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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. But enough with that,

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Speaker 2: let's get to the show. Hello everybody, Happy election day.

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Speaker 2: It's Tuesday, it's here, it's the morning. It's time for

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Speaker 2: the update. All four of us are in the house,

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Speaker 2: I guess in our respective houses. We will be in

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Speaker 2: our studio as a reminder, two thousand what is it?

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Speaker 2: We will be doing the live stream at six thirty

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Speaker 2: pm Eastern time, all four of us at the desk,

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Speaker 2: and we will.

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Speaker 3: Go as long as we need to go.

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Speaker 2: We're really excited for it, and we thought we just

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Speaker 2: what is it, Crystal. We've got tea leaves leaves.

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Speaker 1: Yeah, all the two. So you have a few votes

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Speaker 1: cast in Dick Much, New Hampshire. This is like you

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Speaker 1: know every time election traditions. So we'll give you those results.

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Speaker 1: You can way overanalyze them as people always do. Yeah,

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Speaker 1: we have the very last of like the predictions like

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Speaker 1: the official you know, Nate Silver, John Ralston, Larry Sabato,

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Speaker 1: So break that down for You've got a little glimpse

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Speaker 1: in the final rallies and a big endorsement on the

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Speaker 1: Trump side, Joe Rogan making it official that he is

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Speaker 1: endorsing Donald Trump. So bring all that to you also,

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Speaker 1: because basically spoiler alert, all of these predictions, like the

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Speaker 1: fancy models just came down to like, eh, go either way,

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Speaker 1: I don't know. So we thought we would also take

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Speaker 1: a look at some of the dumber ways that people

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Speaker 1: do election predictions, some of the like traditions, superstictions, things

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Speaker 1: like which Halloween mask sells more, what this viral hippo

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Speaker 1: did with the cake, all these sorts of things. So

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Speaker 1: we'll dig into those tea leaves like a real tea

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Speaker 1: leaves as well, and also things that are equally scientific

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Speaker 1: like Alan Lichtman's keys. Yes, so we can all make

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Speaker 1: of that what we will.

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Speaker 3: Yeah, it's exciting.

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Speaker 2: As a reminder to everybody, six thirty pm, like I said,

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Speaker 2: we'll be live, go ahead and sign out Breakingpoints dot com.

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Speaker 2: You will want to do that because we'll also be

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Speaker 2: taking questions from our subscribers during the stream.

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Speaker 3: But yeah, what's first on deck? Crystal? What do you well?

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Speaker 1: I want to pull out Dixville Notch, but really quick

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Speaker 1: before I do that, Emily and Ryan, you guys we

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Speaker 1: made you give us predictions like last week, and there's

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Speaker 1: been a bunch of new poles and whatever the drop

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Speaker 1: since then, any any revisions while I'm getting the Dixville

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Speaker 1: Notch votes pulled up here, it.

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Speaker 4: Would be smart to make revisions because then, no matter

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Speaker 4: what happens one of the.

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Speaker 1: That's true, giving you that opportunity. That's like file he

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Speaker 1: made three different maps. I was like, that's yeah, that's that's.

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Speaker 3: Not fair, that's smart. No, that's very smart. Ridiculous.

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Speaker 4: Yeah, one of my maps has Trump winning and one

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Speaker 4: has Kamala winning.

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Speaker 5: Come on.

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Speaker 1: Right, thanks Silver, Yeah, how about you? Amy?

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Speaker 5: And that's basically it's a cop out.

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Speaker 6: But it's like when you look at the you go

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Speaker 6: to do the maps, as we've all done, and you're

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Speaker 6: calling certain states and then you're looking at where the

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Speaker 6: polling averages are in those states and you're just like,

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Speaker 6: this is insane. I mean, it could with what you

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Speaker 6: guys have been talking about a lot in terms of

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Speaker 6: not knowing the direction of the polling era, like we

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Speaker 6: don't even have a good sense. In twenty twenty, it

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Speaker 6: was like, well, they might be under estimating Trump, Like

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Speaker 6: if I had to guess the polls, l still underestimate Trump.

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Speaker 5: Do not have that this time around at all.

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Speaker 6: So that's where you get these two different versions of

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Speaker 6: the map where you can have like a Kammalo sweep

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Speaker 6: or a Trump sweep, or you can have something like

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Speaker 6: what Sager predicted, which is sort of what I'm leaning

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Speaker 6: towards right now.

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Speaker 5: Which is a really really close electoral college battle.

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Speaker 6: Because it's split, the results end up being as split

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Speaker 6: as they seemed like they would.

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Speaker 1: Yeah, I mean, it's kind of funny because everybody knows

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Speaker 1: the Poles are wrong, like the Poles are, the Polsters

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Speaker 1: are definitely just like hurting, it's a tie. I don't

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Speaker 1: know what's to tell you, but we have no idea

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Speaker 1: what direction they're.

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Speaker 4: Wrong, which means there's a strong chance that we could

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Speaker 4: expect to not know until like Friday or something, but

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Speaker 4: might actually know tonight. Like that there's a strong chance

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Speaker 4: we actually are shocked and are like, oh wow, this

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Speaker 4: this thing's over.

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Speaker 1: Well the leam and we're like, oh, we actually know

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Speaker 1: what happened.

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Speaker 2: I think just to prepare for people like if Kamala right,

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Speaker 2: because the polls close in Georgia at seven and then

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Speaker 2: North Carolina seven thirty, and then Pennsylvania at eight.

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Speaker 3: If the polls close.

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Speaker 2: In Georgia and North Carolina and you can call, we

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Speaker 2: will reminder, will have decision desk. If Decision desk can

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Speaker 2: call those two states, you know, almost immediately and then

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Speaker 2: maybe put a trajectory for her in Pennsylvania. I'm not

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Speaker 2: going to say it's over, but you know, the math

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Speaker 2: becomes very difficult for Trump. Similarly, you know, if Trump

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Speaker 2: has big strength in Georgia and in North Carolina, which

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Speaker 2: were two tiede states, and then shows a lot of

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Speaker 2: potential going into the wee hours of like one or

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Speaker 2: two am, they call PA at two am in twenty sixteen,

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Speaker 2: although everything I've read says it may take a little

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Speaker 2: bit longer. Pennsylvania election workers are not allowed to count

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Speaker 2: ballots until actually is it the start of polls, like

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Speaker 2: or on election day or whatever, so processing can take

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Speaker 2: a little while. But there is a strong chance we're

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Speaker 2: gonna know a lot, you know tonight.

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Speaker 1: Yeah, I think so. I mean even last time when

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Speaker 1: it came down to the wire, we really did go

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Speaker 1: to bed pretty much knowing, Yeah, that's right, the way

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Speaker 1: things were. We know, yeah, we knew all right. So

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Speaker 1: here we go the much anticipated Dixville Notch presidential results.

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Speaker 1: And guess what, guys, is a tie? Yeah, Paris three

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Speaker 1: Trump three. Now, if you are a Democrat, you may

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Speaker 1: look at this and go Dixville Notch haz zero registered Democrats.

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Speaker 1: This is four Republicans and two independents. So we're already

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Speaker 1: picking up those Nicky Haley voters. And all six of

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Speaker 1: these people voted for Nikki Haley in the Republican primary.

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Speaker 1: If you're a Republican, you look at these and go,

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Speaker 1: Joe Biden swept. Yeah, take five h last time around.

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Speaker 1: So you know, really choose your own adventure with this

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Speaker 1: particular one. So make of it what you will, and

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Speaker 1: I recommend you make nothing of it. Yeah, let me

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Speaker 1: go and show you John Ralston's predictions. Now this made

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Speaker 1: quite a bit of waves. I'm curious, in particular, what

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Speaker 1: Emily and Zager have thought about this. So Ralston is,

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Speaker 1: you know, the guru of early vote in Nevada. His

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Speaker 1: presidential prediction record is actually perfect since he started making

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Speaker 1: these predictions. And the TLDR here is that after he

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Speaker 1: looked at the early vote and what male ballots he

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Speaker 1: thinks are still outstanding, and his historical experience with the

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Speaker 1: effectiveness of the Harry Reid machine, he is very narrowly

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Speaker 1: predicting a Harris victory forty eight point five percent to

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Speaker 1: Trump's forty eight point two percent, which is like a

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Speaker 1: preposterously narrow margin. But you know, part of what was

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Speaker 1: consequential about this is that the Republicans really felt like

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Speaker 1: this was the place where the early vote numbers were

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Speaker 1: most clearly in their favor, and so I think, you know,

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Speaker 1: there was an expectation that he was probably going to

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Speaker 1: call it for Trump, and instead he narrowly goes for Kamala.

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Speaker 2: Yeah, I mean, well, you know, by his own admission,

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Speaker 2: he's got Kamala there by point three. It's also so

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Speaker 2: funny if you read the analysis, He's basically like, yeah,

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Speaker 2: Trump is up, but I'm just gonna guess that the

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Speaker 2: Red machine knows exactly how many votes that they need

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Speaker 2: to be postmarked by election day. And I've never bet

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Speaker 2: against the red machine and the read machine will turn

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Speaker 2: those folks out, which is kind of a hilarious, almost

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Speaker 2: like mafioso style analysis of the state. But he's not wrong.

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Speaker 2: I mean, you know, he's called it correctly every single time.

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Speaker 2: He has a perfect record. But he did, of course

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Speaker 2: leave himself there where if he is wrong, like let's

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Speaker 2: say Trump wins byo point six, well, you know he's

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Speaker 2: got a decent out so last time around, you know, regardless,

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Speaker 2: it does show a much closer election. Joe Biden got

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Speaker 2: fifty percent of the vote donald Trump at forty seven

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Speaker 2: point six, so narrowing the gap there three. I took

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Speaker 2: a little bit hard in this, even though he did

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Speaker 2: predict a Kamala win, just because this would verify a

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Speaker 2: lot of the Sun Belt strength for Donald Trump that

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Speaker 2: I had included in my map, including with the Latino vote.

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Speaker 2: So I'm interested to see, you know, which way it goes,

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Speaker 2: because even if he is predicting such a narrow result,

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Speaker 2: that actually would be a sign of strength for the

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Speaker 2: tightness of the polling, particularly in those states Nevada and Arizona,

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Speaker 2: and not necessarily a large miss.

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Speaker 3: So that was just my general read of it.

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Speaker 4: And from my perciective. If Ralston does have a bias,

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Speaker 4: it's too much confidence in the read machine. And so

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Speaker 4: I've covered Harry Reid for so long. I've also known

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Speaker 4: and interacted with and had a lot of respect for

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Speaker 4: Ralston for like twenty years now or something, and covering

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Speaker 4: Harry Reid, you do get this sense of like, Wow,

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Speaker 4: this is an old school machine that like actually knows

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Speaker 4: how to do machine politics, and so you can't the

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Speaker 4: questions do you get carried away with your faith in

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Speaker 4: it after Harry Reid has died, Like we don't know

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Speaker 4: yet like the strength of this machine. So if he's wrong,

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Speaker 4: and that from the grave Harry Reid is not able

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Speaker 4: to run this machine as effectively as he was, you know,

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Speaker 4: from the earth, then that could make up the couple

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Speaker 4: of points that you're talking about.

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Speaker 6: I'm also sort of curious about I think Republicans getting

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Speaker 6: really excited about having you know, more registered Republicans turning

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Speaker 6: out in some states where they've been working really hard.

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Speaker 6: I mean, if you Ryan was at a Trump rally

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Speaker 6: just last night, like they have the I don't know

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Speaker 6: if they're still doing it, but a month ago, you know,

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Speaker 6: they were doing swamp the vote too big to rig.

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Speaker 5: They were pushing it really hard, and you know, part of.

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Speaker 6: Me wonders if what we're seeing is just really high

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Speaker 6: early voting from suburban women. So Republicans are getting excited

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Speaker 6: about more registered Republican women returning their ballots early, or

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Speaker 6: in areas where they expect to do well returning their

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Speaker 6: ballots early. And actually it was a lot of women

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Speaker 6: who weren't voting for Trump but are registered Republicans. So

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Speaker 6: I don't know, like if that's the case, but I

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Speaker 6: am curious about that.

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Speaker 1: If Anne Selzer is even directionally correct, there is some

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Speaker 1: of that going on, because she found Kamala winning something

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Speaker 1: like ten percent of republic That would be the theoretical

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Speaker 1: sort of like Nikki Haley voter. And the other thing

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Speaker 1: that is different in Nevada this year versus other years

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Speaker 1: is that they passed a law that automatically registers anyone

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Speaker 1: who you know, goes the DMB gets a driver's license,

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Speaker 1: and the default is that you're just registered, you know,

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Speaker 1: non affiliated. And so Democrats believe a lot of those voters,

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Speaker 1: because they tend to be younger, are their voters. And

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Speaker 1: that's part of what factored into his analysis here too.

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Speaker 1: But yeah, I mean he talks about how this was,

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Speaker 1: you know, maybe the hardest election for him to be

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Speaker 1: able to predict. So I don't think anyone would be

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Speaker 1: surprised if it comes in significantly differently from this. All right,

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Speaker 1: let me show you this next prediction. This is the

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Speaker 1: Larry Sabateaux crystal ball no relation prediction. And again this

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Speaker 1: is another I mean, he has it narrowly in favor

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Speaker 1: of Kamala Harris. Basically, this is kind of like, you know,

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Speaker 1: before these campaigns really started and got into full effect,

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Speaker 1: this is kind of what you would predict the map

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Speaker 1: would be. She holds the blue Wall, she holds Nevada

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Speaker 1: where they've been consistently winning, and Trump takes everything else.

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Speaker 1: And there you go. It's you know, it's narrow, but

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Speaker 1: it's enough for her to be able to pull out

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Speaker 1: a win. You guys have any any thoughts or reactions

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Speaker 1: to this one.

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Speaker 3: I think it's a very possible map.

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Speaker 2: I was at around this map for a while and

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Speaker 2: the real reason I bet against it with Pa and

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Speaker 2: with North Carolina was some different demographic reasons, but for specifically,

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Speaker 2: what this really is is a bet on you know,

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Speaker 2: narrow victory in Nevada and then white over performance with

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Speaker 2: seniors and with college educated voters across the Blue Wall

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Speaker 2: states while losing the sun Belt. And I think it's

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Speaker 2: a highly logical map. It's a bet that I would

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Speaker 2: not feel uncomfortable taking in a scenario for a Harris win.

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Speaker 2: It's also one which was their most plausible path to victory.

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Speaker 2: I listened to an interview that David Pluff, who was

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Speaker 2: the Obama two thousand and eight campaign manager, gave on CNN,

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Speaker 2: and he got pretty specific. He was basically like, look,

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Speaker 2: we believe this is going to be a razor tight election.

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Speaker 2: We are trying to operate in a sense where there

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Speaker 2: is no polling error and we could still be able

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Speaker 2: to drive things out. And we see a scenario where

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Speaker 2: any of the states that we win will be by

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Speaker 2: point six point eight or any of that. So that's

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Speaker 2: basically a bet where a Kamala victory. And if you know,

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Speaker 2: in that point eight marginal victory scenario, this is what

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Speaker 2: a kamal Of victory would most likely look like with

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Speaker 2: no major polling error. A polling error in either direction

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Speaker 2: would just be read across the board for all of them.

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Speaker 1: Ryan was this your mount basically.

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Speaker 7: More or less.

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Speaker 4: I think I had Nevada going to Trump.

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Speaker 1: Yeah, so you added actually two seventy two sixty eight right,

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Speaker 1: like as narrow as possible. Yep, got it all right.

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Speaker 1: Let's take a look at NAT's silver it's very ying.

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Speaker 3: Yeah.

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Speaker 1: Yeah, it says we ran eighty thousand simulations tonight, Harris

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Speaker 1: one in forty thousand and twelve.

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Speaker 4: So, I mean his problem is if it's garbage in,

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Speaker 4: garbage out, is that's what the simulations are going to produce.

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Speaker 1: That's my thing is, like he has been arguing correctly

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Speaker 1: and vociferously online for like these polsters are basically just

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Speaker 1: making stuff up. And it's not just the recall to

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Speaker 1: twenty twenty thing. He was in this whole fight with

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Speaker 1: this economist Justin Wolfers, who was like, they're taking low

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Speaker 1: quality surveys, then they look at what the average is

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Speaker 1: and they rig the results to match that average. But

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Speaker 1: it's like, if you think that's true, then how can

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Speaker 1: you justify feeding this into your model and pretending like

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Speaker 1: you're getting anything approxy? You're gonna get the same, Like

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Speaker 1: it's fifty to fifty. It's tied that these posters are

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Speaker 1: giving so that's you know, that's the part of this

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Speaker 1: is questions, what else is he supposed to do?

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Speaker 2: I was gonna say, you go to war with the

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Speaker 2: troops he have. I mean, it's just like what else

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Speaker 2: can you do? Like there's no other option? Like he's

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Speaker 2: got a decent I mean, he does attempt at scoring

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Speaker 2: like a lot of those hurting ones, I think they

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Speaker 2: get scored less inside. But at a certain point, you know,

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Speaker 2: we only have a few people who are out there

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Speaker 2: doing high quality surveys who are not doing recall to vote.

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Speaker 3: And or not hurting.

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Speaker 2: And even then, you know, there's a big question mark

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Speaker 2: in that last New York Times Siena pol like they

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Speaker 2: had it, they basically said jump ball in all of

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Speaker 2: the blue wall states, where you do have to ask

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Speaker 2: was that just a hurting thing because they don't want

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Speaker 2: to deal with the blowback. You know, in a scenario

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Speaker 2: where Donald Trump overperforms by four or five points or

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Speaker 2: Harris over performs by four or five there's also just

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Speaker 2: you know, like the more meta questions that we always

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Speaker 2: have to confront about overresponse bias, about the makeup of

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Speaker 2: the electorate, people who literally are like an entire generation

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Speaker 2: now with no landline. How are they even contactable? So

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Speaker 2: this will look the country changes a lot.

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Speaker 1: You know, this is partly a monster of his own

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Speaker 1: making because of the Polster rating that you're talking about.

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Speaker 1: It's based on how close they were the actual results,

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Speaker 1: which incentivizes you know, if you say it's fifty to fifty,

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Speaker 1: then you're not going to be too far off any direction.

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Speaker 1: And so partly the you know, I don't they didn't

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Speaker 1: intend to do this, but that was partly the impact.

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Speaker 1: So you know, that's why I look, yeah, it's good

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Speaker 1: that he's out there arguing, like, look, all these polsters

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Speaker 1: are just basically making things up. They're hurting. It would

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Speaker 1: be one in nine trillion chance literally that they would

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Speaker 1: be getting the results that they are. But then I

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Speaker 1: think you have to factor in more humility to your

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Speaker 1: own model at the end of the day, if you

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Speaker 1: know that the data that's going in is basically fictional, right,

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Speaker 1: in other words, vindication for Alan Lichtman and his keys

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Speaker 1: right as to the keys dang yeah, also for Mudang yeah.

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Speaker 3: I'll get to moodang yeah.

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Speaker 4: And I got into like a famous or infamous argument

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Speaker 4: with Nate silver back in twenty sixteen, and I ended

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Speaker 4: up looking like a fool. But the actual point that

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Speaker 4: I was trying to make is is this one right here.

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Speaker 4: What I said is that, look, all of the polling

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Speaker 4: models are saying that Hillary Clinton has a like ninety

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Speaker 4: plus percent chance of winning, and in the last week,

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Speaker 4: he was artificially pushing his number down to try to

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Speaker 4: hedge for a Trump win because he felt in his

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Speaker 4: gut that there was a chance that Trump might actually win.

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Speaker 4: And I said in the piece, I feel like that's

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Speaker 4: true too, that it seems absurd to say that there's

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Speaker 4: a ninety plus percent chance that he's gonna win. But

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Speaker 4: you're a polling model guy, Like, you're not a pundit.

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Speaker 4: Your job is to follow your own polling model. If

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Speaker 4: you don't want to do that, then go be a

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Speaker 4: pundit and say this seems silly. But you can't kind

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Speaker 4: of have it both ways. And so I think you're

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Speaker 4: right that it's a problem of his own creation, Like

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Speaker 4: he has created this idea that you can put these

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Speaker 4: numbers into this machine and it's gonna spit out for

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Speaker 4: you results. But then as as human beings, we look

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Speaker 4: at those results and we're like, this doesn't look right.

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Speaker 4: So then what was the whole point of the machine

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Speaker 4: to be given was fair?

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Speaker 3: That was my point.

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Speaker 2: I think that's totally fair, and it does get to

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Speaker 2: the whole idea of this is all human in terms

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Speaker 2: like there's no sign, there is no science, you know,

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Speaker 2: behind prediction. All of it comes down to human assumptions.

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Speaker 2: And by the way, keep us in mind for AI

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Speaker 2: and for any large like data machine or for anything,

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Speaker 2: this is the case for the assumptions that are programmed

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Speaker 2: into these models are what determine the model itself. And

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Speaker 2: that's why people have heard here a lot about it.

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Speaker 2: I actually think it's very vindicating where a lot of

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Speaker 2: people just have a lot more humility around these things

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Speaker 2: and just really, yeah, nobody knows anything, and that's actually fine,

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Speaker 2: It's okay.

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Speaker 1: I mean, the truth of the matter is that as

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Speaker 1: much as Nate Silver and Alan Lichtman and his keys

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Speaker 1: like fought back and forth, they're actually not doing anything

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Speaker 1: different from one another.

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Speaker 3: Yeah, they're both.

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Speaker 1: Just let you know, they have their own ideas about

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Speaker 1: how to bake in the assumptions and what are the

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Speaker 1: keys to the election. And you know, if you go

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Speaker 1: into Name's opera, he has things like you know, the

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Speaker 1: fundamentals and what's the economy and whatever? Those are all

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Speaker 1: just your assumptions about how those things play into the election.

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Speaker 1: And then because you have this illusion of feeding it

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Speaker 1: into an impartial machine, you come out with what appears

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Speaker 1: like a more scientific result. You're not actually doing anything

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Speaker 1: different than Alan Lichtmann is when it comes down to it,

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Speaker 1: And at least with Lichman, he puts his thumb on that.

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Speaker 1: He says it's gonna be Kamala Harris. You can check

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Speaker 1: whether he got a write or not. In the end,

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Speaker 1: where is Nate Silver? Whichever way it goes, you can

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Speaker 1: be like I told you it was fifty to fifty.

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Speaker 1: You could go either way.

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Speaker 6: Right.

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Speaker 1: That's funny, all right, So let's go ahead and get

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Speaker 1: to the big news yesterday evening, which is that Joe

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Speaker 1: Rogan Oh Moodang lined up next though it's XV famous,

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Speaker 1: which is that Joe Rogan did He had Elon Musk

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Speaker 1: on his podcast and then he came out and tweeted

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Speaker 1: this official endorsement of Trump. Now he had had Trump

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Speaker 1: on the podcast, then he had jad Vans on, and

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Speaker 1: then he had Elon Musk on. Of course, Trump's biggest backer,

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Speaker 1: and he says the great and powerful Elon Musk. If

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Speaker 1: it wasn't for him, we'd be f't He makes what

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Speaker 1: I think is the most compelling case for Trump, you'll hear,

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Speaker 1: and I agree with him every step of the way.

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Speaker 1: For the record, yes, that's an endorsement of Trump. Enjoy

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Speaker 1: the podcast, Emily, let me hear from you on You know,

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Speaker 1: what do you make of this endorsement? And you know

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Speaker 1: Rogan's evolution to this position, because we've tracked some of

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Speaker 1: his you know, he's previously back Bertie Sanders, his very

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Speaker 1: left wing positions that he took at some point in

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Speaker 1: the past. But he's clearly made a transition into both

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Speaker 1: his audience and himself being more you know, solidly in

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Speaker 1: the pro Trump right wing space. So what do you

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Speaker 1: make of the significance both from a Rogan evolution and

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Speaker 1: a political electoral impact.

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Speaker 6: Yeah, well, I mean Republicans just on the electoral front

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Speaker 6: desperately desperately need to get low propensity young male voters

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Speaker 6: to turn out, so they're interpreting this as a major gift.

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Speaker 6: Now will it actually be a major gift? I don't

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Speaker 6: know that a bunch of people are going to turn

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Speaker 6: out to the polls because Joe Rogan literally on the

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Speaker 6: eve of the election, as people had already had their

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Speaker 6: plans in place for the next day and all that

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Speaker 6: good stuff, made his endorsement. But everything is marginal this election,

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Speaker 6: so I guess Republicans maybe are clinging to that as

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Speaker 6: some just something that can get young male voters actually

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Speaker 6: to the polls, because they're doing better than Republicans have

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Speaker 6: in I think decades with young male voters under this

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Speaker 6: Trump run, so they really I mean, that's the worst

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Speaker 6: demographic basically to be doing well with because it's just

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Speaker 6: the lowest turnout slice of the electorate. So maybe this

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Speaker 6: is motivating and animating on that front. On the second question,

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Speaker 6: it's really wild to see the changes in the MAGA

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Speaker 6: movement post twenty twenty because, and I know we've all

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Speaker 6: talked about this before. What's happened is a lot of

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Speaker 6: Silicon Valley guys have sort of stepped into the ring

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Speaker 6: and become MAGA. And I don't think this changes Joe

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Speaker 6: Rogan's and you guys would know better, but from my perspective,

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Speaker 6: it's not a lot of people are going to be

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Speaker 6: tempted to see this as mutually exclude with the Bernie ism,

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Speaker 6: but there were a lot of voters that kind of

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Speaker 6: had both in their heads too, back in twenty sixteen

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Speaker 6: and twenty twenty, like.

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Speaker 5: The Trump stuff and the Bernie stuff.

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Speaker 6: And it might not be that Joe Rogan is like

435
00:21:12,240 --> 00:21:15,919
Speaker 6: fully anti establishment or is fully non anti establishment, that

436
00:21:15,960 --> 00:21:18,520
Speaker 6: now he's pro billionaire, pro Trump, pro all of that.

437
00:21:18,840 --> 00:21:22,359
Speaker 6: He just sees it as the best shot at breaking

438
00:21:22,520 --> 00:21:26,119
Speaker 6: up what the kind of swamp has come to signify

439
00:21:26,359 --> 00:21:30,480
Speaker 6: and represent. So that is a totally arguable point. But

440
00:21:30,560 --> 00:21:34,240
Speaker 6: I think just trying to parse his argument on behalf

441
00:21:34,280 --> 00:21:37,440
Speaker 6: of Trump, it's sort of similar to some other people's Fundamentally,

442
00:21:37,440 --> 00:21:40,879
Speaker 6: it's like it's still seen as the best anti establishment

443
00:21:41,119 --> 00:21:43,160
Speaker 6: candidate to explode everything in DC.

444
00:21:43,760 --> 00:21:46,560
Speaker 5: So that's sort of an initial reaction.

445
00:21:47,600 --> 00:21:50,400
Speaker 2: Yeah, I mostly agree with that. I mean, I think

446
00:21:50,400 --> 00:21:53,080
Speaker 2: it's I mean, look, it's kind of interesting to see

447
00:21:53,119 --> 00:21:55,159
Speaker 2: it in different directions.

448
00:21:55,040 --> 00:21:55,760
Speaker 3: In a lot of ways.

449
00:21:55,840 --> 00:21:58,520
Speaker 2: Joe is actually following the trend you know, in the

450
00:21:58,560 --> 00:22:02,360
Speaker 2: podcast demographic. It's not a secret the podcast demographic has

451
00:22:02,359 --> 00:22:05,320
Speaker 2: been trending right wing for like what three years, maybe

452
00:22:05,359 --> 00:22:05,880
Speaker 2: four years?

453
00:22:05,960 --> 00:22:08,639
Speaker 3: Honestly, I think of it, and I think you.

454
00:22:08,640 --> 00:22:11,199
Speaker 2: Tweeted something about pop culture, and I think it's a

455
00:22:11,320 --> 00:22:15,280
Speaker 2: really interesting view into the bifurcated way that we experience

456
00:22:15,359 --> 00:22:18,199
Speaker 2: pop culture in twenty twenty four, where you have like

457
00:22:18,280 --> 00:22:20,720
Speaker 2: Internet celebrities, Joe kind of being the king of that,

458
00:22:20,920 --> 00:22:24,000
Speaker 2: the person the driver, the new Oprah quote unquote, you know,

459
00:22:24,040 --> 00:22:26,840
Speaker 2: for the non mainstream, but then you also have literally

460
00:22:26,880 --> 00:22:29,440
Speaker 2: Oprah on the same night, you know, giving a speech

461
00:22:29,800 --> 00:22:34,320
Speaker 2: in favor of Kamala Harris. You have Hollywood the celebrity

462
00:22:34,480 --> 00:22:36,040
Speaker 2: kind of establishment.

463
00:22:35,520 --> 00:22:37,040
Speaker 3: Quote unquote on that side.

464
00:22:37,080 --> 00:22:38,800
Speaker 2: And then I would also say that, you know, amongst

465
00:22:38,800 --> 00:22:41,639
Speaker 2: the Internet celebrities, I don't think it's a secret, you know,

466
00:22:41,760 --> 00:22:44,200
Speaker 2: looking around. If you look at the top twenty you

467
00:22:44,280 --> 00:22:48,080
Speaker 2: know shows or whatever on Apple podcasts, probably eighteen out

468
00:22:48,080 --> 00:22:51,000
Speaker 2: of twenty of them are going to be directionally right wing.

469
00:22:51,119 --> 00:22:54,119
Speaker 2: So that does tell you something about the audience and

470
00:22:54,160 --> 00:22:56,919
Speaker 2: about the space in which that that is both permissible

471
00:22:57,000 --> 00:22:58,560
Speaker 2: and for a lot of reasons.

472
00:22:58,600 --> 00:22:59,520
Speaker 3: I actually think it makes.

473
00:22:59,400 --> 00:23:02,680
Speaker 2: Perfect sense because you know, in general, outside of Fox News,

474
00:23:02,720 --> 00:23:05,359
Speaker 2: like being quote unquote right wing is not really acceptable

475
00:23:05,440 --> 00:23:07,639
Speaker 2: or possible, and so you start your own thing on

476
00:23:07,680 --> 00:23:10,679
Speaker 2: the Internet. That's also where you can have like literally

477
00:23:10,680 --> 00:23:15,040
Speaker 2: the ability to congregate without a gatekeeper, which is predominantly

478
00:23:15,040 --> 00:23:17,920
Speaker 2: more friendly to the establishment left. And so Joe has

479
00:23:17,920 --> 00:23:20,560
Speaker 2: been now a part of this for several years. If

480
00:23:20,600 --> 00:23:24,280
Speaker 2: you track his like ideological evolution, I don't think it's

481
00:23:24,320 --> 00:23:27,360
Speaker 2: all that like surprising if you look at the politicians

482
00:23:27,400 --> 00:23:30,040
Speaker 2: people like he likes, like Tulsey Gabbard and others. Who

483
00:23:30,080 --> 00:23:33,200
Speaker 2: is now literally a Republican calls She's like, I became

484
00:23:33,200 --> 00:23:37,560
Speaker 2: a Republican campaigning for Donald Trump for RFK Junior, who

485
00:23:37,640 --> 00:23:40,320
Speaker 2: has now endorsed Donald Trump and is urging people to

486
00:23:40,520 --> 00:23:43,760
Speaker 2: vote for him. So this is like an ideological valance

487
00:23:43,760 --> 00:23:46,359
Speaker 2: which has been present there for quite a long time.

488
00:23:46,480 --> 00:23:49,280
Speaker 2: And if anything, he's very late to it because, like

489
00:23:49,320 --> 00:23:51,919
Speaker 2: you said, Emily, you know twenty I mean when did

490
00:23:51,920 --> 00:23:54,679
Speaker 2: it drop? Yeah, eight forty five pm last night. So

491
00:23:54,720 --> 00:23:56,560
Speaker 2: it's not like this is going to be top of

492
00:23:56,640 --> 00:23:59,760
Speaker 2: mind for people who are organizing or anything. It's an

493
00:23:59,760 --> 00:24:03,399
Speaker 2: in seeing cultural like demarcation point to me, because it

494
00:24:03,520 --> 00:24:07,080
Speaker 2: is just like the final apotheosis of what I think

495
00:24:07,320 --> 00:24:11,119
Speaker 2: a lot of the podcast audience, demographic, et cetera. It

496
00:24:11,280 --> 00:24:14,880
Speaker 2: all began with Dave Portnoy in twenty fifteen, and secular

497
00:24:14,920 --> 00:24:19,000
Speaker 2: conservatism kind of crowns itself here today with a Rogan endorsement.

498
00:24:19,480 --> 00:24:21,719
Speaker 6: Can I just say, like, I don't think this is

499
00:24:21,840 --> 00:24:23,680
Speaker 6: durable beyond Trump, Like that's another thing.

500
00:24:23,720 --> 00:24:29,720
Speaker 3: I mean, solutely yes, sobsolutely.

501
00:24:29,720 --> 00:24:33,680
Speaker 1: About that, because I think it depends. I mean, Rogan

502
00:24:33,760 --> 00:24:34,960
Speaker 1: loved JD Vance.

503
00:24:35,920 --> 00:24:38,840
Speaker 2: But that was the JD talking about trans that was

504
00:24:38,920 --> 00:24:42,600
Speaker 2: not fantas right. But that's JD talking about trans, not

505
00:24:42,760 --> 00:24:43,959
Speaker 2: JD talking about abortion.

506
00:24:44,000 --> 00:24:45,520
Speaker 3: And that's where things got a little bit testy.

507
00:24:46,359 --> 00:24:48,720
Speaker 1: Like I don't know, I mean, I just like Joe's

508
00:24:48,720 --> 00:24:50,600
Speaker 1: top issues that he talks the most about are like

509
00:24:50,720 --> 00:24:55,960
Speaker 1: anti wokeism and trans stuff and immigration and immigration and

510
00:24:56,240 --> 00:24:58,480
Speaker 1: like that being anti vax which there was a funny

511
00:24:58,480 --> 00:25:00,800
Speaker 1: part of a conversation with you one where they're talking

512
00:25:00,800 --> 00:25:02,600
Speaker 1: about that. It's like Trump is the person who did

513
00:25:02,640 --> 00:25:06,080
Speaker 1: Operation Warp speed and coke but whatever, Like I understand

514
00:25:06,119 --> 00:25:07,800
Speaker 1: all of that is right when godd but it's just

515
00:25:07,800 --> 00:25:10,879
Speaker 1: a funny, funny note. So I don't think I really

516
00:25:10,960 --> 00:25:13,840
Speaker 1: agree with that, because I do. I think he and

517
00:25:14,040 --> 00:25:15,879
Speaker 1: you know, most of the people in that ecosystem, like

518
00:25:15,920 --> 00:25:19,359
Speaker 1: they just have pretty conventional Republican views at this point,

519
00:25:20,200 --> 00:25:20,720
Speaker 1: I don't.

520
00:25:20,560 --> 00:25:24,320
Speaker 3: Think so, Like here, why did he like Ron DeSantis, covid.

521
00:25:24,160 --> 00:25:27,840
Speaker 1: An and covid whoa right, but it likes Jad because

522
00:25:28,119 --> 00:25:32,679
Speaker 1: I don't know, like because he can And these are

523
00:25:32,720 --> 00:25:37,480
Speaker 1: all very different, like sort of ideological flavors within the

524
00:25:37,520 --> 00:25:41,719
Speaker 1: Republican movement. So I you know, yeah, I think that

525
00:25:41,920 --> 00:25:44,680
Speaker 1: these fears are just like they're just a Republican.

526
00:25:45,160 --> 00:25:45,960
Speaker 3: But I don't see.

527
00:25:46,040 --> 00:25:48,240
Speaker 2: That's that's why I would disagree. For example, put Ron

528
00:25:48,280 --> 00:25:49,919
Speaker 2: Johnson on there. You think Joe is gonna like him?

529
00:25:49,960 --> 00:25:50,480
Speaker 2: I don't think so.

530
00:25:51,040 --> 00:25:54,200
Speaker 1: Or what Ron Johnson's not running for president certainly.

531
00:25:53,880 --> 00:25:54,399
Speaker 3: But whoever.

532
00:25:54,520 --> 00:25:56,280
Speaker 2: I mean, But this is this is kind of like

533
00:25:56,440 --> 00:25:59,920
Speaker 2: the big thing that goes into this is a concern

534
00:26:00,160 --> 00:26:03,600
Speaker 2: vatism quote unquote or Republican party which can appeal to

535
00:26:03,680 --> 00:26:07,520
Speaker 2: the secular, you know, kind of libertarian mail is mutually

536
00:26:07,600 --> 00:26:12,159
Speaker 2: exclusive from a conservatism of Mitch McConnell, Ron Johnson or

537
00:26:12,200 --> 00:26:13,879
Speaker 2: any of these other folks. I think it's quite clear

538
00:26:13,920 --> 00:26:16,560
Speaker 2: what is one out under Donald Trump. But the big

539
00:26:16,640 --> 00:26:19,480
Speaker 2: question is if Donald Trump goes down, right, if Trump

540
00:26:19,520 --> 00:26:23,919
Speaker 2: goes down tomorrow or today at the ballot box, then

541
00:26:23,920 --> 00:26:25,639
Speaker 2: there's going to be a big fight and.

542
00:26:25,560 --> 00:26:27,119
Speaker 3: A lot of a lot of the strategy will get

543
00:26:27,160 --> 00:26:27,680
Speaker 3: blamed here.

544
00:26:27,960 --> 00:26:30,360
Speaker 1: I mean, policy wise, I just don't see Trump as

545
00:26:30,400 --> 00:26:33,359
Speaker 1: being really different from the Mitch McConnell's of the world

546
00:26:33,400 --> 00:26:35,960
Speaker 1: or whatever. Right, I think you're running around and talking

547
00:26:35,960 --> 00:26:38,360
Speaker 1: about cutting two trillion dollars from the government. He wants

548
00:26:38,400 --> 00:26:40,480
Speaker 1: to put Mike Pompeio back in the cabinet. Like I

549
00:26:40,600 --> 00:26:43,120
Speaker 1: just there's a stylistic difference.

550
00:26:43,359 --> 00:26:45,719
Speaker 4: Sure, that's a key point though, but I think what

551
00:26:45,880 --> 00:26:48,159
Speaker 4: I think what you're missing is that though so the

552
00:26:48,160 --> 00:26:51,240
Speaker 4: real we all see that the realignment is happening between

553
00:26:51,400 --> 00:26:54,200
Speaker 4: the two parties, and one thing that that realignment is

554
00:26:54,240 --> 00:26:57,000
Speaker 4: going to bring is it's going to turn the Republican

555
00:26:57,040 --> 00:27:00,320
Speaker 4: Party into a part into a coalition party, way that

556
00:27:00,359 --> 00:27:03,119
Speaker 4: the Democratic Party previously, you know, for the last forty

557
00:27:03,160 --> 00:27:06,320
Speaker 4: years or more, has been this party of coalitions, where

558
00:27:06,359 --> 00:27:09,720
Speaker 4: the Republican Party was more of kind of a uniform

559
00:27:09,800 --> 00:27:12,439
Speaker 4: party that had people who had the same interest same genders,

560
00:27:12,720 --> 00:27:14,600
Speaker 4: same well not same gender, but same race.

561
00:27:14,600 --> 00:27:17,000
Speaker 1: As many instances, yes now.

562
00:27:17,480 --> 00:27:20,840
Speaker 4: Now and right increasingly so, and so now the Republicans

563
00:27:20,880 --> 00:27:23,159
Speaker 4: are going to it's going to be a confusing process

564
00:27:23,200 --> 00:27:24,680
Speaker 4: for them because they're like, wait a minute, we don't

565
00:27:24,680 --> 00:27:27,120
Speaker 4: all actually agree on all of these things, yet we're

566
00:27:27,160 --> 00:27:29,960
Speaker 4: all part of the same coalition. And so how does

567
00:27:29,960 --> 00:27:33,760
Speaker 4: the Rogan kind of faction of the coalition fit in

568
00:27:34,160 --> 00:27:37,920
Speaker 4: with the kind of evangelical faction of the coalition which

569
00:27:37,960 --> 00:27:41,000
Speaker 4: agrees on some things but disagrees on others. And that's so,

570
00:27:41,320 --> 00:27:43,480
Speaker 4: but doesn't mean you can't put together a party in

571
00:27:43,520 --> 00:27:44,399
Speaker 4: a two party system.

572
00:27:44,440 --> 00:27:47,959
Speaker 2: Yeah, the entire the tent that holds the GOP together

573
00:27:48,119 --> 00:27:52,960
Speaker 2: is hating the left and specifically hating ideological like leftism

574
00:27:53,280 --> 00:27:55,080
Speaker 2: in all of its forms. That's the only thing that

575
00:27:55,200 --> 00:27:59,080
Speaker 2: unites a Tulsa Gabbard and what like Tom Cotton right.

576
00:27:59,400 --> 00:28:03,879
Speaker 2: And so that's the key of the Rogan podcast. You know,

577
00:28:04,000 --> 00:28:06,879
Speaker 2: male demographic is that they hate the left and they

578
00:28:06,880 --> 00:28:09,160
Speaker 2: hate the higher institutions of culture and they're really going

579
00:28:09,200 --> 00:28:10,680
Speaker 2: to give a shit. And Crystal, I don't disagree with

580
00:28:10,720 --> 00:28:11,840
Speaker 2: you when you're talking about the policy.

581
00:28:12,280 --> 00:28:15,280
Speaker 1: No matter who is at the head of the Republican Party,

582
00:28:15,400 --> 00:28:16,760
Speaker 1: that's still going to be the case.

583
00:28:16,840 --> 00:28:19,360
Speaker 3: Maybe I'm seeing again if a Trump goes.

584
00:28:19,160 --> 00:28:23,359
Speaker 1: Down not going out of style anytime soon.

585
00:28:23,720 --> 00:28:26,200
Speaker 2: I can only say inshallah to what you just said, Crystal,

586
00:28:26,280 --> 00:28:29,840
Speaker 2: But I do worry that if you know, like, for example,

587
00:28:29,880 --> 00:28:31,840
Speaker 2: I think that if Trump goes down big, let's say

588
00:28:31,840 --> 00:28:35,439
Speaker 2: a Harris Landslide, a lot of this podcast bro energy,

589
00:28:35,600 --> 00:28:38,960
Speaker 2: like no Republican is going to be courting shit like that. Ever, again,

590
00:28:39,000 --> 00:28:41,520
Speaker 2: they're going to have to take big lessons away from

591
00:28:41,600 --> 00:28:42,120
Speaker 2: suburban win.

592
00:28:42,160 --> 00:28:43,320
Speaker 3: If anything, you're going to see opposite.

593
00:28:43,360 --> 00:28:44,560
Speaker 2: You're going to see a lot of these people going on

594
00:28:44,560 --> 00:28:46,840
Speaker 2: the View and going on Oprah trying to like beg

595
00:28:46,880 --> 00:28:49,520
Speaker 2: these ladies to come back and to vote for them.

596
00:28:49,680 --> 00:28:49,960
Speaker 3: I mean.

597
00:28:50,040 --> 00:28:52,240
Speaker 2: But the other side is so that Trump does win big,

598
00:28:52,280 --> 00:28:54,720
Speaker 2: then you are going to see a lot more of

599
00:28:54,880 --> 00:28:57,400
Speaker 2: you know, you've had John Fetterman go on the podcast

600
00:28:57,400 --> 00:29:00,320
Speaker 2: with Joe. I think you will see you know, Pete Boudage, Edger,

601
00:29:00,480 --> 00:29:03,720
Speaker 2: whoever you know, didn't Boodo Jet just go on what

602
00:29:03,840 --> 00:29:06,440
Speaker 2: you call it Jubilee right like he knows what time

603
00:29:06,480 --> 00:29:06,760
Speaker 2: it is.

604
00:29:07,120 --> 00:29:07,920
Speaker 3: He's a smart guy.

605
00:29:07,960 --> 00:29:10,800
Speaker 2: He can he can see where the trend is going

606
00:29:11,200 --> 00:29:14,040
Speaker 2: the future primary. You will have the Gavin Newsom's and

607
00:29:14,080 --> 00:29:16,320
Speaker 2: all these other people go on Joe. I actually think

608
00:29:16,360 --> 00:29:18,520
Speaker 2: Gavin would do an incredible job on Joe Rogan, which

609
00:29:18,520 --> 00:29:20,160
Speaker 2: is crazy thing to say because I know that Joe

610
00:29:20,360 --> 00:29:25,040
Speaker 2: hates him, but he's a masterful, skilled wordsmith politician, and

611
00:29:25,120 --> 00:29:28,160
Speaker 2: so this is, uh, I'm curious to see how it

612
00:29:28,200 --> 00:29:31,880
Speaker 2: shakes out, entirely dependent on the result of what happens

613
00:29:32,080 --> 00:29:32,920
Speaker 2: and where things go.

614
00:29:33,320 --> 00:29:35,200
Speaker 6: I was gonna just quickly say on the pop culture

615
00:29:35,200 --> 00:29:38,320
Speaker 6: point this, it's not silly to remember that the days

616
00:29:38,360 --> 00:29:41,600
Speaker 6: of like Sea Pack people like freaking out because they

617
00:29:41,640 --> 00:29:45,960
Speaker 6: had celebrities. And by celebrities, I mean Kid Rock. I'm sorry,

618
00:29:46,000 --> 00:29:48,600
Speaker 6: I mean Ted Nugent and right, Kid Rock's a bona

619
00:29:48,600 --> 00:29:50,959
Speaker 6: fide celebrity, but I mean like washed up Ted Nugent

620
00:29:51,000 --> 00:29:53,480
Speaker 6: and Kelsey Grammer or something like that, right, And so

621
00:29:54,000 --> 00:29:56,920
Speaker 6: it's just looking back to twenty sixteen and looking now,

622
00:29:57,840 --> 00:30:00,920
Speaker 6: it's everything is shattered, like it feels this is though, Uh,

623
00:30:01,120 --> 00:30:03,320
Speaker 6: the all we have like monoculture.

624
00:30:03,360 --> 00:30:05,440
Speaker 5: I think we all know this is completely shattering.

625
00:30:05,440 --> 00:30:09,320
Speaker 6: And what that's meant is more mainstream people like more

626
00:30:09,320 --> 00:30:12,200
Speaker 6: people in the quote unquote mainstream because the mainstream is

627
00:30:12,240 --> 00:30:16,520
Speaker 6: just bigger, and that has meant some inroads for Republicans

628
00:30:16,600 --> 00:30:20,520
Speaker 6: with popular in popular popular culture. But does that translate

629
00:30:20,560 --> 00:30:23,680
Speaker 6: outside of Trump and JD. Vance as Trump's running mate.

630
00:30:24,360 --> 00:30:25,360
Speaker 6: I don't know, maybe it does.

631
00:30:25,680 --> 00:30:27,520
Speaker 5: I think it was a kind of interesting conversation.

632
00:30:27,800 --> 00:30:30,400
Speaker 1: My point is just like, I don't think that the

633
00:30:30,440 --> 00:30:34,040
Speaker 1: whether politicians this election will really be a verdict if

634
00:30:34,080 --> 00:30:37,680
Speaker 1: in the near term Republican politicians once again kind of

635
00:30:37,680 --> 00:30:40,080
Speaker 1: bet the farm on the bro s fear podcast world.

636
00:30:40,080 --> 00:30:40,520
Speaker 3: That's right.

637
00:30:40,600 --> 00:30:42,960
Speaker 1: I do not think the brose fear podcast world is

638
00:30:43,000 --> 00:30:46,960
Speaker 1: going back to being like dem or lib curious. I

639
00:30:46,960 --> 00:30:49,360
Speaker 1: think that's definitely not Like that's what I'm saying. They're

640
00:30:49,520 --> 00:30:52,600
Speaker 1: they're republic like, they're just Republicans now. Their views fit

641
00:30:52,920 --> 00:30:57,880
Speaker 1: very neatly into the broader Republican like coalition, and I

642
00:30:57,960 --> 00:31:00,480
Speaker 1: don't I don't see that changing, Like I to me,

643
00:31:00,600 --> 00:31:03,200
Speaker 1: the Rogan endorsement is like the final you know, it's

644
00:31:03,200 --> 00:31:08,080
Speaker 1: like the crown jewel of Trump's bro podcast strategy coming

645
00:31:08,120 --> 00:31:11,280
Speaker 1: on the night before the election, and you know, and

646
00:31:11,320 --> 00:31:13,880
Speaker 1: so it will be the election will in some ways

647
00:31:13,880 --> 00:31:17,959
Speaker 1: be a real verdict on how ultimately consequential that strategy was.

648
00:31:18,480 --> 00:31:21,200
Speaker 2: Absolutely maybe this is one of the reasons I'm laying

649
00:31:21,240 --> 00:31:23,880
Speaker 2: the groundwork for if there's a big Trump loss. Is

650
00:31:24,000 --> 00:31:25,760
Speaker 2: a lot of it, and people should remember this. Look

651
00:31:25,840 --> 00:31:28,680
Speaker 2: like anti establishment is not always loved by people who

652
00:31:28,800 --> 00:31:31,560
Speaker 2: like the establishment. In fact, there's a lot of women

653
00:31:31,640 --> 00:31:34,160
Speaker 2: and people out there who if you ever showed them,

654
00:31:34,480 --> 00:31:37,520
Speaker 2: you know, a comedy set of Andrew Schultz or a

655
00:31:37,520 --> 00:31:40,800
Speaker 2: Theo von or of a Rogan or any others Day

656
00:31:40,840 --> 00:31:43,320
Speaker 2: or Tony Hinchcliff, they would really hate it. And so

657
00:31:43,840 --> 00:31:46,360
Speaker 2: that's a question too about who those people are. The

658
00:31:46,360 --> 00:31:48,960
Speaker 2: people who love Oprah or The View or the Today

659
00:31:48,960 --> 00:31:52,160
Speaker 2: Show or Good Morning America, those people vote, you know,

660
00:31:52,280 --> 00:31:53,560
Speaker 2: possibly just as much.

661
00:31:53,640 --> 00:31:54,960
Speaker 3: So it was a big risk.

662
00:31:55,000 --> 00:31:56,680
Speaker 2: I mean, it was a titanic risk really to be

663
00:31:57,160 --> 00:32:00,600
Speaker 2: the bet the farm on this. But again the question

664
00:32:00,640 --> 00:32:03,040
Speaker 2: too is you're not wrong, Crystal that they'll be Republicans.

665
00:32:03,040 --> 00:32:06,560
Speaker 2: But there's also an energy level to this, like for example,

666
00:32:06,680 --> 00:32:09,760
Speaker 2: Kumaru Usman, who is one of the greatest fighters in

667
00:32:09,800 --> 00:32:12,440
Speaker 2: the UFC, came out and endorsed Donald Trump.

668
00:32:12,720 --> 00:32:13,960
Speaker 3: I just don't.

669
00:32:13,680 --> 00:32:16,800
Speaker 2: Really see him ever doing that for Glenn Youngkin or

670
00:32:17,360 --> 00:32:20,120
Speaker 2: maybe even honestly like a jd Vance like yeah, or

671
00:32:20,240 --> 00:32:23,320
Speaker 2: Jake Paul or Logan Paul or I mean, I'm trying

672
00:32:23,320 --> 00:32:25,400
Speaker 2: to think all the bodybuilders you know that I follow

673
00:32:25,600 --> 00:32:29,560
Speaker 2: on Instagram. Trump is a deeply unique cultural figure in

674
00:32:29,600 --> 00:32:34,800
Speaker 2: a way that is like like psychologically very different than

675
00:32:34,880 --> 00:32:36,920
Speaker 2: a lot of these other folks. Dana White, you know,

676
00:32:37,000 --> 00:32:39,760
Speaker 2: look at him, and his friendship with Donald Trump was

677
00:32:39,800 --> 00:32:41,920
Speaker 2: a huge engine, you know, behind a.

678
00:32:41,840 --> 00:32:42,240
Speaker 3: Lot of this.

679
00:32:42,360 --> 00:32:44,600
Speaker 2: In fact, if it does work out, Dana will be

680
00:32:44,640 --> 00:32:47,840
Speaker 2: probably more responsible for this than anyone else him, his

681
00:32:47,880 --> 00:32:50,760
Speaker 2: friendship with Donald Trump Junior and his ability you know,

682
00:32:50,920 --> 00:32:52,760
Speaker 2: you know, Joe even said on his podcast with Trump

683
00:32:52,800 --> 00:32:55,640
Speaker 2: the number one reason he's doing this because of Dana White. Well,

684
00:32:55,720 --> 00:32:58,640
Speaker 2: Dana White go to bat for traditional or even like

685
00:32:58,800 --> 00:33:02,280
Speaker 2: whatever comes next after Trump. I'm not so sure, to

686
00:33:02,360 --> 00:33:03,720
Speaker 2: be honest, I don't.

687
00:33:03,560 --> 00:33:04,200
Speaker 3: Really think so.

688
00:33:04,760 --> 00:33:07,840
Speaker 2: And so that's why Trump is just such He's so

689
00:33:07,840 --> 00:33:10,360
Speaker 2: so unique in his ability. So will they always be

690
00:33:10,440 --> 00:33:12,640
Speaker 2: right wing? Will they always hate the left? Absolutely? But

691
00:33:12,720 --> 00:33:16,680
Speaker 2: will they be as galvanized as united you know, around Trump?

692
00:33:17,040 --> 00:33:19,120
Speaker 2: I don't think so, not in the current way.

693
00:33:19,200 --> 00:33:22,680
Speaker 1: Well we'll see. I just these guys loved Ron DeSantis

694
00:33:22,680 --> 00:33:26,000
Speaker 1: five seconds ago. Yeah, yes, So it's not like there

695
00:33:26,200 --> 00:33:28,640
Speaker 1: was aren't other Republican figures out there that they're also

696
00:33:28,720 --> 00:33:30,440
Speaker 1: really excited about. It's just my point.

697
00:33:30,600 --> 00:33:33,200
Speaker 2: But Santas also, let's be honest, the guy kind of sucks, right,

698
00:33:33,240 --> 00:33:35,680
Speaker 2: So it's like when you get reality Santas, like what

699
00:33:35,840 --> 00:33:37,480
Speaker 2: it is compared to Trump.

700
00:33:38,600 --> 00:33:39,760
Speaker 3: That's a whole other ballgame.

701
00:33:40,480 --> 00:33:42,320
Speaker 4: One other point that might be obvious, but I think

702
00:33:42,320 --> 00:33:43,920
Speaker 4: worth making before we move on. And I have a

703
00:33:43,920 --> 00:33:46,000
Speaker 4: section of this in my book on the Squad about

704
00:33:46,480 --> 00:33:52,600
Speaker 4: the moment when Rogan endorsed Bernie Sanders in that weird

705
00:33:52,640 --> 00:33:54,920
Speaker 4: interview with Barry Weiss. Yeah, I'm voting for me. He's

706
00:33:54,960 --> 00:33:58,120
Speaker 4: been consistent, he's and like lays out like the reason

707
00:33:58,160 --> 00:34:02,800
Speaker 4: he loves Bernie Sanders. The reaction from the Democratic Party

708
00:34:02,880 --> 00:34:05,800
Speaker 4: kind of set a course that we're on today, Like

709
00:34:05,840 --> 00:34:08,799
Speaker 4: that was a moment of agency where a different kind

710
00:34:08,800 --> 00:34:12,400
Speaker 4: of party, in a different moment, less kind of consumed

711
00:34:12,400 --> 00:34:16,279
Speaker 4: by identity politics at the time, could have embraced Joe

712
00:34:16,320 --> 00:34:18,640
Speaker 4: Rogan and his entire coalition at that time and said,

713
00:34:18,880 --> 00:34:22,359
Speaker 4: we don't agree with everything that Rogan believes, but he

714
00:34:22,440 --> 00:34:25,319
Speaker 4: agrees with us eighty percent of the time. So we're

715
00:34:25,360 --> 00:34:27,879
Speaker 4: bringing him in and we're going to build a coalition here.

716
00:34:28,320 --> 00:34:31,600
Speaker 4: And if you even have half the bros fear like

717
00:34:31,760 --> 00:34:34,839
Speaker 4: the Democrats are, you know, have an extra couple point

718
00:34:34,880 --> 00:34:42,040
Speaker 4: advantage long term, the long term trajectory of losing young men,

719
00:34:43,400 --> 00:34:45,520
Speaker 4: you know, it's going to add up over the years,

720
00:34:45,520 --> 00:34:49,040
Speaker 4: and it all goes back to this deeply emotional moment.

721
00:34:49,080 --> 00:34:51,600
Speaker 4: You know, Rogan said, I'm never getting involved in politics again.

722
00:34:51,640 --> 00:34:54,680
Speaker 4: Like I think a lot of his like hostility towards

723
00:34:54,680 --> 00:34:59,440
Speaker 4: wokeism was ramped up in that moment and still fuels

724
00:34:59,440 --> 00:35:00,440
Speaker 4: his politics today.

725
00:35:00,680 --> 00:35:02,440
Speaker 3: And then Gasoline Port on that by Covid.

726
00:35:02,480 --> 00:35:05,000
Speaker 2: I don't disagree. I think that's totally right. I'm not

727
00:35:05,200 --> 00:35:07,360
Speaker 2: so much. I don't know if there was much you

728
00:35:07,360 --> 00:35:09,560
Speaker 2: could do about it, to be honest, because we're talking

729
00:35:09,640 --> 00:35:13,520
Speaker 2: about like way bigger cultural figure or like way bigger

730
00:35:13,560 --> 00:35:16,800
Speaker 2: cultural shifts that have happened in the higher like commanding

731
00:35:16,840 --> 00:35:19,319
Speaker 2: heights of culture than just Rogan or not.

732
00:35:19,480 --> 00:35:20,799
Speaker 3: You knows, that's why I met.

733
00:35:20,840 --> 00:35:22,880
Speaker 2: That's why I met in terms of he's following the

734
00:35:22,920 --> 00:35:25,960
Speaker 2: trend right by coming in this late like everyone else

735
00:35:26,080 --> 00:35:28,759
Speaker 2: is just pro Trump, like Patrick, Bette, David and all

736
00:35:28,760 --> 00:35:32,239
Speaker 2: these other these guys have built massive channels they I

737
00:35:32,280 --> 00:35:34,959
Speaker 2: mean think about it, like in terms of how big

738
00:35:35,000 --> 00:35:38,360
Speaker 2: they are relative to even four years ago, of how

739
00:35:38,520 --> 00:35:41,239
Speaker 2: gigantic the trend has come. So you know, that's why

740
00:35:41,280 --> 00:35:44,160
Speaker 2: it makes sense logically to me, because the mill you

741
00:35:44,719 --> 00:35:46,880
Speaker 2: the Rogan is swimming in of UFC and all these

742
00:35:47,080 --> 00:35:49,240
Speaker 2: these people have been wearing MAGA hats for three years.

743
00:35:49,280 --> 00:35:51,200
Speaker 2: It's sometime ten years, you know, in case of like

744
00:35:51,480 --> 00:35:54,280
Speaker 2: Dana White. So I don't know. I'm not so sure

745
00:35:54,480 --> 00:35:56,680
Speaker 2: that there was anything to be done about this. I

746
00:35:56,680 --> 00:35:58,600
Speaker 2: think way more of it has to do with just

747
00:35:59,000 --> 00:36:02,600
Speaker 2: mainstream media, with the broader like culture and with everything.

748
00:36:02,760 --> 00:36:04,960
Speaker 3: So yeah, I don't know. Clearly, we have a lot

749
00:36:05,000 --> 00:36:05,600
Speaker 3: to say about it.

750
00:36:05,800 --> 00:36:07,600
Speaker 1: Yeah, and this will be I think this will be

751
00:36:07,640 --> 00:36:10,520
Speaker 1: one of the conversations that will continue, you know, kind

752
00:36:10,520 --> 00:36:13,239
Speaker 1: of will be shaped by what happens tonight for sure,

753
00:36:13,480 --> 00:36:17,160
Speaker 1: when we see how successful this strategy ends up being.

754
00:36:17,719 --> 00:36:20,000
Speaker 1: I can just show you guys if you want. I've

755
00:36:20,040 --> 00:36:24,360
Speaker 1: got the Trump reaction to learning that Joe Rogan endorsed

756
00:36:24,440 --> 00:36:27,040
Speaker 1: him from last night. I think this was that was

757
00:36:27,080 --> 00:36:28,320
Speaker 1: this at his final rally.

758
00:36:28,360 --> 00:36:29,760
Speaker 3: I'm not grand Rapids.

759
00:36:29,800 --> 00:36:33,880
Speaker 1: It was the one before the one was Okay, Ryan

760
00:36:33,920 --> 00:36:36,200
Speaker 1: went to one of Trump's rallies yesterday. Just quickly, Ryan,

761
00:36:36,239 --> 00:36:39,400
Speaker 1: give us a give us a vibe check on on

762
00:36:39,480 --> 00:36:40,520
Speaker 1: the how the rally was.

763
00:36:41,160 --> 00:36:43,000
Speaker 4: So it was it was pretty low energy and Trump

764
00:36:43,040 --> 00:36:47,279
Speaker 4: didn't really rile folks up. But you know it's in Reading, Pennsylvania.

765
00:36:47,320 --> 00:36:49,920
Speaker 4: He had he had been in Reading a month earlier.

766
00:36:49,960 --> 00:36:53,040
Speaker 4: He was in nearby Allentown like a week earlier. If

767
00:36:53,080 --> 00:36:55,799
Speaker 4: you're a Trump supporter and you want to see Trump, like,

768
00:36:55,880 --> 00:37:00,160
Speaker 4: you've had scores, like maybe one hundred plus chances in

769
00:37:00,160 --> 00:37:03,000
Speaker 4: the Redding area to see him over the last nine years.

770
00:37:03,440 --> 00:37:07,520
Speaker 4: So I don't want to discount his his potential just

771
00:37:07,600 --> 00:37:09,520
Speaker 4: based on that. But yeah, there's a bunch of old

772
00:37:09,520 --> 00:37:11,600
Speaker 4: people who've been sitting there for four hours waiting for him,

773
00:37:11,640 --> 00:37:14,200
Speaker 4: and so it's not surprising that they couldn't get whipped

774
00:37:14,680 --> 00:37:15,200
Speaker 4: too much of.

775
00:37:15,120 --> 00:37:18,520
Speaker 1: Any may not even be advisable.

776
00:37:18,080 --> 00:37:18,719
Speaker 3: To what he did.

777
00:37:19,800 --> 00:37:21,880
Speaker 4: He rocked out to Y m c A. And you

778
00:37:21,880 --> 00:37:24,120
Speaker 4: know he did the whole whole show you his dance.

779
00:37:25,640 --> 00:37:28,120
Speaker 1: Okay, let's let's take a listen to Trump learning on

780
00:37:28,160 --> 00:37:29,400
Speaker 1: stage that Rogan indors.

781
00:37:29,239 --> 00:37:32,800
Speaker 7: Oh wow, I have some bigs, Megan. I'm just getting

782
00:37:32,840 --> 00:37:39,280
Speaker 7: this right now. So somebody that's very very respected show

783
00:37:40,320 --> 00:37:44,920
Speaker 7: two weeks ago, and I said, why not? And to

784
00:37:45,000 --> 00:37:48,920
Speaker 7: me it's very big because he's, uh, the biggest there is,

785
00:37:48,960 --> 00:37:51,360
Speaker 7: I guess in that world by far. Somebody said, the

786
00:37:51,360 --> 00:37:55,439
Speaker 7: biggest beyond anybody in a long time and his name

787
00:37:55,560 --> 00:38:04,080
Speaker 7: is Joe Rogan and he's never done this before. And

788
00:38:04,160 --> 00:38:07,080
Speaker 7: it just came over the wires that Joe Rogan just

789
00:38:07,200 --> 00:38:07,799
Speaker 7: indorsed me.

790
00:38:07,960 --> 00:38:21,080
Speaker 8: Is that that's so nice.

791
00:38:20,719 --> 00:38:21,759
Speaker 3: And he doesn't do that.

792
00:38:22,280 --> 00:38:28,399
Speaker 5: He doesn't do that, and he tends to.

793
00:38:28,360 --> 00:38:30,799
Speaker 7: Be a little bit more liberal than some of the

794
00:38:30,800 --> 00:38:32,919
Speaker 7: people in this No. I had a lot of fun

795
00:38:32,960 --> 00:38:36,759
Speaker 7: and he was amazing and he was It was a

796
00:38:36,840 --> 00:38:37,800
Speaker 7: three hour interview.

797
00:38:37,840 --> 00:38:39,920
Speaker 1: In fact, I was two.

798
00:38:39,800 --> 00:38:42,400
Speaker 7: Hours late for a rally that we had had explained

799
00:38:42,400 --> 00:38:44,799
Speaker 7: that a little bit and it was cold out that night.

800
00:38:45,600 --> 00:38:47,960
Speaker 7: We flew and I but we had to make it.

801
00:38:47,960 --> 00:38:51,520
Speaker 7: We were two more than two hours late. And they understood.

802
00:38:51,560 --> 00:38:53,800
Speaker 7: I said, you know, I just was interviewed by a

803
00:38:53,920 --> 00:38:56,360
Speaker 7: very interesting guy and he just kept going on. They

804
00:38:56,440 --> 00:38:58,960
Speaker 7: called it long form, and it could have gone a

805
00:38:58,960 --> 00:38:59,520
Speaker 7: lot longer.

806
00:38:59,520 --> 00:39:00,239
Speaker 1: But he was right.

807
00:39:00,800 --> 00:39:03,960
Speaker 7: And he's not a person that does endorsements, but he

808
00:39:04,000 --> 00:39:06,320
Speaker 7: did it endorsements. So I just want to thank Joe Rogan.

809
00:39:06,400 --> 00:39:10,680
Speaker 1: That spans there you go, Ryan. One thing. I saw

810
00:39:10,680 --> 00:39:12,759
Speaker 1: a couple of people who were at like reporters who

811
00:39:12,800 --> 00:39:15,960
Speaker 1: are at Trump rallies, detecting a bit of a wistful

812
00:39:16,320 --> 00:39:19,879
Speaker 1: tone because obviously this could be you know, very likely

813
00:39:19,960 --> 00:39:23,840
Speaker 1: we're the last Trump rallies that we'll ever see, which is,

814
00:39:23,960 --> 00:39:27,080
Speaker 1: you know, kind of the end of a very specific

815
00:39:27,440 --> 00:39:30,759
Speaker 1: and very tumultuous era in American politics. Did you pick

816
00:39:30,800 --> 00:39:31,400
Speaker 1: up on any of that?

817
00:39:31,800 --> 00:39:34,360
Speaker 4: I mean, yeah, he was withful. He's like, you know, guys,

818
00:39:34,840 --> 00:39:37,200
Speaker 4: we've been doing this for nine years now. Think about that.

819
00:39:38,000 --> 00:39:40,960
Speaker 4: I think he counted something like nine hundred rallies.

820
00:39:41,200 --> 00:39:41,480
Speaker 7: Wow.

821
00:39:41,840 --> 00:39:44,239
Speaker 4: And he said, you know, and then his quip there

822
00:39:44,360 --> 00:39:46,160
Speaker 4: was He's like, and I've always been, you know, very

823
00:39:46,200 --> 00:39:48,840
Speaker 4: well behaved. Once or twice, I may have made a

824
00:39:48,880 --> 00:39:53,000
Speaker 4: slightly aggressive statement, and the fake news media has called

825
00:39:53,000 --> 00:39:54,680
Speaker 4: me out on it, but in general, I've been I've

826
00:39:54,680 --> 00:39:55,400
Speaker 4: been perfect.

827
00:39:55,480 --> 00:39:59,279
Speaker 3: They've been perfect rallies, perfect rallies, perfect phone calls, perfect.

828
00:39:59,000 --> 00:40:03,839
Speaker 4: Perfect, perfect. And then he said, you know, it's this

829
00:40:03,880 --> 00:40:07,080
Speaker 4: is not an end. It's it's beginning and we'll be back,

830
00:40:07,120 --> 00:40:09,920
Speaker 4: but we'll be talking about, you know, our achievements and

831
00:40:09,920 --> 00:40:11,960
Speaker 4: what we've what we've accomplished. And people in the crowd

832
00:40:11,960 --> 00:40:15,279
Speaker 4: are yelling out, we'll see you at the inauguration. So yeah,

833
00:40:15,480 --> 00:40:17,600
Speaker 4: the waterworks were flowing and reading no doubt.

834
00:40:17,440 --> 00:40:21,080
Speaker 1: About it, gotcha right, well, Trump was not the only

835
00:40:21,160 --> 00:40:26,240
Speaker 1: one receiving a Big Joe endorsement. Yes, Tamala Harris also

836
00:40:26,960 --> 00:40:31,640
Speaker 1: receiving the endorsement of Fat Joe actually who I believe

837
00:40:31,719 --> 00:40:35,640
Speaker 1: is Puerto Rican and spoke about the Tony Hinchcliff comments

838
00:40:35,640 --> 00:40:38,480
Speaker 1: and made an appeal specifically to Latino voters. Let's take

839
00:40:38,480 --> 00:40:39,239
Speaker 1: a listen to that.

840
00:40:41,680 --> 00:40:44,839
Speaker 9: I really want you to feel the moment. I am

841
00:40:44,880 --> 00:40:48,600
Speaker 9: a sucker for a good laugh. I'm the guy they

842
00:40:48,640 --> 00:40:51,720
Speaker 9: throw out the movie theater because the movie's too funny

843
00:40:51,719 --> 00:40:55,520
Speaker 9: and I'm making too much noise. I'm the guy that

844
00:40:55,680 --> 00:40:58,239
Speaker 9: you can roast me in the comedy club and I

845
00:40:58,320 --> 00:41:02,439
Speaker 9: will laugh with you all the way out. The other

846
00:41:02,520 --> 00:41:07,400
Speaker 9: day in Madison Square Garden, that was no joke, Ladies

847
00:41:07,400 --> 00:41:13,840
Speaker 9: and gentlemen, that was no joke. And it was filled

848
00:41:13,880 --> 00:41:17,840
Speaker 9: with so much hate, Hatred of Jewish people, hatred of

849
00:41:17,920 --> 00:41:24,960
Speaker 9: black people, garving watermelons, calling Puerto Rico in the island

850
00:41:25,040 --> 00:41:31,200
Speaker 9: of garbage, my Latinos, where is your pride?

851
00:41:32,920 --> 00:41:34,520
Speaker 1: So you get a sense of that, I will tell

852
00:41:34,600 --> 00:41:36,319
Speaker 1: I mean this is kind of you know, contrast to

853
00:41:36,440 --> 00:41:43,920
Speaker 1: the the Republican closing strategy. Democrats genuinely feel that the

854
00:41:43,960 --> 00:41:47,280
Speaker 1: Madison Square Garden rally was a bit of a turning

855
00:41:47,320 --> 00:41:50,520
Speaker 1: point in terms of late breaking voters going for them

856
00:41:50,520 --> 00:41:53,680
Speaker 1: a Latino voters in particular, but voters in general. There

857
00:41:53,760 --> 00:41:56,799
Speaker 1: is significant decent amount of data that says, for whatever reason,

858
00:41:56,880 --> 00:42:00,239
Speaker 1: late breaking voters do seem to be going more formal.

859
00:42:00,280 --> 00:42:02,799
Speaker 1: Harris will find out tonight how true that ultimately is.

860
00:42:03,200 --> 00:42:06,360
Speaker 1: I texted our friend Chuck Roache yesterday to say, you know,

861
00:42:06,440 --> 00:42:08,760
Speaker 1: this is a real thing, and he's like, one hundred percent,

862
00:42:08,880 --> 00:42:11,480
Speaker 1: this is definitely a real thing. And the sense was

863
00:42:11,520 --> 00:42:13,640
Speaker 1: it was kind of like a final straw for voters,

864
00:42:13,680 --> 00:42:15,279
Speaker 1: reminding them of some of the worst parts of Trump

865
00:42:15,280 --> 00:42:19,319
Speaker 1: blah blah blah. So they're leaning in hard to this

866
00:42:19,400 --> 00:42:22,719
Speaker 1: final strategy and reminding people of, you know, some of

867
00:42:22,800 --> 00:42:25,600
Speaker 1: the more noxious or toxic traits of Trump that have

868
00:42:25,640 --> 00:42:27,560
Speaker 1: made him unpopular over the years.

869
00:42:27,960 --> 00:42:28,080
Speaker 2: Yea.

870
00:42:28,160 --> 00:42:31,560
Speaker 4: And anecdotally, for whatever this is worth, my stepmother and

871
00:42:31,800 --> 00:42:37,080
Speaker 4: sister were in voted in Allentown yesterday and said that

872
00:42:37,160 --> 00:42:40,839
Speaker 4: like it was mostly Puerto Ricans in line to vote.

873
00:42:40,880 --> 00:42:43,640
Speaker 3: Now Puerto Rican population, it.

874
00:42:43,600 --> 00:42:47,120
Speaker 4: Would be probably a third Puerto Rican like normally anyway,

875
00:42:47,640 --> 00:42:51,920
Speaker 4: it's you know, sixty percent population roughly in Allentown. You know,

876
00:42:52,000 --> 00:42:54,120
Speaker 4: but are they showing up late and deciding to vote?

877
00:42:54,200 --> 00:42:59,120
Speaker 4: Because because of this, the Democrats certainly think that that

878
00:42:59,239 --> 00:43:01,680
Speaker 4: it has gotten some people to vote who otherwise would

879
00:43:01,680 --> 00:43:03,319
Speaker 4: not have voted. Like it's not as if they were

880
00:43:03,360 --> 00:43:06,759
Speaker 4: like Trump and now they're Kamala, or they were like

881
00:43:06,800 --> 00:43:09,200
Speaker 4: on the fence and now they're Kamala. It was like

882
00:43:09,239 --> 00:43:11,920
Speaker 4: they were they were the classic voter of the choice

883
00:43:11,920 --> 00:43:13,440
Speaker 4: is not who you're going to vote for us whether

884
00:43:13,520 --> 00:43:15,480
Speaker 4: or not you actually decide to vote now they have

885
00:43:15,520 --> 00:43:16,040
Speaker 4: a reason to.

886
00:43:16,320 --> 00:43:19,640
Speaker 2: It's the couch that would actually make more sense to

887
00:43:19,680 --> 00:43:21,960
Speaker 2: me than like people who are swishing right.

888
00:43:22,400 --> 00:43:24,000
Speaker 3: Who was the guy that we was?

889
00:43:24,000 --> 00:43:26,440
Speaker 2: It like, I forget there was the artist who had

890
00:43:26,520 --> 00:43:29,239
Speaker 2: already endorsed Trump, and he's like now I cannot stand.

891
00:43:28,960 --> 00:43:32,239
Speaker 3: With I'm just like, bro, shut shut the fuck up.

892
00:43:32,320 --> 00:43:34,120
Speaker 2: You know, it's like you know exactly what you're getting

893
00:43:34,120 --> 00:43:35,200
Speaker 2: into first time.

894
00:43:35,520 --> 00:43:39,160
Speaker 3: I don't remember who his name is, Nick.

895
00:43:39,280 --> 00:43:42,040
Speaker 1: Jam, Nicky Jam Jam.

896
00:43:42,120 --> 00:43:46,040
Speaker 2: Yeah, I apologize, Jam, but it's like, bro, like, I'm sorry,

897
00:43:46,200 --> 00:43:51,400
Speaker 2: zero respect. Yeah for that idea of somebody who was

898
00:43:51,560 --> 00:43:54,800
Speaker 2: not voting and then decided to vote. That makes actually

899
00:43:54,800 --> 00:43:57,080
Speaker 2: a little bit more sense to me in terms of

900
00:43:57,560 --> 00:43:59,359
Speaker 2: the overall turnout. But you know, part of the reason

901
00:43:59,360 --> 00:44:01,400
Speaker 2: why I'm just so skeptical of all of this is

902
00:44:01,440 --> 00:44:03,000
Speaker 2: I just feel like if Kamala wins is going to

903
00:44:03,040 --> 00:44:04,759
Speaker 2: come down to a huge poling error like we see

904
00:44:04,760 --> 00:44:07,319
Speaker 2: with the Iowa Seltzer pole, and the vast majority of

905
00:44:07,320 --> 00:44:10,719
Speaker 2: it will be abortion right. And you know, in the

906
00:44:10,760 --> 00:44:13,880
Speaker 2: scenario even where she does win in the blue wall, like,

907
00:44:13,920 --> 00:44:15,680
Speaker 2: it's not like there's a ton of Puerto Ricans who

908
00:44:15,719 --> 00:44:18,520
Speaker 2: live in Michigan and Wisconsin. Like, it's going to be

909
00:44:18,800 --> 00:44:21,600
Speaker 2: white women who really push you over the end.

910
00:44:21,680 --> 00:44:22,200
Speaker 3: Now they know.

911
00:44:22,360 --> 00:44:24,920
Speaker 2: Look, it's highly possible a white ladies get very offended

912
00:44:24,960 --> 00:44:27,640
Speaker 2: by this, like I was talking about earlier, No, I

913
00:44:27,640 --> 00:44:30,680
Speaker 2: needed like statistically the most likely to get offended by it.

914
00:44:30,760 --> 00:44:31,480
Speaker 1: Yeah, definitely.

915
00:44:31,640 --> 00:44:33,920
Speaker 3: And so that is the way I would, you know,

916
00:44:34,000 --> 00:44:34,600
Speaker 3: kind of look at it.

917
00:44:34,640 --> 00:44:38,760
Speaker 2: But that's where Trump's affect and character and all this stuff,

918
00:44:38,800 --> 00:44:42,280
Speaker 2: where these people were probably just voting anyways and couldn't

919
00:44:42,320 --> 00:44:44,359
Speaker 2: wait to go and to vote against him.

920
00:44:44,400 --> 00:44:47,240
Speaker 3: So we'll see. I don't know, I'm still curious.

921
00:44:49,920 --> 00:44:52,879
Speaker 1: Okay, guys, so we took you through these supposedly scientific

922
00:44:53,360 --> 00:44:57,560
Speaker 1: election prediction models. But the reality is they all are

923
00:44:57,600 --> 00:44:59,360
Speaker 1: just like it's a toss up, it's fifty to fifty.

924
00:44:59,400 --> 00:45:00,960
Speaker 1: So we thought we would give you some of the

925
00:45:01,520 --> 00:45:05,719
Speaker 1: less scientific election prediction models that you know, don't even

926
00:45:05,800 --> 00:45:08,799
Speaker 1: have a semblance of a mirror of anything you know,

927
00:45:08,960 --> 00:45:10,920
Speaker 1: real to them, but people still look at them, and

928
00:45:10,920 --> 00:45:12,560
Speaker 1: some of them have a pretty good track record. So

929
00:45:12,880 --> 00:45:16,520
Speaker 1: we'll start with this one. There was a panda, sorry,

930
00:45:16,520 --> 00:45:21,200
Speaker 1: not a panda, a hippo that became very famous. I

931
00:45:21,520 --> 00:45:21,839
Speaker 1: was not.

932
00:45:21,760 --> 00:45:28,520
Speaker 3: Actual he canceled this.

933
00:45:29,640 --> 00:45:32,960
Speaker 1: I was actually not familiar with this baby hippo.

934
00:45:32,840 --> 00:45:35,960
Speaker 3: With mood Yeah, with mood very popular.

935
00:45:35,719 --> 00:45:39,120
Speaker 1: But she's apparently very popular. And they had her pick

936
00:45:39,200 --> 00:45:43,759
Speaker 1: from two cakes, one that said Kamala Harris and the

937
00:45:43,800 --> 00:45:48,959
Speaker 1: other that said Donald Trump and drumroll please moodang went

938
00:45:49,080 --> 00:45:51,960
Speaker 1: for the Trump cake. Now, I have seen that the

939
00:45:52,520 --> 00:45:57,360
Speaker 1: conventional wisdom is that that would indicate a Trump win. However,

940
00:45:57,440 --> 00:46:01,919
Speaker 1: I have seen the contrarian cake that eating the Trump cake,

941
00:46:02,000 --> 00:46:04,360
Speaker 1: which she's really signaling is she wants to eat the

942
00:46:04,440 --> 00:46:07,880
Speaker 1: rich and she actually a secret Kamala Harris fan.

943
00:46:08,800 --> 00:46:09,480
Speaker 3: Very possible.

944
00:46:09,719 --> 00:46:11,680
Speaker 2: And what did they say there that they they're the

945
00:46:11,719 --> 00:46:14,080
Speaker 2: conspiracy is that there was a bigger piece of dragon

946
00:46:14,160 --> 00:46:14,920
Speaker 2: fruit that was.

947
00:46:14,920 --> 00:46:16,720
Speaker 3: On the on the cake, and that's.

948
00:46:16,600 --> 00:46:19,279
Speaker 1: What so they raised it.

949
00:46:19,280 --> 00:46:20,200
Speaker 3: It's certainly possible.

950
00:46:20,480 --> 00:46:22,799
Speaker 2: Listen, if you ever been to Thailand, dragon fruit is

951
00:46:22,840 --> 00:46:26,920
Speaker 2: the ship whenever it is fresh. So yeah, shout out

952
00:46:26,920 --> 00:46:31,719
Speaker 2: to Moodang. I no disrespect for choosing that cake. It

953
00:46:31,760 --> 00:46:34,440
Speaker 2: was funny though, this one I've seen go viral everywhere now.

954
00:46:34,520 --> 00:46:38,520
Speaker 1: Ryan asked, Ryan asked, what was before we did them?

955
00:46:38,880 --> 00:46:41,200
Speaker 3: And I was like, well, mood Egg's a baby four.

956
00:46:41,160 --> 00:46:43,480
Speaker 1: Months old, I think so, so she's perfect.

957
00:46:43,520 --> 00:46:44,160
Speaker 2: She never got it.

958
00:46:46,920 --> 00:46:47,319
Speaker 5: All right.

959
00:46:47,400 --> 00:46:51,799
Speaker 1: In addition, we have a superstition about what's called the

960
00:46:51,840 --> 00:46:54,640
Speaker 1: Redskins rule. Of course, now the Redskins are the Commanders,

961
00:46:55,040 --> 00:46:57,960
Speaker 1: but for a long time there was a relationship between

962
00:46:58,239 --> 00:47:02,120
Speaker 1: did the Redskins whin they're final home game and who

963
00:47:02,280 --> 00:47:05,680
Speaker 1: ultimately took the White House. So actually in the last

964
00:47:05,719 --> 00:47:08,200
Speaker 1: Commander's home game, it was against the Bears, and I

965
00:47:08,239 --> 00:47:10,239
Speaker 1: don't know if you guys saw, even as like not

966
00:47:10,280 --> 00:47:14,400
Speaker 1: a big sports person, I saw this unbelievable touchdown pass,

967
00:47:14,480 --> 00:47:18,719
Speaker 1: crazy hail Mary, the clocks expired, etc. To launch them

968
00:47:18,719 --> 00:47:22,640
Speaker 1: to a victory over the Bears. However, there's a catch

969
00:47:23,000 --> 00:47:27,439
Speaker 1: which is that apparently literally they have recently revised the

970
00:47:27,480 --> 00:47:30,719
Speaker 1: Redskins Rule to be the Redskins Rule two point zero

971
00:47:30,760 --> 00:47:33,560
Speaker 1: because it like stopped being predictive sometime in the two thousands.

972
00:47:33,560 --> 00:47:35,839
Speaker 1: They were like, actually, now it's flipped, and if the

973
00:47:35,840 --> 00:47:39,480
Speaker 1: Redskins win, then the party in power is gonna lose.

974
00:47:40,080 --> 00:47:42,160
Speaker 1: So I sort of rate this one a toss up

975
00:47:42,160 --> 00:47:42,680
Speaker 1: at this point.

976
00:47:43,040 --> 00:47:46,400
Speaker 2: Okay, yeah, I don't even know what to say about that.

977
00:47:46,800 --> 00:47:48,960
Speaker 2: You could also read it as fifty to fifty because

978
00:47:49,000 --> 00:47:51,320
Speaker 2: the Bears had basically won the game up until the

979
00:47:51,440 --> 00:47:54,560
Speaker 2: very last second whenever they threw the Hail Mary.

980
00:47:54,680 --> 00:47:57,279
Speaker 3: So yeah, I don't know the game.

981
00:47:57,480 --> 00:47:57,920
Speaker 1: It could be.

982
00:47:58,080 --> 00:47:59,000
Speaker 3: That could be it.

983
00:47:59,040 --> 00:48:00,759
Speaker 1: We could look back at that and say, you know what,

984
00:48:00,840 --> 00:48:03,960
Speaker 1: that actually perfectly predicted what we're going to see on

985
00:48:04,080 --> 00:48:08,040
Speaker 1: election day. This one I had heard about before. There's

986
00:48:08,080 --> 00:48:14,160
Speaker 1: this monogram shop in East Hampton, New York that sells

987
00:48:14,719 --> 00:48:19,359
Speaker 1: cups of the different presidential contenders, and so they look

988
00:48:19,360 --> 00:48:21,880
Speaker 1: at like which candidate sells the most cups and then

989
00:48:21,920 --> 00:48:24,440
Speaker 1: not supposed to be predictive. I don't actually have what

990
00:48:24,520 --> 00:48:27,040
Speaker 1: the record here of how accurate this has been. I

991
00:48:27,040 --> 00:48:30,120
Speaker 1: think it's been decently accurate though, and they found that

992
00:48:30,200 --> 00:48:34,240
Speaker 1: the Harris cups have been out selling the Trump cups

993
00:48:34,239 --> 00:48:37,719
Speaker 1: pretty significantly, most recently. Oh this is this the Yeah,

994
00:48:37,719 --> 00:48:39,120
Speaker 1: this is the monogram East Hampton.

995
00:48:39,880 --> 00:48:40,080
Speaker 5: Yeah.

996
00:48:40,600 --> 00:48:43,800
Speaker 2: Isn't this like one of the wealthiest neighborhoods like in

997
00:48:44,520 --> 00:48:45,200
Speaker 2: New York?

998
00:48:45,920 --> 00:48:47,200
Speaker 3: Isn't this like Long Island?

999
00:48:47,239 --> 00:48:52,520
Speaker 1: Think that this is extremely predictive soccer, Like I dare you?

1000
00:48:52,880 --> 00:48:53,840
Speaker 3: Okay, do we even.

1001
00:48:53,719 --> 00:48:54,880
Speaker 1: Know how to turn the keys?

1002
00:48:57,440 --> 00:48:59,320
Speaker 2: That's like going to Nantucket and being like, oh the

1003
00:48:59,440 --> 00:49:01,720
Speaker 2: Nantucket shop is it going big for comma?

1004
00:49:01,719 --> 00:49:03,759
Speaker 3: I'm like, wow, reallynoy So.

1005
00:49:03,960 --> 00:49:07,799
Speaker 1: I think they predicted Trump over Hillary. I think in

1006
00:49:07,840 --> 00:49:08,560
Speaker 1: twenty fifteen.

1007
00:49:09,200 --> 00:49:10,120
Speaker 5: Oh so, how.

1008
00:49:10,040 --> 00:49:12,319
Speaker 2: Many those people are buying Trump is a lark? See

1009
00:49:12,320 --> 00:49:14,760
Speaker 2: that's the other problem with Trump. Trump isually a memeable figure.

1010
00:49:15,040 --> 00:49:17,840
Speaker 2: We're about to get to Halloween masks right where.

1011
00:49:17,880 --> 00:49:21,120
Speaker 1: Yes, so this is This is another interesting one because

1012
00:49:21,920 --> 00:49:28,200
Speaker 1: so traditionally the Halloween mask predictor is which candidate should

1013
00:49:28,360 --> 00:49:32,239
Speaker 1: sells more Halloween masks from Spirit Halloween? Like, who how

1014
00:49:32,280 --> 00:49:35,480
Speaker 1: many people buy which candidate's masks? I couldn't find that

1015
00:49:35,600 --> 00:49:38,280
Speaker 1: data this year, even though I've seen it every other election.

1016
00:49:38,760 --> 00:49:44,439
Speaker 1: But the number one costume was apparently the crazy cat

1017
00:49:44,520 --> 00:49:45,360
Speaker 1: Lady costume.

1018
00:49:45,719 --> 00:49:47,560
Speaker 3: Yeah, that tracks.

1019
00:49:47,760 --> 00:49:49,040
Speaker 1: So what do we make is that?

1020
00:49:49,600 --> 00:49:52,839
Speaker 4: Is that a pro common commo nominated too lately to

1021
00:49:52,840 --> 00:49:56,200
Speaker 4: get those to get those masks shipped from China.

1022
00:49:56,440 --> 00:49:57,200
Speaker 3: Yeah, good point.

1023
00:49:57,360 --> 00:50:00,840
Speaker 1: Oh, that probably is what happened, right, That lady thing

1024
00:50:00,960 --> 00:50:04,319
Speaker 1: has hit in a degree which is like insane to me.

1025
00:50:04,480 --> 00:50:07,080
Speaker 2: So like in my neighborhood here there's literally the lady

1026
00:50:07,080 --> 00:50:09,720
Speaker 2: two doors down as a childless cat lity for Kamala

1027
00:50:09,760 --> 00:50:12,799
Speaker 2: sign in her window, and our local cat cafe is

1028
00:50:12,840 --> 00:50:13,720
Speaker 2: selling merch.

1029
00:50:13,840 --> 00:50:16,280
Speaker 3: That's like childless cat ladies for Kamala.

1030
00:50:16,320 --> 00:50:18,759
Speaker 1: Harris so Keaylor Swift referenced it in her Taylor.

1031
00:50:18,520 --> 00:50:23,000
Speaker 2: Swift references in her endorsement. So did Jennifer Aniston. There's

1032
00:50:23,000 --> 00:50:24,840
Speaker 2: a lot you could say about that, but yeah, it

1033
00:50:24,960 --> 00:50:27,359
Speaker 2: certainly it certainly hit else just's put it that way.

1034
00:50:27,440 --> 00:50:29,560
Speaker 1: So you would say that's that's a pro Kamala sign

1035
00:50:29,600 --> 00:50:31,319
Speaker 1: because I was going to be a toss up because

1036
00:50:31,320 --> 00:50:32,359
Speaker 1: it's a little ambiguous.

1037
00:50:32,560 --> 00:50:33,680
Speaker 3: No, No, it's pro Kammala.

1038
00:50:33,800 --> 00:50:36,080
Speaker 1: This is hit there too.

1039
00:50:36,440 --> 00:50:38,560
Speaker 6: I can combine the last two. I think it's pro Kamala.

1040
00:50:38,560 --> 00:50:40,560
Speaker 6: I think that's a positive sign for Kamala. I was

1041
00:50:40,560 --> 00:50:42,560
Speaker 6: on a flight about a month ago and there was

1042
00:50:42,600 --> 00:50:46,640
Speaker 6: a woman who seemed absolutely bananas while we were the

1043
00:50:46,640 --> 00:50:49,640
Speaker 6: flight was delayed and she started doing her workouts.

1044
00:50:49,960 --> 00:50:52,399
Speaker 5: Was elderly. She started doing her workouts at.

1045
00:50:52,320 --> 00:50:55,960
Speaker 6: The gate, full like lunging, all kinds of stuff, just

1046
00:50:56,040 --> 00:50:59,440
Speaker 6: going for it. She was wearing a childless cat Ladies

1047
00:50:59,480 --> 00:51:00,520
Speaker 6: for Kamala shirt.

1048
00:51:01,600 --> 00:51:04,120
Speaker 5: And I know what's going on here. Get on the flight.

1049
00:51:04,960 --> 00:51:08,920
Speaker 5: She was in first class. She was in first So

1050
00:51:09,000 --> 00:51:12,120
Speaker 5: you find the two of them, Yes, East Hampton.

1051
00:51:13,520 --> 00:51:17,799
Speaker 1: All it all adds up. It's a landslide, all right.

1052
00:51:17,960 --> 00:51:21,720
Speaker 1: Next one, we've got the S and P five hundred index.

1053
00:51:22,680 --> 00:51:25,520
Speaker 1: And if it's they say, if it's up between August

1054
00:51:25,560 --> 00:51:27,520
Speaker 1: and November, and come at parties likely to keep the

1055
00:51:27,520 --> 00:51:32,520
Speaker 1: White House. This one is a good metric for Kamala. However,

1056
00:51:32,800 --> 00:51:36,200
Speaker 1: candidate height, which was indicative in eighteen or the last

1057
00:51:36,200 --> 00:51:40,280
Speaker 1: twenty four elections, would indicate a Trump landslide Becausekamala is actually.

1058
00:51:40,120 --> 00:51:41,200
Speaker 3: Quite Yeah, she's short.

1059
00:51:41,320 --> 00:51:45,440
Speaker 1: Short, she's five foot four and what is Trumple's like

1060
00:51:45,320 --> 00:51:50,719
Speaker 1: six three? Yeah, well short, large, Emily, that's all.

1061
00:51:51,920 --> 00:51:53,600
Speaker 3: I think she would be our shortest president.

1062
00:51:53,640 --> 00:51:56,640
Speaker 1: Now, Yeah, you know, I think that might.

1063
00:51:56,560 --> 00:52:01,279
Speaker 2: Be right, James James Monroe was like five. Sorry, she

1064
00:52:01,360 --> 00:52:02,800
Speaker 2: might be slightly taller than Monroe.

1065
00:52:02,880 --> 00:52:04,920
Speaker 1: They talked about the Redskins rule here, but then they

1066
00:52:04,960 --> 00:52:10,319
Speaker 1: also talk about this bakery, Buskin Bakery in Ohio that's

1067
00:52:10,360 --> 00:52:14,120
Speaker 1: predicted almost every presidential election correctly since eighty four through

1068
00:52:14,120 --> 00:52:16,200
Speaker 1: cookie sales. Team. You guys tell how much research I've

1069
00:52:16,200 --> 00:52:20,560
Speaker 1: put into Yes, and they have Trump way up. But

1070
00:52:20,960 --> 00:52:22,839
Speaker 1: a confounding variable I have to say is that Elon

1071
00:52:22,920 --> 00:52:27,360
Speaker 1: did tweet about this bakery and so on Monday the

1072
00:52:27,360 --> 00:52:30,760
Speaker 1: cookie count was roughly eleven thousand for Harris and roughly

1073
00:52:30,880 --> 00:52:34,200
Speaker 1: thirty thousand for Trump. So landslide for Trump in that

1074
00:52:34,239 --> 00:52:35,680
Speaker 1: particular count, all.

1075
00:52:35,640 --> 00:52:39,160
Speaker 4: Right, does show that Elon, you know, translates IRL.

1076
00:52:39,320 --> 00:52:41,719
Speaker 1: So there you go. That can mean something translates to

1077
00:52:41,760 --> 00:52:44,960
Speaker 1: cookie sales. I guess that's true. Okay, this is an

1078
00:52:45,000 --> 00:52:47,960
Speaker 1: interesting one. This one actually might be somewhat interesting to

1079
00:52:47,960 --> 00:52:51,880
Speaker 1: look at. This is the stock performance of the DJT

1080
00:52:52,360 --> 00:52:57,879
Speaker 1: like the Truth Social stock, and at the you know,

1081
00:52:57,920 --> 00:53:01,120
Speaker 1: when Trump was at his most confident, it was very high.

1082
00:53:01,239 --> 00:53:03,799
Speaker 1: Then there was a period of decline, but there has

1083
00:53:03,880 --> 00:53:08,399
Speaker 1: been a rebound recently. So you know, again I feel

1084
00:53:08,440 --> 00:53:11,160
Speaker 1: like this is a little bit of an ambiguous indicator

1085
00:53:11,320 --> 00:53:14,520
Speaker 1: of what could potentially happen on election day, but could

1086
00:53:14,520 --> 00:53:17,040
Speaker 1: reflect how people genuinely think about, like, you know, how

1087
00:53:17,040 --> 00:53:19,400
Speaker 1: are things going to go for Trump here on the election?

1088
00:53:19,840 --> 00:53:21,960
Speaker 2: Yeah, definitely people have been looking at that as like

1089
00:53:22,000 --> 00:53:24,600
Speaker 2: an inverse for how things are going to go. At

1090
00:53:24,640 --> 00:53:27,719
Speaker 2: the same time, didn't Jim Kramer say that there's been

1091
00:53:27,760 --> 00:53:30,640
Speaker 2: some pro Harris trading that's been happening. But then there's

1092
00:53:30,680 --> 00:53:33,560
Speaker 2: also inverse Kramer where you're like, oh, no, that's really

1093
00:53:33,560 --> 00:53:35,120
Speaker 2: bad from Kamala Harris.

1094
00:53:35,200 --> 00:53:37,399
Speaker 1: I'm glad you brought that up, because here we go.

1095
00:53:37,840 --> 00:53:40,960
Speaker 1: Jim Kramer says marketing action anticipate to Harris wind, which

1096
00:53:41,000 --> 00:53:41,960
Speaker 1: is devastating for her.

1097
00:53:42,960 --> 00:53:47,040
Speaker 3: Yes, there you go. Inverse Kramer never met against it.

1098
00:53:47,760 --> 00:53:50,319
Speaker 1: And the last one I have for you guys is

1099
00:53:51,280 --> 00:53:57,640
Speaker 1: the Alan Lichtman. Yep, the keys. He turned the keys.

1100
00:53:58,120 --> 00:54:00,320
Speaker 1: He says that Kamala Harris will be the next president

1101
00:54:00,320 --> 00:54:03,759
Speaker 1: of the United States. So you know, he likes to

1102
00:54:03,719 --> 00:54:06,120
Speaker 1: teut his track record. They say, correctly predicted nine of

1103
00:54:06,200 --> 00:54:08,279
Speaker 1: last ten. I think he claims he predicted all of

1104
00:54:08,320 --> 00:54:10,279
Speaker 1: the last ten, but some of them was like, oh,

1105
00:54:10,320 --> 00:54:12,320
Speaker 1: I got the popular vote, right, but that the elector

1106
00:54:12,440 --> 00:54:14,600
Speaker 1: was something like, he's got what did he hope around

1107
00:54:14,600 --> 00:54:15,000
Speaker 1: some of these?

1108
00:54:15,080 --> 00:54:16,080
Speaker 3: Which one did he miss?

1109
00:54:16,120 --> 00:54:16,279
Speaker 5: Then?

1110
00:54:16,400 --> 00:54:18,560
Speaker 3: But he did call Trump in twenty sixteen, right, he

1111
00:54:18,600 --> 00:54:19,120
Speaker 3: did call.

1112
00:54:19,040 --> 00:54:20,240
Speaker 1: Trump in twenty sixteen.

1113
00:54:20,600 --> 00:54:24,120
Speaker 4: Yes, I'm not sure what two thousand, Maybe that's the

1114
00:54:24,160 --> 00:54:25,720
Speaker 4: one he I think.

1115
00:54:25,520 --> 00:54:28,600
Speaker 1: It might be two thousand that he called for Gore,

1116
00:54:29,280 --> 00:54:32,560
Speaker 1: which or did win. But yeah, I think it might

1117
00:54:32,600 --> 00:54:35,120
Speaker 1: be that one. So still I'm willing to give him

1118
00:54:35,120 --> 00:54:35,640
Speaker 1: that one.

1119
00:54:35,680 --> 00:54:37,920
Speaker 4: I feel like this should be single elimination. Like you

1120
00:54:37,920 --> 00:54:40,640
Speaker 4: don't get to say you've got nine out of ten, yeah,

1121
00:54:40,680 --> 00:54:42,919
Speaker 4: because it's once every four years you got one job,

1122
00:54:43,680 --> 00:54:44,520
Speaker 4: you miss one, you're done.

1123
00:54:44,640 --> 00:54:45,000
Speaker 1: That's right.

1124
00:54:45,080 --> 00:54:45,960
Speaker 3: Yeah, I agree with you.

1125
00:54:47,239 --> 00:54:48,960
Speaker 1: So there you go. Guys.

1126
00:54:49,080 --> 00:54:51,279
Speaker 4: The hippo better take notes, that's right.

1127
00:54:51,520 --> 00:54:53,319
Speaker 1: Yeah, could be one and done for her man.

1128
00:54:53,640 --> 00:54:55,080
Speaker 5: What's the life span of a hippo?

1129
00:54:55,600 --> 00:54:56,880
Speaker 4: I got two hundred years or.

1130
00:54:58,520 --> 00:55:02,160
Speaker 1: Because when I was researching these yesterday, a lot of

1131
00:55:02,160 --> 00:55:05,440
Speaker 1: things came up in twenty sixteen or fifty that no

1132
00:55:05,440 --> 00:55:09,040
Speaker 1: one ever talked about again. Yeah yes, yeah, right, profit

1133
00:55:09,200 --> 00:55:13,000
Speaker 1: monkey or something like that, and you never heard of

1134
00:55:13,040 --> 00:55:14,640
Speaker 1: it again because I guess it got it wrong and

1135
00:55:14,719 --> 00:55:17,960
Speaker 1: let's to your point where like over one single elimination,

1136
00:55:18,400 --> 00:55:21,240
Speaker 1: you can't get it wrong, and especially that particular election,

1137
00:55:21,360 --> 00:55:24,280
Speaker 1: that was your time to shine and go against conventional wisdom.

1138
00:55:24,360 --> 00:55:26,320
Speaker 1: And if you're the profit monkey and you picked Hillary,

1139
00:55:26,400 --> 00:55:32,080
Speaker 1: like sorry, that's it all right, that's in Asia. So guys,

1140
00:55:32,480 --> 00:55:36,080
Speaker 1: we'll be doing live stream tonight six thirty. We'll be

1141
00:55:36,120 --> 00:55:42,520
Speaker 1: there till I don't know when. It's gonna be fun

1142
00:55:42,560 --> 00:55:44,680
Speaker 1: and we've got a logan, it's gonna be in the studio.

1143
00:55:44,800 --> 00:55:48,280
Speaker 1: We've got the decision dusk HQ data there. They usually

1144
00:55:48,320 --> 00:55:51,320
Speaker 1: are the fastest with updates and calls. They're really excited

1145
00:55:51,320 --> 00:55:54,399
Speaker 1: to have that resource available to us, and of course

1146
00:55:54,400 --> 00:55:56,000
Speaker 1: we will all be there trying to figure out what

1147
00:55:56,040 --> 00:55:57,520
Speaker 1: the hell is going on, what the world's going to

1148
00:55:57,560 --> 00:55:58,160
Speaker 1: look like tomorrow.

1149
00:55:58,280 --> 00:55:59,120
Speaker 3: So it's exciting.

1150
00:55:59,480 --> 00:56:01,440
Speaker 2: We'll see you guys is then indeed, enjoy the day

1151
00:56:01,520 --> 00:56:05,360
Speaker 2: y'allah