaker 8: kind of inverse phenomenon, this like opposite way we'd respect,

681
00:31:40,400 --> 00:31:43,040
Speaker 8: but we don't know that other polsters who may not

682
00:31:43,160 --> 00:31:46,720
Speaker 8: even be as aware of this are are still like

683
00:31:46,880 --> 00:31:48,920
Speaker 8: trying to hedge on the side of caution and react

684
00:31:49,280 --> 00:31:52,640
Speaker 8: to what happened in twenty twenty. If they're doing that properly,

685
00:31:53,040 --> 00:31:55,760
Speaker 8: so quite ironically, and you can say whatever the reason

686
00:31:55,800 --> 00:31:58,360
Speaker 8: for this like might be. I think probably the funniest

687
00:31:58,360 --> 00:32:01,680
Speaker 8: one is that liberal's got the mainstream media after the

688
00:32:01,720 --> 00:32:04,120
Speaker 8: Biden drop out saga where they thought that they were

689
00:32:04,120 --> 00:32:07,719
Speaker 8: trying to end as presidency, that they stop responding to pollsters,

690
00:32:08,560 --> 00:32:12,000
Speaker 8: but like this, uh, that's what I think should be

691
00:32:12,000 --> 00:32:13,920
Speaker 8: the ending of this election, because that's very fun to me.

692
00:32:14,920 --> 00:32:16,760
Speaker 3: That would be genuinely hilarious.

693
00:32:17,040 --> 00:32:17,280
Speaker 9: Yeah.

694
00:32:17,400 --> 00:32:20,040
Speaker 8: Yeah, it's like they got so pissed as reclined they

695
00:32:20,040 --> 00:32:20,760
Speaker 8: stopped picking up.

696
00:32:20,720 --> 00:32:23,280
Speaker 4: The That would be incredible.

697
00:32:23,400 --> 00:32:25,200
Speaker 3: I do have a question for you, Josh, because you're

698
00:32:25,360 --> 00:32:26,040
Speaker 3: in Georgia.

699
00:32:26,400 --> 00:32:28,720
Speaker 2: I'm assuming you know, beyond pulling you and looking at

700
00:32:28,720 --> 00:32:31,680
Speaker 2: early voting, are there any trends there that could pick

701
00:32:31,720 --> 00:32:33,320
Speaker 2: up on your thesis and bear that out.

702
00:32:33,880 --> 00:32:34,120
Speaker 5: Yeah.

703
00:32:34,160 --> 00:32:36,680
Speaker 8: Well, the early voting here is a really tough one

704
00:32:36,680 --> 00:32:40,440
Speaker 8: to take too much stock. In Georgia does not have

705
00:32:40,560 --> 00:32:44,280
Speaker 8: party registration. You can't track it by like what the

706
00:32:44,280 --> 00:32:47,960
Speaker 8: partisanship like you cant in Nevada. They do have for

707
00:32:48,280 --> 00:32:51,280
Speaker 8: whatever reason. This is probably for not very great reasons.

708
00:32:51,360 --> 00:32:54,520
Speaker 8: They do track voters by race here, which can be

709
00:32:54,640 --> 00:32:58,560
Speaker 8: an interesting heuristic for how the parties are doing because

710
00:32:58,920 --> 00:33:02,280
Speaker 8: in Georgia, deb non whites vote Democratic and white voters

711
00:33:02,360 --> 00:33:05,600
Speaker 8: vote Republican, and elections here can often be just turnout

712
00:33:05,640 --> 00:33:08,479
Speaker 8: battles more than they are persuasion battles, although that's been

713
00:33:08,520 --> 00:33:11,640
Speaker 8: different in recent years where the suburbs have really become

714
00:33:11,680 --> 00:33:14,560
Speaker 8: big battlegrounds in the way that they were before, and

715
00:33:14,680 --> 00:33:18,479
Speaker 8: we've been seeing the black vote here is about in

716
00:33:18,560 --> 00:33:22,320
Speaker 8: line with what it was in twenty twenty among habitual

717
00:33:22,400 --> 00:33:25,080
Speaker 8: early voters. So among the same population that voted early

718
00:33:25,160 --> 00:33:28,120
Speaker 8: in twenty twenty, it's about the same. The difference is

719
00:33:28,200 --> 00:33:30,840
Speaker 8: is that there are a lot of Republicans who voted

720
00:33:30,840 --> 00:33:33,040
Speaker 8: on election day in twenty twenty, a lot of white

721
00:33:33,440 --> 00:33:36,200
Speaker 8: votes who are like I think that of the voters

722
00:33:36,200 --> 00:33:38,320
Speaker 8: who voted on election day in twenty twenty and are

723
00:33:38,400 --> 00:33:40,840
Speaker 8: voting early now, they're like over they're like seventy two

724
00:33:40,960 --> 00:33:44,880
Speaker 8: seventy five percent Republican. So we have a slightly wider

725
00:33:46,720 --> 00:33:50,360
Speaker 8: like voting pool right now in Georgia then we've had

726
00:33:50,360 --> 00:33:52,680
Speaker 8: it like maybe in a couple of cycles. But a

727
00:33:52,680 --> 00:33:55,280
Speaker 8: lot of that is because we have a more depolarized

728
00:33:55,320 --> 00:33:57,680
Speaker 8: early voting elector than we have before. There are a

729
00:33:57,720 --> 00:34:00,360
Speaker 8: lot of Republicans who vote on election day and who

730
00:34:00,400 --> 00:34:02,560
Speaker 8: are voting now. There are probably some Democrats who voted

731
00:34:02,600 --> 00:34:04,400
Speaker 8: early in twenty twenty because I didn't want to get

732
00:34:04,400 --> 00:34:07,600
Speaker 8: COVID through mail, who are going to vote on election day,

733
00:34:07,720 --> 00:34:12,520
Speaker 8: and I would say generally the number you're probably going

734
00:34:12,600 --> 00:34:15,560
Speaker 8: to get about twenty six twenty seven percent black turnout,

735
00:34:15,680 --> 00:34:18,799
Speaker 8: like with the way things are going here in Georgia,

736
00:34:18,840 --> 00:34:22,160
Speaker 8: which isn't like ideal for Democrats, but like it's winnable.

737
00:34:22,239 --> 00:34:24,160
Speaker 8: I think Warnock one with the like was able to

738
00:34:24,160 --> 00:34:28,520
Speaker 8: go run off with similar demographics in twenty twenty two.

739
00:34:28,760 --> 00:34:29,920
Speaker 3: Gotcha very interesting.

740
00:34:30,200 --> 00:34:33,960
Speaker 1: I've seen some theories about a shy Kamala voter this time,

741
00:34:34,040 --> 00:34:37,040
Speaker 1: and some Democratic groups seem to be buying into this idea.

742
00:34:37,040 --> 00:34:39,600
Speaker 1: I'm thinking most specifically of this ad that went viral

743
00:34:39,760 --> 00:34:44,120
Speaker 1: of like women secretly, yeah vote, bucking their husband's will

744
00:34:44,239 --> 00:34:47,200
Speaker 1: and voting for Kamala Harris. And these you know stickers

745
00:34:47,200 --> 00:34:49,200
Speaker 1: that are popping up in women's restrooms that are like

746
00:34:49,280 --> 00:34:51,640
Speaker 1: your vote secret, don't worry, you can vote for Kamala

747
00:34:51,680 --> 00:34:51,880
Speaker 1: and not.

748
00:34:51,960 --> 00:34:52,839
Speaker 4: Feel bad about it.

749
00:34:53,120 --> 00:34:56,400
Speaker 1: So clearly some parts of the Democratic coalition believe that

750
00:34:56,440 --> 00:34:59,640
Speaker 1: this could be a thing of like Nikki Hayley, voters

751
00:35:00,040 --> 00:35:03,120
Speaker 1: who are in you know, Republican households or their husbands

752
00:35:03,200 --> 00:35:05,520
Speaker 1: or Republicans who are secretly going to go and cast

753
00:35:05,520 --> 00:35:07,719
Speaker 1: their ballot for Kamala Harris and may not be telling

754
00:35:07,760 --> 00:35:10,040
Speaker 1: polsters the truth about the way that they're votings or

755
00:35:10,080 --> 00:35:13,200
Speaker 1: the inverse of the quote unquote shy Trump voter theory

756
00:35:13,239 --> 00:35:15,920
Speaker 1: back in twenty sixteen. Do you see any indications that

757
00:35:15,920 --> 00:35:16,719
Speaker 1: that could be the case.

758
00:35:17,200 --> 00:35:19,239
Speaker 8: I don't know. I've heard people say that actually for

759
00:35:19,280 --> 00:35:21,760
Speaker 8: a while. I remember people saying it in like twenty

760
00:35:22,000 --> 00:35:25,759
Speaker 8: sixteen about Hillary and they ended up being true. Like

761
00:35:25,800 --> 00:35:28,080
Speaker 8: it makes sense in theory, but like, I mean.

762
00:35:28,000 --> 00:35:29,240
Speaker 4: It's not really evidence.

763
00:35:29,239 --> 00:35:29,399
Speaker 3: Farr.

764
00:35:29,640 --> 00:35:31,640
Speaker 8: Yeah, it's kind of a bit of a fan fiction.

765
00:35:31,719 --> 00:35:32,200
Speaker 8: I'd say.

766
00:35:33,040 --> 00:35:34,560
Speaker 4: My last question for you, Josh, is.

767
00:35:36,120 --> 00:35:39,759
Speaker 1: How, like, what's your confidence level that your analysis is

768
00:35:39,920 --> 00:35:42,560
Speaker 1: correct and that Democrats are going to end up being

769
00:35:42,800 --> 00:35:46,400
Speaker 1: underestimated or in a strong position to win this election.

770
00:35:46,880 --> 00:35:49,920
Speaker 8: Yeah. Well, I feel like I think we have a

771
00:35:49,920 --> 00:35:52,279
Speaker 8: lot of evidence right now from the modeling that my

772
00:35:52,440 --> 00:35:56,160
Speaker 8: friend also Josh did to like what we know these

773
00:35:56,200 --> 00:35:59,200
Speaker 8: polsters have done to I think maybe over correct or

774
00:35:59,200 --> 00:36:02,200
Speaker 8: correct for their miss that there shouldn't be something significantly

775
00:36:02,239 --> 00:36:05,680
Speaker 8: overestimating Trump. If that happens, it's for something we have

776
00:36:05,800 --> 00:36:08,040
Speaker 8: no clue about right now, and we'll only be able

777
00:36:08,080 --> 00:36:10,920
Speaker 8: to know after the election. There's nothing we know about

778
00:36:10,920 --> 00:36:13,560
Speaker 8: politics or polling right now that indicates, like if that

779
00:36:13,600 --> 00:36:16,239
Speaker 8: should be happening to a significant degree, unless like it's

780
00:36:16,320 --> 00:36:19,520
Speaker 8: just a non response thing again, in which case polling

781
00:36:19,600 --> 00:36:24,000
Speaker 8: really like is a little screwed. Yes, But what is

782
00:36:24,120 --> 00:36:26,560
Speaker 8: kind of compelling to me is that we do have

783
00:36:26,920 --> 00:36:30,040
Speaker 8: past precedent for this exact kind of thing happening where

784
00:36:30,120 --> 00:36:33,759
Speaker 8: posters get like really insecure and overclock in favor of

785
00:36:33,760 --> 00:36:36,360
Speaker 8: the right, not in the US, but actually in the

786
00:36:36,440 --> 00:36:40,480
Speaker 8: United Kingdom in twenty seventeen, the election between Jeremy Corbett

787
00:36:40,480 --> 00:36:45,359
Speaker 8: and Theresa May. There polsters two years prior had incorrectly

788
00:36:45,400 --> 00:36:48,120
Speaker 8: called a labor winner a hung parliament. When the Conservatives

789
00:36:48,200 --> 00:36:52,439
Speaker 8: ended up winning a majority two years later, they changed.

790
00:36:52,440 --> 00:36:54,600
Speaker 8: They made a lot of changes to their polls, a

791
00:36:54,600 --> 00:36:57,840
Speaker 8: lot of which were ad hoc changes, a lot like

792
00:36:57,960 --> 00:37:02,160
Speaker 8: how posters are arguably making ad hoc changes now. Crystal.

793
00:37:02,280 --> 00:37:05,279
Speaker 8: You mentioned that they were doing recall to vote, which

794
00:37:05,360 --> 00:37:09,840
Speaker 8: is something that can really only generously be called quite suspect,

795
00:37:10,280 --> 00:37:12,880
Speaker 8: like if applied like a cone showed that if applied

796
00:37:12,920 --> 00:37:14,919
Speaker 8: to past elections over the past twenty years, it would

797
00:37:14,960 --> 00:37:16,600
Speaker 8: have made all of the polling in all those years

798
00:37:16,680 --> 00:37:20,200
Speaker 8: less accurate, because people will tell you that they voted

799
00:37:20,239 --> 00:37:22,200
Speaker 8: for people like people like saying that they voted for

800
00:37:22,239 --> 00:37:25,400
Speaker 8: the winner. So an accurate sample is going to usually

801
00:37:25,440 --> 00:37:28,040
Speaker 8: get you people saying they voted more for the winner

802
00:37:28,280 --> 00:37:30,160
Speaker 8: than for the loser, and by shifting that to the

803
00:37:30,200 --> 00:37:32,719
Speaker 8: actual results, you're kind of just putting your thumb on

804
00:37:32,719 --> 00:37:35,160
Speaker 8: the scale. British posters did a lot of that in

805
00:37:35,200 --> 00:37:37,640
Speaker 8: twenty seventeen and what ended up happening was that they

806
00:37:37,680 --> 00:37:42,000
Speaker 8: ended up underestimating Labor. Every single forecaster at the end

807
00:37:42,040 --> 00:37:45,239
Speaker 8: of the election called a Labor a Conservative majority, in

808
00:37:45,280 --> 00:37:48,200
Speaker 8: some cases a very large conservative majority, and it only

809
00:37:48,239 --> 00:37:51,160
Speaker 8: resulted in a hung parliament. Yes, the exception was Yuk

810
00:37:51,200 --> 00:37:55,600
Speaker 8: of funnily enough, but so that is like a degree

811
00:37:55,640 --> 00:37:58,359
Speaker 8: of like having that actual example if this is something

812
00:37:58,400 --> 00:38:01,719
Speaker 8: that has happened before, makes me feel like it's something

813
00:38:01,760 --> 00:38:04,879
Speaker 8: that could happen. I won't make any guarantees. I don't

814
00:38:04,880 --> 00:38:07,520
Speaker 8: think anybody can, especially in a race that's this close.

815
00:38:08,000 --> 00:38:11,320
Speaker 8: But the ingredients deuce that we're there in Britten in

816
00:38:11,400 --> 00:38:14,680
Speaker 8: twenty seventeen do seem to be here seven years later?

817
00:38:14,840 --> 00:38:15,000
Speaker 4: Yeah?

818
00:38:15,040 --> 00:38:15,560
Speaker 3: Very interesting.

819
00:38:15,640 --> 00:38:17,080
Speaker 4: Well, it is very interesting.

820
00:38:17,160 --> 00:38:20,160
Speaker 1: And certainly if she ends up winning and outperforming the polls,

821
00:38:20,200 --> 00:38:21,399
Speaker 1: I think a lot of people are going to look

822
00:38:21,400 --> 00:38:23,560
Speaker 1: back at your nelsons and say, you know this guy

823
00:38:23,600 --> 00:38:27,120
Speaker 1: had it right on. So guys go subscribe to Edinger

824
00:38:27,480 --> 00:38:30,000
Speaker 1: Mentum's substack, and thank you so much for joining us.

825
00:38:30,000 --> 00:38:32,320
Speaker 4: As Marie s great to thank you, Josh, It's our pleasure.

826
00:38:35,200 --> 00:38:38,720
Speaker 2: Kamala Harris is now responding to Joe Biden's quote unquote

827
00:38:38,800 --> 00:38:41,239
Speaker 2: garbage comment trying to clean up his mess. Never at

828
00:38:41,280 --> 00:38:43,640
Speaker 2: underestimate his ability to f things up, in the words

829
00:38:43,640 --> 00:38:46,759
Speaker 2: of Barack Obama, and he certainly has done that. He's

830
00:38:46,840 --> 00:38:49,560
Speaker 2: created an entire news cycle around it. So here we

831
00:38:49,640 --> 00:38:50,840
Speaker 2: have Kamala responding.

832
00:38:50,920 --> 00:38:53,640
Speaker 3: Let's take a lesson. President Biden took this comment last

833
00:38:53,719 --> 00:38:54,960
Speaker 3: night about garbage.

834
00:38:56,840 --> 00:39:01,120
Speaker 9: Listen. I think that, first of all, he clarified his comments.

835
00:39:01,160 --> 00:39:05,240
Speaker 9: But let me be clara. I strongly disagree with any

836
00:39:06,280 --> 00:39:10,160
Speaker 9: criticism of people based on who they vote for.

837
00:39:10,440 --> 00:39:11,359
Speaker 1: It's you hurt.

838
00:39:11,360 --> 00:39:15,200
Speaker 9: In my speech last night and continuously throughout my career,

839
00:39:16,200 --> 00:39:20,480
Speaker 9: I believe that the work that I do is about

840
00:39:20,840 --> 00:39:23,680
Speaker 9: representing all the people, whether they support me or not.

841
00:39:24,480 --> 00:39:26,400
Speaker 9: And as President of the United States, I will be

842
00:39:26,400 --> 00:39:28,520
Speaker 9: a president for all Americans.

843
00:39:28,400 --> 00:39:29,480
Speaker 3: Whether you vote for me or not.

844
00:39:30,239 --> 00:39:33,040
Speaker 9: Satisfy responsibility, and that's the kind of work that I've

845
00:39:33,080 --> 00:39:35,600
Speaker 9: done my entire career, and I take a very serious.

846
00:39:35,280 --> 00:39:39,440
Speaker 2: He trying desperately to avoid any deplorables magic from twenty

847
00:39:39,880 --> 00:39:43,880
Speaker 2: and sixteen, and she said Biden clarified his comments. If

848
00:39:43,880 --> 00:39:45,360
Speaker 2: you want to know why you had to quote unquote

849
00:39:45,360 --> 00:39:48,560
Speaker 2: clarify it, it's because he's so cooked that he can

850
00:39:48,600 --> 00:39:52,680
Speaker 2: barely issue a single coherent sentence. So I know they

851
00:39:52,680 --> 00:39:54,920
Speaker 2: played this yesterday, but you know we'll include it here.

852
00:39:55,120 --> 00:39:57,600
Speaker 2: These are the full comments from Joe Biden.

853
00:39:57,520 --> 00:40:01,800
Speaker 5: Or Puerto Rico, where I'm in my home stated, they're good, decent,

854
00:40:01,880 --> 00:40:04,960
Speaker 5: honorable people. The only garbage I see floating down there

855
00:40:05,040 --> 00:40:07,160
Speaker 5: is just supporters.

856
00:40:06,520 --> 00:40:09,600
Speaker 10: His demonizational scene is unconsctable.

857
00:40:09,960 --> 00:40:12,399
Speaker 2: Okay, So I thought it was pretty clear the only

858
00:40:12,400 --> 00:40:14,320
Speaker 2: garbage I see out there is supporters.

859
00:40:14,320 --> 00:40:16,280
Speaker 3: But he is also like slurring his words.

860
00:40:16,840 --> 00:40:18,839
Speaker 2: If you want to listen to the case for why

861
00:40:18,880 --> 00:40:22,239
Speaker 2: he wasn't saying his supporters he was it hinges on

862
00:40:22,280 --> 00:40:26,359
Speaker 2: an apostrophe of whether he meant Tony Hinchcliffe there as

863
00:40:26,400 --> 00:40:29,960
Speaker 2: the support ers for his rhetoric.

864
00:40:30,040 --> 00:40:32,960
Speaker 3: Regardless, it's pretty clear it didn't work out so well.

865
00:40:33,040 --> 00:40:35,640
Speaker 2: And that the Kamala campaign, I mean, I saw people

866
00:40:35,719 --> 00:40:37,480
Speaker 2: defending it, and one of the ways that you know

867
00:40:37,520 --> 00:40:40,000
Speaker 2: that you shouldn't defend it is that Kamala herself immediately

868
00:40:40,120 --> 00:40:41,680
Speaker 2: was like, I have nothing to do with that, and

869
00:40:41,760 --> 00:40:44,239
Speaker 2: Jos Shapiro also went on television immediately was like, no,

870
00:40:44,360 --> 00:40:45,360
Speaker 2: we're not doing that today.

871
00:40:45,440 --> 00:40:47,440
Speaker 1: Well, listen, Kama's been looking for a way to separate

872
00:40:47,440 --> 00:40:49,680
Speaker 1: herself from Joe Biden. I guess she just handed her

873
00:40:49,719 --> 00:40:53,600
Speaker 1: an opportunity to do exactly that. You know, Yeah, they're

874
00:40:53,680 --> 00:40:57,520
Speaker 1: terrified of it being another deplorables moment. The Republicans are

875
00:40:57,560 --> 00:41:00,759
Speaker 1: making as much of it as they possibly and you know.

876
00:41:00,680 --> 00:41:02,279
Speaker 4: Trump, Bretroun in a garbage truck.

877
00:41:02,320 --> 00:41:04,759
Speaker 1: The Vake Ramaswamy also like going out and picking up

878
00:41:04,800 --> 00:41:07,720
Speaker 1: garbage and yeah whatever. So they're doing with it everything

879
00:41:07,760 --> 00:41:10,239
Speaker 1: that they possibly can. But I mean, it strikes a

880
00:41:10,239 --> 00:41:14,320
Speaker 1: few things like Number one, no surprise that Joe Biden

881
00:41:14,320 --> 00:41:17,000
Speaker 1: has been asking to campaign with Kamala Harris apparently for

882
00:41:17,080 --> 00:41:19,200
Speaker 1: weeks and weeks and they're like, we'll get.

883
00:41:19,080 --> 00:41:21,520
Speaker 4: Back to you on that. Yeah, that's not going to

884
00:41:21,600 --> 00:41:23,680
Speaker 4: happen because this is what you get.

885
00:41:24,120 --> 00:41:27,040
Speaker 1: Number Two, how many times has she had to clean

886
00:41:27,120 --> 00:41:30,480
Speaker 1: up for the men in particular who are campaigning for

887
00:41:30,560 --> 00:41:32,680
Speaker 1: I mean, I think about Barack Obama going out and

888
00:41:32,760 --> 00:41:35,759
Speaker 1: lecturing the brothers, you know, about how they need to vote,

889
00:41:35,760 --> 00:41:38,440
Speaker 1: et cetera, et cetera, And when she's asked about it,

890
00:41:38,440 --> 00:41:43,440
Speaker 1: she didn't directly, you know, like rebuke President Obama. But

891
00:41:43,520 --> 00:41:46,480
Speaker 1: she also made it clear like I'm not taking anyone's

892
00:41:46,560 --> 00:41:48,759
Speaker 1: vote for granted, and I have to earn the vote

893
00:41:48,840 --> 00:41:51,080
Speaker 1: of course, like this is going to be difficult. I'm

894
00:41:51,080 --> 00:41:53,600
Speaker 1: also reminded of when she was in that difficult interview

895
00:41:53,640 --> 00:41:57,040
Speaker 1: with Brett Baer. He tried multiple times to try to

896
00:41:57,080 --> 00:42:01,400
Speaker 1: get her to have a moment where she derived Trump supporters,

897
00:42:01,480 --> 00:42:05,160
Speaker 1: and she adamantly refused to take the bait. We're also

898
00:42:05,160 --> 00:42:07,000
Speaker 1: going to show you later in the show some comments

899
00:42:07,000 --> 00:42:11,200
Speaker 1: from Bill Clinton out there in Michigan making somewhat less

900
00:42:11,239 --> 00:42:16,600
Speaker 1: than compelling case, we'll say, to Arab American, Muslim American voters,

901
00:42:16,680 --> 00:42:18,680
Speaker 1: you know, to not Lei Kamlo has been good on

902
00:42:18,719 --> 00:42:21,680
Speaker 1: that topic either. She said that the main problem they

903
00:42:21,719 --> 00:42:24,960
Speaker 1: have is obviously the reality of the policy that she supports,

904
00:42:25,000 --> 00:42:27,120
Speaker 1: and her refusal to put on any distance from her

905
00:42:27,200 --> 00:42:30,279
Speaker 1: between herself and Joe Biden on that. But again at

906
00:42:30,320 --> 00:42:32,880
Speaker 1: a time when she's trying to say, listen, I'm concerned

907
00:42:32,880 --> 00:42:37,080
Speaker 1: about Palestinian civilian death. Bill Clinton goes out there and

908
00:42:37,120 --> 00:42:39,640
Speaker 1: steps all over that. So you know, with this Joe

909
00:42:39,680 --> 00:42:42,080
Speaker 1: Biden thing like this is also not even the first

910
00:42:42,120 --> 00:42:47,360
Speaker 1: time that he's stepped on her messaging. During this particular campaign,

911
00:42:47,400 --> 00:42:48,839
Speaker 1: she had a big event that she wanted to get

912
00:42:48,880 --> 00:42:50,799
Speaker 1: a lot of attention, and he at the same time

913
00:42:50,880 --> 00:42:52,920
Speaker 1: came out and gave a press conference, which he almost

914
00:42:53,040 --> 00:42:56,040
Speaker 1: never does. There was that issue she was in this

915
00:42:56,160 --> 00:42:59,440
Speaker 1: sort of like battle with Ronda Santis over hurricane relief,

916
00:42:59,840 --> 00:43:02,759
Speaker 1: and he wasn't responding to her calls, and they were

917
00:43:02,760 --> 00:43:05,040
Speaker 1: into this whole thing, and then Joe Biden gets asked

918
00:43:05,080 --> 00:43:06,520
Speaker 1: about He's like, Ron's been great.

919
00:43:07,680 --> 00:43:09,080
Speaker 4: He's been very responsive to me.

920
00:43:09,640 --> 00:43:13,239
Speaker 1: So, you know, coming at this moment when she just

921
00:43:13,280 --> 00:43:16,640
Speaker 1: gave this big speech on the Ellipse where she made

922
00:43:16,640 --> 00:43:19,080
Speaker 1: a point in that speech of trying to reach her

923
00:43:19,080 --> 00:43:22,640
Speaker 1: hand out to Trump supporters, obviously the campaign is not

924
00:43:22,719 --> 00:43:23,400
Speaker 1: very out about them.

925
00:43:23,400 --> 00:43:27,040
Speaker 4: No, there's much of a differences. I mean, it's like the.

926
00:43:27,280 --> 00:43:29,919
Speaker 2: Trump got a decent stunt off of it with the garbage. Sure,

927
00:43:30,239 --> 00:43:32,040
Speaker 2: we have some of that video that we can go

928
00:43:32,080 --> 00:43:33,160
Speaker 2: ahead and play for everybody.

929
00:43:33,239 --> 00:43:34,040
Speaker 3: Let's take a lesson.

930
00:43:34,200 --> 00:43:38,360
Speaker 11: Two hundred and fifty million people are not garbage.

931
00:43:38,719 --> 00:43:39,319
Speaker 5: I could tell you.

932
00:43:39,360 --> 00:43:41,560
Speaker 2: Who the real garbage is, but we won't say that.

933
00:43:43,920 --> 00:43:46,880
Speaker 3: How do you like my garbage truck? This truck is

934
00:43:46,920 --> 00:43:48,880
Speaker 3: an honor of Kamala and Joe.

935
00:43:48,640 --> 00:43:50,879
Speaker 2: Biden is in the garbage truck to do a little

936
00:43:50,880 --> 00:43:52,799
Speaker 2: press comp We'll get to the most consequential part of that.

937
00:43:52,880 --> 00:43:54,320
Speaker 4: I mean, it's just hard for me to take.

938
00:43:54,120 --> 00:43:55,680
Speaker 3: He's recreating the McDonald's magic.

939
00:43:55,800 --> 00:43:58,400
Speaker 1: Yeah, he's definitely And we have some polling later to

940
00:43:58,440 --> 00:44:00,880
Speaker 1: show you that that McDonald's done got a lot of

941
00:44:01,000 --> 00:44:03,080
Speaker 1: It was like the number one story that people are

942
00:44:03,160 --> 00:44:06,640
Speaker 1: aware of its Yeah, because the images are so striking.

943
00:44:06,680 --> 00:44:09,200
Speaker 2: It's one of the most indelible photos of American It's

944
00:44:09,280 --> 00:44:12,040
Speaker 2: up there with the tank photo. Like that's it's it's

945
00:44:12,080 --> 00:44:15,200
Speaker 2: going to be burned in to my like Dewey defeats Truman.

946
00:44:15,560 --> 00:44:17,880
Speaker 3: The tank, the McDonald's thing, I'll never forget it.

947
00:44:17,880 --> 00:44:20,040
Speaker 4: You talk about the Doucocot tank, Yeah, the Douacacus. That's

948
00:44:20,080 --> 00:44:21,200
Speaker 4: not a favorable comparison.

949
00:44:21,880 --> 00:44:23,239
Speaker 3: I didn't say it was good or bad. I just

950
00:44:23,280 --> 00:44:24,960
Speaker 3: said it's burning burn into the mind.

951
00:44:25,080 --> 00:44:26,600
Speaker 4: Yeah, yeah.

952
00:44:26,640 --> 00:44:29,680
Speaker 1: I mean I think it's hard for obviously, it's hard

953
00:44:29,680 --> 00:44:31,840
Speaker 1: for me to take seriously like all of their angst

954
00:44:31,880 --> 00:44:34,680
Speaker 1: and upset over it because Trump goes out there all

955
00:44:34,719 --> 00:44:39,000
Speaker 1: the time and trash's Democrats and Democratic supporters and the

956
00:44:39,120 --> 00:44:42,600
Speaker 1: enemy within and whatever. So but you know, yeah, they're

957
00:44:42,640 --> 00:44:44,680
Speaker 1: going to make of it what they can. I don't

958
00:44:44,719 --> 00:44:48,360
Speaker 1: think it's number one. Back in twenty sixteen, when Hillary

959
00:44:48,360 --> 00:44:50,080
Speaker 1: said that, first of all, she was the one who

960
00:44:50,160 --> 00:44:53,960
Speaker 1: was running, and second of all, there were people who genuinely,

961
00:44:54,080 --> 00:44:56,799
Speaker 1: you know, potentially could have been swayed between Trump and

962
00:44:56,840 --> 00:44:59,359
Speaker 1: Hillary Clinton. That was still a theoretical possibility.

963
00:45:00,080 --> 00:45:01,640
Speaker 4: Trump's face is so locked.

964
00:45:01,360 --> 00:45:05,600
Speaker 1: In, Like what person who sees himself as a Trump

965
00:45:05,640 --> 00:45:08,680
Speaker 1: supporter was really considering Kamala Harris at this point is

966
00:45:08,719 --> 00:45:10,920
Speaker 1: going to be like, oh, Joe Biden said that we're garbage, Like,

967
00:45:10,960 --> 00:45:13,080
Speaker 1: forget about it. I guess the best you can hope

968
00:45:13,120 --> 00:45:15,880
Speaker 1: is that it helps the juice turnout on that side.

969
00:45:15,920 --> 00:45:18,319
Speaker 1: And maybe that's a maybe that's a possibility. I don't

970
00:45:18,360 --> 00:45:20,560
Speaker 1: want to discount it, but like you know, I just

971
00:45:21,320 --> 00:45:23,839
Speaker 1: it doesn't have quite the magic that the Deplorables moment

972
00:45:23,840 --> 00:45:24,800
Speaker 1: did back in twenty sixty.

973
00:45:24,880 --> 00:45:27,200
Speaker 3: Well, yeah, I mean deplorables was everything.

974
00:45:27,280 --> 00:45:29,120
Speaker 2: And first of all, I mean, the main reason why

975
00:45:29,160 --> 00:45:31,000
Speaker 2: Trump gets away with it and they don't is that

976
00:45:31,080 --> 00:45:33,319
Speaker 2: because a huge part of the Trump base is literally

977
00:45:33,400 --> 00:45:36,600
Speaker 2: animated by being pissed and shit on by elites and

978
00:45:36,640 --> 00:45:37,399
Speaker 2: feeling that way.

979
00:45:37,680 --> 00:45:39,440
Speaker 3: They that's an entire part of it.

980
00:45:39,480 --> 00:45:43,200
Speaker 2: So in a sense, that is why it is so

981
00:45:43,320 --> 00:45:46,799
Speaker 2: animating to the Trump voter and not maybe not necessarily

982
00:45:46,840 --> 00:45:49,960
Speaker 2: to the Democratic voter, just because there's like a there's

983
00:45:49,960 --> 00:45:53,040
Speaker 2: a much bigger Zeitgeist thing happening. Regardless, this tells us

984
00:45:53,040 --> 00:45:55,160
Speaker 2: a couple of things. Why Biden is it that terrible candidate?

985
00:45:55,360 --> 00:45:58,520
Speaker 2: Why Biden is cooked old and shouldn't even be in office?

986
00:45:58,560 --> 00:46:01,320
Speaker 2: And the key was a i'm all immediately distanced herself

987
00:46:01,320 --> 00:46:05,279
Speaker 2: from it. But also Josh Shapiro immediately on television that night,

988
00:46:05,320 --> 00:46:07,600
Speaker 2: I think maybe an hour or so after the comment

989
00:46:07,760 --> 00:46:10,200
Speaker 2: was made, I mean as forcefully distanced himself as he

990
00:46:10,239 --> 00:46:10,759
Speaker 2: could from it.

991
00:46:10,880 --> 00:46:11,879
Speaker 3: Let's take a listen to that.

992
00:46:12,120 --> 00:46:15,520
Speaker 12: Governor Vershoff, just what's your response to that comment from

993
00:46:15,600 --> 00:46:19,560
Speaker 12: President Biden where it sounds like he's calling Trump supporters

994
00:46:19,560 --> 00:46:22,480
Speaker 12: their garbage.

995
00:46:23,120 --> 00:46:26,240
Speaker 13: Yeah, Look, I had not heard that until now, Caitlin,

996
00:46:26,440 --> 00:46:29,080
Speaker 13: so I'm kind of giving you my fresh reaction to it.

997
00:46:29,600 --> 00:46:33,880
Speaker 13: I would never insult the good people of Pennsylvania or

998
00:46:33,920 --> 00:46:37,320
Speaker 13: any Americans, even if they chose to support a candidate

999
00:46:37,880 --> 00:46:40,439
Speaker 13: that I didn't support. I think the real issue here

1000
00:46:40,520 --> 00:46:45,480
Speaker 13: is Donald Trump and his inability to simply say comments

1001
00:46:45,560 --> 00:46:48,600
Speaker 13: like that are wrong, to simply stand up for his

1002
00:46:48,680 --> 00:46:51,840
Speaker 13: fellow Americans. He is incapable of doing that. When he

1003
00:46:51,880 --> 00:46:54,320
Speaker 13: had the keys to the White House before, he didn't

1004
00:46:54,360 --> 00:46:57,600
Speaker 13: do it. He separated people out, he divided people. He's

1005
00:46:57,640 --> 00:47:00,279
Speaker 13: promised more of the same, and that's why I think

1006
00:47:00,320 --> 00:47:03,160
Speaker 13: he's dangerous, and that's why I'm working as hard as

1007
00:47:03,200 --> 00:47:06,480
Speaker 13: I can to support Kamala Harris and do everything we

1008
00:47:06,560 --> 00:47:09,880
Speaker 13: can to defeat Donald Trump again in Pennsylvania.

1009
00:47:09,960 --> 00:47:13,399
Speaker 2: That is what a competent politician looks like. And that's

1010
00:47:13,400 --> 00:47:15,600
Speaker 2: that's really what it comes down to me. You know,

1011
00:47:15,680 --> 00:47:17,920
Speaker 2: I got no love for Joshapiro, and he won a

1012
00:47:17,920 --> 00:47:20,920
Speaker 2: lot Kamala Harris Trump supporters pennsylvani The thing is, remember

1013
00:47:20,960 --> 00:47:25,200
Speaker 2: how he responded to the assassination attempt, and he went

1014
00:47:25,320 --> 00:47:29,080
Speaker 2: and did a conference where he read from the from

1015
00:47:29,120 --> 00:47:32,080
Speaker 2: the widow the newly like I forget them. I think

1016
00:47:32,080 --> 00:47:35,000
Speaker 2: it's Corey competore and he gave him a great eulogy,

1017
00:47:35,080 --> 00:47:37,160
Speaker 2: and he was a girl dad and all that spoke

1018
00:47:37,239 --> 00:47:41,120
Speaker 2: to the his what the widow read a statement from

1019
00:47:41,160 --> 00:47:43,359
Speaker 2: her even though literally they were killed at a Trump brother.

1020
00:47:43,440 --> 00:47:46,880
Speaker 2: That's what actual competent, you know, politicians look like. And

1021
00:47:46,920 --> 00:47:48,400
Speaker 2: then you compare that to buy it in it's just

1022
00:47:48,440 --> 00:47:49,239
Speaker 2: like ridiculous.

1023
00:47:49,320 --> 00:47:53,560
Speaker 1: Yeah, I mean I genuinely, like, I genuinely if you

1024
00:47:53,600 --> 00:47:55,880
Speaker 1: look at his whole quote, like if you read the

1025
00:47:55,920 --> 00:48:00,400
Speaker 1: transcript in come, it's incomprehensible. Yeah, And so, like, I

1026
00:48:00,400 --> 00:48:02,680
Speaker 1: don't know what he was trying to say. I find

1027
00:48:02,680 --> 00:48:04,640
Speaker 1: it believable that because it is kind of on a

1028
00:48:04,719 --> 00:48:06,680
Speaker 1: character for him, he's wanted to be more like, oh,

1029
00:48:06,719 --> 00:48:08,239
Speaker 1: I worked with strom Thurmon, like.

1030
00:48:08,200 --> 00:48:10,600
Speaker 4: I love Republicans. That's more of his mo o.

1031
00:48:10,840 --> 00:48:13,880
Speaker 1: Right, So I find it believable that he didn't really

1032
00:48:13,920 --> 00:48:17,200
Speaker 1: mean to say that all Trump supporters are quote unquote garbage.

1033
00:48:17,440 --> 00:48:20,040
Speaker 1: But like, that's the thing is if you are so

1034
00:48:20,880 --> 00:48:25,040
Speaker 1: adult and incoherent and still occupying the office of the

1035
00:48:25,120 --> 00:48:28,640
Speaker 1: White House, like what are we doing here? And Alex

1036
00:48:28,680 --> 00:48:32,720
Speaker 1: Thompson made that made that point effectively Axios reporters. CNN

1037
00:48:32,760 --> 00:48:35,960
Speaker 1: contributor Alex Thompson made that point of like, that's the

1038
00:48:36,080 --> 00:48:39,480
Speaker 1: really sad reality is that you can parse these words

1039
00:48:39,480 --> 00:48:41,960
Speaker 1: and it's like trying you need a decoder ring to

1040
00:48:42,000 --> 00:48:43,919
Speaker 1: try to figure out what the hell he's even talking about.

1041
00:48:43,920 --> 00:48:46,600
Speaker 1: Some of his supporters have also brought back up like, oh,

1042
00:48:46,640 --> 00:48:49,239
Speaker 1: he has a stutter. Oh my god, Like, please, come on,

1043
00:48:49,360 --> 00:48:51,440
Speaker 1: what are we doing here at this point? Let's listen

1044
00:48:51,440 --> 00:48:52,759
Speaker 1: to the point that Alex Thompson made.

1045
00:48:52,800 --> 00:48:55,040
Speaker 7: Though no one here at this table knows what Joe

1046
00:48:55,080 --> 00:48:57,920
Speaker 7: Biden meant unless someone here has talked to Joe Biden

1047
00:48:58,200 --> 00:49:01,200
Speaker 7: when he made those when he made those comments, and

1048
00:49:01,640 --> 00:49:07,799
Speaker 7: the sad reality is that they were indecipherable because this

1049
00:49:07,920 --> 00:49:12,000
Speaker 7: president is no longer able to coherently and consistently articulate

1050
00:49:12,040 --> 00:49:15,000
Speaker 7: a message. And that's just the sad reality. That is

1051
00:49:15,040 --> 00:49:17,640
Speaker 7: why he's no longer the nominee because of the debate.

1052
00:49:17,840 --> 00:49:20,799
Speaker 7: We all saw that very clearly, and it's also why

1053
00:49:20,840 --> 00:49:23,120
Speaker 7: Kamala Harris does not want him on the trail. This

1054
00:49:23,160 --> 00:49:25,959
Speaker 7: is a guy that just last week referred to former

1055
00:49:26,000 --> 00:49:28,759
Speaker 7: Representative Gabby Giffords in the past tense. She's very much

1056
00:49:28,800 --> 00:49:32,279
Speaker 7: alive as a person that last week said that he wanted.

1057
00:49:32,040 --> 00:49:33,600
Speaker 3: To throw Donald Trump in jail and then.

1058
00:49:33,560 --> 00:49:36,560
Speaker 7: Very quickly tried to backtrack and said he just politically

1059
00:49:36,719 --> 00:49:40,040
Speaker 7: meant to lock him up. And we are in this

1060
00:49:40,200 --> 00:49:43,520
Speaker 7: sad scenario where clearly Joe Biden and the aids around

1061
00:49:43,560 --> 00:49:45,920
Speaker 7: him that want to make him feel better, want him

1062
00:49:46,000 --> 00:49:47,799
Speaker 7: to be able to sort of be inserted into this

1063
00:49:47,960 --> 00:49:52,880
Speaker 7: race that see Kamala Harris's potential election as an affirmation

1064
00:49:52,920 --> 00:49:55,839
Speaker 7: of his record, but that Kamala Harris does not want

1065
00:49:55,920 --> 00:49:57,960
Speaker 7: him to be inserted, and it would prefer that he

1066
00:49:58,280 --> 00:49:59,839
Speaker 7: basically be absent. This last week.

1067
00:50:01,160 --> 00:50:03,160
Speaker 3: I love how he had you could hear a pin

1068
00:50:03,280 --> 00:50:03,680
Speaker 3: drop on.

1069
00:50:03,640 --> 00:50:06,399
Speaker 1: That in that yea, because how can you how can

1070
00:50:06,480 --> 00:50:09,160
Speaker 1: you disagree with any of that? And I agree with

1071
00:50:09,239 --> 00:50:11,239
Speaker 1: Nate Silver who says there should be a lot more

1072
00:50:11,360 --> 00:50:14,640
Speaker 1: journalistic scrutiny of his capability to function in this job,

1073
00:50:14,680 --> 00:50:16,680
Speaker 1: because yeah, I mean, he's almost not out of there,

1074
00:50:16,719 --> 00:50:19,160
Speaker 1: but we still got months to go. Well, he is

1075
00:50:19,239 --> 00:50:21,440
Speaker 1: still president of the United States, and he is so

1076
00:50:21,600 --> 00:50:25,560
Speaker 1: addled that he can't even in the one CNN appearance

1077
00:50:25,600 --> 00:50:29,520
Speaker 1: that he did competently deliver a basic point and a

1078
00:50:29,560 --> 00:50:33,040
Speaker 1: basic message. Like, the other thing that's insane to me

1079
00:50:33,600 --> 00:50:35,760
Speaker 1: is that you still have people out there running around

1080
00:50:35,920 --> 00:50:37,960
Speaker 1: like maybe he would have been a better candidate and

1081
00:50:38,000 --> 00:50:41,040
Speaker 1: I don't know why we switch. And if you go back,

1082
00:50:41,160 --> 00:50:45,399
Speaker 1: especially at the time, the number of Democratic partisans who

1083
00:50:45,440 --> 00:50:47,080
Speaker 1: were like, no, we got to stick with Joe, and

1084
00:50:47,160 --> 00:50:49,400
Speaker 1: Joe's great and he's going to be way more electable.

1085
00:50:49,480 --> 00:50:52,440
Speaker 1: Like these people should never be taken seriously again, because

1086
00:50:53,520 --> 00:50:56,600
Speaker 1: this this is not new, right. I mean, we all

1087
00:50:56,640 --> 00:51:00,400
Speaker 1: saw the debate, which was shocking, But even before the debate,

1088
00:51:00,800 --> 00:51:01,880
Speaker 1: how long have we been.

1089
00:51:01,719 --> 00:51:03,920
Speaker 3: Covering years, literally years.

1090
00:51:03,719 --> 00:51:08,400
Speaker 1: His rapid decline. So I think it is a tremendous

1091
00:51:08,520 --> 00:51:10,799
Speaker 1: journalistic fit. Look, I get it, it's the election, and

1092
00:51:10,880 --> 00:51:12,800
Speaker 1: so you know, there's a lot of coverage of Kamlin

1093
00:51:12,840 --> 00:51:15,520
Speaker 1: Trump and what they're doing, and that's all justified. But

1094
00:51:15,600 --> 00:51:19,000
Speaker 1: the fact that when Biden decided to step out of

1095
00:51:19,040 --> 00:51:22,800
Speaker 1: the race, that almost all the scrutiny about his age

1096
00:51:22,800 --> 00:51:25,919
Speaker 1: and capability went away, even though he's still the guy

1097
00:51:25,920 --> 00:51:28,439
Speaker 1: with the nuclear codes, you know, I do think that

1098
00:51:28,440 --> 00:51:33,000
Speaker 1: that is a tremendous a tremendous failure and dramatic inadequacy

1099
00:51:33,040 --> 00:51:33,560
Speaker 1: of the process.

1100
00:51:33,920 --> 00:51:37,279
Speaker 2: He is the most incapacitated president in office. Sin's like

1101
00:51:37,280 --> 00:51:40,839
Speaker 2: Woodrow Wilson, and you know it maybe just like at

1102
00:51:40,840 --> 00:51:42,600
Speaker 2: that time, they didn't want to cover it and his

1103
00:51:42,680 --> 00:51:45,200
Speaker 2: wife was running the White House and all of that.

1104
00:51:45,320 --> 00:51:47,560
Speaker 2: I think we're seen very similar dynamics to this. Five

1105
00:51:47,600 --> 00:51:49,839
Speaker 2: years from now, we'll get the whole inside story from

1106
00:51:49,960 --> 00:51:53,440
Speaker 2: Jake Sullivan and Anthony Blincoln about what it was really like.

1107
00:51:53,560 --> 00:51:55,560
Speaker 2: You know, he'll probably have to like die, you know

1108
00:51:55,640 --> 00:51:58,720
Speaker 2: eventually before you can tell the real truth about what happened.

1109
00:51:58,760 --> 00:52:01,400
Speaker 2: And then it's it's going to be insane, Like whenever

1110
00:52:01,480 --> 00:52:05,279
Speaker 2: that book comes out, it's, oh my god. And the

1111
00:52:05,360 --> 00:52:07,560
Speaker 2: worst part is is that we won't even be surprised

1112
00:52:07,760 --> 00:52:11,600
Speaker 2: by any of it. All right, let's move on to

1113
00:52:11,800 --> 00:52:14,200
Speaker 2: Madison Square Garden, and there's been quite a lot of

1114
00:52:14,239 --> 00:52:18,319
Speaker 2: discussion there about the or men of grbage Gate. Garbage Gate,

1115
00:52:18,480 --> 00:52:21,319
Speaker 2: that's right, I like that Puerto Rico Gate. You could

1116
00:52:21,360 --> 00:52:23,440
Speaker 2: also call hingecliff Gate. You could take it back as

1117
00:52:23,480 --> 00:52:25,640
Speaker 2: far as he wants. So we've got a couple of

1118
00:52:25,880 --> 00:52:30,760
Speaker 2: Republicans actually who came out to criticize first was Nikki Haley,

1119
00:52:30,800 --> 00:52:33,880
Speaker 2: who took to Fox News to critique the Trump campaign.

1120
00:52:34,080 --> 00:52:34,880
Speaker 3: Let's take a listen.

1121
00:52:35,080 --> 00:52:37,759
Speaker 14: They were right to denounce the comedian. They need to

1122
00:52:37,800 --> 00:52:40,319
Speaker 14: go and tell Puerto Ricans how much you know they

1123
00:52:40,360 --> 00:52:42,520
Speaker 14: do value them. They need to tell Latino's that. But

1124
00:52:42,560 --> 00:52:45,680
Speaker 14: they also need to look at how they're talking about women.

1125
00:52:46,040 --> 00:52:49,719
Speaker 14: I mean that this romance and this masculinity stuff, I

1126
00:52:49,719 --> 00:52:52,960
Speaker 14: mean it borders on edgy to the point that it's

1127
00:52:52,960 --> 00:52:55,960
Speaker 14: going to make women uncomfortable. You know, you've got affiliated

1128
00:52:55,960 --> 00:52:59,399
Speaker 14: packs that are doing commercials about calling Kamala the sea word,

1129
00:52:59,520 --> 00:53:02,319
Speaker 14: or you had speakers at Madison Square Gardens, you know,

1130
00:53:02,440 --> 00:53:05,440
Speaker 14: referring to her and her pimps. That is not the

1131
00:53:05,480 --> 00:53:07,600
Speaker 14: way to win women. That is not the way to

1132
00:53:07,640 --> 00:53:10,719
Speaker 14: win people who are concerned about Trump style. This is

1133
00:53:10,760 --> 00:53:12,759
Speaker 14: a time to talk about the economy, This is the

1134
00:53:12,800 --> 00:53:14,880
Speaker 14: time to talk about immigration, this is the time to

1135
00:53:14,920 --> 00:53:17,400
Speaker 14: talk about national security, and this is the time to

1136
00:53:17,440 --> 00:53:18,720
Speaker 14: talk about Kamwa Harris.

1137
00:53:18,960 --> 00:53:19,960
Speaker 3: So you could see there.

1138
00:53:20,000 --> 00:53:22,520
Speaker 2: I mean my take actionally Crystal was I think she's

1139
00:53:22,560 --> 00:53:25,680
Speaker 2: betting on a Trump loss. That's my guess from this clip,

1140
00:53:25,760 --> 00:53:29,080
Speaker 2: because I actually spoke to some others, people who are

1141
00:53:29,120 --> 00:53:31,560
Speaker 2: concerned but who are your friend and they want to

1142
00:53:31,560 --> 00:53:33,479
Speaker 2: help you win. They call you behind the scenes, right,

1143
00:53:33,560 --> 00:53:36,120
Speaker 2: they don't want to give the other side ammunition. But

1144
00:53:36,200 --> 00:53:38,920
Speaker 2: when you're betting on a loss, you take to the

1145
00:53:38,960 --> 00:53:41,000
Speaker 2: TV and you're like, well, they need to do a

1146
00:53:41,000 --> 00:53:43,160
Speaker 2: better job reaching out to women, and this isn't this.

1147
00:53:43,840 --> 00:53:45,560
Speaker 2: But of course I still want him to win because

1148
00:53:45,800 --> 00:53:48,480
Speaker 2: I'm working on policy, trying to cover her bases. This

1149
00:53:48,560 --> 00:53:51,440
Speaker 2: seemed like a soft twenty twenty eight launch just from

1150
00:53:51,480 --> 00:53:54,560
Speaker 2: what I could tell. She's not a commentator, she's a politician, right.

1151
00:53:54,719 --> 00:53:56,759
Speaker 2: She also has not she's been rejected by the Trump

1152
00:53:56,840 --> 00:54:00,120
Speaker 2: campaign effectively, will not campaign with him. It looks like

1153
00:54:00,360 --> 00:54:03,239
Speaker 2: up until election day they previously had thought that they might.

1154
00:54:03,280 --> 00:54:05,400
Speaker 2: But there's still quite a bit of bad blood between

1155
00:54:05,440 --> 00:54:07,440
Speaker 2: the two camps. So there's quite a bit of jocking

1156
00:54:07,480 --> 00:54:08,240
Speaker 2: that's also happening.

1157
00:54:08,360 --> 00:54:10,680
Speaker 1: Yes, yeah, I think the comments are accurate, but they're

1158
00:54:10,719 --> 00:54:14,319
Speaker 1: also motivated because it's not just her positioning herself for

1159
00:54:14,400 --> 00:54:16,719
Speaker 1: what comes next, and she fancies herself potentially the next

1160
00:54:16,719 --> 00:54:21,640
Speaker 1: Republican contenderlution, but whatever, that's how she's thinking. And she

1161
00:54:21,760 --> 00:54:24,520
Speaker 1: also is bitter because to your point about you know,

1162
00:54:24,520 --> 00:54:25,960
Speaker 1: you call him behind the scenes, him.

1163
00:54:25,880 --> 00:54:26,800
Speaker 4: Not picking up those calls.

1164
00:54:26,920 --> 00:54:29,400
Speaker 1: Yeah, that has made herself available to go a campaign

1165
00:54:29,400 --> 00:54:32,160
Speaker 1: for him, and he's like no. And in fact, when

1166
00:54:32,160 --> 00:54:34,439
Speaker 1: he and I think this is foolish by the way

1167
00:54:34,560 --> 00:54:38,959
Speaker 1: on his part, because clearly the Kamala campaign thinks whether

1168
00:54:38,960 --> 00:54:41,120
Speaker 1: they're right about this or not, there's a possibility that

1169
00:54:41,120 --> 00:54:43,439
Speaker 1: they're right about it, that there are some of those

1170
00:54:43,560 --> 00:54:49,040
Speaker 1: Nikki Haley primary voters who are potential persuadable targets for them.

1171
00:54:49,400 --> 00:54:51,719
Speaker 1: They have made a very concerted effort. That's like the

1172
00:54:51,800 --> 00:54:54,719
Speaker 1: bulk of their campaign is a concerted effort to reach

1173
00:54:54,760 --> 00:54:57,319
Speaker 1: out to those voters. And recently, when Trump was asked

1174
00:54:57,320 --> 00:55:00,319
Speaker 1: about Nicki Haley on a Fox and Friends or some thing,

1175
00:55:00,800 --> 00:55:03,839
Speaker 1: rather than trying to appeal to them, he trashes Nicky

1176
00:55:03,880 --> 00:55:06,359
Speaker 1: Haley and rags about how much he beat her by

1177
00:55:06,400 --> 00:55:09,120
Speaker 1: et cetera, et cetera. So you know, I mean, this

1178
00:55:09,280 --> 00:55:11,759
Speaker 1: is just Trump and the way he operates, and his

1179
00:55:11,840 --> 00:55:15,640
Speaker 1: ego always comes first, even when it's totally inadvisable from

1180
00:55:15,680 --> 00:55:18,759
Speaker 1: any sort of a rational campaign perspective, like what would

1181
00:55:18,800 --> 00:55:21,880
Speaker 1: it hurt you to have her come and be on

1182
00:55:21,920 --> 00:55:23,719
Speaker 1: the stage with you. She's willing to do it, etc.

1183
00:55:24,080 --> 00:55:26,319
Speaker 1: It's certainly not going to be a loss, and potentially

1184
00:55:26,680 --> 00:55:29,520
Speaker 1: it's a gain for you, but that's that's just not

1185
00:55:29,640 --> 00:55:32,040
Speaker 1: in him to do so. I think the Yale is

1186
00:55:32,480 --> 00:55:35,839
Speaker 1: right fundamentally in her comments. We've played that the Megan

1187
00:55:35,920 --> 00:55:38,080
Speaker 1: Kelly comments in a moment, which are are very similar.

1188
00:55:38,120 --> 00:55:39,120
Speaker 4: She had a very similar take.

1189
00:55:39,160 --> 00:55:43,040
Speaker 1: You had Tommy Laren earlier on critical of some of

1190
00:55:43,080 --> 00:55:46,799
Speaker 1: the lack of outreach to women, and you know, it

1191
00:55:46,840 --> 00:55:50,880
Speaker 1: really has. They have actually primarily turned it into a

1192
00:55:51,000 --> 00:55:53,600
Speaker 1: real battle of the sexes kind of a vibe. And

1193
00:55:53,640 --> 00:55:55,239
Speaker 1: we've done a lot of scrutiny. There's been a lot

1194
00:55:55,280 --> 00:55:58,759
Speaker 1: of conversation about Kamala Harris knowing to do better with men.

1195
00:55:58,880 --> 00:56:01,760
Speaker 1: She had that like hop like moment with Russian Whitmer,

1196
00:56:01,800 --> 00:56:03,920
Speaker 1: et cetera, et cetera. And you see them trying to

1197
00:56:03,960 --> 00:56:06,319
Speaker 1: make some efforts. You see her going on with you know,

1198
00:56:06,400 --> 00:56:08,799
Speaker 1: Shannon Sharp on Club Shasha, you see her going on

1199
00:56:08,920 --> 00:56:11,040
Speaker 1: The Shade Room and some of these like more male

1200
00:56:11,120 --> 00:56:15,120
Speaker 1: dominated podcasts. You don't really see equivalent outreach from the

1201
00:56:15,320 --> 00:56:18,960
Speaker 1: Trump side, like he didn't go on with Brene Brown

1202
00:56:19,080 --> 00:56:21,520
Speaker 1: or whoever the you know, and Kamala did, by the way,

1203
00:56:21,920 --> 00:56:26,520
Speaker 1: some feat more female dominated spaces. Instead, they've just consistently

1204
00:56:26,600 --> 00:56:29,600
Speaker 1: leaned into the like camp masculinity theme that you saw

1205
00:56:29,680 --> 00:56:31,200
Speaker 1: at the R and C and that you certainly saw

1206
00:56:31,239 --> 00:56:34,040
Speaker 1: in Madison Square, Garden, Hule Cogan, Dana White, you know

1207
00:56:34,160 --> 00:56:37,480
Speaker 1: that whole sense. And so you have a few women

1208
00:56:37,520 --> 00:56:39,840
Speaker 1: in the party at least saying and now actually Cernovich

1209
00:56:39,880 --> 00:56:41,799
Speaker 1: and Charlie Kirk as well saying like, eh, this might

1210
00:56:41,840 --> 00:56:42,719
Speaker 1: be a little bit of an issue.

1211
00:56:42,719 --> 00:56:44,120
Speaker 3: They're talking about the vote, right.

1212
00:56:44,120 --> 00:56:46,640
Speaker 4: Yeah, but the downstream of the bro this.

1213
00:56:46,600 --> 00:56:48,839
Speaker 2: Is where I'm like, I don't think that she's necessarily right,

1214
00:56:48,920 --> 00:56:50,759
Speaker 2: like in terms of what does she say in terms

1215
00:56:50,760 --> 00:56:53,480
Speaker 2: of like, this is not the way to win women.

1216
00:56:53,640 --> 00:56:56,840
Speaker 2: I mean that also presumes like what is the correct

1217
00:56:56,840 --> 00:56:59,480
Speaker 2: way to quote unquote win women? Like that just seems

1218
00:56:59,520 --> 00:57:03,200
Speaker 2: like a very I don't know, it doesn't seem like

1219
00:57:03,320 --> 00:57:06,480
Speaker 2: it's a comment that is directed in any reality. If

1220
00:57:06,480 --> 00:57:09,640
Speaker 2: we look at quote unquote women voters, they're voting on

1221
00:57:09,680 --> 00:57:10,880
Speaker 2: the issue of abortion.

1222
00:57:11,080 --> 00:57:13,000
Speaker 3: You can't win on the issue of abortion.

1223
00:57:13,360 --> 00:57:17,440
Speaker 2: Your best case scenario is to win men, drive up

1224
00:57:17,440 --> 00:57:20,360
Speaker 2: the margin, and then just hope that married women come

1225
00:57:20,400 --> 00:57:22,480
Speaker 2: along as they have in the past. I think in

1226
00:57:22,520 --> 00:57:24,840
Speaker 2: twenty sixteen and in twenty twenty to make sure that

1227
00:57:24,840 --> 00:57:26,400
Speaker 2: you don't get drowned out. That's part of the reason

1228
00:57:26,400 --> 00:57:28,919
Speaker 2: that I think. Didn't you reference those ads where it's like, women,

1229
00:57:28,960 --> 00:57:30,960
Speaker 2: it's okay to lie to your husband or what, which is,

1230
00:57:30,960 --> 00:57:33,800
Speaker 2: by the way, psychotic and weird, and if you're in

1231
00:57:33,840 --> 00:57:35,560
Speaker 2: a marriage, we had to lie to your husband or

1232
00:57:35,560 --> 00:57:37,360
Speaker 2: your wife your body for marriage.

1233
00:57:37,360 --> 00:57:39,040
Speaker 3: You got to get out, all right, let me just

1234
00:57:39,080 --> 00:57:39,680
Speaker 3: tell you that right now.

1235
00:57:39,760 --> 00:57:42,520
Speaker 1: I saw I tweet about this too, where a woman

1236
00:57:42,640 --> 00:57:45,320
Speaker 1: was saying like, she who knows if this is even accurate,

1237
00:57:45,360 --> 00:57:47,200
Speaker 1: but that like every four years as hell because her

1238
00:57:47,240 --> 00:57:49,200
Speaker 1: husband's a big Trump supporter and she's not. And he

1239
00:57:49,320 --> 00:57:50,959
Speaker 1: was like, so where are you gonna move if Trump

1240
00:57:51,000 --> 00:57:53,280
Speaker 1: wins again? I'm like, what kind of a marriage is this?

1241
00:57:53,600 --> 00:57:54,640
Speaker 4: Jesus Christ to.

1242
00:57:54,640 --> 00:57:58,600
Speaker 2: Her, get out to him, you're a loser. You're like sir,

1243
00:57:58,920 --> 00:58:01,120
Speaker 2: and vice versa. If you're aiding your husband or whatever,

1244
00:58:01,200 --> 00:58:02,080
Speaker 2: you're also a loser.

1245
00:58:02,160 --> 00:58:05,439
Speaker 1: I just would say, and uh, and then we'll play

1246
00:58:05,440 --> 00:58:07,280
Speaker 1: the Megan Kelly comments and get her take on this

1247
00:58:07,360 --> 00:58:09,880
Speaker 1: as well. Nikki Haley reference to the fact that you

1248
00:58:09,920 --> 00:58:13,680
Speaker 1: know you had multiple sort of like sexually lude jokes

1249
00:58:13,680 --> 00:58:16,600
Speaker 1: made at Kamala Harris's expense, You had the other Joe

1250
00:58:16,600 --> 00:58:18,960
Speaker 1: from Tony Hinchcliff that we haven't even played as about,

1251
00:58:18,960 --> 00:58:24,080
Speaker 1: like you know, Latino's coming inside, and you have Elon

1252
00:58:24,200 --> 00:58:29,320
Speaker 1: Musk's super pack literally running an ad insinuating Kamala Harris

1253
00:58:29,400 --> 00:58:33,120
Speaker 1: is the C word. So you know, I certainly there

1254
00:58:33,120 --> 00:58:34,680
Speaker 1: are some women that are just voting on abortion.

1255
00:58:34,760 --> 00:58:35,000
Speaker 3: That's it.

1256
00:58:35,040 --> 00:58:37,000
Speaker 1: They're not going to change their mind. It's done, it's over,

1257
00:58:37,040 --> 00:58:39,120
Speaker 1: there's nothing they can say about it. But there are

1258
00:58:39,400 --> 00:58:42,520
Speaker 1: also are some norm and I know some of these

1259
00:58:42,520 --> 00:58:47,800
Speaker 1: women normy like previously voter Republican women who the tone

1260
00:58:48,240 --> 00:58:50,880
Speaker 1: is in the crassness is a problem where they're not

1261
00:58:50,920 --> 00:58:55,240
Speaker 1: particularly like ideologically or issue focused. And so when that's

1262
00:58:55,280 --> 00:58:59,240
Speaker 1: the entire vibe of the campaign. In the same way,

1263
00:58:59,240 --> 00:59:01,200
Speaker 1: I think the Democrat at times, with like all the

1264
00:59:01,240 --> 00:59:04,160
Speaker 1: talk about toxic masculinity, et cetera, have put out the

1265
00:59:04,240 --> 00:59:05,919
Speaker 1: vibe of like this is just not really a place

1266
00:59:05,960 --> 00:59:07,240
Speaker 1: for you if you're you know.

1267
00:59:07,280 --> 00:59:08,160
Speaker 4: A white dude bro.

1268
00:59:08,840 --> 00:59:11,680
Speaker 1: That's the vibe that women are getting from these events

1269
00:59:11,720 --> 00:59:14,560
Speaker 1: of like this is just not really like this is

1270
00:59:14,600 --> 00:59:17,040
Speaker 1: just not really a place for me, Like this is

1271
00:59:17,080 --> 00:59:21,080
Speaker 1: not you're not putting out welcoming vibes, and I'm put

1272
00:59:21,120 --> 00:59:24,280
Speaker 1: off by you know, the quote unquote locker room talk

1273
00:59:24,680 --> 00:59:27,480
Speaker 1: is really not for me. It's in a locker room

1274
00:59:27,600 --> 00:59:30,200
Speaker 1: with other men. That's the reason why it's quote unquote

1275
00:59:30,240 --> 00:59:31,040
Speaker 1: locker room talk.

1276
00:59:31,120 --> 00:59:33,480
Speaker 2: But he did bring white women in twenty sixteen, you know,

1277
00:59:33,640 --> 00:59:34,640
Speaker 2: that's why, and that's fact.

1278
00:59:34,720 --> 00:59:37,000
Speaker 1: But that's one But that's one of the groups that's

1279
00:59:37,040 --> 00:59:40,760
Speaker 1: been edging away from him. Sure, and there's very real

1280
00:59:40,840 --> 00:59:44,200
Speaker 1: Posmost of the polling suggests that Kamala Harrison Will is

1281
00:59:44,240 --> 00:59:47,560
Speaker 1: in position to potentially win white women this time around,

1282
00:59:47,560 --> 00:59:52,160
Speaker 1: and certainly she's banking on she's betting on suburban women,

1283
00:59:52,400 --> 00:59:54,240
Speaker 1: which would be, you know, the type of people that

1284
00:59:54,360 --> 00:59:57,280
Speaker 1: Nikki Haley is most in touch with and is thinking

1285
00:59:57,360 --> 01:00:00,400
Speaker 1: about with these comments just to show you, like, you know,

1286
01:00:00,440 --> 01:00:03,320
Speaker 1: also Megan Kelly, who's also very you know, very strong

1287
01:00:03,440 --> 01:00:07,040
Speaker 1: Trump supporter, certainly this time around, she also had some

1288
01:00:07,160 --> 01:00:10,360
Speaker 1: similar concerns about the tone of the MSG rally.

1289
01:00:10,440 --> 01:00:11,840
Speaker 4: Let's take a listen to what she had to say.

1290
01:00:12,000 --> 01:00:15,200
Speaker 10: Trump was not well served by those around him last night.

1291
01:00:16,160 --> 01:00:18,560
Speaker 4: It wasn't a Nazi rally. All that's nonsense.

1292
01:00:19,000 --> 01:00:21,800
Speaker 10: But I'm telling you, even for me and I voted

1293
01:00:21,800 --> 01:00:26,800
Speaker 10: for Donald Trump last week. It was too grotastic, Okay,

1294
01:00:27,080 --> 01:00:30,320
Speaker 10: it was you're trying to win an election in which

1295
01:00:30,360 --> 01:00:35,040
Speaker 10: you're hemorrhaging female voters. Maybe when you present in front

1296
01:00:35,080 --> 01:00:39,160
Speaker 10: of hundreds of thousands, at least at Madison Square Garden,

1297
01:00:39,720 --> 01:00:42,600
Speaker 10: you clean up the bro talk just a little so

1298
01:00:42,640 --> 01:00:46,080
Speaker 10: you don't alienate women in the middle of America who

1299
01:00:46,120 --> 01:00:50,040
Speaker 10: are already on the fence about Republicans. Do they have

1300
01:00:50,080 --> 01:00:53,880
Speaker 10: no women advising their campaign? Is there no actual woman

1301
01:00:54,120 --> 01:00:56,840
Speaker 10: sitting behind the scenes coming up with the guest lineup

1302
01:00:57,120 --> 01:01:00,000
Speaker 10: and saying, let's just have a word with the guy

1303
01:01:00,080 --> 01:01:03,160
Speaker 10: who are going to be speaking about. This isn't the bar,

1304
01:01:04,040 --> 01:01:08,440
Speaker 10: this isn't their living room. This is a campaign. This

1305
01:01:08,520 --> 01:01:12,160
Speaker 10: is politics. We're trying to get him elected. We don't

1306
01:01:12,280 --> 01:01:17,480
Speaker 10: need to rally the base or guys anymore. And it's

1307
01:01:17,520 --> 01:01:19,840
Speaker 10: not helpful even if we do want to rally the

1308
01:01:19,880 --> 01:01:25,240
Speaker 10: base or guys, to go full off color insults to

1309
01:01:25,440 --> 01:01:28,320
Speaker 10: different racial groups and so on. I get it, Trust me,

1310
01:01:28,560 --> 01:01:32,080
Speaker 10: nothing that was said offended me. I'm almost unoffendable. But

1311
01:01:32,200 --> 01:01:36,240
Speaker 10: I understand how this plays, especially with women, and it

1312
01:01:36,320 --> 01:01:38,080
Speaker 10: was an e ft up choice.

1313
01:01:38,280 --> 01:01:40,600
Speaker 4: I do love how it's never terms fault. It's always he's.

1314
01:01:40,800 --> 01:01:41,320
Speaker 3: Failed by that.

1315
01:01:41,400 --> 01:01:43,400
Speaker 4: He can never fail. He's failed by the people around now.

1316
01:01:43,440 --> 01:01:44,440
Speaker 3: But sure I agree with her.

1317
01:01:44,640 --> 01:01:46,680
Speaker 2: Honestly, I think the base strategy is the one that works.

1318
01:01:46,720 --> 01:01:49,760
Speaker 2: I mean, women are predomint college educated women or the

1319
01:01:49,800 --> 01:01:52,280
Speaker 2: Kamala base. That's where they're going after Liz Change and

1320
01:01:52,320 --> 01:01:54,840
Speaker 2: all that. Bros Are the basically the Republican base, gen

1321
01:01:54,920 --> 01:01:56,320
Speaker 2: z split amongst men.

1322
01:01:56,400 --> 01:01:58,000
Speaker 3: That's what you need now, look right.

1323
01:01:58,000 --> 01:02:01,000
Speaker 4: Smart, But Ryan's sweet thing on the margins.

1324
01:02:00,680 --> 01:02:02,800
Speaker 2: Ryan Sweet, that you read is accurate and it is

1325
01:02:02,800 --> 01:02:04,000
Speaker 2: one where it could be scary.

1326
01:02:04,080 --> 01:02:05,360
Speaker 4: Right, So I write it again since there was a

1327
01:02:05,600 --> 01:02:07,680
Speaker 4: more segment. Go ahead, keep talking, I'll pull it up.

1328
01:02:07,840 --> 01:02:08,040
Speaker 3: Fine.

1329
01:02:08,360 --> 01:02:11,520
Speaker 2: Yeah, it's something about like dudes don't have the capacity.

1330
01:02:11,120 --> 01:02:14,680
Speaker 1: The organizational capacity, he says. I love my dudes. But

1331
01:02:14,720 --> 01:02:17,240
Speaker 1: if there is a nationwide contest between men and women,

1332
01:02:17,320 --> 01:02:19,120
Speaker 1: that goes to the group that managed to show up

1333
01:02:19,160 --> 01:02:21,120
Speaker 1: at the right place, fill out their forms on time,

1334
01:02:21,160 --> 01:02:23,240
Speaker 1: and do all those things, the men would need to

1335
01:02:23,240 --> 01:02:25,480
Speaker 1: start with an advantage of several millions to be competitive.

1336
01:02:25,480 --> 01:02:27,240
Speaker 1: Don't come at me. Those are Ryan Grimm's words. You

1337
01:02:27,240 --> 01:02:28,800
Speaker 1: can take it up with him. I'm not being sexist

1338
01:02:28,800 --> 01:02:29,400
Speaker 1: against men.

1339
01:02:29,320 --> 01:02:29,720
Speaker 3: That's fair.

1340
01:02:30,200 --> 01:02:32,240
Speaker 2: It's just one of those where I think that this

1341
01:02:32,360 --> 01:02:34,760
Speaker 2: is a base turnout election. I don't think that this

1342
01:02:34,840 --> 01:02:38,120
Speaker 2: is necessarily like, look, you're right, you know everything with margins,

1343
01:02:38,120 --> 01:02:39,640
Speaker 2: et cetera. But if you won't up your margin with

1344
01:02:39,720 --> 01:02:42,120
Speaker 2: men enough, then that actually is one that goes to

1345
01:02:42,120 --> 01:02:44,880
Speaker 2: the Trump campaign strategy. They just think that they're literally

1346
01:02:44,920 --> 01:02:47,760
Speaker 2: not winnable, and I honestly think they're probably correct at

1347
01:02:47,760 --> 01:02:51,040
Speaker 2: this point, Like we're so bifurcated in terms of also

1348
01:02:51,360 --> 01:02:53,680
Speaker 2: the issue set that we look at for men and

1349
01:02:53,720 --> 01:02:58,280
Speaker 2: women are completely at odds. Immigration and the economy way

1350
01:02:58,360 --> 01:03:00,360
Speaker 2: higher for men and for women.

1351
01:03:00,440 --> 01:03:01,960
Speaker 3: Abortion is just so sky high.

1352
01:03:02,040 --> 01:03:04,640
Speaker 2: Like what can you possibly say as a Republican to

1353
01:03:04,720 --> 01:03:07,600
Speaker 2: win that type of voter over I mean, tone is

1354
01:03:07,640 --> 01:03:08,400
Speaker 2: really not going to come.

1355
01:03:08,520 --> 01:03:09,440
Speaker 3: Maybe I am.

1356
01:03:09,400 --> 01:03:13,040
Speaker 2: Totally wrong, Okay, I really I might be. That said,

1357
01:03:13,400 --> 01:03:15,840
Speaker 2: the data tells us that if you drive up the

1358
01:03:16,120 --> 01:03:19,800
Speaker 2: margin with men, you will still get a substantial portion

1359
01:03:20,200 --> 01:03:22,200
Speaker 2: of married conservative women to come with you.

1360
01:03:22,400 --> 01:03:24,480
Speaker 3: That's enough to win the election. You don't necessarily have

1361
01:03:24,520 --> 01:03:25,480
Speaker 3: to win, you know, like.

1362
01:03:25,400 --> 01:03:27,880
Speaker 2: Single ladies are voting for trumpet like or sorry for Kamma,

1363
01:03:27,960 --> 01:03:29,320
Speaker 2: like a forty percent margin.

1364
01:03:29,600 --> 01:03:31,200
Speaker 3: It's crazy if you look at it.

1365
01:03:31,240 --> 01:03:33,560
Speaker 2: I mean, by the way, anthropologically, none of this is good,

1366
01:03:33,600 --> 01:03:35,680
Speaker 2: but that's a separate story. Like, if you're purely trying

1367
01:03:35,720 --> 01:03:36,960
Speaker 2: to win, that's what I would do.

1368
01:03:37,120 --> 01:03:41,320
Speaker 1: Massive divide between college educated women and non college educated men.

1369
01:03:41,440 --> 01:03:42,840
Speaker 4: Yes, but you know, I.

1370
01:03:42,800 --> 01:03:44,760
Speaker 1: Mean the numbers are what they are. In terms of

1371
01:03:44,840 --> 01:03:48,000
Speaker 1: every election, women vote more than men. You're going to

1372
01:03:48,080 --> 01:03:51,320
Speaker 1: pick one side, you know, but Ryan's comments, putting Ryan's

1373
01:03:51,360 --> 01:03:54,000
Speaker 1: comments aside. If you're going to pick one side of

1374
01:03:54,040 --> 01:03:56,560
Speaker 1: the Battle of the sexes to be on, just based

1375
01:03:56,600 --> 01:03:58,880
Speaker 1: on the fact that women always vote high rates the men,

1376
01:03:59,080 --> 01:04:00,520
Speaker 1: you would want to be on the side women.

1377
01:04:00,640 --> 01:04:02,320
Speaker 4: So listen, how much is I don't know.

1378
01:04:02,360 --> 01:04:04,080
Speaker 1: I mean, you're right, like the Trump's space is so

1379
01:04:04,160 --> 01:04:06,600
Speaker 1: locked in people, but we're talking about.

1380
01:04:06,240 --> 01:04:07,680
Speaker 4: A game of inches.

1381
01:04:08,120 --> 01:04:10,400
Speaker 1: So if you have some people who are on the fence,

1382
01:04:11,160 --> 01:04:15,280
Speaker 1: reminding them of everything that comes with Trump, and you know,

1383
01:04:15,360 --> 01:04:19,000
Speaker 1: there are a lot of like normy women out there,

1384
01:04:19,040 --> 01:04:21,960
Speaker 1: normally people in general who are just like, oh, you know,

1385
01:04:22,120 --> 01:04:23,360
Speaker 1: this is just gross.

1386
01:04:24,600 --> 01:04:25,040
Speaker 4: I don't know.

1387
01:04:25,320 --> 01:04:28,960
Speaker 1: I do think her point too about he doesn't have

1388
01:04:29,040 --> 01:04:31,520
Speaker 1: any well he does this Susie Wilds on the campaign.

1389
01:04:31,600 --> 01:04:32,880
Speaker 3: She's literally the number two on the camp.

1390
01:04:33,080 --> 01:04:35,200
Speaker 1: I mean, there are women's voices that are in there,

1391
01:04:35,240 --> 01:04:40,080
Speaker 1: but clearly, whereas the comment team has really tried to

1392
01:04:40,160 --> 01:04:44,240
Speaker 1: minimize their losses with men and try to keep as many,

1393
01:04:44,600 --> 01:04:48,080
Speaker 1: especially black and brown men in the tent as they

1394
01:04:48,120 --> 01:04:50,920
Speaker 1: possibly can, there has not been similar outreach from the

1395
01:04:50,960 --> 01:04:53,000
Speaker 1: Trump campaign. And you're right that it is consistent with

1396
01:04:53,040 --> 01:04:55,680
Speaker 1: two different strategies. The common strategy if you look at

1397
01:04:55,680 --> 01:04:57,520
Speaker 1: the ad campaign that's been run, if you look at

1398
01:04:57,560 --> 01:04:59,640
Speaker 1: the like the Liz training out region, all those stuff,

1399
01:05:00,080 --> 01:05:03,920
Speaker 1: it has really been an attempt at persuasion, and the

1400
01:05:03,920 --> 01:05:06,400
Speaker 1: Trump campaign has been very much an attempt at like,

1401
01:05:06,680 --> 01:05:09,120
Speaker 1: let's get some of these infrequent voters out, let's drive

1402
01:05:09,160 --> 01:05:10,920
Speaker 1: up turn out in the base. And like I said,

1403
01:05:10,960 --> 01:05:13,480
Speaker 1: you see that in closing ads where the Trump campaign's

1404
01:05:13,480 --> 01:05:15,840
Speaker 1: going all in on these like culture war issues. The

1405
01:05:15,920 --> 01:05:18,440
Speaker 1: Kamala campaign is featuring a lot of former Republicans who

1406
01:05:18,440 --> 01:05:20,280
Speaker 1: are voting for her, and they're doing like more of

1407
01:05:20,280 --> 01:05:22,280
Speaker 1: a class war. He's a billionaire, he's not for you,

1408
01:05:22,280 --> 01:05:24,880
Speaker 1: et cetera, et cetera. So you know, I guess that's

1409
01:05:24,960 --> 01:05:26,720
Speaker 1: that's the bet that both campaigns are placing.

1410
01:05:26,760 --> 01:05:27,720
Speaker 4: We'll see, We're going.

1411
01:05:27,680 --> 01:05:31,000
Speaker 2: To see, let's stick with the MSG theme. There was

1412
01:05:31,040 --> 01:05:34,640
Speaker 2: some more quote unquote fallout here. We have a Nikki Jam.

1413
01:05:34,880 --> 01:05:37,640
Speaker 2: He's a reggae tone star, he's half Puerto Rican. He

1414
01:05:37,680 --> 01:05:41,600
Speaker 2: has now withdrawn his endorsement of Donald Trump. He said

1415
01:05:41,640 --> 01:05:43,560
Speaker 2: he thought Trump would be good for the economy, but

1416
01:05:43,560 --> 01:05:45,920
Speaker 2: he cannot look past the disrespect that has now been

1417
01:05:45,960 --> 01:05:47,480
Speaker 2: shown to Puerto Rico.

1418
01:05:47,560 --> 01:05:48,160
Speaker 3: So I don't know.

1419
01:05:48,280 --> 01:05:51,040
Speaker 2: I mean, everyone thinks that this stuff will be consequential.

1420
01:05:51,080 --> 01:05:53,760
Speaker 2: They're like, oh, bet Bunny is endorsed Kamla again. I

1421
01:05:53,800 --> 01:05:57,040
Speaker 2: have no idea. I find it very difficult to believe

1422
01:05:57,080 --> 01:05:59,040
Speaker 2: that if you're Puerto Rican, who is going to vote

1423
01:05:59,040 --> 01:06:02,560
Speaker 2: for Trump? Tony hinchclub at a Trump rally, made a

1424
01:06:02,640 --> 01:06:05,040
Speaker 2: joke about Puerto Rico that isn't enough for you to

1425
01:06:05,080 --> 01:06:05,640
Speaker 2: switch your boat.

1426
01:06:05,680 --> 01:06:07,280
Speaker 3: Maybe it's enough for Nikki Jam.

1427
01:06:07,360 --> 01:06:09,240
Speaker 2: It just seems a little weird to me, especially if

1428
01:06:09,280 --> 01:06:10,880
Speaker 2: you live in the Continental US. You don't even live

1429
01:06:10,920 --> 01:06:13,320
Speaker 2: in Puerto Rico. What are you so upset about? So anyway,

1430
01:06:13,440 --> 01:06:13,760
Speaker 2: that's me.

1431
01:06:14,000 --> 01:06:18,480
Speaker 1: I think the case for which I don't know, but

1432
01:06:18,720 --> 01:06:22,120
Speaker 1: the case for it mattering is number one in Philly.

1433
01:06:22,680 --> 01:06:25,640
Speaker 1: The places in the actual city of Philadelphia that had

1434
01:06:25,720 --> 01:06:28,000
Speaker 1: moved the most to the right were also the most

1435
01:06:28,000 --> 01:06:31,280
Speaker 1: heavily Puerto Rican parts of the city. And there's something

1436
01:06:31,320 --> 01:06:34,640
Speaker 1: like half a million Puerto Ricans in the in the

1437
01:06:34,640 --> 01:06:38,680
Speaker 1: state of Pennsylvania. So obviously you know that's an important constituency.

1438
01:06:39,120 --> 01:06:43,160
Speaker 1: And then it's the issue, isn't just this common? It

1439
01:06:43,280 --> 01:06:48,320
Speaker 1: reminds of the like Trump Hurricane Maria response and sort

1440
01:06:48,360 --> 01:06:51,120
Speaker 1: of brings up those memories, et cetera. Because I mean,

1441
01:06:51,160 --> 01:06:52,800
Speaker 1: I don't know, I like you. When I'm looking at

1442
01:06:52,800 --> 01:06:54,640
Speaker 1: this nicky jam thing, I'm like, really, it didn't already

1443
01:06:54,840 --> 01:06:55,520
Speaker 1: like this guy was.

1444
01:06:56,400 --> 01:06:59,800
Speaker 2: Okay, the joke's on you, bro. You like an idiot,

1445
01:07:00,040 --> 01:07:02,320
Speaker 2: And I'll say it like it's like, oh, I thought

1446
01:07:02,360 --> 01:07:03,760
Speaker 2: I never thought this could ever happen.

1447
01:07:04,320 --> 01:07:06,240
Speaker 3: Who did you think this was what's happening here? But

1448
01:07:07,200 --> 01:07:08,680
Speaker 3: it's better for the economy. Why does it matter?

1449
01:07:08,800 --> 01:07:10,960
Speaker 2: Huh So your feelings about Puerto Rico are more important

1450
01:07:10,960 --> 01:07:13,120
Speaker 2: than the economy, then you're an idiot. Okay, it must

1451
01:07:13,160 --> 01:07:14,600
Speaker 2: be nice to be a rich reggaetne star.

1452
01:07:14,720 --> 01:07:18,040
Speaker 1: I mean, I feel like it's reasonable for people to

1453
01:07:18,040 --> 01:07:21,400
Speaker 1: feel like, oh, you just like hate me and the

1454
01:07:21,600 --> 01:07:25,600
Speaker 1: you know place him from and whatever, like disrespect.

1455
01:07:24,680 --> 01:07:25,919
Speaker 3: You know Hinchcliffe thing.

1456
01:07:26,240 --> 01:07:28,360
Speaker 2: It's not even like Trump said, Hurricane Rie is a

1457
01:07:28,360 --> 01:07:30,680
Speaker 2: way more legit reason by the way to be upset

1458
01:07:30,680 --> 01:07:33,040
Speaker 2: about it. But clearly Nicky didn't think about that whenever

1459
01:07:33,080 --> 01:07:35,000
Speaker 2: he was up on the stage. So okay, I'll just

1460
01:07:35,080 --> 01:07:35,560
Speaker 2: leave it at that.

1461
01:07:35,640 --> 01:07:38,360
Speaker 1: So anyway, again, it's one of these micro stories that

1462
01:07:38,480 --> 01:07:41,520
Speaker 1: after election day we may look back and be like, oh, well,

1463
01:07:41,560 --> 01:07:43,680
Speaker 1: this doesn't matter at all, or we may look back

1464
01:07:43,680 --> 01:07:46,480
Speaker 1: and be like, damn, Trump lost Pennsylvania about like five

1465
01:07:46,480 --> 01:07:49,840
Speaker 1: thousand votes, and maybe this was the difference maker right there.

1466
01:07:49,760 --> 01:07:51,960
Speaker 4: Which would be really crazy. But you just never know.

1467
01:07:52,000 --> 01:07:54,120
Speaker 2: How pray that that doesn't happen, just so I don't

1468
01:07:54,160 --> 01:07:56,200
Speaker 2: have to be subjected to this discourse for another three years.

1469
01:07:56,680 --> 01:07:59,000
Speaker 2: Let's continue with Trump. He was asked about Tony Hinchcliff

1470
01:07:59,040 --> 01:08:00,680
Speaker 2: in the garbage truck. Take a listen.

1471
01:08:00,960 --> 01:08:03,000
Speaker 9: I don't know anything about the comedian. I don't know

1472
01:08:03,000 --> 01:08:03,360
Speaker 9: who he is.

1473
01:08:03,400 --> 01:08:04,480
Speaker 11: I've never seen him.

1474
01:08:04,840 --> 01:08:07,080
Speaker 5: I heard he made a statement, but it was just

1475
01:08:07,120 --> 01:08:08,080
Speaker 5: a statement that he made.

1476
01:08:08,120 --> 01:08:08,840
Speaker 6: He's a comedian.

1477
01:08:08,880 --> 01:08:11,320
Speaker 5: What can I tell you. I know nothing about him.

1478
01:08:11,360 --> 01:08:12,360
Speaker 11: I don't know why he's there.

1479
01:08:12,520 --> 01:08:15,600
Speaker 1: You put comedians up and I guess you went on

1480
01:08:16,000 --> 01:08:16,320
Speaker 1: the show.

1481
01:08:16,360 --> 01:08:19,200
Speaker 2: I don't know who he is one of the most

1482
01:08:19,200 --> 01:08:20,679
Speaker 2: classic Trumps of all time.

1483
01:08:20,760 --> 01:08:21,479
Speaker 4: I don't even know who.

1484
01:08:21,720 --> 01:08:22,880
Speaker 3: I don't even know who the guy is.

1485
01:08:22,920 --> 01:08:24,800
Speaker 4: Well, really never heard. It was at your rally and

1486
01:08:24,840 --> 01:08:26,280
Speaker 4: the team vetted his comments.

1487
01:08:26,360 --> 01:08:28,360
Speaker 2: He also said that about he continues to say about

1488
01:08:28,360 --> 01:08:28,920
Speaker 2: Mark Robinson.

1489
01:08:28,920 --> 01:08:30,120
Speaker 3: He goes, I don't know what you're talking about.

1490
01:08:30,840 --> 01:08:32,840
Speaker 1: He did, didn't he call him like a modern day

1491
01:08:33,080 --> 01:08:34,800
Speaker 1: MLK junior or something like that.

1492
01:08:36,120 --> 01:08:37,320
Speaker 3: You're telling me for the first time.

1493
01:08:37,600 --> 01:08:41,000
Speaker 1: Yeah, it's just it's not really It's obviously not particularly credible,

1494
01:08:41,040 --> 01:08:43,559
Speaker 1: but it shows you that many people have also pointed

1495
01:08:43,560 --> 01:08:45,639
Speaker 1: out that like, if Trump's had said the same thing,

1496
01:08:45,880 --> 01:08:47,840
Speaker 1: he would get away with it and everyone would double

1497
01:08:47,880 --> 01:08:49,599
Speaker 1: down on it and no one would criticize it. But

1498
01:08:50,160 --> 01:08:52,000
Speaker 1: only Trump can get away with those things. And so

1499
01:08:52,080 --> 01:08:54,680
Speaker 1: even the campaign obviously, you know, in similar way that

1500
01:08:54,720 --> 01:08:57,360
Speaker 1: Kama was distancing herself from the Biden comments, like they

1501
01:08:57,400 --> 01:09:00,639
Speaker 1: realize it's a problem. Republicans in Florida ran away from

1502
01:09:00,680 --> 01:09:03,639
Speaker 1: it immediately, and other Republicans across the country, and Trump

1503
01:09:03,720 --> 01:09:05,280
Speaker 1: himself is like, wow, I don't even know who this

1504
01:09:05,320 --> 01:09:09,120
Speaker 1: guy is, Like, you know this. They clearly recognize this

1505
01:09:09,200 --> 01:09:09,640
Speaker 1: is a bit of a.

1506
01:09:09,640 --> 01:09:10,160
Speaker 4: Problem for them.

1507
01:09:10,200 --> 01:09:12,840
Speaker 1: They did the Allentown rally, made sure to have lots

1508
01:09:12,840 --> 01:09:16,760
Speaker 1: of Puerto Ricans there to validate on stage whatever, so

1509
01:09:17,680 --> 01:09:20,559
Speaker 1: they know it is a potential issue. Is it a

1510
01:09:21,080 --> 01:09:23,040
Speaker 1: reality electoral issue?

1511
01:09:24,040 --> 01:09:27,240
Speaker 2: We'll find out him bingo, all right, let's continue. Let's

1512
01:09:27,240 --> 01:09:29,160
Speaker 2: put this up on the screen. Just the last thing

1513
01:09:29,200 --> 01:09:31,439
Speaker 2: on this. So in terms of these stunts, one of

1514
01:09:31,479 --> 01:09:34,639
Speaker 2: the reasons kind of interesting is Data for Progress asked

1515
01:09:34,680 --> 01:09:38,240
Speaker 2: about people who had seen the recent news about certain

1516
01:09:38,439 --> 01:09:41,840
Speaker 2: events in the campaign. Number one was actually Trump serving

1517
01:09:41,880 --> 01:09:45,559
Speaker 2: fries at McDonald's. In terms of the meme mobility of that,

1518
01:09:46,040 --> 01:09:48,320
Speaker 2: you only had seventeen percent of voters who said they

1519
01:09:48,320 --> 01:09:51,320
Speaker 2: had heard nothing at all about it. Carris calling Trump

1520
01:09:51,400 --> 01:09:53,840
Speaker 2: a fascist, you only had twenty one percent who had

1521
01:09:53,840 --> 01:09:57,200
Speaker 2: heard nothing about it. But actually Madison Square Garden was

1522
01:09:57,280 --> 01:10:01,360
Speaker 2: number three. So I have you know again, Americans, you guys,

1523
01:10:01,560 --> 01:10:05,519
Speaker 2: you fascinate me every day. What breaks through and what doesn't?

1524
01:10:05,800 --> 01:10:09,080
Speaker 2: Why that one that one really crystal. It broke through

1525
01:10:09,120 --> 01:10:12,280
Speaker 2: more than enemy from within, what it broke through more

1526
01:10:12,280 --> 01:10:15,760
Speaker 2: than Trump doing on Joe Rogan, which you heard some

1527
01:10:15,840 --> 01:10:17,920
Speaker 2: thirty two percent of people saying never heard about it,

1528
01:10:17,960 --> 01:10:20,280
Speaker 2: Harri's campaigning with Liz Cheney.

1529
01:10:20,439 --> 01:10:23,040
Speaker 3: I mean, what like how people.

1530
01:10:22,760 --> 01:10:26,120
Speaker 2: Consume the news, what they choose to like click on

1531
01:10:26,320 --> 01:10:28,719
Speaker 2: and send to their friends. I again will never understand,

1532
01:10:28,720 --> 01:10:31,960
Speaker 2: but that does show you that sometimes things just break

1533
01:10:31,960 --> 01:10:33,719
Speaker 2: through for reasons that are far beyond our.

1534
01:10:33,920 --> 01:10:35,479
Speaker 4: Just some things just take off.

1535
01:10:35,520 --> 01:10:36,280
Speaker 3: See McDonald's.

1536
01:10:36,280 --> 01:10:40,400
Speaker 2: I guess that's just an MSG, like what you know,

1537
01:10:40,880 --> 01:10:44,400
Speaker 2: that's the one where I don't understand it anyway, all right,

1538
01:10:44,439 --> 01:10:48,479
Speaker 2: let's continue. Joe Rogan actually Rogan recently, he was talking

1539
01:10:48,720 --> 01:10:52,160
Speaker 2: on his own podcast about the Tony Hinchcliff comments, and

1540
01:10:52,479 --> 01:10:56,920
Speaker 2: he actually made some interesting comments, specifically about how he

1541
01:10:57,000 --> 01:10:59,600
Speaker 2: had warned Tony in the past that that joke was

1542
01:10:59,600 --> 01:11:00,639
Speaker 2: gonna get him stabbed.

1543
01:11:00,760 --> 01:11:01,479
Speaker 3: Let's take a listen.

1544
01:11:01,760 --> 01:11:04,200
Speaker 6: I don't really blame Tony though, because Tony is what

1545
01:11:04,240 --> 01:11:06,320
Speaker 6: Tony is, right, Like, if you want an insult, call

1546
01:11:06,360 --> 01:11:07,760
Speaker 6: make Tony is the best in the world.

1547
01:11:07,840 --> 01:11:12,000
Speaker 11: That's literally his His his great specialty is roasting.

1548
01:11:12,200 --> 01:11:16,720
Speaker 6: Right, So if you booked Tony Hinchcliffe, Tony Hinchcliff is

1549
01:11:16,760 --> 01:11:19,599
Speaker 6: going to be Tony Hinchcliff exactly. So whoever fucking booked him,

1550
01:11:19,600 --> 01:11:21,759
Speaker 6: that's the person that's that's made the mistakes.

1551
01:11:21,920 --> 01:11:24,760
Speaker 11: Booked him but apparently went over his material.

1552
01:11:24,880 --> 01:11:26,320
Speaker 3: Did they go over his materials?

1553
01:11:27,840 --> 01:11:31,360
Speaker 11: Oh my, this is what I've read on the internet,

1554
01:11:31,439 --> 01:11:32,320
Speaker 11: so it must be true.

1555
01:11:33,000 --> 01:11:35,360
Speaker 3: In the words of Donald Trump, someone's getting fired.

1556
01:11:35,400 --> 01:11:35,559
Speaker 9: Man.

1557
01:11:35,600 --> 01:11:38,240
Speaker 11: I'm gonna tell you that joke kills at comedy clubs.

1558
01:11:38,400 --> 01:11:39,240
Speaker 3: I don't like the joke.

1559
01:11:39,760 --> 01:11:41,880
Speaker 11: It kills. And I said to him, I don't. It's

1560
01:11:41,960 --> 01:11:44,280
Speaker 11: just like if you're Puerto Rican and you hear that

1561
01:11:44,280 --> 01:11:46,479
Speaker 11: in the eudis like, oh, it's a funny joke. The

1562
01:11:46,560 --> 01:11:49,280
Speaker 11: joke does well. But I said to him, I go, dude,

1563
01:11:49,320 --> 01:11:52,599
Speaker 11: that's the one's gonna get you stabbed. Really yes, And

1564
01:11:52,640 --> 01:11:55,559
Speaker 11: he used to talk about it on stage, saying, Joe

1565
01:11:55,680 --> 01:11:58,160
Speaker 11: Rogan always says, that's the one's gonna get me stabbed,

1566
01:11:58,200 --> 01:12:02,439
Speaker 11: Like wow, which is so great. Here's what's where that

1567
01:12:02,560 --> 01:12:06,800
Speaker 11: joke comes from. Tony is actually obsessed with the Pacific

1568
01:12:06,840 --> 01:12:11,559
Speaker 11: garbage patch. And unlike the fact that we just throw

1569
01:12:11,600 --> 01:12:13,560
Speaker 11: it like we were talking about like recycling, Like the

1570
01:12:13,640 --> 01:12:16,360
Speaker 11: recycling doesn't work. They don't do it. Most of your

1571
01:12:16,360 --> 01:12:18,880
Speaker 11: bottles and you're thrown in recycling, they get put in landfills.

1572
01:12:19,200 --> 01:12:23,120
Speaker 11: So there's a landfill in Puerto Rico that's way overflowed.

1573
01:12:23,360 --> 01:12:27,000
Speaker 11: Puerto Rico has a legitimate trash problem because they're on

1574
01:12:27,040 --> 01:12:28,920
Speaker 11: an island like where you're gonna put it, right, there's

1575
01:12:28,960 --> 01:12:30,599
Speaker 11: all these people living on that island where you're gonna

1576
01:12:30,640 --> 01:12:32,960
Speaker 11: put it, and so they have landfills. Their landfills are

1577
01:12:33,040 --> 01:12:36,080
Speaker 11: way over capacity. So that's where the joke came from, right,

1578
01:12:36,240 --> 01:12:41,839
Speaker 11: Like the joke came from, like Tony being environmentally conscious, well.

1579
01:12:43,920 --> 01:12:47,439
Speaker 1: Laid down a little bit of that battle.

1580
01:12:46,880 --> 01:12:49,280
Speaker 4: Could make a little more since I thought i'd just

1581
01:12:49,360 --> 01:12:49,720
Speaker 4: be I.

1582
01:12:49,720 --> 01:12:51,599
Speaker 2: Will say, I've seen Tony a couple of times. I've

1583
01:12:51,600 --> 01:12:53,439
Speaker 2: never heard the Puerto Rico joke. I've seen him do.

1584
01:12:54,080 --> 01:12:56,120
Speaker 2: I've seen his act quite a few times. I have

1585
01:12:56,240 --> 01:12:57,880
Speaker 2: not heard that one, although I guess I haven't been

1586
01:12:57,920 --> 01:13:00,640
Speaker 2: lostin in quite a while. But it is funny that

1587
01:13:00,680 --> 01:13:02,479
Speaker 2: were Even Rogan himself was like, that's a joke that's

1588
01:13:02,479 --> 01:13:03,280
Speaker 2: going to get you stabbed.

1589
01:13:03,520 --> 01:13:05,519
Speaker 1: Yeah, and he said I don't like the joke, but

1590
01:13:05,720 --> 01:13:08,479
Speaker 1: you know, I was environmentally conscious that.

1591
01:13:08,560 --> 01:13:10,759
Speaker 4: He does it. Okay, this was funny.

1592
01:13:10,760 --> 01:13:12,760
Speaker 1: I put this up on the screen Apparently this was

1593
01:13:13,360 --> 01:13:15,360
Speaker 1: some of the backs, which also indicated that he had

1594
01:13:15,400 --> 01:13:17,600
Speaker 1: delivered it, you know, in clubs before. He says it

1595
01:13:17,640 --> 01:13:19,360
Speaker 1: was not the first time Hinchcliff had used the Porto

1596
01:13:19,400 --> 01:13:21,200
Speaker 1: Rico line. He practiced at the Stan Comedy Club in

1597
01:13:21,200 --> 01:13:23,519
Speaker 1: New York City, where he made a surprise appearance Saturday night.

1598
01:13:23,600 --> 01:13:26,120
Speaker 1: According to an NBC News producer who happened to be there,

1599
01:13:26,320 --> 01:13:28,800
Speaker 1: the joke did not draw laughs, just a handful of

1600
01:13:28,800 --> 01:13:31,639
Speaker 1: awkward chuckles. And you guys will recall at the rally too,

1601
01:13:32,000 --> 01:13:34,439
Speaker 1: it bombed. No one was like just a little bit

1602
01:13:34,479 --> 01:13:35,200
Speaker 1: of nervous laughter.

1603
01:13:35,360 --> 01:13:35,839
Speaker 4: Maybe.

1604
01:13:36,120 --> 01:13:38,200
Speaker 1: Hinchcliffe told the audience he would be performing at the

1605
01:13:38,200 --> 01:13:40,559
Speaker 1: Madison Square Garden rally the next day, and said multiple

1606
01:13:40,600 --> 01:13:42,599
Speaker 1: times during his routine that he would get a better

1607
01:13:42,680 --> 01:13:47,759
Speaker 1: reaction tomorrow at the rally. Famous last words, this person opines,

1608
01:13:47,800 --> 01:13:49,680
Speaker 1: I like that Tony bombed at the rally, being like

1609
01:13:49,880 --> 01:13:52,040
Speaker 1: this usually does better at clubs, after he bombed at

1610
01:13:52,040 --> 01:13:54,200
Speaker 1: the club, saying this is going to kill at the rally.

1611
01:13:54,320 --> 01:13:57,439
Speaker 1: So listen, you know, there's a lot of Puerto Ricans

1612
01:13:57,439 --> 01:13:59,760
Speaker 1: in New York City, and maybe it's just not really

1613
01:13:59,800 --> 01:14:03,360
Speaker 1: the location for this line to that's a work out

1614
01:14:03,400 --> 01:14:04,000
Speaker 1: for he guys.

1615
01:14:04,040 --> 01:14:05,840
Speaker 2: He got to keep saying it in Austin, Texas, where

1616
01:14:05,880 --> 01:14:07,880
Speaker 2: Mexicans are allowed to laugh at Puerto Ricans.

1617
01:14:07,920 --> 01:14:09,720
Speaker 3: That's that's what you got to keep doing. You got

1618
01:14:09,720 --> 01:14:10,920
Speaker 3: to know your demographics, Tony.

1619
01:14:11,080 --> 01:14:13,120
Speaker 1: Yeah, I do have to say, like I do kind

1620
01:14:13,120 --> 01:14:16,720
Speaker 1: of agree with a if it's it really is the

1621
01:14:16,720 --> 01:14:19,120
Speaker 1: fault of the Trump campaign, Like it really is there

1622
01:14:19,520 --> 01:14:22,519
Speaker 1: because look at Tony Hinchcliffe's.

1623
01:14:22,000 --> 01:14:23,719
Speaker 3: Body of work. That's correct, like if.

1624
01:14:23,560 --> 01:14:27,880
Speaker 4: You don't want and they vetted, they vetted his remarks.

1625
01:14:27,520 --> 01:14:30,040
Speaker 1: And reportedly he was planning to call Kama Harris the

1626
01:14:30,040 --> 01:14:32,320
Speaker 1: C word and they were like that's too far. But

1627
01:14:32,880 --> 01:14:36,160
Speaker 1: everything else and not just this Puerto Rico joke they

1628
01:14:36,280 --> 01:14:39,120
Speaker 1: left in. So I mean, I do sort of agree

1629
01:14:39,160 --> 01:14:42,680
Speaker 1: that the blame really lies at the feet of the

1630
01:14:42,680 --> 01:14:46,280
Speaker 1: Trump campaign for thinking that this was a good idea,

1631
01:14:46,800 --> 01:14:49,439
Speaker 1: knowing like looking at his body of work and being like, yes,

1632
01:14:49,520 --> 01:14:51,120
Speaker 1: this is a great thing to have at our political

1633
01:14:51,200 --> 01:14:53,000
Speaker 1: rally because of course he's going to do the thing

1634
01:14:53,040 --> 01:14:54,120
Speaker 1: that he does.

1635
01:14:54,800 --> 01:14:56,799
Speaker 3: Yeah, yeah, I mean, I have no idea why it committe.

1636
01:14:56,840 --> 01:14:58,880
Speaker 2: I think I said that too, even though I'm not

1637
01:14:58,920 --> 01:15:00,640
Speaker 2: you know whatever, I still think A lot of it

1638
01:15:00,680 --> 01:15:02,519
Speaker 2: is overblown. I don't know why you would have a

1639
01:15:02,520 --> 01:15:05,400
Speaker 2: comedian at a freaking rally period, Like, there's really no

1640
01:15:05,520 --> 01:15:08,080
Speaker 2: reason for it. Yeah, it has no precedence. It's not

1641
01:15:08,120 --> 01:15:10,599
Speaker 2: even one of those like old timey things that people

1642
01:15:10,720 --> 01:15:12,679
Speaker 2: used to do to like jazz up the crowd.

1643
01:15:12,720 --> 01:15:14,760
Speaker 3: They used to just have bands. We should bring that back, right,

1644
01:15:14,760 --> 01:15:15,400
Speaker 3: we should have band.

1645
01:15:15,439 --> 01:15:17,280
Speaker 1: It's also like, if you want to be edgy in

1646
01:15:17,360 --> 01:15:20,360
Speaker 1: that context, wouldn't you, as a roast comedian go after

1647
01:15:20,439 --> 01:15:22,559
Speaker 1: some of the like people that are there.

1648
01:15:22,600 --> 01:15:25,240
Speaker 4: Like that's the thing that really is like you know, I.

1649
01:15:25,200 --> 01:15:27,640
Speaker 2: Mean he hit some of the core constitutions easy went

1650
01:15:27,680 --> 01:15:29,800
Speaker 2: after some what did he tell some Jewish jokes? And

1651
01:15:29,800 --> 01:15:32,040
Speaker 2: he was telling some uh there there was there was

1652
01:15:32,040 --> 01:15:34,519
Speaker 2: some there. I think that well again and also a

1653
01:15:34,520 --> 01:15:36,120
Speaker 2: lot of it didn't hit land version Like.

1654
01:15:36,080 --> 01:15:37,760
Speaker 1: That's the whole thing of roast is like the Tom

1655
01:15:37,800 --> 01:15:39,840
Speaker 1: Brady Like Tom Brady's right there and you're saying some

1656
01:15:39,880 --> 01:15:41,920
Speaker 1: wild shit to his face instead of like, here's some

1657
01:15:41,960 --> 01:15:44,800
Speaker 1: demographic groups that we all hate, right, let's let's go

1658
01:15:44,920 --> 01:15:48,920
Speaker 1: after them. So anyway, that's uh, this Rogan's take on it.

1659
01:15:48,960 --> 01:15:49,360
Speaker 3: There you go

1660
01:16:02,560 --> 01:16:02,600
Speaker 5: A