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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.

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Speaker 3: Coverage that is possible.

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

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Speaker 2: the absolute world to have your support.

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

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Speaker 2: Good morning, everybody, Happy Thursday. We have a great show

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Speaker 2: for everybody today. But of course we have the statness

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Speaker 2: going on right now with Hurricane Milton making landfall, but

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Speaker 2: we are going to cover that and we're going to

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Speaker 2: get to a lot of other political stuff as well.

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Speaker 1: Yeah, that's right. All eyes certainly on Florida right now

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Speaker 1: as residents there are waking up to scenes of mass devastations,

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Speaker 1: So we'll bring you everything we know about that storm

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Speaker 1: and the chaos that it is wrought in that state.

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Speaker 1: Were also are going to take a look at some

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Speaker 1: of the political news coming out. We have some new

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Speaker 1: data on a rare Republican advantage in voter self identification

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Speaker 1: and what that could mean for twenty twenty four. Also,

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Speaker 1: take you look at Trump went on Sager's friend Andrew

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Speaker 1: Schultz's podcast, and the results are actually pretty interesting. We'll

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Speaker 1: share you with you some of those clips. Mark Cuban

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Speaker 1: is joining the war against Lena Khan. Bernie Sanders and

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Speaker 1: AOC are diving into that as well, so we'll intra

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Speaker 1: party fight there. As Kamala continues to be silent about

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Speaker 1: whether or not she would keep Lena Khan, who's the

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Speaker 1: head of the FTC and has been quite aggressive about

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Speaker 1: challenging corporate power, probably the best, the best bureaucrat in

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Speaker 1: the Biden administration, Kamala's can continue to be silent about

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Speaker 1: whether or not she would keep her on board. We're

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Speaker 1: also keeping her eyes on the Middle East. BB apparently

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Speaker 1: spoke with Joe Biden. We are still waiting whatever that

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Speaker 1: attack on Iran is going to be. You have Galant

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Speaker 1: giving some indications of what it might look like. A

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Speaker 1: Fox News reporter also sparking some backlash from the right

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Speaker 1: after making sympathetic comments for Palestinian journalists. Will show you

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Speaker 1: that clip, and there's some UFO stuff that Sager's going

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Speaker 1: to explain to me into all of you as well.

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Speaker 2: Immaculate Constellation that's the only thing that you need to

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Speaker 2: know and get to the hurricane.

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Speaker 1: So, guys, we are just getting our first look at

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Speaker 1: the damage wrought by Hurricane Milton, which slammed into Florida

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Speaker 1: as a Category three hurricane. It had previously been as

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Speaker 1: high as category five, escalating rapidly within a single day

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Speaker 1: from a tropical storm to a category five hurricane with

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Speaker 1: winds so strong that actually they're contemplating adding another category

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Speaker 1: category six to deal with storms of this strength fueled

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Speaker 1: by incredibly hot waters in the Gulf of Mexico. There,

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Speaker 1: we can go ahead and put up some of the

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Speaker 1: images that we've gathered to show you this morning, so

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Speaker 1: you can just see the torrential rain and the palm

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Speaker 1: trees blowing in that incredible wind. This appears to be

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Speaker 1: some transformers that are blowing. This was I believe this

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Speaker 1: was from some Weather Channel footage. This was I believe

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Speaker 1: where Jim Canty was in a parking garage, and you

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Speaker 1: can see the way that the storm surge flooded many

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Speaker 1: areas of Florida. Three million residents this morning without power,

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Speaker 1: and part of what was so dangerous here too is

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Speaker 1: you can see this tornado. There were many tornadoes across

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Speaker 1: the state. Some people were killed by those tornadoes. This

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Speaker 1: is a look at Tropicana Field and the roof was

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Speaker 1: torn off. This is a crane that was blown off

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Speaker 1: of the roof of a building here right outside of

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Speaker 1: the Tampa Bay Times. Very very dangerous because you actually

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Speaker 1: you know reporters who were there at the Times building

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Speaker 1: trying to do their job and cover this hurricane. When

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Speaker 1: this crane comes down. I also have a lot of

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Speaker 1: questions about why you leave a crane up when you're

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Speaker 1: facing a Category three hurricane. But what we know this

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Speaker 1: morning is that the flooding is incredibly substantial. We know

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Speaker 1: that some unknown number of people lost their lives because

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Speaker 1: they were killed by tornadoes. We know a few people

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Speaker 1: were killed by tornadoes in a retirement community. We know

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Speaker 1: that there are three million people who lost power as

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Speaker 1: a result of this. And in advance, there was a

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Speaker 1: huge effort to evacuate as many people as possible from Florida,

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Speaker 1: because if you looked at the track of this thing,

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Speaker 1: which hit the west coast of Florida near Sarasota and

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Speaker 1: then traveled across the state, it was a good a

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Speaker 1: large portion of the state, almost the entirety of the

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Speaker 1: state that was impacted in one way or another by

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Speaker 1: Hurricane Milton. This also coming, as we know, on the

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Speaker 1: heels of Hurricane Helen. Some of these places were still

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Speaker 1: cleaning up and hadn't recovered from Helene, and less than

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Speaker 1: two weeks later hit by another brutally strong storm that

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Speaker 1: was again fueled by those very warm waters in the

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Speaker 1: Gulf of Mexico. So, you know, we're still still getting

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Speaker 1: a sense of just how much devastation and damage here

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Speaker 1: and what the death toel will ultimately be.

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Speaker 2: Yeah, what's really crazy is all those tornadoes that also

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Speaker 2: ripped Florida at the same time. So I have the

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Speaker 2: numbers here in front of me. There were one hundred

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Speaker 2: and sixteen tornado warnings that were issued across the state,

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Speaker 2: including far away on the different side of the coast.

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Speaker 2: I'm not exactly sure why, whatever, something to do with

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Speaker 2: barometric pressure, etc. But nearly one hundred and twenty five

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Speaker 2: homes were destroyed. A lot of these were areas that

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Speaker 2: didn't actually expect, you know, to be hit by the

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Speaker 2: hurricane or were kind of less of an impact, and

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Speaker 2: so they were pretty surprised by all of that happening.

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Speaker 2: But so far, I mean the damage still well a

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Speaker 2: damage in Saint Petersburg and in some of these other areas.

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Speaker 2: The initial stormstters that was indicated was almost like six

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Speaker 2: to seven feet that came through, So I mean that's

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Speaker 2: just devastating for a lot of the homes and the

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Speaker 2: other areas. It's still very early in the morning, so

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Speaker 2: we don't have like a full account, but you know,

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Speaker 2: people are going to have to return. There's going to

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Speaker 2: have to be quite a lot of rebuilding, especially in

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Speaker 2: a lot of those downtown areas. Florida authorities and others

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Speaker 2: are already preparing for the influx of people coming back

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Speaker 2: and seeming to prepare, so I think it's going to

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Speaker 2: be a days and perhaps like a week's even years

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Speaker 2: long thing, you know, to recover the area.

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Speaker 1: Yeah, there were some people who had fled from the

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Speaker 1: inland devastation of Hurricane Helene to some of these areas

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Speaker 1: in Florida and then had to evacuate and flee again

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Speaker 1: because of this storm. It reminds me a little bit

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Speaker 1: of you know, I don't know if you guys recall

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Speaker 1: after Katrina hit famously in Louisiana New Orleans area, a

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Speaker 1: lot of people fled to Texasca. I'm sure you remember this.

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Speaker 1: And then Hurricane Rita hits and devastates areas of Texas,

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Speaker 1: and you know, they're left once again fleeing and trying

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Speaker 1: to preserve their lives and their livelihoods and their possessions.

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Speaker 1: Just to echo what you were saying about the storm

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Speaker 1: surge and the amount of water here truly astonishing. So

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Speaker 1: this is from Matthew Capucci. He says, this is insane.

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Speaker 1: Saint Petersburg. He's an atmospheric scientist, by the way, In

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Speaker 1: self described stormchaser, Saint Petersburg has reported five point h

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Speaker 1: nine inches of rain in one hour, more than five

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Speaker 1: inches of rain in a single hour, and nine inches

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Speaker 1: in three hours. He says that is actually more rare

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Speaker 1: than a one thousand year rain event. So just wrap

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Speaker 1: your head around the fact that we had two storms

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Speaker 1: in a two weeks time period that were one in

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Speaker 1: one thousand year events, or at least they previously were.

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Speaker 1: And you know, of course, you can't say exactly how

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Speaker 1: much is climate change and how much is just you know,

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Speaker 1: things that happened during hurricane season. But every meteorologist seems

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Speaker 1: to indicate that the exceptionally warm waters in the Gulf

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Speaker 1: of Mexico are part of what contributed to the strength

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Speaker 1: and ultimate devastation of both of these storms. And so

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Speaker 1: in a lot of ways, this is sort of, you know,

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Speaker 1: the devastating new normal. You already have a situation in

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Speaker 1: Florida that we've covered before, Soger, where insurance you can't

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Speaker 1: in certain parts of Florida, in certain areas, you literally

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Speaker 1: cannot get homeowners insurance. So Florida while it's seeing you know,

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Speaker 1: huge population boom, a lot of people moving to the

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Speaker 1: state because they like some of the you know, the

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Speaker 1: polite policies, they like the quality of life, they like

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Speaker 1: the sunshine and the ocean, all of that. Then not

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Speaker 1: only is housing of course expensive everywhere and expensive in Florida,

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Speaker 1: but then you're hit with this additional massive cost for

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Speaker 1: homeowners insurance if you can even get it. This is

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Speaker 1: going to continue to be an escalating problem. I know

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Speaker 1: some of these insurers dropped another several hundred thousand customers

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Speaker 1: from their policies. The state really has no idea how

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Speaker 1: to handle this, doesn't have the money or sufficient funds

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Speaker 1: in order to themselves run a home owner's insurance market

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Speaker 1: for areas that are essentially uninsurable. Now, so that's sort of,

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Speaker 1: you know, some of the longer term projection. Here soccer

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Speaker 1: was talking about the tornadoes. Let's go and put a

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Speaker 1: three up on the screen. This gives you a sense

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Speaker 1: of how many tornadoes were cited and hit Florida as

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Speaker 1: a consequence of Hurricane Milton. So the National Weather Service

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Speaker 1: issued about one hundred tornado warnings between noon and sea

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Speaker 1: yesterday in Florida, at least twenty reports of sightings and damage.

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Speaker 1: And as I mentioned before, usually with hurricanes, it's not

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Speaker 1: uncommon that you have tornadoes that spin up as a

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Speaker 1: result of hurricanes or storms in general. But apparently I

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Speaker 1: was reading this morning that these were exceptionally strong. Usually

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Speaker 1: the tornadoes that you get associated with hurricanes are relatively

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Speaker 1: weak and Pete arount quickly. These were exceptionally strong, and

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Speaker 1: we know, as I said before, that they claimed some

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Speaker 1: number of lives. We're not sure how many at this point.

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Speaker 1: So that was part of what caused so much damage

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Speaker 1: and devastation in the state in advance of the hurricane. Yesterday,

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Speaker 1: Kamala Harris took the opportunity actually to call into the

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Speaker 1: Weather Channel and to explain to people what benefits were

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Speaker 1: available for them, and also notably to issue a warning

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Speaker 1: to would be price gouchers about taking advantage of people's desperation.

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Speaker 1: Let's go ahead and take a listen to that.

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Speaker 4: There are federal resources you are in hide to receive

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Speaker 4: without conditions, and there are resources that are available to

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Speaker 4: you to deal with what you need right now to

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Speaker 4: for example, get money to be able to fill your

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Speaker 4: prescriptions and deal with a hotel expense and help you

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Speaker 4: recover over the longer term. The other point I would

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Speaker 4: make is this, and I've done this work in my career. Sadly,

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Speaker 4: there are some folks who during the moment of a

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Speaker 4: crisis will be very predatory and start jacking up prices

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Speaker 4: like gasoline or hotels or airlines to take advantage of

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Speaker 4: the desperation people experiencing. So to anybody who's thinking about

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Speaker 4: jacket up those prices those companies they are thinking about

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Speaker 4: doing it, know that we are monitoring and we're launching

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Speaker 4: and if there is that kind of price gouging, that

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Speaker 4: there will be a consequence for that, because it's just

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Speaker 4: wrong to take advantage of people who are desperate for help,

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Speaker 4: and so know that we're watching that as well.

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Speaker 1: So using natural disasters and hurricanes as for political purpose

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Speaker 1: in age old age old pursue.

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Speaker 3: I was going to say, we're in it right now.

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Speaker 2: Ron DeSantis just went on television this morning because there's

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Speaker 2: been this whole like beef between It was like Kamala

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Speaker 2: called DeSantis and apparently he was like, I'm not going

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Speaker 2: to take the call. And then actually, Kamala's advisors are

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Speaker 2: very upset right now with Joe Biden because Biden was like, oh, he's.

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Speaker 3: Doing a great job.

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Speaker 2: Ron Santas De Santas and Biden apparently have been on

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Speaker 2: the phone.

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Speaker 1: The Santas also compliment in Biden's yes.

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Speaker 2: Compliment Biden. He's like, well, Biden's actually the president. I

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Speaker 2: mean he does have a point, he went on televiend,

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Speaker 2: I mean, what does the.

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Speaker 1: Vice president really have to do anything? And we're being honest, Yeah.

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Speaker 2: All the storms I've dealt with under this administration, she

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Speaker 2: has never called into Florida or offered any support. She's

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Speaker 2: trying to inject herself into this because of her political campaign.

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Speaker 2: I also read that Kama advisors were furious that Biden

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Speaker 2: took the podium at the press briefing because it kind

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Speaker 2: of I believe there was like a counter programming going

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Speaker 2: on because she had had some event that she was

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Speaker 2: participating in. So, yes, the political maschinations behind the hurricane

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Speaker 2: are alive, and well almost certainly we'll see it too

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Speaker 2: with visits. You maybe separate visits by Kamala and Biden

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Speaker 2: in to view the destruction. That's another time honored tradition

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Speaker 2: for Presidents Trump as well in terms of how they

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Speaker 2: decide to respond. But you know, overall, I would just hope,

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Speaker 2: especially that Florida can do what North Carolina's done now

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Speaker 2: so far, North Carolina in general actually seems quite committed

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Speaker 2: to being like, hey, we're temporarily going to make sure

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Speaker 2: that people who were at displays can be able to vote.

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Speaker 2: We're going to figure this out, set up precincts, et cetera.

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Speaker 2: Let's set have the same thing for Florida. Let's try

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Speaker 2: not with us at least, you know, make it too political,

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Speaker 2: and let's just make sure people in Tampas, Sarrisota, et cetera,

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Speaker 2: come home, rebuild. Definitely thinking about that insurance thing. In fact,

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Speaker 2: this could be a landmark moment in the history of

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Speaker 2: insurance because we're going to find out just how much

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Speaker 2: the devastation is, whether that state backed insurance company has

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Speaker 2: the funds and is able to meet those goals. It

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Speaker 2: could require billions of bailout from the federal government, which

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Speaker 2: I don't know.

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Speaker 3: It's complicated because we.

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Speaker 2: Got California and Florida, two of the most popular states

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Speaker 2: in the entire Union, who now basically are unensurable from

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Speaker 2: wildfires and from hurricanes. And then is the federal government's

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Speaker 2: going to backstop that? Like how does it work?

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Speaker 1: Right?

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Speaker 3: You know, it's a crazy system. I don't know. I'm

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Speaker 3: up two minds.

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Speaker 2: You know, they both create a ton for the economy,

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Speaker 2: both Florida and California, and massive GDP and you know

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Speaker 2: benefit to the nation. But it's also kind of crazy,

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Speaker 2: you know, if the government's coming to bail you out

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Speaker 2: every single time that there is a storm or wildfire.

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Speaker 1: Yeah, I mean Also what Florida doesn't have an income tax,

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Speaker 1: They have no one, so it's like, yeah, so they

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Speaker 1: don't tax their people and then they whatever the rest

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Speaker 1: of the country you pick up the tab for.

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Speaker 2: Actually, in fact, to be fair, ye, the way that

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Speaker 2: they make all their money is because people go to

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Speaker 2: Florida for they charge these exorbitant like hotel and visitors taxes,

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Speaker 2: so America is paying for it no matter what. That's

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Speaker 2: how they make all their revenue. Every time they go

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Speaker 2: to Disney World, you pay the state with Florida. So

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Speaker 2: I don't know, well the O.

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Speaker 1: The other thing is that you know the the risk

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Speaker 1: is accelerated, or you know, is higher in certain parts

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Speaker 1: of the country. Certainly you think about your Florida, I

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Speaker 1: think about California wildfires. Colorado is another place that suffers

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Speaker 1: from wildfires. But I think one of the things we

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Speaker 1: learned from Halllene is that really nowhere it is safe.

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Speaker 1: You know, if you were living in Asheville, North Carolina,

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Speaker 1: western North Carolina, in Appalachia, you were not thinking that

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Speaker 1: a hurricane was going to come through and destroy everything

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Speaker 1: that you have ever known and loved, Like that was

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Speaker 1: not on your radar whatsoever. In fact, there were articles

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Speaker 1: about people who moved to Asheville specifically because they thought

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Speaker 1: this was a quote unquote climate change haven where they

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Speaker 1: weren't going to be impacted. And so, you know, it's

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Speaker 1: very hard to predict the impact that you're going to

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Speaker 1: have from these storms and these increasingly regular extreme events. Again,

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Speaker 1: these two storms back to back, both one in one

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Speaker 1: thousand year occurrences that now are just happening on you know,

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Speaker 1: a weekly basis, and we'll see what the rest of

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Speaker 1: hurricane season has in store for us. But you know,

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Speaker 1: right now, we're just praying for the people who are

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Speaker 1: there in Florida and praying for a quick recovery and

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Speaker 1: that the loss of life in particular is as minimal

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Speaker 1: as absolutely possible. I think I hope that most people

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Speaker 1: heeded the calls to evacuate, which were quite strenuous. I

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Speaker 1: know you and Ryan covered what was it, the Tampa

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Speaker 1: mayor lay the first day you will die, so please leave.

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Speaker 1: So hopefully most people heeded those calls and were able

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Speaker 1: to survive this farm.

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Speaker 2: Like I said on that show, Florida it's either one,

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Speaker 2: number two or number three state that listens to breaking points.

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Speaker 2: So to all of our people out there there you go,

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Speaker 2: we love you, Thank you about you safe.

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Speaker 1: So we continue to get really interesting indications out of

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Speaker 1: the twenty twenty four race. Harrenton, who really has done

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Speaker 1: a good job crunching the numbers over at CNN, did

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Speaker 1: a segment recently about just how close this race is,

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Speaker 1: which has been kind of the steady state for a

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Speaker 1: number of weeks. Now, let's take a listen to what

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Speaker 1: he had to say.

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Speaker 5: How much for the state pole averages miss by all

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Speaker 5: right ridge error since nineteen hundred and seventy two in

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Speaker 5: the close races in those battleground states, we've been looking

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Speaker 5: at three point four points, three point four points every

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Speaker 5: single state, All seven of those key battleground states are

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Speaker 5: within three point four points. What's the chance for an

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Speaker 5: even larger err You know, we talk about the margin

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Speaker 5: of error, right, So what is that ninety five percent

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Speaker 5: confidence interval? What is that true margin of error? Five

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Speaker 5: percent of errors in state polling averages are off by

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Speaker 5: more off by more than nine point four points.

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Speaker 3: These battleground states are well within that.

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Speaker 5: I want you to remember this number because the bottom

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Speaker 5: line is this race is going to be too close

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Speaker 5: to call almost certainly all the way till election day.

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Speaker 5: It's definitely going to be within this interval, and it's

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Speaker 5: most likely going to be within this interval. So the

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Speaker 5: bottom line is the state polling averages tell us. What

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Speaker 5: it tells us is it's just a race that is

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Speaker 5: too close to call.

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Speaker 1: I mean It makes a lot of Sun Soccer. When

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Speaker 1: you look at the Trump era, you have twenty sixteen,

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Speaker 1: extremely close election, twenty eighteen midterms, very close election, twenty twenty,

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Speaker 1: extremely closed election twenty twenty two. You know, Democrats did

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Speaker 1: what that was a weird one because you add Democrats

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Speaker 1: doing really well in certain states and then Republican surging

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Speaker 1: in Florida and New York in particular. Nate Cohne over

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Speaker 1: the New York Times, his theory of the case is

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Speaker 1: basically that the election is shaping up to look a

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Speaker 1: lot like the twenty twenty two midterms. That would have

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Speaker 1: huge implications also for the popular vote advantage in electoral

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Speaker 1: college advantage that the Republicans have. It would actually narrow

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Speaker 1: that gap. But the bottom line is that if the

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Speaker 1: polls are off three points in one way, you get

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Speaker 1: a Trump landslide in terms of the electoral college. If

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Speaker 1: they're off three points the other way, you get a

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Speaker 1: Kamala landslide in the electoral college. All those things are

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Speaker 1: on the table. And in spite of all of the

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Speaker 1: wild twists and turns that we have seen throughout this campaign,

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Speaker 1: since Kamala came in and kind of rose to the

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Speaker 1: position that she's at right now, it has basically been stable.

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Speaker 3: Yeah, exactly, and that's kind of the fascinating part.

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Speaker 2: In fact, this morning I was reading a lot of

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Speaker 2: Democratic activists who were talking to Axios about their consternation

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Speaker 2: that they've thrown almost three hundred million dollars of advertising

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Speaker 2: into the Blue Wall states so far with the reserved

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Speaker 2: another three hundred million coming even just in the state

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Speaker 2: of Pennsylvania, and they have seen basically no movement whatsoever.

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Speaker 2: So the dollar figure, like I said previously, comparing it

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Speaker 2: to Coca Cola, we know that if it wasn't there,

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Speaker 2: it would have an impact, But we do know that

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Speaker 2: its presence, we're not really sure what that impact is.

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Speaker 2: Not to build on this theory because this is a

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Speaker 2: little bit complicated, but everybody stick with us. Let's put

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Speaker 2: the next one, please up on the screen. From the

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Speaker 2: Wall Street Journal, more Americans identify as Republican than Democrat. Now,

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Speaker 2: as they say, here's what that means for the election. Now,

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Speaker 2: this could mean could mean that there would be a

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Speaker 2: narrower Trump Trump Harris margin in the popular vote, but

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Speaker 2: does not necessarily mean that Trump will win the electoral college.

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Speaker 3: This is complicated, but just stick with us.

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Speaker 2: The reason why is that just because more Americans identify

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Speaker 2: as Republican, if Trump wins Florida by thirteen points, which

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Speaker 2: was the New York Times has as opposed to the

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Speaker 2: three points that he won back in twenty twenty, it

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Speaker 2: doesn't make all that much of a difference in the

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Speaker 2: electoral cause it was going read anyway, right, It's going

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Speaker 2: right anyway, and you win the electoral votes no matter what.

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Speaker 2: But let's say that the margin or for Harris drops

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Speaker 2: in Texas, or sorry, goes up in Texas, goes down

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Speaker 2: in Florida, all these other places like for example, Trump

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Speaker 2: is doing rallies in Coachella in California, He's doing a

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Speaker 2: rally in Madison Square Garden, which is twenty eight days

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Speaker 2: to election day.

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Speaker 3: I mean, it doesn't make a whole lot of electoral sense.

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Speaker 2: But what we know from both of those things is

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Speaker 2: that you're watching the margins of victory for Republicans go

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Speaker 2: up in traditionally blue states and.

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Speaker 3: Probably go up even more in the Sunbelt.

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Speaker 2: The problem that they accurately point out is that in

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Speaker 2: twenty twenty two, even though Republican identification was very very high,

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Speaker 2: and in fact beat Democrats on the generic ballot, it's

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Speaker 2: that all of the swing voters broke so hard for

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Speaker 2: the Democrats that was able to put the Democratic candidates

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Speaker 2: up over the margin of victory. So just because Republicans

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Speaker 2: have a major ballot advantage here does not mean that

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Speaker 2: they will win again. I understand that this is counterintuitive,

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Speaker 2: but the thing is is that more Americans identifying as

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Speaker 2: Republican actually just means there are less swing voters.

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Speaker 3: So in some ways that's good.

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Speaker 2: It means that you have people who are explicitly like

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Speaker 2: parts and are they're going to come out and they're

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Speaker 2: going to vote for you, But it will not take

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Speaker 2: you across the finish line. You still need to win

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Speaker 2: the swing voters, and if the swing voters break in

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Speaker 2: the same way they did in twenty twenty two, you

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Speaker 2: will lose every single one of the swing states.

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Speaker 1: I mean, the TLDR of this is that the electoral

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Speaker 1: college is really stupid, because it makes no sense that

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Speaker 1: if you win a bunch more voters in Florida or

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Speaker 1: California or in New York that it literally does not

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Speaker 1: matter at all. But that's the reality. I mean, all

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Speaker 1: indications are that Republicans have improved their position in New York,

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Speaker 1: but Democrats are still going to win New York. So yeah,

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Speaker 1: that helps reduce your popular vote totals, it does not

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Speaker 1: matter at all in terms of the electoral out That's

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Speaker 1: a stupid situation, like it should not be the case

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Speaker 1: that what voters think in New York matter, like doesn't

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Speaker 1: matter at all versus what voters think in Michigan or

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Speaker 1: Wisconsin or Pennsylvania in particular. But that's a system that

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Speaker 1: we have. So this, again is what Nate Cone has

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Speaker 1: been writing about over at the New York Times, and

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Speaker 1: I encourage you to go read because it's a very

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Speaker 1: interesting analysis. There's something else that ties into this that

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Speaker 1: I'll do my best to explain, but basically, the idea

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Speaker 1: is that Trump is picking up a lot of voters

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Speaker 1: in those two states, in particular, Florida and New York,

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Speaker 1: so that means that he is narrowing That helps to

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Speaker 1: narrow that electoral college edge, which means that when you're

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Speaker 1: seeing these national polls that have Kamala Harris up three

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Speaker 1: points or even up two points, that might be sufficient

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Speaker 1: to get her over the top, whereas previously Democrats needed

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Speaker 1: to win by a closer to certainly three to four

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Speaker 1: points in order to be able to win the electoral

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00:21:55,880 --> 00:21:58,240
Speaker 1: college vote. So in any case, you know, it was

436
00:21:58,320 --> 00:22:01,720
Speaker 1: interesting reading about this Republican self identification. One of the

437
00:22:01,720 --> 00:22:04,480
Speaker 1: things that they find in terms of driving some of

438
00:22:04,480 --> 00:22:07,160
Speaker 1: these movements is just like who's the party that holds

439
00:22:07,160 --> 00:22:09,280
Speaker 1: the White House and how do people feel about that person.

440
00:22:09,680 --> 00:22:12,480
Speaker 1: So if you have you know, George b. Bush, for example,

441
00:22:12,520 --> 00:22:16,240
Speaker 1: after nine to eleven, he has this huge surgeon popularity

442
00:22:16,480 --> 00:22:18,480
Speaker 1: and that was the last time that you even had

443
00:22:18,480 --> 00:22:23,040
Speaker 1: for a brief period Republicans outpacing Democrats in terms of

444
00:22:23,119 --> 00:22:26,040
Speaker 1: how people describe themselves because he was very popular and

445
00:22:26,080 --> 00:22:28,920
Speaker 1: he was a Republican. Now, obviously you have Joe Biden

446
00:22:28,960 --> 00:22:31,679
Speaker 1: in the White House. He is very unpopular. So that

447
00:22:31,760 --> 00:22:35,080
Speaker 1: has caused democratic self identification to take a hit. To

448
00:22:35,119 --> 00:22:37,560
Speaker 1: your point, Soccer, I mean, it certainly is something that

449
00:22:37,600 --> 00:22:39,760
Speaker 1: Republicans will be happy about. It gives them a little

450
00:22:39,760 --> 00:22:41,640
Speaker 1: bit of a leg up and an edge going into

451
00:22:41,680 --> 00:22:46,200
Speaker 1: this election. But in twenty twenty two, Democrats won independence

452
00:22:46,320 --> 00:22:51,160
Speaker 1: sufficiently to overcome that edge that Republicans had at that time.

453
00:22:51,240 --> 00:22:53,600
Speaker 1: So it's an interesting note, but it is not by

454
00:22:53,640 --> 00:22:57,359
Speaker 1: any means determinative of what the outcome may actually be.

455
00:22:57,800 --> 00:22:59,480
Speaker 1: Let's go and put this next piece up on the screen.

456
00:22:59,520 --> 00:23:02,320
Speaker 1: This is the late New York Times poll which finds

457
00:23:02,359 --> 00:23:06,240
Speaker 1: that Kamala Harris has taken a narrow three point lead

458
00:23:06,440 --> 00:23:10,040
Speaker 1: in the national popular vote forty nine forty six. The

459
00:23:10,160 --> 00:23:13,400
Speaker 1: same poll last time around had the two of them

460
00:23:13,880 --> 00:23:18,719
Speaker 1: tied I believe at forty seven percent if memory serves,

461
00:23:19,160 --> 00:23:23,000
Speaker 1: this is the first time in this particular poll that

462
00:23:23,080 --> 00:23:26,879
Speaker 1: Kamala Harris has actually led Trump since July when Biden

463
00:23:26,920 --> 00:23:28,919
Speaker 1: dropped out of the race. So certainly she'll be happy

464
00:23:28,960 --> 00:23:31,159
Speaker 1: with that outcome from the New York Times. Has just

465
00:23:31,160 --> 00:23:33,600
Speaker 1: taken very seriously, whether it deserves all of the like,

466
00:23:33,760 --> 00:23:35,600
Speaker 1: you know, this is one of the ones that everybody

467
00:23:35,600 --> 00:23:37,800
Speaker 1: stops and takes note of when these polls come out.

468
00:23:39,000 --> 00:23:42,120
Speaker 1: She has short up her support, they say, among older voters,

469
00:23:42,400 --> 00:23:46,639
Speaker 1: has begun making inroads among Republicans. Nine percent say they

470
00:23:46,640 --> 00:23:49,680
Speaker 1: plan to support her, up slightly from five percent last month,

471
00:23:50,080 --> 00:23:51,920
Speaker 1: and she appears to have closed the gap on the

472
00:23:52,000 --> 00:23:55,199
Speaker 1: question of change, a critical factor, they write, in an

473
00:23:55,200 --> 00:23:57,680
Speaker 1: election where voters have repeatedly told pollsters they believe the

474
00:23:57,760 --> 00:24:00,560
Speaker 1: nation is heading in the wrong direction in that one,

475
00:24:00,600 --> 00:24:03,800
Speaker 1: because she did herself no favors this week on that

476
00:24:03,880 --> 00:24:06,080
Speaker 1: question of who would be the change candidate when she

477
00:24:06,119 --> 00:24:09,160
Speaker 1: could not literally name a single thing that she would

478
00:24:09,160 --> 00:24:11,480
Speaker 1: do different than Joe Biden, which is like, how do

479
00:24:11,520 --> 00:24:13,680
Speaker 1: you not know how to answer this question? At this point,

480
00:24:13,800 --> 00:24:16,520
Speaker 1: all of the polling shows that it is important to

481
00:24:16,640 --> 00:24:19,719
Speaker 1: voters that she separate herself from Joe Biden. But what

482
00:24:19,760 --> 00:24:22,000
Speaker 1: I took note of here is, you know, this kind

483
00:24:22,000 --> 00:24:24,600
Speaker 1: of dovetails with idea that more people are self identifying

484
00:24:24,600 --> 00:24:30,000
Speaker 1: as Republicans. This helps to explain their Liz Cheney strategy.

485
00:24:30,400 --> 00:24:33,160
Speaker 1: You know, this is not a strategy that I would

486
00:24:33,160 --> 00:24:36,280
Speaker 1: personally go forward with. I think they should be emphasizing

487
00:24:36,359 --> 00:24:39,199
Speaker 1: more of their economic positions. I think they should be

488
00:24:39,280 --> 00:24:41,200
Speaker 1: doing more work to close that gap with Trump on

489
00:24:41,240 --> 00:24:44,200
Speaker 1: the economy and on inflation, in particular in those industrial

490
00:24:44,240 --> 00:24:48,919
Speaker 1: Midwestern states. But they believe that there is a sufficient

491
00:24:49,040 --> 00:24:53,240
Speaker 1: majority anti Trump coalition that the more they lean into

492
00:24:53,320 --> 00:24:56,040
Speaker 1: like oh my god, Dick Cheney and Liz Cheney endorsed us,

493
00:24:56,440 --> 00:24:59,600
Speaker 1: and the less they talk about hard policy proposals that

494
00:24:59,680 --> 00:25:04,040
Speaker 1: could be controversial among some Republican voters, the better off

495
00:25:04,080 --> 00:25:06,919
Speaker 1: they'll be that's their theory is basically like, we're going

496
00:25:07,000 --> 00:25:11,639
Speaker 1: to put back together the pro Joe Biden, anti Trump coalition,

497
00:25:12,119 --> 00:25:15,640
Speaker 1: and we believe, based on the electoral results in eighteen

498
00:25:15,760 --> 00:25:18,800
Speaker 1: and twenty and twenty two that that's enough to get

499
00:25:18,840 --> 00:25:21,080
Speaker 1: us over the top. And you know, they'll look at

500
00:25:21,080 --> 00:25:23,479
Speaker 1: this and say, oh, see it's working. Now we've got

501
00:25:23,600 --> 00:25:26,080
Speaker 1: nine percent of Republicans supporting us instead of five percent.

502
00:25:26,160 --> 00:25:27,560
Speaker 3: Maybe that's an awful lot.

503
00:25:27,600 --> 00:25:30,000
Speaker 2: I mean, these polls are only one thousand people, you know,

504
00:25:30,080 --> 00:25:31,399
Speaker 2: that are even the sample size.

505
00:25:31,440 --> 00:25:33,040
Speaker 3: So how many is that? Like ninety?

506
00:25:33,240 --> 00:25:35,600
Speaker 2: It's like, are you really going to base your political

507
00:25:35,680 --> 00:25:36,600
Speaker 2: theory based on that?

508
00:25:36,720 --> 00:25:37,119
Speaker 3: I don't know.

509
00:25:37,160 --> 00:25:39,200
Speaker 2: I mean, this is all the problem with a lot

510
00:25:39,240 --> 00:25:42,040
Speaker 2: of cross tab diving is these samples are not that big.

511
00:25:42,080 --> 00:25:44,680
Speaker 2: And by the way, that's actually quite big for a

512
00:25:44,880 --> 00:25:47,760
Speaker 2: over national poll and where they do live caller, but

513
00:25:48,080 --> 00:25:50,640
Speaker 2: statistical sampling and all that. The deeper that you try

514
00:25:50,680 --> 00:25:52,720
Speaker 2: and go, it's very difficult. That's why you should try

515
00:25:52,760 --> 00:25:55,800
Speaker 2: to look at averages and ultimately the actual outcome on

516
00:25:55,880 --> 00:25:58,160
Speaker 2: election day will tell us a lot more. I did

517
00:25:58,200 --> 00:26:01,600
Speaker 2: see this morning I possible indication from Reuters. They say

518
00:26:01,600 --> 00:26:05,159
Speaker 2: that Kamala Harris is quote erased Republican Trump advantage in

519
00:26:05,200 --> 00:26:08,040
Speaker 2: the vast middle of American society, suburban residents and middle

520
00:26:08,080 --> 00:26:11,040
Speaker 2: income households. That's basically who they're trying to go after

521
00:26:11,200 --> 00:26:12,880
Speaker 2: with that Republican theory.

522
00:26:13,000 --> 00:26:15,400
Speaker 1: Again, they're going after those Nikki Hayley voter.

523
00:26:15,560 --> 00:26:17,600
Speaker 2: Here's the reason, the main one that I just don't

524
00:26:17,640 --> 00:26:21,000
Speaker 2: think that that is correct is that, yes, those Republicans

525
00:26:21,000 --> 00:26:23,640
Speaker 2: and all that may be disgusted with Trump, but when

526
00:26:23,680 --> 00:26:26,800
Speaker 2: you're also not acting as a change agent, which we're

527
00:26:26,800 --> 00:26:29,240
Speaker 2: about to get to in her answer on the view,

528
00:26:29,560 --> 00:26:31,680
Speaker 2: that's where I think it's going to be really difficult.

529
00:26:31,720 --> 00:26:34,400
Speaker 2: Like I also saw Crystal you know, remember that JD

530
00:26:34,520 --> 00:26:37,040
Speaker 2: Van's answer about the election, and we thought it was

531
00:26:37,080 --> 00:26:39,320
Speaker 2: going to be everywhere. So it turns out that those

532
00:26:39,400 --> 00:26:41,800
Speaker 2: ads Dave Wigel pointed this out, those ads don't have

533
00:26:41,880 --> 00:26:42,919
Speaker 2: a lot of money behind them.

534
00:26:43,119 --> 00:26:45,240
Speaker 3: Yeah, Internet, they're just on Twitter.

535
00:26:45,280 --> 00:26:48,439
Speaker 2: They're not actually like the major ones that are playing

536
00:26:48,440 --> 00:26:50,800
Speaker 2: in the battleground states. So I can tell you, like

537
00:26:50,880 --> 00:26:54,160
Speaker 2: I said, I was in Pennsylvania, every anti Trump ad

538
00:26:54,400 --> 00:26:58,080
Speaker 2: was just about abortion, and that I mean, clearly that's something.

539
00:26:58,280 --> 00:26:59,920
Speaker 3: But abortion's not going to be enough.

540
00:27:00,000 --> 00:27:02,520
Speaker 2: We talked to Logan Phillips just to carry you across

541
00:27:02,560 --> 00:27:05,320
Speaker 2: because not aspressions in twenty twenty two. I think the

542
00:27:05,359 --> 00:27:08,200
Speaker 2: economy and feeling like you're going to do something different

543
00:27:08,600 --> 00:27:11,720
Speaker 2: is really important. And that answer that she gave on

544
00:27:11,800 --> 00:27:14,080
Speaker 2: the view, I don't think that that. I think the

545
00:27:14,160 --> 00:27:16,440
Speaker 2: fact that the Trump campaign is now planning on blasting

546
00:27:16,440 --> 00:27:19,160
Speaker 2: that everywhere that does tell you actually quite a bit.

547
00:27:19,280 --> 00:27:20,760
Speaker 2: Why don't we take a listen. I know Ryan and

548
00:27:20,760 --> 00:27:23,080
Speaker 2: Emiley brought some of these yesterday, but let's relive it.

549
00:27:23,320 --> 00:27:26,720
Speaker 6: What do you think would be the biggest specific difference

550
00:27:27,200 --> 00:27:33,080
Speaker 6: between your presidency and a Biden see a Biden presidency. Well,

551
00:27:33,080 --> 00:27:36,400
Speaker 6: we're obviously two different people, and we have a lot

552
00:27:36,400 --> 00:27:39,720
Speaker 6: of shared life experiences, for example, the way we feel

553
00:27:39,760 --> 00:27:43,119
Speaker 6: about our family and our parents and so on. But

554
00:27:43,400 --> 00:27:46,919
Speaker 6: we're also different people, and I will bring those sensibilities

555
00:27:46,920 --> 00:27:51,119
Speaker 6: to how I lead by part. Listen, I plan on

556
00:27:51,160 --> 00:27:52,520
Speaker 6: having a Republican in my cabinet.

557
00:27:52,520 --> 00:27:55,200
Speaker 3: You got a list Yeah, right, you have to.

558
00:27:55,640 --> 00:27:58,479
Speaker 6: What's the difference between Joe Biden and me? Well, that

559
00:27:58,520 --> 00:27:58,960
Speaker 6: will be.

560
00:27:58,840 --> 00:27:59,760
Speaker 3: One of the differences.

561
00:27:59,800 --> 00:28:01,160
Speaker 1: I'm going to have a Republican in my.

562
00:28:01,160 --> 00:28:02,479
Speaker 3: Cabinet because I don't.

563
00:28:03,040 --> 00:28:06,159
Speaker 6: I don't feel burdened, but letting pride get in the

564
00:28:06,160 --> 00:28:07,800
Speaker 6: way of a good idea, yeah.

565
00:28:07,520 --> 00:28:11,440
Speaker 2: Right, right, So that exactly is the dynamic that we're

566
00:28:11,480 --> 00:28:13,639
Speaker 2: talking about here. So let mean, look, we'll see, we

567
00:28:13,720 --> 00:28:17,080
Speaker 2: got twenty seven more days. We'll find out on election

568
00:28:17,240 --> 00:28:20,520
Speaker 2: day whether she's correct, If this turns out to be

569
00:28:20,560 --> 00:28:22,600
Speaker 2: the right theory, I don't know. I give up because

570
00:28:22,640 --> 00:28:25,040
Speaker 2: it's like just saying, oh, Liz Cheney is going to

571
00:28:25,080 --> 00:28:27,119
Speaker 2: be my secretary of Defense, which you know, between the

572
00:28:27,160 --> 00:28:30,119
Speaker 2: Iran comments about our greatest adversary, the Liz Cheney thing,

573
00:28:30,160 --> 00:28:32,439
Speaker 2: and now seeing Republican in my cabinet, I think we

574
00:28:32,480 --> 00:28:34,800
Speaker 2: all know who that person is going to be, or

575
00:28:34,920 --> 00:28:37,600
Speaker 2: at least it's going to be somewhere there in the cabinet,

576
00:28:37,680 --> 00:28:40,400
Speaker 2: and then we combine it with I actually wouldn't change

577
00:28:40,440 --> 00:28:43,200
Speaker 2: anything from the Biden administration except bringing in somebody who

578
00:28:43,280 --> 00:28:45,840
Speaker 2: is way worse on national policy.

579
00:28:45,920 --> 00:28:49,200
Speaker 3: If that's enough. I honestly, I don't really know what

580
00:28:49,200 --> 00:28:49,520
Speaker 3: to say.

581
00:28:49,560 --> 00:28:52,480
Speaker 1: But it's not even true that Biden doesn't have Republicans

582
00:28:52,520 --> 00:28:56,040
Speaker 1: in his government. I mean, Ray is a Republican, right,

583
00:28:56,080 --> 00:28:57,640
Speaker 1: aren't there other people anything?

584
00:28:57,920 --> 00:28:59,840
Speaker 3: What does the term mean at this point.

585
00:29:00,160 --> 00:29:04,600
Speaker 1: It's so here's here's what I would say. I'm inclined

586
00:29:04,640 --> 00:29:07,360
Speaker 1: to agree with you that they are really screwing up

587
00:29:07,440 --> 00:29:09,720
Speaker 1: right now, like they should be leaning into economics because

588
00:29:09,760 --> 00:29:12,000
Speaker 1: my view is people who are going to vote for

589
00:29:12,080 --> 00:29:14,960
Speaker 1: democracy and part of the anti DRUP like they already

590
00:29:15,160 --> 00:29:18,240
Speaker 1: know where they stand. That's why they're not running ads

591
00:29:18,320 --> 00:29:21,120
Speaker 1: about JD Vance's answer, because it's like, yeah, those people

592
00:29:21,120 --> 00:29:23,280
Speaker 1: are already locked in, right and now we just got

593
00:29:23,280 --> 00:29:26,560
Speaker 1: to turn them out. And they do have massive funds

594
00:29:26,600 --> 00:29:29,600
Speaker 1: in terms of their you know, overall operation turnout operations.

595
00:29:29,640 --> 00:29:33,240
Speaker 1: She raised a billion dollars we just learned as of

596
00:29:33,360 --> 00:29:37,920
Speaker 1: yesterday in less than three months time. Like that is

597
00:29:38,280 --> 00:29:42,040
Speaker 1: unheard of, ungodly amounts of money that she's been able

598
00:29:42,080 --> 00:29:43,800
Speaker 1: to raise. So they're gonna have plenty of money for

599
00:29:43,840 --> 00:29:45,480
Speaker 1: the field operation, whatever they want to do down the

600
00:29:45,520 --> 00:29:48,560
Speaker 1: stretch ads, et cetera, et cetera. The only thing is

601
00:29:48,600 --> 00:29:51,720
Speaker 1: like I just I just a very humble at this

602
00:29:51,800 --> 00:29:54,240
Speaker 1: point about what is actually going to work, because in

603
00:29:54,280 --> 00:29:57,360
Speaker 1: twenty twenty two, they were doing the same, like let's

604
00:29:57,360 --> 00:30:00,880
Speaker 1: just talk about democracy and extremism and not offer literally

605
00:30:00,920 --> 00:30:04,080
Speaker 1: anything on economics. In twenty twenty Joe Biden did not

606
00:30:04,240 --> 00:30:07,120
Speaker 1: run on a single economic position, to the best of

607
00:30:07,200 --> 00:30:09,640
Speaker 1: my recollection, even though we ended up, you know, having

608
00:30:09,680 --> 00:30:13,280
Speaker 1: a pretty good domestic economic agenda, but did not run

609
00:30:13,440 --> 00:30:16,080
Speaker 1: on really any of that in terms of the general election.

610
00:30:16,680 --> 00:30:18,640
Speaker 1: And it worked out. You know, in twenty twenty two,

611
00:30:18,800 --> 00:30:21,400
Speaker 1: I really thought I was I thought, like, the red

612
00:30:21,440 --> 00:30:23,760
Speaker 1: wave is coming. People are angry about inflation, y'all aren't

613
00:30:23,800 --> 00:30:26,240
Speaker 1: talking about it. You know, there's people think you're on

614
00:30:26,280 --> 00:30:28,720
Speaker 1: the wrong track, et cetera, et cetera. Joe Biden is

615
00:30:28,760 --> 00:30:34,400
Speaker 1: already pretty unpopular, and I was wrong. So maybe they're right.

616
00:30:34,560 --> 00:30:37,240
Speaker 1: Maybe it's enough. Maybe just getting together the old like

617
00:30:37,360 --> 00:30:40,680
Speaker 1: anti Trump band again. Maybe they're correct that there just

618
00:30:40,840 --> 00:30:43,200
Speaker 1: is an anti Trump majority, and the less that you

619
00:30:43,240 --> 00:30:46,240
Speaker 1: promise on policy, the less divisive that you are, And

620
00:30:46,240 --> 00:30:48,840
Speaker 1: if you just lean into that like generic Democrat position,

621
00:30:49,200 --> 00:30:51,720
Speaker 1: maybe it's sufficient. I genuinely do not know.

622
00:30:51,880 --> 00:30:52,760
Speaker 3: Oh, I agree with you.

623
00:30:52,840 --> 00:30:54,719
Speaker 2: That's my point is I'm like, I really hope this

624
00:30:54,760 --> 00:30:57,000
Speaker 2: isn't correct, but I fear that it is. It might

625
00:30:57,040 --> 00:30:59,440
Speaker 2: shaybe let's put this on the screen. Well, actually, at

626
00:30:59,440 --> 00:31:01,360
Speaker 2: the very least this time we're going to get a

627
00:31:01,400 --> 00:31:05,680
Speaker 2: real test, because what we have here from Axios is

628
00:31:05,720 --> 00:31:08,240
Speaker 2: that Republicans are again planning to spend heavily to promote

629
00:31:08,280 --> 00:31:10,320
Speaker 2: this clip of saying that there's not a thing that

630
00:31:10,360 --> 00:31:12,400
Speaker 2: she would do differently. I saw Jade vanc and Trump

631
00:31:12,440 --> 00:31:15,520
Speaker 2: are now playing that clip actually at their rallies. Obviously,

632
00:31:15,520 --> 00:31:18,200
Speaker 2: people go into a Trump rally are already voting for Republican.

633
00:31:18,280 --> 00:31:20,360
Speaker 2: But the point is is that they see that as

634
00:31:20,480 --> 00:31:21,800
Speaker 2: very beneficial to them.

635
00:31:22,040 --> 00:31:23,720
Speaker 3: There is a significant.

636
00:31:23,160 --> 00:31:27,400
Speaker 2: Amount of dollars behind. We need an actual change in

637
00:31:27,400 --> 00:31:29,920
Speaker 2: the White House. One of the areas that she's always

638
00:31:29,960 --> 00:31:33,360
Speaker 2: struggled the most with is differentiating herself from Biden. The

639
00:31:33,400 --> 00:31:35,240
Speaker 2: only thing she can really come up with is, I

640
00:31:35,280 --> 00:31:38,240
Speaker 2: would have a Republican in my cabinet. I would also

641
00:31:38,360 --> 00:31:40,960
Speaker 2: know maybe part of this is media, because Christal, what

642
00:31:41,040 --> 00:31:42,680
Speaker 2: you and I know is that even if there isn't

643
00:31:42,840 --> 00:31:47,080
Speaker 2: a necessarily major constituency for this stuff, there's nobody who

644
00:31:47,160 --> 00:31:51,320
Speaker 2: loves it more than Morning Joe or like MSNBC's with

645
00:31:51,440 --> 00:31:54,480
Speaker 2: Nicole Wallace, this idea of a Republican in the cabinet,

646
00:31:54,560 --> 00:31:57,040
Speaker 2: So it could be to get more favorable media attention.

647
00:31:57,120 --> 00:31:58,760
Speaker 3: The other thing is maybe it's just what she believes.

648
00:31:58,760 --> 00:32:00,000
Speaker 3: She doesn't want to do anyth different.

649
00:32:00,560 --> 00:32:02,640
Speaker 1: She does not believe anything. Yeah, I mean, and that's

650
00:32:02,680 --> 00:32:06,120
Speaker 1: part of why my theory of why she does so

651
00:32:06,120 --> 00:32:08,760
Speaker 1: poorly in these interviews, and her aids were basically right,

652
00:32:08,840 --> 00:32:13,200
Speaker 1: like she shouldn't have done these from just pure tactical perspective,

653
00:32:13,960 --> 00:32:15,680
Speaker 1: just don't do the interview. I send him walls down.

654
00:32:15,720 --> 00:32:19,000
Speaker 1: He does a fantastic job in these interview situations. But yeah,

655
00:32:19,080 --> 00:32:22,120
Speaker 1: they were correct about the basement strategy. Not that I'm

656
00:32:22,160 --> 00:32:24,160
Speaker 1: saying it's a good thing for democracy. It's not, but

657
00:32:24,240 --> 00:32:27,320
Speaker 1: in terms of tactical political decision making, they were probably

658
00:32:27,400 --> 00:32:31,160
Speaker 1: right about that. But it's just because it's a lot

659
00:32:31,240 --> 00:32:34,720
Speaker 1: harder to nail an interview like this when you don't

660
00:32:34,720 --> 00:32:37,760
Speaker 1: believe anything, because you just have to memorize every answer

661
00:32:38,160 --> 00:32:40,640
Speaker 1: versus asking yourself like, oh, well, what do I actually

662
00:32:40,640 --> 00:32:43,400
Speaker 1: think about that? Let me use my brain to articulate it.

663
00:32:43,400 --> 00:32:45,760
Speaker 1: It's like, oh, what am I supposed to say to this?

664
00:32:46,040 --> 00:32:48,400
Speaker 1: What a my aids tell me the right answer is here.

665
00:32:48,880 --> 00:32:51,239
Speaker 1: And then this was a classic Kabala moment in that

666
00:32:51,360 --> 00:32:55,440
Speaker 1: it's a totally foreseeable question, Like totally foreseeable question it

667
00:32:55,440 --> 00:32:57,640
Speaker 1: should be a lip. She should be hoping to get

668
00:32:57,640 --> 00:32:59,920
Speaker 1: that question so that she can knock it out of

669
00:33:00,080 --> 00:33:02,040
Speaker 1: the park of the ways in which she's going to

670
00:33:02,120 --> 00:33:04,880
Speaker 1: be different from Joe Biden, like that would only serve her.

671
00:33:05,160 --> 00:33:10,480
Speaker 1: And yet on this very predictable soft ball question she

672
00:33:10,960 --> 00:33:15,240
Speaker 1: completely whiffs. So you know, this is what happens when

673
00:33:15,640 --> 00:33:19,120
Speaker 1: she I don't know if the preparation fails her or

674
00:33:19,520 --> 00:33:21,640
Speaker 1: brain fails her in terms of trying to recall what

675
00:33:21,640 --> 00:33:24,440
Speaker 1: our advisors want her to say in that moment, because yeah,

676
00:33:24,480 --> 00:33:27,640
Speaker 1: it's really difficult to try to memorize a thousand different

677
00:33:27,640 --> 00:33:30,000
Speaker 1: answers on things when you yourself don't really have any

678
00:33:30,040 --> 00:33:32,880
Speaker 1: core beliefs to draw in and go back to. Like

679
00:33:32,920 --> 00:33:34,479
Speaker 1: she doesn't have the ability to say, oh, well, how

680
00:33:34,520 --> 00:33:37,680
Speaker 1: would I actually be different from Joe Biden, Because to

681
00:33:37,720 --> 00:33:40,120
Speaker 1: be honest with you, if she was really answering that

682
00:33:40,200 --> 00:33:42,280
Speaker 1: question truthfully, it'd be like, oh, I'm probably going to

683
00:33:42,320 --> 00:33:46,200
Speaker 1: be more receptive to like the crypto bros. And you know,

684
00:33:46,560 --> 00:33:49,000
Speaker 1: maybe get rid of Gary Gensler and Lena Khan and

685
00:33:49,040 --> 00:33:52,240
Speaker 1: be more friendly to Silicon Valley to Wall Street, which

686
00:33:53,040 --> 00:33:54,960
Speaker 1: I guess, as we'll get to you later in the show.

687
00:33:55,000 --> 00:33:57,120
Speaker 1: The Mark Cubans of the world and others would be

688
00:33:57,240 --> 00:34:00,000
Speaker 1: happy about, but may not be the best general election message.

689
00:34:00,000 --> 00:34:00,560
Speaker 3: That's a good point.

690
00:34:00,600 --> 00:34:02,920
Speaker 2: I mean, look, I think the part of the issue

691
00:34:02,960 --> 00:34:05,000
Speaker 2: is I feel like in previous elections we had a

692
00:34:05,000 --> 00:34:08,320
Speaker 2: lot more either events or ten pole news. So twenty twenty,

693
00:34:08,400 --> 00:34:10,680
Speaker 2: obviously there was COVID, and that was just such a

694
00:34:10,760 --> 00:34:14,040
Speaker 2: crazy environment that we were covering. Not only the attempts

695
00:34:14,080 --> 00:34:17,560
Speaker 2: at interview or sorry rallies, but we had the traditional debates.

696
00:34:17,560 --> 00:34:20,239
Speaker 2: Trump had COVID, that was all happening. Then we had

697
00:34:20,280 --> 00:34:23,520
Speaker 2: the whole mail in balloting thing. Had this just election,

698
00:34:24,040 --> 00:34:27,680
Speaker 2: especially since Kamla got picked, just feels remarkably stable to me.

699
00:34:27,719 --> 00:34:29,840
Speaker 2: And I know that's a weird thing to say, but

700
00:34:30,080 --> 00:34:32,680
Speaker 2: the test case and the theories put forth by both

701
00:34:32,719 --> 00:34:36,560
Speaker 2: of these candidates have been effectively locked in stone and absent.

702
00:34:36,920 --> 00:34:40,600
Speaker 2: You know, a quote unquote October surprise of a genuine

703
00:34:40,680 --> 00:34:44,120
Speaker 2: war in Lebanon and Iran some you know, obviously the

704
00:34:44,200 --> 00:34:45,280
Speaker 2: hurricane that just happened.

705
00:34:45,480 --> 00:34:46,239
Speaker 3: It's devastating.

706
00:34:46,320 --> 00:34:49,279
Speaker 2: Hopefully we don't see any major fallout or anything from that,

707
00:34:49,840 --> 00:34:50,959
Speaker 2: hoping it doesn't change anything.

708
00:34:50,960 --> 00:34:52,960
Speaker 3: But wait, you know, we don't yet know. But between

709
00:34:53,000 --> 00:34:54,960
Speaker 3: those like I don't see anything yet.

710
00:34:55,080 --> 00:34:57,839
Speaker 2: Of course, look the Comy letter came when October twenty third,

711
00:34:58,120 --> 00:35:00,360
Speaker 2: I want to say, so there's still plenty of time

712
00:35:00,719 --> 00:35:02,840
Speaker 2: that something could significantly change. In fact, I read a

713
00:35:03,040 --> 00:35:05,319
Speaker 2: insane statistic. I think I talked about this with you

714
00:35:05,400 --> 00:35:08,640
Speaker 2: last time. In the twenty sixteen election, nine percent of

715
00:35:08,640 --> 00:35:10,719
Speaker 2: the electorate did not make up their minds until a

716
00:35:10,760 --> 00:35:11,680
Speaker 2: week before the election.

717
00:35:12,120 --> 00:35:13,440
Speaker 3: That is crazy to me.

718
00:35:13,680 --> 00:35:17,000
Speaker 1: We just do I just if we have that electorate anymore.

719
00:35:16,960 --> 00:35:19,359
Speaker 7: You might be right, but I'm like, wow, like not

720
00:35:19,480 --> 00:35:21,960
Speaker 7: knowing who you're going to vote for it nine days

721
00:35:22,000 --> 00:35:24,480
Speaker 7: before election day and just be like okay, yeah, I

722
00:35:24,480 --> 00:35:26,920
Speaker 7: mean I agree, it's probably less.

723
00:35:26,960 --> 00:35:28,520
Speaker 2: It may not be nine, but let's say it's four.

724
00:35:28,600 --> 00:35:30,600
Speaker 2: I mean four is enough. You could swing the entire thing.

725
00:35:30,920 --> 00:35:32,239
Speaker 3: So there are a lot of people out there that

726
00:35:32,320 --> 00:35:32,799
Speaker 3: think that way.

727
00:35:32,920 --> 00:35:35,239
Speaker 1: The other thing is though, that we just we vote

728
00:35:35,239 --> 00:35:37,880
Speaker 1: differently than we did in twenty sixteen. I mean early

729
00:35:38,160 --> 00:35:42,680
Speaker 1: voting and mail in voting, you know, obviously spiked massively

730
00:35:42,760 --> 00:35:44,360
Speaker 1: during the pandemic, and it's not going to be as

731
00:35:44,440 --> 00:35:46,759
Speaker 1: high as the levels in twenty twenty. But it's not

732
00:35:47,080 --> 00:35:49,560
Speaker 1: going back to twenty sixteen either, so you'd almost need

733
00:35:49,600 --> 00:35:52,800
Speaker 1: more of like a September surprise than an October surprise.

734
00:35:53,239 --> 00:35:56,680
Speaker 1: And we've had a lot of surprises in thrace so far,

735
00:35:56,880 --> 00:35:59,359
Speaker 1: and after Joe Biden is out, you know, everything that's

736
00:35:59,400 --> 00:36:02,880
Speaker 1: happened since you know, maybe the polls take one point

737
00:36:02,920 --> 00:36:06,120
Speaker 1: this way or one point that way, but overall it

738
00:36:06,200 --> 00:36:10,000
Speaker 1: is just really stable and the steady like stable place

739
00:36:10,040 --> 00:36:12,720
Speaker 1: it is is common well, hovering with like a maybe

740
00:36:12,719 --> 00:36:16,479
Speaker 1: about a three point lead in the popular vote, which

741
00:36:16,520 --> 00:36:20,880
Speaker 1: is like translates into every swing state effectively being tied

742
00:36:20,920 --> 00:36:23,799
Speaker 1: and within the margin of error. So you know, it's

743
00:36:23,840 --> 00:36:26,799
Speaker 1: really a question of are the polls accurate? Are they

744
00:36:27,480 --> 00:36:29,520
Speaker 1: you know, are they missing some dynamic. That's the other

745
00:36:29,560 --> 00:36:31,520
Speaker 1: thing that Nate Cone has been talking about is some

746
00:36:31,560 --> 00:36:35,480
Speaker 1: of the pollsters they're very worried about understating Republican support

747
00:36:35,520 --> 00:36:38,560
Speaker 1: and Trump support again, right, because they really blew it.

748
00:36:38,640 --> 00:36:41,360
Speaker 1: You know, they're they famously blew it in twenty sixteen,

749
00:36:41,400 --> 00:36:43,680
Speaker 1: but they actually were more wrong in twenty twenty. Yeah,

750
00:36:43,680 --> 00:36:47,120
Speaker 1: that's correct, they were more off. It's just that Joe

751
00:36:47,160 --> 00:36:49,080
Speaker 1: Biden still had enough of an edge to win, so

752
00:36:49,120 --> 00:36:51,280
Speaker 1: they didn't get quite as much backlash as twenty sixteen,

753
00:36:51,280 --> 00:36:53,799
Speaker 1: but they were actually more wrong in twenty twenty and

754
00:36:53,880 --> 00:36:55,920
Speaker 1: so some of the pollsters who've been burned by that

755
00:36:56,440 --> 00:36:59,759
Speaker 1: have adopted this practice, which was previously seen as like

756
00:37:00,000 --> 00:37:05,080
Speaker 1: ad polling practice, where they weight the voter sample based

757
00:37:05,120 --> 00:37:08,720
Speaker 1: on how people self report who they voted for last time.

758
00:37:09,160 --> 00:37:12,840
Speaker 1: People are not reliable actually even about who they voted

759
00:37:12,840 --> 00:37:15,640
Speaker 1: for in the last election. And without getting into the

760
00:37:15,640 --> 00:37:21,319
Speaker 1: technical details of this, the impact of using that method

761
00:37:21,480 --> 00:37:24,279
Speaker 1: as for waiting the electorate is that you're likely to

762
00:37:24,520 --> 00:37:28,480
Speaker 1: overstate the amount of support for the last loser of

763
00:37:28,520 --> 00:37:31,320
Speaker 1: the election. But they feel like, okay, but we've gotten

764
00:37:31,360 --> 00:37:33,479
Speaker 1: it so wrong so many times, we need to use

765
00:37:33,480 --> 00:37:37,800
Speaker 1: Trump's numbers so that we don't have a similar miss again. Now,

766
00:37:37,880 --> 00:37:40,040
Speaker 1: if you look at the polsters who were doing that

767
00:37:40,440 --> 00:37:42,640
Speaker 1: versus the polsters who are not doing that, and by

768
00:37:42,640 --> 00:37:45,040
Speaker 1: the way, the New York Times, where Nate con Is

769
00:37:45,040 --> 00:37:47,279
Speaker 1: is one of the polsters that is not doing that,

770
00:37:48,000 --> 00:37:51,360
Speaker 1: the results in most cases are not all that much different.

771
00:37:52,160 --> 00:37:56,480
Speaker 1: But the subtle difference is that the polsters who are

772
00:37:56,560 --> 00:37:59,960
Speaker 1: using that you know, waiting based on self identification method,

773
00:38:00,600 --> 00:38:04,640
Speaker 1: their polls are sort of mirroring what happened in twenty twenty,

774
00:38:05,200 --> 00:38:07,920
Speaker 1: and using that method is likely to get you to

775
00:38:08,239 --> 00:38:12,239
Speaker 1: basically mirror the last results of the last election, and

776
00:38:12,960 --> 00:38:14,880
Speaker 1: the New York Times, Poles and other ones that aren't

777
00:38:14,960 --> 00:38:18,160
Speaker 1: using that method, they're seeing warshifts that look more like

778
00:38:18,200 --> 00:38:21,160
Speaker 1: the twenty twenty two midterm elections. That's why you end

779
00:38:21,239 --> 00:38:23,720
Speaker 1: up with the New York Times poll of Florida being

780
00:38:23,800 --> 00:38:27,600
Speaker 1: a thirteen point lead for Donald Trump, where other polls

781
00:38:27,600 --> 00:38:30,960
Speaker 1: have shown it actually quite close, being like three points

782
00:38:31,000 --> 00:38:34,040
Speaker 1: even two points, four points more in the single digits

783
00:38:34,120 --> 00:38:38,920
Speaker 1: versus thirteen points. So Nate's point is basically like, you know,

784
00:38:39,400 --> 00:38:41,920
Speaker 1: I understand, I'm sympathetic to why polsters are doing this,

785
00:38:42,280 --> 00:38:44,840
Speaker 1: but if you are using this method, you may be

786
00:38:45,040 --> 00:38:48,800
Speaker 1: missing some shift that has happened post twenty twenty. And

787
00:38:48,840 --> 00:38:50,840
Speaker 1: I don't think any of us would be surprised, especially

788
00:38:50,880 --> 00:38:53,120
Speaker 1: given what we saw on the twenty twenty two midterms

789
00:38:53,120 --> 00:38:55,960
Speaker 1: and how that was such a like different election. In

790
00:38:56,040 --> 00:38:58,760
Speaker 1: terms of some places, the red wave did actually materialize,

791
00:38:58,800 --> 00:39:00,719
Speaker 1: but in a lot of key swings states it did not.

792
00:39:01,239 --> 00:39:05,200
Speaker 1: Any other direction, the map in the country looks a

793
00:39:05,200 --> 00:39:08,000
Speaker 1: lot different than it did pre pandemic, So in any case,

794
00:39:08,040 --> 00:39:12,799
Speaker 1: that's something else to watch out for. Let's go and

795
00:39:12,840 --> 00:39:14,800
Speaker 1: put this next piece up on the screen. This is

796
00:39:14,840 --> 00:39:17,880
Speaker 1: a report out of Pennsylvania, which it really is kind

797
00:39:17,920 --> 00:39:20,560
Speaker 1: of the ballgame. Pennsylvania really is kind of the ballgame.

798
00:39:20,600 --> 00:39:24,640
Speaker 1: Whichever two of these two candidates wins Pennsylvania is very

799
00:39:24,800 --> 00:39:27,200
Speaker 1: likely to be the next president in the United States,

800
00:39:27,239 --> 00:39:31,800
Speaker 1: and they are going all in the head. The beginning

801
00:39:32,000 --> 00:39:34,560
Speaker 1: paragraph here in this article sets when Vice President Kamala

802
00:39:34,560 --> 00:39:37,000
Speaker 1: Harris rolled out her economic agenda. She went to Pittsburgh

803
00:39:37,080 --> 00:39:39,040
Speaker 1: when she unveiled her running mate. She went to Philly.

804
00:39:39,160 --> 00:39:41,000
Speaker 1: When she had to pick a place for Obama's first

805
00:39:41,040 --> 00:39:44,120
Speaker 1: ball rally, it was back to Pittsburgh. Trump has earmarked

806
00:39:44,160 --> 00:39:47,080
Speaker 1: the greatest share of his advertising budget for Pennsylvania's held

807
00:39:47,160 --> 00:39:49,719
Speaker 1: more rallies in the state than in any other battleground

808
00:39:49,920 --> 00:39:52,480
Speaker 1: since Miss Harris joined the race, including two on Wednesday,

809
00:39:52,520 --> 00:39:55,239
Speaker 1: three in the last week. And they talk about why

810
00:39:55,360 --> 00:39:58,200
Speaker 1: Pennsylvania is so compelling. It's sort of like a microcosm

811
00:39:58,600 --> 00:40:01,319
Speaker 1: of all of America. They write. It's home to urban

812
00:40:01,360 --> 00:40:04,120
Speaker 1: centers like Philly. Of course, large population of black voters

813
00:40:04,120 --> 00:40:07,480
Speaker 1: who Dems need to mobilize, has fast growing, highly educated

814
00:40:07,520 --> 00:40:10,920
Speaker 1: mostly white suburbs outside of Philadelphia and Pittsburgh, where Republicans

815
00:40:10,960 --> 00:40:13,319
Speaker 1: have been bleeding support in the Trump years. There are

816
00:40:13,360 --> 00:40:16,760
Speaker 1: struggling industrial towns where Trump needs to maximize the vote,

817
00:40:16,840 --> 00:40:19,560
Speaker 1: smaller cities that are actually booming with Latino immigrants where

818
00:40:19,560 --> 00:40:22,400
Speaker 1: Harris wants to make gains, And there's a significant, although

819
00:40:22,440 --> 00:40:26,520
Speaker 1: shrinking rural population. White voters without college degrees who make

820
00:40:26,600 --> 00:40:30,640
Speaker 1: up mister Trump's base, still account for roughly half the vote.

821
00:40:30,680 --> 00:40:35,040
Speaker 1: And if you look at Pennsylvania politically, Democrats did very

822
00:40:35,040 --> 00:40:39,279
Speaker 1: well there in twenty twenty two, in part because Republicans

823
00:40:39,280 --> 00:40:43,280
Speaker 1: had sort of like uniquely poor candidates in doctor Oz

824
00:40:43,320 --> 00:40:45,759
Speaker 1: and what's the other dud's name Haran for governor called

825
00:40:45,800 --> 00:40:49,120
Speaker 1: Lostria Mastriano, who was really really out there. And so

826
00:40:49,200 --> 00:40:52,000
Speaker 1: Democrats did well at the Senate and the guminatorial level.

827
00:40:52,440 --> 00:40:55,080
Speaker 1: But if you look at the legislature, it's the only

828
00:40:55,160 --> 00:40:58,000
Speaker 1: state in the country where Democrats have one chamber and

829
00:40:58,080 --> 00:41:01,360
Speaker 1: Republicans have the other. And the margin in the state's

830
00:41:01,360 --> 00:41:05,360
Speaker 1: lower chamber is a single seat. That tells you just

831
00:41:05,400 --> 00:41:08,560
Speaker 1: how closely Pennsylvania is divided is divided, and why this

832
00:41:08,600 --> 00:41:09,919
Speaker 1: is such a dogfight between them.

833
00:41:09,920 --> 00:41:12,560
Speaker 2: And the dollars show that dollars are the only place

834
00:41:12,560 --> 00:41:14,560
Speaker 2: where there is not as much of a disparity between

835
00:41:14,560 --> 00:41:16,440
Speaker 2: the Democrats and the Republicans. You have one hundred and

836
00:41:16,480 --> 00:41:19,480
Speaker 2: eighty million being spent so far by the Democrats, one

837
00:41:19,520 --> 00:41:22,680
Speaker 2: hundred and seventy million by the Republicans. In fact, they

838
00:41:22,719 --> 00:41:25,120
Speaker 2: look at Pennsylvania. I think part of the reason too

839
00:41:25,200 --> 00:41:27,520
Speaker 2: is not only is that Pennsylvania is the tipping point state,

840
00:41:27,600 --> 00:41:30,479
Speaker 2: but the general theory that both of the campaigns seem

841
00:41:30,560 --> 00:41:32,160
Speaker 2: to have is that a polling error is going to

842
00:41:32,239 --> 00:41:35,080
Speaker 2: miss in either way. And so the theory behind that

843
00:41:35,200 --> 00:41:38,120
Speaker 2: means that it would be very unlikely to let's say,

844
00:41:38,400 --> 00:41:43,520
Speaker 2: win Pennsylvania and not also win Wisconsin, yeah, or Michigan, right,

845
00:41:43,760 --> 00:41:46,279
Speaker 2: although I guess to be fair, in twenty sixteen, Trump

846
00:41:46,320 --> 00:41:48,879
Speaker 2: only won Michigan by like ten thousand votes. I did

847
00:41:48,880 --> 00:41:51,640
Speaker 2: look at his victory margin or lost margin in twenty twenty.

848
00:41:51,760 --> 00:41:54,160
Speaker 2: It's eight five hundred and fifty five votes in the

849
00:41:54,200 --> 00:41:57,800
Speaker 2: state of Pennsylvania. That's incredibly close after almost six million,

850
00:41:58,200 --> 00:42:00,880
Speaker 2: or more than six million votes that were in the election.

851
00:42:01,120 --> 00:42:03,719
Speaker 3: So what that means, you know, for this is that

852
00:42:03,800 --> 00:42:04,839
Speaker 3: it really is just.

853
00:42:04,840 --> 00:42:08,400
Speaker 2: Get down to turn out in the constituencies that you

854
00:42:08,440 --> 00:42:10,560
Speaker 2: can and then just kind of hope and pray for

855
00:42:10,640 --> 00:42:13,400
Speaker 2: those swing voters to come out. The point about Pennsylvania

856
00:42:13,440 --> 00:42:16,600
Speaker 2: being America is very important. They have those industrial steel towns.

857
00:42:16,800 --> 00:42:19,520
Speaker 2: They have a long legacy of you know, half the

858
00:42:19,680 --> 00:42:22,840
Speaker 2: half of the state. It's gigantic, as more in common

859
00:42:22,840 --> 00:42:25,720
Speaker 2: with the industrial Midwest than it does with the East coast.

860
00:42:25,960 --> 00:42:29,120
Speaker 2: The east coast side has a lot of more economic dynamism.

861
00:42:29,239 --> 00:42:31,680
Speaker 2: You've got the you know, the Pennsylvania mainline suburbs has

862
00:42:31,760 --> 00:42:34,080
Speaker 2: quite a bit of wealth that is there. Pennsylvania or

863
00:42:34,120 --> 00:42:38,000
Speaker 2: Philadelphia itself has got its own like constituencies. You've got

864
00:42:38,120 --> 00:42:41,520
Speaker 2: black population, Hispanic population as well. We saw previously that

865
00:42:41,560 --> 00:42:43,680
Speaker 2: we played here on the show about how the poor

866
00:42:43,840 --> 00:42:46,960
Speaker 2: areas of Philadelphia are actually swinging to Trump the biggest.

867
00:42:47,239 --> 00:42:50,000
Speaker 2: We don't know if that's an urban only phenomenon, if

868
00:42:50,040 --> 00:42:51,680
Speaker 2: it also translates.

869
00:42:51,160 --> 00:42:53,880
Speaker 3: To poorer, more rural areas in the state.

870
00:42:53,960 --> 00:42:55,680
Speaker 2: So the point is that it's changing a lot, and

871
00:42:55,719 --> 00:42:59,120
Speaker 2: obviously it's very up for grabs. Doctor Oz lost the

872
00:42:59,160 --> 00:43:02,120
Speaker 2: state by almost six points back in twenty twenty two,

873
00:43:02,280 --> 00:43:05,760
Speaker 2: so that could be an indicator. Also made an indicator

874
00:43:05,760 --> 00:43:08,520
Speaker 2: to me about the strength of abortion. John Fetterman, if

875
00:43:08,520 --> 00:43:10,200
Speaker 2: you remember that time, it barely even you know, put

876
00:43:10,200 --> 00:43:10,759
Speaker 2: a comp Yeah, he.

877
00:43:10,760 --> 00:43:13,280
Speaker 1: Had just had that stroke. It was like basically remember

878
00:43:13,320 --> 00:43:14,440
Speaker 1: the debate performance.

879
00:43:14,520 --> 00:43:16,160
Speaker 2: It was crazy, right, yeah, I mean it's only recently

880
00:43:16,239 --> 00:43:18,640
Speaker 2: these like regained his power of speech. But my point

881
00:43:18,680 --> 00:43:20,920
Speaker 2: is that, you know, Fetterman, I thought he was going

882
00:43:21,000 --> 00:43:22,839
Speaker 2: to lose. To this day, I still don't really get

883
00:43:22,880 --> 00:43:25,239
Speaker 2: it how anybody voted for him back in twenty twenty two,

884
00:43:25,280 --> 00:43:27,080
Speaker 2: But he won, not just by a little he won

885
00:43:27,120 --> 00:43:30,120
Speaker 2: by a lot, like six points in a major swing state.

886
00:43:30,360 --> 00:43:32,440
Speaker 2: So that was the power of abortion at that time.

887
00:43:32,920 --> 00:43:36,960
Speaker 2: Shapiro beat Mastriano by thirteen points. So the question about

888
00:43:37,000 --> 00:43:39,800
Speaker 2: the whole polling thing and the tightness is if twenty

889
00:43:39,920 --> 00:43:42,920
Speaker 2: twenty is the benchmark for what they're pulling against, I

890
00:43:42,920 --> 00:43:45,520
Speaker 2: don't know. To me, the state seems to have shifted

891
00:43:45,600 --> 00:43:48,160
Speaker 2: quite a bit in that time. At the same time,

892
00:43:48,320 --> 00:43:51,040
Speaker 2: Trump is such a unique figure that he's so much

893
00:43:51,080 --> 00:43:53,879
Speaker 2: better and more appealing to Republican voters or even swing

894
00:43:53,960 --> 00:43:56,759
Speaker 2: voters than an Oz or a Mastriano or even a

895
00:43:56,800 --> 00:43:59,080
Speaker 2: Dave McCormick. If you look at how much more he's

896
00:43:59,120 --> 00:44:01,440
Speaker 2: running ahead of him, that maybe he's the only guy

897
00:44:01,520 --> 00:44:03,879
Speaker 2: left in America who could still win of this state

898
00:44:03,880 --> 00:44:07,080
Speaker 2: of Pennsylvania as a Republican. So it's complicated, you know,

899
00:44:07,200 --> 00:44:08,960
Speaker 2: in terms of how we look at it, but the

900
00:44:09,000 --> 00:44:11,359
Speaker 2: inside battle for it and the likelihood of it being

901
00:44:11,360 --> 00:44:14,120
Speaker 2: the tipping point state is so high. I was just

902
00:44:14,160 --> 00:44:16,640
Speaker 2: looking again, it's almost like thirty or forty percent or

903
00:44:16,680 --> 00:44:19,520
Speaker 2: whatever for the Nate Silver forecast of whoever wins it,

904
00:44:19,600 --> 00:44:22,120
Speaker 2: and just in general, if we think about it like

905
00:44:22,400 --> 00:44:25,239
Speaker 2: that will be the main one to watch on election night.

906
00:44:25,280 --> 00:44:27,920
Speaker 2: We're lucky who were on East coast time, so is Pennsylvania.

907
00:44:28,040 --> 00:44:30,200
Speaker 2: We can see it was called at roughly two am

908
00:44:30,440 --> 00:44:33,720
Speaker 2: on twenty sixteen election night. If it is as close

909
00:44:33,760 --> 00:44:35,879
Speaker 2: as again as it was in twenty twenty, it could

910
00:44:35,920 --> 00:44:38,719
Speaker 2: take a while, especially with mail in ballots, although I

911
00:44:38,800 --> 00:44:40,920
Speaker 2: do believe the mail in balloting. You know, things have

912
00:44:41,000 --> 00:44:43,040
Speaker 2: changed in the last couple of years for processing, it's

913
00:44:43,040 --> 00:44:44,440
Speaker 2: no longer COVID and all of that.

914
00:44:44,800 --> 00:44:46,560
Speaker 3: Yeah, But basically take brace for a long election.

915
00:44:46,680 --> 00:44:50,080
Speaker 1: They still, I believe, have law on the books that

916
00:44:50,080 --> 00:44:53,480
Speaker 1: says you can't even begin processing those mail in ballots

917
00:44:53,560 --> 00:44:56,400
Speaker 1: until election day. That means even just like smoothing out

918
00:44:56,440 --> 00:44:59,600
Speaker 1: the envelopes and preparing them to be fed into the machine,

919
00:45:00,000 --> 00:45:03,000
Speaker 1: which means that it's going to take longer. And it

920
00:45:03,040 --> 00:45:05,759
Speaker 1: also means that just like last time around, Remember we

921
00:45:05,800 --> 00:45:07,839
Speaker 1: talked about that quote unquote red mirage because a lot

922
00:45:07,880 --> 00:45:10,839
Speaker 1: of the Republican in person votes on that day and

923
00:45:10,920 --> 00:45:13,400
Speaker 1: in smaller counties where they're able to count things more quickly,

924
00:45:13,880 --> 00:45:17,480
Speaker 1: those came in first, and that's what helped Trump to

925
00:45:17,480 --> 00:45:19,800
Speaker 1: spin his tales about how the election was stolen and

926
00:45:19,960 --> 00:45:23,200
Speaker 1: you know, Philadelphia blah blah blah, yeah, stop the count

927
00:45:24,320 --> 00:45:26,160
Speaker 1: we are likely to see. It may not be as

928
00:45:26,239 --> 00:45:28,799
Speaker 1: dramatic an effect as last In fact, I don't think

929
00:45:28,840 --> 00:45:30,879
Speaker 1: it will be as dramatic an effect as last time,

930
00:45:30,960 --> 00:45:32,759
Speaker 1: but it will still be something to watch more on

931
00:45:32,840 --> 00:45:36,560
Speaker 1: election day, that is likely, those Republican in person, small

932
00:45:36,600 --> 00:45:40,040
Speaker 1: county votes are likely to come in more quickly than

933
00:45:40,520 --> 00:45:43,440
Speaker 1: the big urban areas or even the big suburban areas

934
00:45:43,480 --> 00:45:45,759
Speaker 1: where Democrats are stronger. The other thing to point out

935
00:45:45,760 --> 00:45:49,480
Speaker 1: about Pennsylvania is Trump just had his giant rally in Butler,

936
00:45:49,520 --> 00:45:53,480
Speaker 1: Pennsylvania with Elon Musk. We're turning to the site of

937
00:45:53,520 --> 00:45:57,840
Speaker 1: that assassination attempt on his life. And that's another like

938
00:45:58,120 --> 00:46:00,200
Speaker 1: X factor in all of this. Whether that helped to

939
00:46:00,320 --> 00:46:04,480
Speaker 1: drive local enthusiasm for Trump the rally with Elin, I

940
00:46:04,520 --> 00:46:06,279
Speaker 1: don't know what the final numbers were, but I mean

941
00:46:06,719 --> 00:46:08,719
Speaker 1: it was huge. There were a lot of people there,

942
00:46:08,719 --> 00:46:11,120
Speaker 1: obviously a lot of energy around that. New York Times

943
00:46:11,120 --> 00:46:14,960
Speaker 1: interviewed some local supporters who were really energized and inspired

944
00:46:15,040 --> 00:46:18,920
Speaker 1: by his reaction in that moment and decided, you know,

945
00:46:18,960 --> 00:46:20,200
Speaker 1: not only am I going to vote for I'm going

946
00:46:20,239 --> 00:46:23,120
Speaker 1: to volunteer, I'm going to be a precinct captain or whatever.

947
00:46:23,440 --> 00:46:27,240
Speaker 1: So that is possible, possible to see that impact as well.

948
00:46:27,560 --> 00:46:29,359
Speaker 1: But just to give you a sense of just how

949
00:46:29,400 --> 00:46:31,840
Speaker 1: close these are the status in terms of the polls,

950
00:46:31,840 --> 00:46:34,640
Speaker 1: we can put up the latest RCP average. Now RCP

951
00:46:35,000 --> 00:46:37,439
Speaker 1: it's not a model. It just literally takes in every pole,

952
00:46:37,640 --> 00:46:39,960
Speaker 1: whether it's a good pollster or a bad pollster or

953
00:46:39,960 --> 00:46:42,359
Speaker 1: an in between polster, whatever their record is, they just

954
00:46:42,400 --> 00:46:45,880
Speaker 1: feed them in to the average. But based on their

955
00:46:46,000 --> 00:46:48,960
Speaker 1: average of every single pole that's out there, the partisan ones,

956
00:46:48,960 --> 00:46:52,600
Speaker 1: on the nonpartisan ones, they've got Trump at point two

957
00:46:53,400 --> 00:46:56,680
Speaker 1: of a lead over Kamala Harris. In other words, it

958
00:46:56,800 --> 00:47:00,439
Speaker 1: could not possibly be closer. And Tiger, I know you've

959
00:47:00,560 --> 00:47:01,880
Speaker 1: spent some time in the state.

960
00:47:01,760 --> 00:47:02,600
Speaker 3: Well just for a weekend.

961
00:47:02,680 --> 00:47:05,560
Speaker 1: Yeah, but you were you and are you still getting

962
00:47:05,640 --> 00:47:06,719
Speaker 1: like detoxing from.

963
00:47:06,680 --> 00:47:10,040
Speaker 3: All the play I'm I'm not lying.

964
00:47:10,080 --> 00:47:12,120
Speaker 2: Like my in lawns, they like to watch actual cable

965
00:47:12,160 --> 00:47:17,160
Speaker 2: television and like sitting there being bombarded with those ads

966
00:47:17,360 --> 00:47:21,080
Speaker 2: is maddening, especially I think we're watching the Phillies game.

967
00:47:21,600 --> 00:47:27,200
Speaker 2: Every single commercial break is just it's literally Trump at Comma,

968
00:47:27,239 --> 00:47:30,040
Speaker 2: at Dave McCormick, ad, Bob Casey, ad, then whoever the

969
00:47:30,040 --> 00:47:30,960
Speaker 2: congressional candidate is.

970
00:47:30,960 --> 00:47:33,480
Speaker 3: I'm like, I'm gonna lose my goddamn mind. It was

971
00:47:33,520 --> 00:47:33,960
Speaker 3: the first time.

972
00:47:34,000 --> 00:47:35,319
Speaker 2: I'm like, I'm happy I don't live in a swing

973
00:47:35,440 --> 00:47:37,600
Speaker 2: state because I don't think I could handle this. Because

974
00:47:37,640 --> 00:47:39,719
Speaker 2: even if you don't want to engage with it and

975
00:47:39,760 --> 00:47:42,160
Speaker 2: you don't watch terrestrial TV, it's on the radio.

976
00:47:42,600 --> 00:47:44,960
Speaker 3: The yard signs are everywhere. I will say they do.

977
00:47:45,080 --> 00:47:47,960
Speaker 3: They just very cypically engage people. I'll give them that.

978
00:47:48,080 --> 00:47:50,960
Speaker 2: Like when you ride along the highway or anywhere here

979
00:47:51,000 --> 00:47:53,520
Speaker 2: in Virginia's I wouldn't say it's as common, but there,

980
00:47:53,600 --> 00:47:56,279
Speaker 2: I mean every house if whoever they're supporting, they're there.

981
00:47:56,320 --> 00:47:58,040
Speaker 2: There does seem to be a lot of neighborliness, so

982
00:47:58,080 --> 00:48:00,440
Speaker 2: that's good, you know, people respect each other. But it

983
00:48:00,520 --> 00:48:02,920
Speaker 2: was fascinating for me to see it like in action

984
00:48:03,080 --> 00:48:05,920
Speaker 2: of what it really looks like in the hardcore wing state.

985
00:48:06,280 --> 00:48:09,760
Speaker 2: For you, right before in election, the ads are just everywhere,

986
00:48:09,760 --> 00:48:12,080
Speaker 2: and also, like I said, the choice of those ads

987
00:48:12,080 --> 00:48:16,320
Speaker 2: for Trump every ad immigration, immigration, immigration, immigration, migrant immigration.

988
00:48:17,000 --> 00:48:20,239
Speaker 2: And then there were some Dave McCormick ads which tried

989
00:48:20,280 --> 00:48:22,080
Speaker 2: to do that thing where he's like Bob Casey's the

990
00:48:22,120 --> 00:48:26,799
Speaker 2: real radical on abortion, but from Bob Casey and from Kamala, it.

991
00:48:26,760 --> 00:48:27,960
Speaker 3: Was abortion all day long.

992
00:48:28,000 --> 00:48:31,040
Speaker 2: It was all ro versus Wade for their paid advertising.

993
00:48:31,080 --> 00:48:34,040
Speaker 2: I mean possibly because I was in literally in the suburbs,

994
00:48:34,080 --> 00:48:37,000
Speaker 2: so maybe that's why the targeting was there, But it

995
00:48:37,120 --> 00:48:40,319
Speaker 2: seemed to me like that I took note of the

996
00:48:40,360 --> 00:48:43,440
Speaker 2: fact that it was so strong, so unified in that

997
00:48:43,480 --> 00:48:47,280
Speaker 2: message from both of those candidates, from the Senate candidates

998
00:48:47,320 --> 00:48:50,040
Speaker 2: as well as the presidential candidates, that clearly they've got

999
00:48:50,080 --> 00:48:52,160
Speaker 2: a lot of internals this is the main thing to go.

1000
00:48:52,120 --> 00:48:55,160
Speaker 3: All in on. Very interesting, it makes sense, yeah, makes sense.

1001
00:48:57,800 --> 00:49:01,600
Speaker 2: Foreign President Donald Trump joined the Flag Podcasts with my

1002
00:49:01,640 --> 00:49:05,480
Speaker 2: friend Andrew Schultz and his entire crew over there, and

1003
00:49:05,520 --> 00:49:07,920
Speaker 2: the results were actually incredible.

1004
00:49:08,200 --> 00:49:09,640
Speaker 3: Before we get into a crystal life.

1005
00:49:09,640 --> 00:49:11,879
Speaker 2: Even though I've been discussing it, the way that they

1006
00:49:11,920 --> 00:49:15,040
Speaker 2: handled this was amazing, And really what it came down

1007
00:49:15,080 --> 00:49:17,839
Speaker 2: to is they didn't have the look.

1008
00:49:17,880 --> 00:49:20,600
Speaker 3: In general, as people know, I advocate dressing up, treating

1009
00:49:20,640 --> 00:49:22,799
Speaker 3: the president, etc. But it's a comedy.

1010
00:49:22,440 --> 00:49:27,319
Speaker 2: Podcast, so in general, their theory of going into it

1011
00:49:27,400 --> 00:49:29,520
Speaker 2: was let's just treat him like a normal guest.

1012
00:49:29,960 --> 00:49:31,480
Speaker 3: And by doing that.

1013
00:49:31,400 --> 00:49:33,440
Speaker 2: There was actually a lot that came out of it,

1014
00:49:33,480 --> 00:49:36,560
Speaker 2: both in the way that Trump obviously was felt very

1015
00:49:36,600 --> 00:49:38,960
Speaker 2: comfortable and all that, but it also led to some

1016
00:49:39,080 --> 00:49:41,439
Speaker 2: real world moments where I'm not sure Trump has ever

1017
00:49:42,280 --> 00:49:46,840
Speaker 2: experienced that from an interviewer. Actually, the most noteworthy moment

1018
00:49:46,960 --> 00:49:50,160
Speaker 2: to me came at the very end where Andrew was

1019
00:49:50,280 --> 00:49:53,279
Speaker 2: challenging and even interrupting Trump and Trump let him, which

1020
00:49:53,320 --> 00:49:57,040
Speaker 2: was even the crazier part about the phrase make America

1021
00:49:57,120 --> 00:49:57,640
Speaker 2: great again.

1022
00:49:57,680 --> 00:49:59,640
Speaker 3: So let's go ahead and let's take a listen to that.

1023
00:50:00,080 --> 00:50:03,440
Speaker 8: Or mega make America great again. I'm gonna make this

1024
00:50:03,520 --> 00:50:05,919
Speaker 8: country great again. It's not a great country right now.

1025
00:50:06,000 --> 00:50:08,160
Speaker 8: It's loaded up. Always a great country, it's a great

1026
00:50:08,200 --> 00:50:11,320
Speaker 8: see that's always a great okay, But I say it

1027
00:50:11,719 --> 00:50:14,359
Speaker 8: has the potential, and it was a great country. I

1028
00:50:14,400 --> 00:50:18,200
Speaker 8: think now there's so much hatred and there's so much dissension.

1029
00:50:18,640 --> 00:50:21,040
Speaker 8: I think when you have people that can't walk down

1030
00:50:21,080 --> 00:50:23,640
Speaker 8: Fifth Avenue, when you have people that can't walk down

1031
00:50:23,719 --> 00:50:25,600
Speaker 8: the street, it ceases to be.

1032
00:50:25,560 --> 00:50:27,680
Speaker 3: You can always be better, We can always be better.

1033
00:50:27,760 --> 00:50:31,240
Speaker 8: Yeah, but you can't blindfold yourself to say I'm honest

1034
00:50:31,280 --> 00:50:31,640
Speaker 8: about it.

1035
00:50:31,680 --> 00:50:34,040
Speaker 3: But to me, I'm really proud of America.

1036
00:50:33,760 --> 00:50:36,160
Speaker 9: Because I think that I don't I think I can

1037
00:50:36,200 --> 00:50:38,960
Speaker 9: be the best version of myself here. You know, I

1038
00:50:39,000 --> 00:50:42,560
Speaker 9: think that Donald Trump can only happen in America. Your life,

1039
00:50:43,040 --> 00:50:45,920
Speaker 9: what has happened to you? This is an American story.

1040
00:50:46,160 --> 00:50:48,279
Speaker 3: I mean, I thought that was interesting. I have not

1041
00:50:48,480 --> 00:50:51,160
Speaker 3: I'm shocked Trump let him talk. Actually, it shows that

1042
00:50:51,200 --> 00:50:52,160
Speaker 3: Trump likes him.

1043
00:50:52,200 --> 00:50:53,799
Speaker 2: Having interviewed Trump in the past, he can be very

1044
00:50:53,840 --> 00:50:57,600
Speaker 2: difficult to interrupt in that regard, but it was it

1045
00:50:57,640 --> 00:51:02,960
Speaker 2: was an interesting moment to me, both in the challenge. Also, look,

1046
00:51:03,160 --> 00:51:05,520
Speaker 2: no surprise, Trump's not going out and flagrant out of

1047
00:51:05,560 --> 00:51:07,319
Speaker 2: the goodness of his own heart. He's doing it to

1048
00:51:07,320 --> 00:51:10,120
Speaker 2: try and reach the people who watched comedy podcasts, which

1049
00:51:10,160 --> 00:51:13,400
Speaker 2: is predominantly young men between the age of like eighteen

1050
00:51:13,440 --> 00:51:15,920
Speaker 2: to thirty five, and so I was like, huh, that

1051
00:51:16,040 --> 00:51:19,879
Speaker 2: is I'd be curious. Look, obviously, I'm in politics all

1052
00:51:19,960 --> 00:51:22,920
Speaker 2: day long. If this is your maybe not first exposure,

1053
00:51:22,960 --> 00:51:25,360
Speaker 2: but maybe your first like long form exposure, Trump, I

1054
00:51:25,360 --> 00:51:26,560
Speaker 2: don't really know what to take away from that.

1055
00:51:26,600 --> 00:51:27,800
Speaker 3: I thought I thought it was interesting.

1056
00:51:27,920 --> 00:51:31,719
Speaker 1: Yeah, they definitely think that there's an opportunity to move

1057
00:51:31,800 --> 00:51:35,000
Speaker 1: some young men their way and that there's an opening

1058
00:51:35,040 --> 00:51:38,000
Speaker 1: there and that podcast like this helped to serve that purpose.

1059
00:51:38,040 --> 00:51:40,680
Speaker 1: And this has been an integral part of their strategy.

1060
00:51:40,960 --> 00:51:42,960
Speaker 1: We saw Kamala Harris do like the Call Her Daddy

1061
00:51:43,160 --> 00:51:46,480
Speaker 1: podcast as well, so you know they also have their

1062
00:51:46,480 --> 00:51:49,120
Speaker 1: own podcast strategy. But I think Trump campaign, I think

1063
00:51:49,160 --> 00:51:52,200
Speaker 1: it's fair to say, has lead more into a podcast

1064
00:51:52,239 --> 00:51:54,279
Speaker 1: strategy this election. So it'll be interesting to see what

1065
00:51:54,280 --> 00:51:55,840
Speaker 1: the results are. But yeah, to go back to your

1066
00:51:55,880 --> 00:51:58,320
Speaker 1: point about like I liked the I mean, Andrew Schultz

1067
00:51:58,440 --> 00:52:00,480
Speaker 1: is not a journalist. He's not going to us like

1068
00:52:01,160 --> 00:52:02,960
Speaker 1: he didn't try to pose. It's like, oh, I'm going

1069
00:52:03,040 --> 00:52:05,120
Speaker 1: to do a bunch of tough followups or whatever. I'm

1070
00:52:05,120 --> 00:52:06,680
Speaker 1: just gonna treat you the way that I would treat

1071
00:52:06,760 --> 00:52:09,239
Speaker 1: any normal guests. And by the way, I'm gonna laugh

1072
00:52:09,239 --> 00:52:11,480
Speaker 1: in your face and mock you effectively to your face.

1073
00:52:11,920 --> 00:52:15,279
Speaker 1: We should watch that when it's called for, and I

1074
00:52:15,360 --> 00:52:17,120
Speaker 1: kind of liked the vibe of that. I think we

1075
00:52:17,120 --> 00:52:20,239
Speaker 1: should normalize laughing and mocking polaticives in her face is

1076
00:52:20,320 --> 00:52:20,920
Speaker 1: more often.

1077
00:52:21,400 --> 00:52:25,080
Speaker 2: Let's take a listen. This was on the basically truthful comment.

1078
00:52:25,400 --> 00:52:27,280
Speaker 8: They can say what they want. I have a hard

1079
00:52:27,280 --> 00:52:30,279
Speaker 8: time doing it to them because I'm basic, you know,

1080
00:52:30,320 --> 00:52:39,280
Speaker 8: I'm basically a truthful person. But frankly, fly, she's given

1081
00:52:39,280 --> 00:52:42,200
Speaker 8: me so much ammunition I don't really have to She's

1082
00:52:42,239 --> 00:52:46,200
Speaker 8: a radical left lunatic who will destroy our nation. Other

1083
00:52:46,280 --> 00:52:48,719
Speaker 8: than that, but she will destroy our nation.

1084
00:52:49,080 --> 00:52:51,399
Speaker 3: That was great. You could see he just couldn't keep

1085
00:52:51,400 --> 00:52:53,399
Speaker 3: it together. You could also see Akash in the frame

1086
00:52:53,440 --> 00:52:54,319
Speaker 3: there just trying to.

1087
00:52:54,880 --> 00:52:58,080
Speaker 2: Trying to feel his eyes because he also was laughing

1088
00:52:58,080 --> 00:52:58,440
Speaker 2: at him.

1089
00:52:58,440 --> 00:53:00,240
Speaker 3: But I mean, yeah, I thought that was good.

1090
00:53:00,440 --> 00:53:03,480
Speaker 2: They genuinely just treated him like you would treat anybody else.

1091
00:53:04,200 --> 00:53:07,040
Speaker 2: He also, it was clear was like picking up on it.

1092
00:53:07,360 --> 00:53:10,960
Speaker 2: He started doing his bit about the weave.

1093
00:53:11,520 --> 00:53:11,600
Speaker 1: Uh.

1094
00:53:11,719 --> 00:53:15,040
Speaker 2: This is perhaps the biggest insight into Trump's mind because

1095
00:53:15,239 --> 00:53:17,880
Speaker 2: what he's picking up on is there have been previous

1096
00:53:17,960 --> 00:53:21,520
Speaker 2: leaks and criticisms of Trump, not even advisors, but also

1097
00:53:21,600 --> 00:53:23,799
Speaker 2: other politicians who are like, hey, he's rambling and he

1098
00:53:23,840 --> 00:53:26,200
Speaker 2: doesn't come to the point. He takes that very much

1099
00:53:26,239 --> 00:53:28,560
Speaker 2: to heart because to him, he calls it the weave,

1100
00:53:28,600 --> 00:53:31,480
Speaker 2: and he believes it's part of his superpower. So he

1101
00:53:31,600 --> 00:53:33,880
Speaker 2: explained this on flagrant Let's take a.

1102
00:53:33,880 --> 00:53:35,600
Speaker 8: Lesson, you know, and I do a thing full the weave.

1103
00:53:36,400 --> 00:53:39,080
Speaker 8: And there were those that are fair that say this

1104
00:53:39,160 --> 00:53:42,919
Speaker 8: guy is so genius, and then others would say, oh,

1105
00:53:42,960 --> 00:53:43,520
Speaker 8: he rambled.

1106
00:53:43,560 --> 00:53:44,200
Speaker 3: I don't ramble.

1107
00:53:45,880 --> 00:53:47,759
Speaker 8: What you do is you weave things, and you do it.

1108
00:53:49,760 --> 00:53:52,719
Speaker 8: You have to have certain things. You need an extraordinary

1109
00:53:52,719 --> 00:53:54,560
Speaker 8: memory because you have to come back to where you started.

1110
00:53:55,560 --> 00:53:58,759
Speaker 8: The weave is only good that you could go all

1111
00:53:58,760 --> 00:54:02,640
Speaker 8: the way over here and so far here or there,

1112
00:54:03,280 --> 00:54:06,120
Speaker 8: and I can come back to exactly where I started. Now,

1113
00:54:06,760 --> 00:54:09,200
Speaker 8: someday when you don't come back to where you started.

1114
00:54:12,680 --> 00:54:14,640
Speaker 8: But the weave is the way when you're telling, like

1115
00:54:14,680 --> 00:54:16,800
Speaker 8: a story, I was telling the story at a rally

1116
00:54:16,800 --> 00:54:19,239
Speaker 8: the other day in front of thousands of people, and

1117
00:54:19,360 --> 00:54:22,680
Speaker 8: I started off, and then something in the story I

1118
00:54:22,719 --> 00:54:27,040
Speaker 8: actually mentioned air Force one. It was air Force one,

1119
00:54:27,080 --> 00:54:28,719
Speaker 8: will say, so I mentioned air Force one, and then

1120
00:54:28,760 --> 00:54:31,600
Speaker 8: I said, how I got one point six billion dollars

1121
00:54:31,600 --> 00:54:33,799
Speaker 8: off the price of air Force one. So but then

1122
00:54:33,840 --> 00:54:35,520
Speaker 8: you have to come back to the story, where was

1123
00:54:35,560 --> 00:54:37,120
Speaker 8: air Force one taken?

1124
00:54:37,560 --> 00:54:39,879
Speaker 3: I do a weave. I call it the weave. There

1125
00:54:39,960 --> 00:54:42,759
Speaker 3: you go, call it to me. From Trump's own break,

1126
00:54:42,840 --> 00:54:43,239
Speaker 3: there was.

1127
00:54:43,960 --> 00:54:45,640
Speaker 1: A few things to say about that. First of all,

1128
00:54:45,760 --> 00:54:48,560
Speaker 1: that feels to me like that New York Times analysis

1129
00:54:48,680 --> 00:54:52,439
Speaker 1: of how he's gotten more rambly and more like disjointed

1130
00:54:52,600 --> 00:54:54,719
Speaker 1: and angrier and all these things like that.

1131
00:54:54,760 --> 00:54:56,719
Speaker 3: He oh of course read that.

1132
00:54:57,200 --> 00:54:59,359
Speaker 1: As much as he loves to hate you New York Times,

1133
00:54:59,400 --> 00:55:01,359
Speaker 1: he's always been sort of like obsessed with what these

1134
00:55:01,440 --> 00:55:04,520
Speaker 1: you know, prestigious mainstream outlets have to say. So that's

1135
00:55:04,560 --> 00:55:07,440
Speaker 1: one thing. The other thing is, my sixteen year old

1136
00:55:07,480 --> 00:55:09,560
Speaker 1: told me soccer that it's a thing on TikTok where

1137
00:55:09,560 --> 00:55:11,880
Speaker 1: they say that Trump talks kind of like he's like

1138
00:55:11,880 --> 00:55:15,200
Speaker 1: a sixteen year old girl. So with the basically truthful

1139
00:55:15,239 --> 00:55:19,120
Speaker 1: thing like being like, yeah, I'm like a basically truthful person.

1140
00:55:20,760 --> 00:55:23,200
Speaker 2: I had not thought about that either. I don't spend

1141
00:55:23,200 --> 00:55:24,560
Speaker 2: a lot of time with sixteen year old girls, but

1142
00:55:24,640 --> 00:55:27,480
Speaker 2: it does make sense. He certainly doesn't make sense basically a.

1143
00:55:27,480 --> 00:55:31,040
Speaker 1: Truthful person, and like Kamala Harris is just she's mean

1144
00:55:31,200 --> 00:55:33,920
Speaker 1: and nasty, and it's like, wow, that actually kind of

1145
00:55:33,920 --> 00:55:35,000
Speaker 1: fits fit.

1146
00:55:35,120 --> 00:55:37,800
Speaker 2: I mean, one of Trump's great strengths is he speaks

1147
00:55:37,920 --> 00:55:39,880
Speaker 2: in incredibly simple sentences.

1148
00:55:39,960 --> 00:55:42,759
Speaker 3: I saw. I remember back in twenty sixteen. People would criticize.

1149
00:55:42,440 --> 00:55:44,160
Speaker 2: Him for that though, like Trump speaks at a third

1150
00:55:44,200 --> 00:55:46,279
Speaker 2: grade reading level or whatever, and I was like, well,

1151
00:55:46,320 --> 00:55:48,200
Speaker 2: you know, you should look at the average reading level

1152
00:55:48,239 --> 00:55:50,479
Speaker 2: of America, because that's usually a good idea.

1153
00:55:50,600 --> 00:55:52,680
Speaker 1: I do think it's fair to say that his communication

1154
00:55:52,920 --> 00:55:55,000
Speaker 1: is not as Sharpert Chris. But if you go back

1155
00:55:55,040 --> 00:55:57,239
Speaker 1: and listen to him in twenty sixteen, especially if you

1156
00:55:57,280 --> 00:56:02,120
Speaker 1: compare his debate performance versus Hillary Clinton in comparison to

1157
00:56:03,000 --> 00:56:08,160
Speaker 1: his matchup versus Kamala Harris, he was way more crisp

1158
00:56:08,280 --> 00:56:11,000
Speaker 1: to the point able to more effectively do the weave,

1159
00:56:11,120 --> 00:56:13,960
Speaker 1: to stay on topic, et cetera, versus you know, in

1160
00:56:14,040 --> 00:56:17,840
Speaker 1: the Kamala Harris matchup, he was easily thrown off course,

1161
00:56:17,920 --> 00:56:20,440
Speaker 1: distracted from the main points he was supposed to hit.

1162
00:56:20,520 --> 00:56:22,080
Speaker 1: In fact, at the very end of the debate, you

1163
00:56:22,120 --> 00:56:24,080
Speaker 1: saw it sort of like click, oh shit, I was

1164
00:56:24,120 --> 00:56:26,400
Speaker 1: supposed to say that she's just like Joe Biden. I

1165
00:56:26,440 --> 00:56:28,000
Speaker 1: was supposed to hit her on these things. Let me

1166
00:56:28,040 --> 00:56:30,360
Speaker 1: just get it in here, you know, before the buzzer.

1167
00:56:31,000 --> 00:56:32,480
Speaker 1: So I do think it's fair to say that he

1168
00:56:32,560 --> 00:56:34,720
Speaker 1: is not as crisp as he was in twenty sixteen,

1169
00:56:34,760 --> 00:56:37,200
Speaker 1: that he's not as able to pull off the weave

1170
00:56:37,280 --> 00:56:38,040
Speaker 1: as he once was.

1171
00:56:38,280 --> 00:56:40,200
Speaker 2: Had some moments there, well, I don't know, you weren't

1172
00:56:40,200 --> 00:56:41,680
Speaker 2: there for the snake, which I had to sit through

1173
00:56:41,719 --> 00:56:49,359
Speaker 2: many times, used to memory. But he had a moment

1174
00:56:49,400 --> 00:56:52,000
Speaker 2: where he talked about nicknames, which is really funny because

1175
00:56:52,000 --> 00:56:54,160
Speaker 2: he was like, you have to make the nickname pew

1176
00:56:55,080 --> 00:56:57,759
Speaker 2: like actually hit. But he acknowledges some of his best

1177
00:56:57,840 --> 00:56:59,760
Speaker 2: nicknames were from twenty sixteen.

1178
00:56:59,800 --> 00:57:00,719
Speaker 3: Yeah, in the primary.

1179
00:57:00,840 --> 00:57:03,319
Speaker 1: He has not had a nickname hit really for a while.

1180
00:57:03,400 --> 00:57:06,200
Speaker 2: Yeah, it's like, Comrade Kamala, He's like, you know, it's

1181
00:57:06,200 --> 00:57:08,120
Speaker 2: hard to say, it's difficult.

1182
00:57:09,800 --> 00:57:11,160
Speaker 1: Which no one even understood.

1183
00:57:11,239 --> 00:57:13,120
Speaker 2: Well, he only he hasn't really said that one. That

1184
00:57:13,160 --> 00:57:16,320
Speaker 2: one was more via text. He's a few times as

1185
00:57:16,360 --> 00:57:20,160
Speaker 2: he but I mean, clearly isn't isn't caught on Tim,

1186
00:57:20,240 --> 00:57:22,080
Speaker 2: It's okay, it doesn't.

1187
00:57:22,280 --> 00:57:25,720
Speaker 3: It doesn't hit, it doesn't. Lion Ted was just so good.

1188
00:57:25,880 --> 00:57:28,480
Speaker 2: I can barely describe it to people about how much

1189
00:57:28,520 --> 00:57:29,840
Speaker 2: it hit at that time.

1190
00:57:30,200 --> 00:57:31,400
Speaker 3: Even Little Marco.

1191
00:57:31,360 --> 00:57:35,200
Speaker 1: Little Marco, yeah, like legendary.

1192
00:57:35,320 --> 00:57:38,080
Speaker 2: He tried the same. Sleepy Joe. I mean it was good,

1193
00:57:38,080 --> 00:57:39,960
Speaker 2: but I don't know for some reason it didn't. It

1194
00:57:39,960 --> 00:57:42,720
Speaker 2: didn't quite hit as well. But this this time around,

1195
00:57:43,000 --> 00:57:44,880
Speaker 2: he doesn't seem to be there. He even said he's like, well,

1196
00:57:44,960 --> 00:57:46,480
Speaker 2: some of my best nick names, I can't talk about

1197
00:57:46,480 --> 00:57:47,840
Speaker 2: them anymore because they're all my friends.

1198
00:57:47,840 --> 00:57:50,080
Speaker 1: Now that's funny.

1199
00:57:50,120 --> 00:57:50,480
Speaker 3: It was good.

1200
00:57:50,560 --> 00:57:53,400
Speaker 1: I think Sleepy Joe. I mean it was. It was

1201
00:57:53,440 --> 00:57:56,000
Speaker 1: good in one sense because it's sort of like Landon

1202
00:57:56,200 --> 00:57:59,040
Speaker 1: was accurate. Like the reason Crooked Hillary was so devastating

1203
00:57:59,080 --> 00:58:02,280
Speaker 1: is because people are like, she's correct, she like it. Yeah, literally,

1204
00:58:02,320 --> 00:58:05,440
Speaker 1: And I think Sleepy Joe, like it just doesn't have

1205
00:58:05,520 --> 00:58:08,760
Speaker 1: the same uh like tuoperative energy, Like it's not as

1206
00:58:08,840 --> 00:58:12,320
Speaker 1: vicious as crooked it. It doesn't bite. Just someone's sleepy. They're

1207
00:58:12,360 --> 00:58:14,440
Speaker 1: not like a threat. They're not scary, they're just kind

1208
00:58:14,440 --> 00:58:16,640
Speaker 1: of like, Okay, they're a little drowsy, you know. So

1209
00:58:16,800 --> 00:58:19,040
Speaker 1: I don't know that it especially an election where people

1210
00:58:19,080 --> 00:58:21,520
Speaker 1: are like, all right, it's been so chaotic, could we

1211
00:58:21,600 --> 00:58:23,360
Speaker 1: just have maybe a little bit of sleepy Joe, Like,

1212
00:58:23,560 --> 00:58:25,120
Speaker 1: let's just have a little sleepy time and be a

1213
00:58:25,160 --> 00:58:27,760
Speaker 1: little calm. I think that's part of why it didn't work.

1214
00:58:27,840 --> 00:58:31,400
Speaker 1: But in this election, he really hasn't had any any

1215
00:58:31,600 --> 00:58:35,320
Speaker 1: w's on the nickname front. And yeah, back in twenty sixteen,

1216
00:58:35,360 --> 00:58:37,280
Speaker 1: that was that was the main skill. There was one

1217
00:58:37,280 --> 00:58:39,440
Speaker 1: other clip that I found funny from this podcast that

1218
00:58:39,480 --> 00:58:43,560
Speaker 1: we didn't pull, but answer was like, so, so, what's

1219
00:58:43,600 --> 00:58:46,680
Speaker 1: what happened with Mike pens caught up with Mike Pence lately?

1220
00:58:47,040 --> 00:58:50,919
Speaker 3: Yeah, with Mike Pence. Well, Mike, he's a nice man,

1221
00:58:51,080 --> 00:58:53,040
Speaker 3: but you know, he didn't do what he said to

1222
00:58:53,080 --> 00:58:54,160
Speaker 3: you what he should have done.

1223
00:58:54,200 --> 00:58:56,600
Speaker 1: And the column when people have already taken that part

1224
00:58:56,920 --> 00:58:59,520
Speaker 1: and spliced it into at least an internet ad where

1225
00:58:59,560 --> 00:59:02,200
Speaker 1: they have that clip and then they flashed to like

1226
00:59:02,720 --> 00:59:05,280
Speaker 1: January sixth, and you know, people running around the Capitol

1227
00:59:05,320 --> 00:59:07,320
Speaker 1: calling for him to be hung, et cetera. Et cetera.

1228
00:59:07,520 --> 00:59:10,880
Speaker 1: So I did think that that was like I think

1229
00:59:11,240 --> 00:59:14,240
Speaker 1: what he did well Andrew Schultz in this podcast is

1230
00:59:14,240 --> 00:59:16,320
Speaker 1: you didn't try to be something other than what he is,

1231
00:59:17,120 --> 00:59:19,160
Speaker 1: and yet he did still did some things that were

1232
00:59:19,520 --> 00:59:22,640
Speaker 1: that were effective that you know, didn't take Trump too seriously,

1233
00:59:22,760 --> 00:59:24,920
Speaker 1: that did treat him just like he was another person

1234
00:59:24,920 --> 00:59:26,720
Speaker 1: who was going to come on the podcast that they

1235
00:59:26,720 --> 00:59:29,360
Speaker 1: were going to screw around with. And you know, I

1236
00:59:29,400 --> 00:59:32,720
Speaker 1: think that was that was a wise and smart approach,

1237
00:59:33,160 --> 00:59:36,680
Speaker 1: and it's going to we're going to have a whole this. Obviously,

1238
00:59:36,720 --> 00:59:39,320
Speaker 1: politicians in the past, they've gone on podcasts. They you know,

1239
00:59:39,360 --> 00:59:41,760
Speaker 1: even in the Obama era he was doing like interviews

1240
00:59:41,760 --> 00:59:42,640
Speaker 1: with YouTubers and.

1241
00:59:42,760 --> 00:59:44,440
Speaker 3: Whatever Marmarage's podcasts, remember that.

1242
00:59:44,560 --> 00:59:46,040
Speaker 1: But I do feel like this is kind of a

1243
00:59:46,080 --> 00:59:49,760
Speaker 1: breakout cycle in terms of podcasts being central to a strategy,

1244
00:59:49,880 --> 00:59:52,880
Speaker 1: because you've had so many cord cutters young Like, you're

1245
00:59:52,920 --> 00:59:56,920
Speaker 1: not going to reach young people going on any of

1246
00:59:57,040 --> 00:59:59,480
Speaker 1: the cable news shows or going on even with a

1247
00:59:59,480 --> 01:00:02,560
Speaker 1: Stephen c there, which still reaches a large audience, but

1248
01:00:02,640 --> 01:00:05,600
Speaker 1: not a young audience. So the best you would get

1249
01:00:05,640 --> 01:00:07,360
Speaker 1: on that is some kind of a viral moment that

1250
01:00:07,400 --> 01:00:10,560
Speaker 1: gets clipped up and put on TikTok. So I do

1251
01:00:10,600 --> 01:00:14,800
Speaker 1: feel like this is the first election where the choosing

1252
01:00:14,880 --> 01:00:19,120
Speaker 1: these podcasts to target some demographic group, whethers young men

1253
01:00:19,160 --> 01:00:21,720
Speaker 1: or young women, where that has really become a central

1254
01:00:21,760 --> 01:00:24,720
Speaker 1: part of both campaign strategies. But certainly the Trump campaign

1255
01:00:24,720 --> 01:00:26,360
Speaker 1: has leaned into it more than the Hairs camp.

1256
01:00:26,480 --> 01:00:29,040
Speaker 2: I was just reading watching post analysis this morning about

1257
01:00:29,040 --> 01:00:32,360
Speaker 2: how central the young male thesis is to the Trump campaign,

1258
01:00:32,480 --> 01:00:35,520
Speaker 2: how not only is the campaign rung by dudes, but

1259
01:00:36,040 --> 01:00:38,960
Speaker 2: very much is like a dude's rock campaign. By the way,

1260
01:00:39,040 --> 01:00:41,720
Speaker 2: I still think that is a very effective strategy. As

1261
01:00:41,720 --> 01:00:44,160
Speaker 2: I've said before, if you look at the main gains

1262
01:00:44,160 --> 01:00:49,439
Speaker 2: for Trump amongst racial minority groups, it's all within men.

1263
01:00:49,480 --> 01:00:51,640
Speaker 2: If anything, there's been more of a polarization. Young men

1264
01:00:51,680 --> 01:00:55,360
Speaker 2: are now more right wing than any previous younger generation,

1265
01:00:55,840 --> 01:00:57,200
Speaker 2: especially in the spread.

1266
01:00:57,280 --> 01:00:58,040
Speaker 3: With young women.

1267
01:00:58,120 --> 01:00:59,560
Speaker 2: So I've always said, I'm like, if you want the

1268
01:00:59,600 --> 01:01:02,080
Speaker 2: real tiracial coalition, it's just dudes.

1269
01:01:02,480 --> 01:01:03,600
Speaker 3: That's what you should be able to run on.

1270
01:01:03,640 --> 01:01:07,760
Speaker 2: I don't think it's necessarily good social sociologically, but electorally

1271
01:01:08,000 --> 01:01:10,960
Speaker 2: it could work. So clearly they think that that can work.

1272
01:01:11,080 --> 01:01:12,960
Speaker 2: Also with Kamala. I mean, I don't want to race

1273
01:01:13,040 --> 01:01:15,000
Speaker 2: like she did go on Call Her Daddy. I've seen

1274
01:01:15,000 --> 01:01:16,560
Speaker 2: a lot of people be like, oh, it didn't even

1275
01:01:16,600 --> 01:01:18,880
Speaker 2: get that many of views guys like Call Her Daddy's

1276
01:01:18,880 --> 01:01:21,280
Speaker 2: not video first podcast like it was a a It's

1277
01:01:21,320 --> 01:01:24,320
Speaker 2: an audio podcast that predominantly is listened to and or

1278
01:01:24,360 --> 01:01:26,960
Speaker 2: downloaded too. I'd be willing to bet it was in

1279
01:01:27,000 --> 01:01:29,880
Speaker 2: the millions. I mean the average download that they're getting

1280
01:01:29,960 --> 01:01:31,600
Speaker 2: is like five million a week or something.

1281
01:01:31,600 --> 01:01:34,520
Speaker 3: That's crazy. It's the second largest podcast in the freaking world.

1282
01:01:34,600 --> 01:01:37,280
Speaker 1: So what you I'm sure predominantly young women.

1283
01:01:37,360 --> 01:01:40,240
Speaker 3: It's almost all young women. So it's like that.

1284
01:01:40,400 --> 01:01:43,680
Speaker 1: And apparently like Bill Crystal, who was did you see that?

1285
01:01:43,720 --> 01:01:44,479
Speaker 3: Wait? What is this?

1286
01:01:45,520 --> 01:01:50,040
Speaker 1: There is some political playbook or some some piece about

1287
01:01:50,160 --> 01:01:52,880
Speaker 1: the Call Her Daddy thing that they had to disclose, like, oh,

1288
01:01:52,880 --> 01:01:53,720
Speaker 1: Bill Crystal's a.

1289
01:01:53,640 --> 01:01:58,160
Speaker 2: Founding member of the Daddy Gang. Oh my god, so

1290
01:01:58,920 --> 01:02:00,000
Speaker 2: wo I didn't even know.

1291
01:02:00,360 --> 01:02:05,720
Speaker 1: Yeah, yeah, yeah. The last thing that I'll say about

1292
01:02:05,760 --> 01:02:07,280
Speaker 1: about this, just to make like trying to make a

1293
01:02:07,320 --> 01:02:10,760
Speaker 1: substance to political point here, is that the polling pretty

1294
01:02:10,760 --> 01:02:14,720
Speaker 1: consistently shows that Kamala is leading. When you talk about

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01:02:14,760 --> 01:02:20,000
Speaker 1: the voters who show up every election. She's leading among those,

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01:02:20,080 --> 01:02:22,160
Speaker 1: like I'm there in the midterms, I'm there for the

1297
01:02:22,200 --> 01:02:25,280
Speaker 1: special elections, I'm there certainly for every presidential election, I'm

1298
01:02:25,280 --> 01:02:28,360
Speaker 1: there for the problem. Like that voter Kamala Harris is

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01:02:28,440 --> 01:02:32,440
Speaker 1: leading with the less frequent voters is where Trump has

1300
01:02:32,480 --> 01:02:35,840
Speaker 1: an edge. And certainly the like young male demographic would

1301
01:02:35,840 --> 01:02:39,720
Speaker 1: be a classic like less frequent voter type of demographic,

1302
01:02:39,800 --> 01:02:42,040
Speaker 1: and that's what they're really counting on, is to to

1303
01:02:42,120 --> 01:02:45,920
Speaker 1: turn out those less frequent voters UH to come and

1304
01:02:45,960 --> 01:02:49,240
Speaker 1: support Donald Trump. So time will tell. I mean, certainly

1305
01:02:49,360 --> 01:02:51,959
Speaker 1: past indications when Trump is on the ballot, it usually

1306
01:02:52,040 --> 01:02:54,560
Speaker 1: leads to very high turnout, so you know, maybe that

1307
01:02:54,840 --> 01:02:57,919
Speaker 1: works out for him. On the other hand, if you're

1308
01:02:58,040 --> 01:03:01,760
Speaker 1: going to have that kind of a strategy where you're

1309
01:03:01,800 --> 01:03:04,520
Speaker 1: trying to turn out less frequent voters, I do think

1310
01:03:04,560 --> 01:03:07,880
Speaker 1: that having a more effective field operation they reportedly have

1311
01:03:08,000 --> 01:03:10,160
Speaker 1: and more money for an effective field operation, which they

1312
01:03:10,160 --> 01:03:13,520
Speaker 1: also are lacking in maybe a critical piece because if

1313
01:03:13,560 --> 01:03:15,960
Speaker 1: you're trying to you know, got to remind someone you

1314
01:03:16,720 --> 01:03:18,840
Speaker 1: got to contact is did you turn out? What's your

1315
01:03:18,840 --> 01:03:20,720
Speaker 1: plan to turn out? Have you voted yet? Who do

1316
01:03:20,840 --> 01:03:23,880
Speaker 1: you like? That requires a lot of energy and effort,

1317
01:03:24,040 --> 01:03:26,560
Speaker 1: So you know that it's surprising to me that they

1318
01:03:26,560 --> 01:03:30,920
Speaker 1: haven't invested more in that turnout operation, that classic field operation,

1319
01:03:31,160 --> 01:03:33,480
Speaker 1: given how much their strategy is reliant on these less

1320
01:03:33,480 --> 01:03:34,120
Speaker 1: frequent voters.

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01:03:34,120 --> 01:03:37,400
Speaker 2: Absolutely, yeah, I mean I think it will be. Look,

1322
01:03:37,440 --> 01:03:39,480
Speaker 2: I mean religious, I can't. I can't wait at this

1323
01:03:39,520 --> 01:03:41,440
Speaker 2: point now for election night, just because there's been so

1324
01:03:41,560 --> 01:03:45,200
Speaker 2: many like theories and tests that I'm so interested in, Like,

1325
01:03:45,280 --> 01:03:46,919
Speaker 2: at this point, I just want to see the data.

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01:03:47,000 --> 01:03:48,720
Speaker 3: I want to see what actually happens.