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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. But enough with that,

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Speaker 2: let's get to the show. Good morning, everybody, Happy Thursday.

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Speaker 2: We have an amazing show for everybody today. What do

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Speaker 2: we have, Crystal, Indeed we do.

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Speaker 1: We got a lot of post debate reaction to bring you,

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Speaker 1: including from former President Trump kind of having a bit

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Speaker 1: of a meltdown on Fox and Friends and also indicating

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Speaker 1: he may not be interested in another debate, even when

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Speaker 1: hosted by Fox News hosts, So we'll bring you all

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Speaker 1: of that. We also have some voter reaction after the debate,

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Speaker 1: the post debate polls, and RFK Junior being a little

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Speaker 1: surprisingly honest about some of Trump's shortcomings during the debates.

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Speaker 1: So we've got all of that where You've also got

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Speaker 1: a panel we're excited to talk to you about the

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Speaker 1: Taylor Swift endorsement, whether or not it won't matter. And

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Speaker 1: also a conversation we have been wanting to have for

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Speaker 1: a while about the massive gen Z gender gap, So

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Speaker 1: we got two young folks to come on.

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Speaker 3: Two gen zs.

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Speaker 1: It's right a Democrat or Republican to talk about what

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Speaker 1: they think is going on there, So that should be

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Speaker 1: an interesting conversation. We also wanted to take a look

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Speaker 1: at this massive case against Google, another anti trust case

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Speaker 1: against Google, what it could mean for the tech giant.

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Speaker 1: And also a really disturbing conflict of interest between the

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Speaker 1: lead lawyer for Google is actually a top advisor, So

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Speaker 1: a lawyer for Google against the government, the DOJ, the

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Speaker 1: federal government is also a top advisor to Kamala Harris.

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Speaker 1: So some deep concerns there about just that reality and

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Speaker 1: what it might mean for her and her approach to

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Speaker 1: anti trust cases were she to win the presidency.

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Speaker 3: Yeah, it'll be interesting to see how that comes out.

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Speaker 2: Now, before we get to that, thank you to all

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Speaker 2: of our premium subscribers and those who signed up during

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Speaker 2: the debate stream to ask some questions.

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Speaker 3: That was a lot of fun.

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Speaker 2: So we want to participate in future AMA which are

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Speaker 2: weekly and they drop exclusively for premiums on locals. You

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Speaker 2: can go ahead and sign up at breakingpoints dot com

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Speaker 2: to get.

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Speaker 3: Access to those exclusive ams.

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Speaker 2: But as Crystal said, let's go ahead and start with

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Speaker 2: Donald Trump and his post debate reaction kind of been

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Speaker 2: all over the place and what he's acquiescing to you

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Speaker 2: or not will he do another debate?

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Speaker 3: He's trying to tease it.

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Speaker 2: He went to familiar territory on Fox and Friends, and

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Speaker 2: Fox and Friends of course, was trying to massage his

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Speaker 2: ego a little bit around the debate. But he kind

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Speaker 2: of even shocked them when they were like, well, oh,

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Speaker 2: it's okay you could do a debate here on Fox, right,

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Speaker 2: mister President, And He's like, well, maybe I actually don't

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Speaker 2: like some of the moderators that you guys would have.

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Speaker 3: Let's take a listen to what Trump had to say.

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Speaker 4: And I know that last night Fox News offered sent

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Speaker 4: letters to your campaign and her campaign offering three dates

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Speaker 4: of debates moderated by Martha and Brett. One is October

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Speaker 4: ninth in Arizona.

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Speaker 5: Well, I wouldn't want to have Mauthor and Brett. I'd

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Speaker 5: love to have somebody else other than Martha and Brett.

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Speaker 5: I'd love to have Frankly Sean or Jesse, Laura, Uh,

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Speaker 5: you know somebody else. Let's give Let's give other people

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Speaker 5: the shot. But I didn't think Martha and Brett were

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Speaker 5: good last night. Jesse was. Jesse was fantastic last night.

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Speaker 5: What he said, Jesse really got it. Jesse said Trump

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Speaker 5: won that debate. That was, we won that debate by

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Speaker 5: a lot. Now I wouldn't want Matha involved, Okay, I would,

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Speaker 5: mister prest. I would take some others.

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Speaker 4: So these are the dates that Fox offered your campaign

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Speaker 4: in Harris's campaign. October ninth in Arizona, October fifteenth in Georgia,

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Speaker 4: October sixteenth in North Carolina. Of course, the big question

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Speaker 4: is would you want the microphones on when when you're

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Speaker 4: not you know, when the other person is talking. You

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Speaker 4: want both microphones on at all times.

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Speaker 5: Well, she wanted a moment like she did with Mike Pence.

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Speaker 5: But I actually got that moment too. I won that too,

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Speaker 5: because she was talking while I was speaking quite a bit. Actually,

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Speaker 5: she didn't know that people weren't listening too much or

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Speaker 5: hear but I was hearing her and I had little

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Speaker 5: fun with that. But look, here's the story when two

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Speaker 5: fighters fight and one loses, the first thing they do

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Speaker 5: is ask for a debate, or they asked for a fight,

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Speaker 5: so in this case, the debate. So we had two

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Speaker 5: people they lost very badly. The first thing they did

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Speaker 5: is ask for a debate, because that's what when a

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Speaker 5: fighter loses, he says, I want to rematch. I want

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Speaker 5: to rematch, say always the losing person, the fighter, the debater,

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Speaker 5: they always ask for a rematch. I won the debate

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Speaker 5: according to c Span by a lot, according to every

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Speaker 5: single I think we had fourteen polls that we said

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Speaker 5: to you, everyone had me winning the debate. So I

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Speaker 5: don't want to do another debate. R.

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Speaker 4: So you do right at the end, as I was interrupting,

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Speaker 4: so you don't know if you want to do another debate.

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Speaker 4: It sounds like you're a no.

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Speaker 5: Well, I'd be less inclined to because we had a

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Speaker 5: great night. We won the debate. We had a terrible

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Speaker 5: a terrible network. I think they were terrible and they

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Speaker 5: should be in embarrassed. I mean, they kept correcting me,

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Speaker 5: and what I said was largely right, or I hope

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Speaker 5: it was right, but what they said was absolutely wrong.

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Speaker 5: The other you know what she said and they refused

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Speaker 5: to correct I even complained a couple of times, why

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Speaker 5: aren't you correcting them? Look, they should have corrected six

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Speaker 5: or seven times. She told it outright, Lie another one

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Speaker 5: who's Project twenty twenty five. They know it has nothing

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Speaker 5: to do with me. She mentioned it all night long,

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Speaker 5: and they refuse to correct it, and everybody, I haven't.

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Speaker 5: I have nothing to do with Project twenty twenty. I

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Speaker 5: was just what they did. But I am not inclined

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Speaker 5: to do it because I want the debate by a lot.

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Speaker 5: But I think we let it settle in and let's

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Speaker 5: see what happens.

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Speaker 3: All right.

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Speaker 2: So that was completely unedited, so you guys a'll get

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Speaker 2: the entire context of that.

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Speaker 3: Trump is.

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Speaker 2: Look, I mean, he's flailing a little bit here. He's like,

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Speaker 2: he doesn't know if he wants to do the Fox debate.

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Speaker 2: He's asking for these moderators who he doesn't even like.

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Speaker 2: The Brett Bayer and Martha McCowan. By the way, Brett

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Speaker 2: and Martha were on the host election analysis. That's what

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Speaker 2: he's upset about. Yes, and they were like, hey, I

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Speaker 2: don't think that Trump, you know, did all that well

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Speaker 2: or I don't even know if they gave opinion analysis.

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Speaker 2: From what I saw, they were accurately pointing out that

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Speaker 2: he was taking debate or they were covering snap polling

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Speaker 2: that showed that people who watched the debate generally did

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Speaker 2: not think he did all that.

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Speaker 1: Yeah, so botch news outside of voter panel, which overwhelmed me.

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Speaker 3: Underside of voter panel and Brit Hume.

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Speaker 2: I mean, these people are not, like, you know, liberals, right,

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Speaker 2: So anyway he was reacting to that, that was just.

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Speaker 1: A small point, like how humiliating and embarrassing for you

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Speaker 1: not to stand up for your colleagues. I was like,

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Speaker 1: I just I mean, it's kind of outrageous, Like there

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Speaker 1: he is smearing Martha McCallum and bare which whatever you

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Speaker 1: think about him, Like these are your colleagues that you

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Speaker 1: work with, and you're just like sit there and take

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Speaker 1: I don't know, it's just it's kind of.

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Speaker 6: Kind of low.

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Speaker 1: And then it's so pathetic he wants the debate to

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Speaker 1: be hosted by Sean Hannity or Josey Waters.

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Speaker 6: Are you kidding me?

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Speaker 1: Like that is an absolute absurdity. So look, you honestly

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Speaker 1: called it right after the debate. You're like, I don't

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Speaker 1: know if do another one, and you know, he certainly

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Speaker 1: he doesn't know if he's going to do another one either,

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Speaker 1: which I think is foolish on his part. I think

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Speaker 1: he should because I think it's unlikely that you would

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Speaker 1: have another outcome that was so lopsided as this one was.

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Speaker 1: I think if you did it again, you would likely

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Speaker 1: have He probably would prepare better. You know, this time

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Speaker 1: he clearly didn't take her seriously, fell into every single trap,

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Speaker 1: you know, completely forgot about what message he was supposed

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Speaker 1: to be delivering until like the last five minutes of

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Speaker 1: the debate. That's unlikely to repeat itself. But you know,

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Speaker 1: I think he just can't handle the risk of being humiliated.

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Speaker 3: I would flip it onto Kamala too.

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Speaker 2: I mean, what is the likelihood that she worked rise

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Speaker 2: to the occasion again, It's not actually that high. We've

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Speaker 2: seen her do multiple repeat performances usually doesn't go all

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Speaker 2: that well.

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Speaker 3: So if you this was her best.

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Speaker 1: This was the better likely to have a reversion to

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Speaker 1: the mean for her and reversion to the meme for him,

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Speaker 1: which would be an improved performance.

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Speaker 3: Yes, that's what I would.

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Speaker 2: I would I would demand a couple of more debates

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Speaker 2: just to keep her off of her toes.

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Speaker 3: Maybe the moderators won't be so favorable this time. Who knows.

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Speaker 2: You can try any you can inject all kinds of

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Speaker 2: chaos into it. You want to increase that likelihood. But

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Speaker 2: clearly he's feeling on the ropes. He continues to tease

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Speaker 2: whether he's going to do it or not. Here he

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Speaker 2: was yesterday asking answering a question about the debate.

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Speaker 3: Let's take a listen, and you're looking at it.

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Speaker 7: But you know, when you win, you don't really necessarily

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Speaker 7: have to do it a second time. You know, we'll see,

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Speaker 7: But we had a I know we had a great debate. Listen,

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Speaker 7: thank you very much. We need to change for you

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Speaker 7: to agree to a second date. Would you want different rules?

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Speaker 7: Would you want to do a format? Well, you know,

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Speaker 7: when you when you don't win, it's like.

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Speaker 5: A fighter, when a fighter is a man, fight gets

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Speaker 5: knocked out or loses the fight.

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Speaker 7: The first thing he says is we want to rematch.

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Speaker 7: So we won the debate, according to every pole, every

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Speaker 7: single pole, I think that are we going to do

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Speaker 7: a rematch? I just don't know, but we'll think about it.

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Speaker 7: Would you still do the one on ABC? You got

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Speaker 7: in September twenty even you'd propose that, and are you

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Speaker 7: still I would do NBC. I do Fox too. I

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Speaker 7: do Fox too. But right now we have to deman

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Speaker 7: whether or not we wanted to do. We had a

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Speaker 7: great night last night, and you see by the ball numbers,

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Speaker 7: it was really fantastic. Thank you very much, everybody.

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Speaker 3: All Right, so there you go.

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Speaker 2: We're sticking with the fighter analogy. Let's put this up

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Speaker 2: there on this I love.

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Speaker 6: I was like, yeah, I do NBC, but I don't know, but.

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Speaker 2: I want to decide that that might be one of

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Speaker 2: the most classic trump isms of all time. Let's put

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Speaker 2: this up there just so everybody understands. The first numbers

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Speaker 2: for the debate are absolutely massive. Revised now currently up

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Speaker 2: to some fifty seven point seventy five million viewers as

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Speaker 2: of this morning, it's actually passed sixty million, according to Nielsen.

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Speaker 2: And that is just the people who watch it on

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Speaker 2: terrestrial television. So if you add in the what tens

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Speaker 2: maybe you know, probably yeah, tens of millions who watched

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Speaker 2: it on YouTube, Twitter or anywhere else, then we're talking.

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Speaker 2: You know, some are almost half of the entire voting

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Speaker 2: age public watched the debate.

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Speaker 3: Which happened on Tuesday night.

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Speaker 2: That's actually a problem for Trump because more people watch

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Speaker 2: the debate with Kamala and Trump then watch the debate

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Speaker 2: of Trump versus Biden. Now, I think there's a lot

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Speaker 2: to be said about that. It's not just enthusiasm, it

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Speaker 2: was the level of interest. People were bored and mostly

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Speaker 2: fed up with the election ahead of that debate. That debate, Yeah,

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Speaker 2: Biden's drop out really invigorated, but not only a lot

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Speaker 2: of Democrats, but it made the race more interesting, which

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Speaker 2: is why a lot of people came to this. But

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Speaker 2: that's part of why Trump does need to do more debates.

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Speaker 2: You can't let that be the only lasting impression in

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Speaker 2: voter's minds going into the election. The obvious though for him,

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Speaker 2: you know, there's downside risk if he were to have

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Speaker 2: a similar performance, and he's doing well enough in the polls,

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Speaker 2: he probably thinks he could just stick to his current strategy.

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Speaker 3: On Kamali's side, clearly she has the incentive.

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Speaker 2: She's already asked for another debate, perhaps the NBC News debate,

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Speaker 2: but at the very least thinks the risk would be

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Speaker 2: worth it of where she is currently. I mean, I

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Speaker 2: don't think the race is fundamentally changed. Yet we haven't

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Speaker 2: seen any polling or any of that to indicate it.

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Speaker 2: But anytime some seventy million people out of one hundred

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Speaker 2: and fifty million people who can vote are watching something,

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Speaker 2: it's you know, it's going to have some impact. I

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Speaker 2: don't really know exactly what it is that's said. I mean,

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Speaker 2: I'll give the boat bear in the bowl case. The

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Speaker 2: bowl case would be Trump allegedly lost all debates according

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Speaker 2: to opinion polls against Hillary.

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Speaker 3: Clinton obviously won that election.

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Speaker 2: Biden, according to the polls that came out at the time,

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Speaker 2: won the debate again Donald Trump, and he barely won

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Speaker 2: that election. So you know, it's not like this is

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Speaker 2: make or break. But I wouldn't let it be the

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Speaker 2: very last impression, is the only thing I would say,

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Speaker 2: especially when things are so tight. Yeah, you don't want

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Speaker 2: to let every you don't want to want to let

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Speaker 2: even a marginal you know, risk like that come to

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Speaker 2: affect her overall performance. Because what if that is the

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Speaker 2: last impression that people have and Kamma and her campaign

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Speaker 2: can just run ads, you know, showing that all for

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Speaker 2: the next couple of months, fifty something days until the election.

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Speaker 2: There's too much risk out there, I think for him

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Speaker 2: to not do another one.

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Speaker 1: Yeah, and it could actually be make or break because

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Speaker 1: if you think about the margin in twenty sixteen and

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Speaker 1: you think about the margin in twenty twenty in terms.

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Speaker 6: Of the if you look at the electoral college.

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Speaker 1: Numbers, it looks like a landslide kind of in both times.

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Speaker 1: But we know, if you look at the number of

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Speaker 1: votes that separated Hillary Clinton from Donald Trump, it was

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Speaker 1: very narrowly that she lost in these key battle ground stations,

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Speaker 1: so he won the popular vote. And then in twenty twenty, again,

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Speaker 1: you know, very narrow victory for Joe Biden. That's just

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Speaker 1: the reality of the country. It's very likely to be

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Speaker 1: just as close, if not even somehow a little bit

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Speaker 1: closer this time around. So yeah, does it like, you know,

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Speaker 1: shift the poles by ten points. No, but we're talking

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Speaker 1: about a race where if you move the poles permanently

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Speaker 1: by half a point, that could be the difference between

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Speaker 1: winning all the swing states and losing all the swing states. So,

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Speaker 1: you know, the other question in my mind is you'll recall,

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Speaker 1: before Joe Biden got out of the race, he was

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Speaker 1: just basically delusional about what the polls showed, and his

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Speaker 1: advisors were shielding him from any negative data, and so

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Speaker 1: he was really convinced that whatever polls had him down,

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Speaker 1: which was like all of them, that they were not real.

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Speaker 1: And in reality, if you look at these like few

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Speaker 1: cherry picked national polls that ignored the battleground states, he

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Speaker 1: was still within the margin of error or whatever. So

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Speaker 1: he was delusional about his standing, and he was surrounded

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Speaker 1: with a bunch of yes men who did not want

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Speaker 1: to upset him, and so cherry picked the data that

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Speaker 1: would keep him happy. I wonder if Trump has a

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Speaker 1: similar level of delusion where he may not really understand

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Speaker 1: the reality of where the race is, or he may,

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Speaker 1: you know, based on his track record in twenty sixteen

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Speaker 1: twenty twenty of outperforming the polls, just assume that that's

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Speaker 1: going to happen again. And maybe it does, but that

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Speaker 1: is not a safe assumption whatsoever, especially given that in

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Speaker 1: the mid terms they undercounted the Democratic Party support. So

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Speaker 1: you know, he may be making strategic miscalculations based on

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Speaker 1: a flawed understanding of where he his position is in

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Speaker 1: this race at this point. With regard to Kamala Harris, Yeah,

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Speaker 1: they came out right away and were like, hey, let's

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Speaker 1: do it again. But you know, you could see them

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Speaker 1: that you could see them putting forward some debate criteria

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Speaker 1: that they kind of know is like a poison pill

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Speaker 1: for Trump, like that maybe he should accept, like, you know,

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Speaker 1: an NBC News debate with whoever whatever moderators are picked

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Speaker 1: for that network to perform, but that they feel pretty

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Speaker 1: confident he's not going to pick, so that they can

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Speaker 1: have the illusion of strength of like, look, we wanted

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Speaker 1: to do it, and he's the one that backed down,

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Speaker 1: but without really having a full intention to do it again.

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Speaker 6: Because listen from her perspective.

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Speaker 1: Frankly, I think the strategic calculus is it's probably better

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Speaker 1: just to let this one ride and not take the

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Speaker 1: risk of a bad debate moment closer to election day.

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Speaker 1: So you know, I think there's a lot of there's

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Speaker 1: a lot of gameplay playing going on here.

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Speaker 3: Of course, I can see it both ways.

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Speaker 2: I will say, in fairness, Trump has always been surrounded

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Speaker 2: by stick of fans who tell him whatever he wants. Here,

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Speaker 2: It's not like it's actually a thing all that different

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Speaker 2: than how things usually go.

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Speaker 1: I mean, Laura Lumer is not telling him the straight

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Speaker 1: truth Sager.

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Speaker 6: I mean, that's shocking.

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Speaker 2: Maybe she is, yes, certainly something to her on that

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Speaker 2: campaign plane. Well, CNN's Harry Anton, she usually does a

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Speaker 2: pretty good job. We relied on him in the past

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Speaker 2: for some good analysis. He generally will tell it straight,

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Speaker 2: I think in terms of how tied things are Trump's

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Speaker 2: favorability and more so, he had an interesting analysis here

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Speaker 2: in terms of who allegedly won pass debates and the margins.

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Speaker 3: Let's take a listen first.

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Speaker 8: Debate winner margins. Look at that she won by a

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Speaker 8: twenty six point margin, nearly matching Trump's margin over Biden

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Speaker 8: back in June when he won by thirty four points,

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Speaker 8: very similar to Joe Biden's margin of over Donald Trump

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Speaker 8: back in twenty of thirty two points, and significantly higher

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Speaker 8: than the twenty sixteen margin that Hillary Clinton had over

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Speaker 8: Donald Trump in their first debate just thirteen points. But

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Speaker 8: the bottom line here is debate watchers and they actually

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Speaker 8: leaned a little bit more Republican than the nation as

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Speaker 8: a whole, believed that Kamala Harris easily easily won this debate. Again,

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Speaker 8: the scientific term is she crushed former President Donald Trull. Right,

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Speaker 8: take a look at the last four times we had

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Speaker 8: a first debate, and look at those winners see a

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Speaker 8: rise in the polls. Yes, Romney in twenty twelve, Hillary

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Speaker 8: Clinton twenty sixteen, Joe Biden twenty twenty, and Donald Trump

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Speaker 8: earlier this year. They all saw rises in the polls

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Speaker 8: of two points or more. And given how close this

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Speaker 8: race is, naturally, given how close it is in the

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Speaker 8: swing states, do not be surprised if Kamala Harris jumps

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Speaker 8: out to a lead nationally, and don't be surprised if

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Speaker 8: she jumps out to a slight small lead in those

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Speaker 8: key battleground states. It should still probably be a close

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Speaker 8: race based upon history.

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Speaker 3: So he's predicting a bump there.

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Speaker 2: Nate Silver was a little bit more equivocal in the

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Speaker 2: analysis that I read. He's like, look, she did everything

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Speaker 2: she needed to do, but we didn't see necessarily a

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Speaker 2: bump come out for her from the convention.

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Speaker 3: Sure, you know, the convention is going to be.

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Speaker 2: A little bit noisy, just because that was at the

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Speaker 2: very same time that RFK Junior also dropped out of

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Speaker 2: the race, which was going to have an impact, especially

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Speaker 2: on the margins.

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Speaker 3: There's no way to know really what happened.

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Speaker 2: Obviously, this time around, you can't expect, you know, literally

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Speaker 2: something as much of a gift like that.

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Speaker 3: So we just don't know where things will stand here.

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Speaker 2: I just think generally, when I look at the number

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Speaker 2: and I see that level, I mean actually think this

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Speaker 2: is not necessarily a decent thing for Trump. High level

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Speaker 2: of specific engagement in this it's not necessarily good. It

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Speaker 2: was good whenever it was against Biden because against Biden,

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Speaker 2: low information, low engagement voters were much more pro Trump.

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Speaker 2: But this time around, I mean Democratic watch parties. This

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Speaker 2: is purely anecdotal, but there were no Biden signs where

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Speaker 2: I live, even though all those people were going to

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Speaker 2: vote for Biden. But the It's quote about Madam time

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Speaker 2: or bat dictatorship bad twenty twenty four signs.

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Speaker 3: The Libs are full force.

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Speaker 2: They've got banners, their windows are dressed, they are ready

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Speaker 2: to roll. The fundraising numbers haven't come out yet from Kamala.

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Speaker 2: I'm willing to bet it's an absolute monster. I'm not

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Speaker 2: going to say Trump isn't going to raise money as well,

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Speaker 2: but you know, just the disparity of the engagement right now,

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Speaker 2: betting on high turnout is not necessarily one that I

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Speaker 2: would say would have been good for Trump, even though

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Speaker 2: I would say that two months ago, this time around,

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Speaker 2: we do know those liberal voters, specifically people who were

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Speaker 2: very activated by Row versus Wade, the high level engagement.

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Speaker 2: They beat the hell out of Republicans back in twenty

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Speaker 2: twenty two, and they were a huge reason why they

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Speaker 2: overperformed expectations. So those high numbers and the high level

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Speaker 2: of certific engagement this time around, I think it might

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Speaker 2: actually be a bad sign.

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Speaker 3: For Trump right now.

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Speaker 6: Yeah, I think that's a good point. We showed before.

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Speaker 1: The voter registration data, which is just like among typically

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Speaker 1: democratic leaning groups, has shot through the roof since Kamala

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Speaker 1: Harris was you know, since Biden was replaced with Kamala Harris.

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Speaker 1: We're going to talk in the Taylor Swift blog about

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Speaker 1: how she appears to have driven some voter registration as well.

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Speaker 1: Now that's very excited about that too. And I would

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Speaker 1: just say, you know, this debate definitely permeated the popular consciousness,

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Speaker 1: like there's no doubt about it that, you know, judging

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Speaker 1: by the conversations that were happening at my kid's soccer

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Speaker 1: practices among people who are not particularly political, who have

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Speaker 1: never who I've never actually said any heard anything political

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Speaker 1: come out of them before, and they're wanting to talk

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Speaker 1: about leg what about that dogs and cats situation? So

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Speaker 1: and that was definitely that was probably the moment that

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Speaker 1: stuck the most in people's minds, just because it was

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Speaker 1: so absurd and outlandish and really captured, you know, the

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Speaker 1: way that Trump was totally off the rails, and also

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Speaker 1: the way that Kamala was able to throw him so

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Speaker 1: severely off his game for you know, after the first

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Speaker 1: maybe fifteen twenty minutes of the debate, where she just

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Speaker 1: really took control. So, you know, between the massively high

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Speaker 1: ratings and the fact that in the you know, the aftermath,

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Speaker 1: all these clips are circulating, They're on TikTok, they're on Twitter,

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Speaker 1: they're everywhere, people are chattering about it. I think it

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Speaker 1: was a significant event and I think it could have

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Speaker 1: a significant and lasting impact. And when I say significant,

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Speaker 1: I mean like one point. You know, I think she'll

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Speaker 1: probably get a couple points. Like I said before, I

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Speaker 1: think she'll probably return to you know, where she was

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Speaker 1: where she was up nationally pretty clearly by a few points,

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Speaker 1: where she had a little bit more of a lead

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Speaker 1: in the battleground states. I think we'll probably were turn

436
00:20:00,320 --> 00:20:02,840
Speaker 1: to that, which is still basically a coin flip election.

437
00:20:03,880 --> 00:20:06,040
Speaker 6: But you know, again, just.

438
00:20:06,000 --> 00:20:09,840
Speaker 1: Those little bit of shifts, if she's able to maintain,

439
00:20:10,080 --> 00:20:10,640
Speaker 1: really matter.

440
00:20:10,800 --> 00:20:10,960
Speaker 6: Now.

441
00:20:10,960 --> 00:20:14,240
Speaker 1: The one word of caution that I'll sound for Kamala

442
00:20:14,280 --> 00:20:17,399
Speaker 1: Harris is she really we talked, We talked about this.

443
00:20:17,600 --> 00:20:20,960
Speaker 1: Her strategy was very much, let me get under his skin,

444
00:20:21,440 --> 00:20:24,520
Speaker 1: let me remind people of who Donald Trump is and

445
00:20:24,560 --> 00:20:27,240
Speaker 1: some of his worst qualities that people were sick and

446
00:20:27,280 --> 00:20:29,040
Speaker 1: tired of, and that's why they tossed him out in

447
00:20:29,040 --> 00:20:32,600
Speaker 1: twenty twenty. She had another job that the voters wanted

448
00:20:32,640 --> 00:20:34,320
Speaker 1: to see her do, which was to really fill in

449
00:20:34,359 --> 00:20:37,800
Speaker 1: the blanks about who she is, how she would govern,

450
00:20:38,000 --> 00:20:40,880
Speaker 1: what her priorities are. You know, what is the Day

451
00:20:40,920 --> 00:20:45,680
Speaker 1: one agenda? That part she really still left very empty.

452
00:20:46,119 --> 00:20:49,240
Speaker 1: You know, it was not a major focus of She

453
00:20:49,800 --> 00:20:52,520
Speaker 1: talked some about policies, but the major focus was more

454
00:20:52,560 --> 00:20:55,719
Speaker 1: about let me get under Trump's skin. And so that

455
00:20:55,720 --> 00:20:58,520
Speaker 1: New York Times Siana pole that showed, you know a

456
00:20:58,520 --> 00:21:00,879
Speaker 1: lot of voters want to know more about her. I

457
00:21:00,880 --> 00:21:04,040
Speaker 1: don't know that she fully answered those questions. And so

458
00:21:04,520 --> 00:21:09,840
Speaker 1: while she certainly burnished her credibility in terms of being presidential,

459
00:21:09,880 --> 00:21:11,800
Speaker 1: being able to go toe to toe with him, you know,

460
00:21:11,840 --> 00:21:14,520
Speaker 1: sort of like filling the shoes, and people being able

461
00:21:14,520 --> 00:21:17,000
Speaker 1: to envision her in the role. I don't think she

462
00:21:17,080 --> 00:21:19,840
Speaker 1: still has answered those questions about well, who are you

463
00:21:20,240 --> 00:21:22,639
Speaker 1: really and who are you really going to govern as?

464
00:21:23,200 --> 00:21:26,719
Speaker 1: And you know, so she still has work to do

465
00:21:26,800 --> 00:21:29,440
Speaker 1: to define herself. That means that there's still an opening

466
00:21:29,840 --> 00:21:32,399
Speaker 1: for Trump to be able to define her in the

467
00:21:32,440 --> 00:21:35,199
Speaker 1: way that he wants to but completely failed to do

468
00:21:35,840 --> 00:21:39,600
Speaker 1: at this debate, And obviously time is relatively.

469
00:21:39,160 --> 00:21:39,720
Speaker 3: Short at this point.

470
00:21:39,760 --> 00:21:41,479
Speaker 2: As a very important point, there was a New York

471
00:21:41,520 --> 00:21:44,040
Speaker 2: Times story where they were like, some undecided voters weren't

472
00:21:44,080 --> 00:21:45,160
Speaker 2: necessarily sure.

473
00:21:45,080 --> 00:21:47,119
Speaker 3: And they picked up exactly on what you said.

474
00:21:47,400 --> 00:21:49,640
Speaker 2: They were like, hey, look, yeah, you definitely did better

475
00:21:49,640 --> 00:21:51,600
Speaker 2: against Trump, but what are you actually going to do?

476
00:21:51,640 --> 00:21:53,399
Speaker 3: I still don't know that much about you.

477
00:21:53,680 --> 00:21:58,119
Speaker 2: She's got a pretty major campaign schedule going into the weekend.

478
00:21:58,600 --> 00:22:00,680
Speaker 2: Apparently you know now that we have after nine eleven,

479
00:22:00,680 --> 00:22:03,240
Speaker 2: Trump is also hitting it. He's going to be in Tucson,

480
00:22:03,320 --> 00:22:06,000
Speaker 2: Harris will hold a rally in Charlotte, North Carolina. I

481
00:22:06,000 --> 00:22:08,440
Speaker 2: think we're learning a lot about that hours later, shall

482
00:22:08,760 --> 00:22:11,600
Speaker 2: fly to Greensboro. Second rally in the states of tube

483
00:22:11,760 --> 00:22:13,360
Speaker 2: rallies in the state of North Carolina.

484
00:22:13,760 --> 00:22:15,199
Speaker 3: It is fascinating just to see that.

485
00:22:15,320 --> 00:22:18,040
Speaker 2: I would never would have thought that North Carolina could

486
00:22:18,080 --> 00:22:20,760
Speaker 2: be a tipping point, but you know, not would have

487
00:22:20,880 --> 00:22:24,399
Speaker 2: necessarily thought that Arizona would be a Democratic state that

488
00:22:24,440 --> 00:22:27,040
Speaker 2: could go blue in twenty twenty. Like I've said, the

489
00:22:27,080 --> 00:22:30,560
Speaker 2: migration data makes a very good case for North Carolina

490
00:22:30,640 --> 00:22:32,720
Speaker 2: and for the exact type of residents that COMMA could

491
00:22:32,760 --> 00:22:35,560
Speaker 2: activate that have all moved probably more than one hundred

492
00:22:35,560 --> 00:22:38,440
Speaker 2: thousand people or so. On top of you got unfavorable

493
00:22:38,520 --> 00:22:41,040
Speaker 2: dynamics at the state level, you got a Democratic governor.

494
00:22:41,200 --> 00:22:43,680
Speaker 2: So I could see that I could see that being

495
00:22:43,720 --> 00:22:46,919
Speaker 2: the case, and it's clearly what they think is their

496
00:22:46,960 --> 00:22:49,840
Speaker 2: path two seventy is very different than Biden. But the

497
00:22:49,960 --> 00:22:52,720
Speaker 2: risk is still exactly what you said, especially to in

498
00:22:52,760 --> 00:22:54,720
Speaker 2: the state of Pennsylvania. I mean, the fact is that

499
00:22:54,800 --> 00:22:57,639
Speaker 2: things are just so tight and so locked there for

500
00:22:57,840 --> 00:23:01,720
Speaker 2: fifty to fifty that I'm very cures where that undecided

501
00:23:01,760 --> 00:23:05,439
Speaker 2: voter profile of who wants to learn more whether that

502
00:23:05,560 --> 00:23:08,639
Speaker 2: was enough for them to eventually come out, Because as

503
00:23:08,720 --> 00:23:10,320
Speaker 2: long as they still see her tied to Biden.

504
00:23:10,560 --> 00:23:12,440
Speaker 3: That's the biggest problem that she's Yeah. Thanks.

505
00:23:12,880 --> 00:23:16,680
Speaker 1: It's perplexing to me because the policies she initially laid

506
00:23:16,720 --> 00:23:20,080
Speaker 1: down in particular, are really popular, and they're kind of

507
00:23:20,119 --> 00:23:23,320
Speaker 1: a no brainer. You know, the price gouging stuff super

508
00:23:23,400 --> 00:23:26,320
Speaker 1: popular and obviously gets at this question of prices that

509
00:23:26,400 --> 00:23:30,320
Speaker 1: people are continue to be very concerned about. The housing

510
00:23:30,359 --> 00:23:34,600
Speaker 1: assistance really popular, getting prescription drug prices down really popular,

511
00:23:34,760 --> 00:23:38,040
Speaker 1: child tax credit, paid family leave, like there's a very

512
00:23:38,080 --> 00:23:41,720
Speaker 1: popular agenda there that she rolled out with some fanfare

513
00:23:42,119 --> 00:23:44,159
Speaker 1: and seems to have been kind of scared off of.

514
00:23:44,240 --> 00:23:46,800
Speaker 1: I mean, they really have retreated into this much more

515
00:23:46,920 --> 00:23:50,080
Speaker 1: like Biden feeling let me cater to the center and

516
00:23:50,119 --> 00:23:53,840
Speaker 1: not really say anything, not really promise anything direction, which

517
00:23:53,880 --> 00:23:58,000
Speaker 1: is you know, classic Democrat and is part of why

518
00:23:58,240 --> 00:24:01,359
Speaker 1: you know, they come so to losing elections or do

519
00:24:01,680 --> 00:24:04,959
Speaker 1: lose elections. So I don't know why she has gotten

520
00:24:05,440 --> 00:24:07,920
Speaker 1: I don't know why she's gotten shy about talking about

521
00:24:07,960 --> 00:24:10,760
Speaker 1: that and making real like, here's my top three priorities.

522
00:24:10,800 --> 00:24:12,719
Speaker 6: Do dot dot. It wouldn't be a hard thing to do.

523
00:24:12,800 --> 00:24:13,960
Speaker 6: It wouldn't have been a hard.

524
00:24:13,760 --> 00:24:17,399
Speaker 1: Thing to put that into the debate in the mix

525
00:24:17,520 --> 00:24:20,440
Speaker 1: with her baiting of Donald Trump. But again, it feels

526
00:24:20,520 --> 00:24:23,080
Speaker 1: like they've just fallen back into this sort of like

527
00:24:23,240 --> 00:24:26,200
Speaker 1: very Biden Esque rut. And look, it might be enough

528
00:24:26,280 --> 00:24:29,600
Speaker 1: Biden one, the press did well running that way in

529
00:24:29,800 --> 00:24:32,159
Speaker 1: the midterms, and so maybe they're looking at that and

530
00:24:32,160 --> 00:24:34,960
Speaker 1: they're like, look, this was a winning strategy two times

531
00:24:34,960 --> 00:24:35,360
Speaker 1: in a row.

532
00:24:35,400 --> 00:24:36,719
Speaker 6: Why we screw with it?

533
00:24:36,760 --> 00:24:39,639
Speaker 1: And I guess there's something to that, But you know,

534
00:24:39,680 --> 00:24:42,080
Speaker 1: they certainly seem to be going more after the like

535
00:24:42,320 --> 00:24:46,840
Speaker 1: Nikki Hayley voter, certainly than trying to further energize and

536
00:24:46,920 --> 00:24:50,760
Speaker 1: further turnout young, more progressive voters and especially voters who

537
00:24:50,800 --> 00:24:54,440
Speaker 1: are you know, also disgusted with the war on.

538
00:24:54,640 --> 00:24:55,880
Speaker 6: The people of Gaza.

539
00:24:56,040 --> 00:24:59,480
Speaker 1: So in any case, just some risks there going forward,

540
00:24:59,560 --> 00:25:01,880
Speaker 1: and we can go ahead and take a look at

541
00:25:02,280 --> 00:25:07,480
Speaker 1: at the polling data and where things stand. So some

542
00:25:07,520 --> 00:25:10,480
Speaker 1: of the networks did some little snap polling after the debate.

543
00:25:10,560 --> 00:25:13,520
Speaker 1: Some of them did some focused groups of undecided voters

544
00:25:13,560 --> 00:25:15,800
Speaker 1: to see what they thought instantly after the debate. Let's

545
00:25:15,840 --> 00:25:17,879
Speaker 1: take a listen to a little bit of what CNN's

546
00:25:18,000 --> 00:25:20,800
Speaker 1: undecided voter panel had to say, who do you think won?

547
00:25:20,880 --> 00:25:27,960
Speaker 9: By a show of hands? Former President Trump two, I'll

548
00:25:28,000 --> 00:25:32,520
Speaker 9: get it four, but tentative two over there? What about

549
00:25:32,600 --> 00:25:38,840
Speaker 9: Vice President Harris More hands? For those of you who

550
00:25:39,480 --> 00:25:42,520
Speaker 9: thought this debate could be determinative, how many of you

551
00:25:42,560 --> 00:25:45,040
Speaker 9: have made up your minds based on what you saw

552
00:25:45,400 --> 00:25:46,879
Speaker 9: tonight on stage in Philadelphia?

553
00:25:46,920 --> 00:25:49,040
Speaker 3: Raise your hands all right?

554
00:25:49,119 --> 00:25:50,000
Speaker 9: I want to ask you why.

555
00:25:50,720 --> 00:25:53,679
Speaker 10: I think it's important to remember that we are voting

556
00:25:53,800 --> 00:25:56,080
Speaker 10: for the leader of our country and not who we

557
00:25:56,280 --> 00:25:58,520
Speaker 10: like the most or who we want in our wedding party,

558
00:25:59,040 --> 00:26:02,920
Speaker 10: but who's actually going to make our country better. And

559
00:26:03,040 --> 00:26:06,440
Speaker 10: we're in an incredibly unique situation where we've had both

560
00:26:06,480 --> 00:26:08,720
Speaker 10: of the candidates in office before and we've gotten to

561
00:26:08,760 --> 00:26:11,560
Speaker 10: see what they would do, and when facts come to facts,

562
00:26:12,040 --> 00:26:14,000
Speaker 10: my life was better when Trump was in office. The

563
00:26:14,040 --> 00:26:18,000
Speaker 10: economy was higher, inflation was lower, things were better overall.

564
00:26:18,040 --> 00:26:23,240
Speaker 10: And now with Kamala's administration, things haven't been so fantastic.

565
00:26:23,640 --> 00:26:26,119
Speaker 6: And she's saying she can fix the problems that her

566
00:26:26,160 --> 00:26:28,560
Speaker 6: administration has caused. But I just don't know if I

567
00:26:28,600 --> 00:26:29,800
Speaker 6: can afford to take that risk.

568
00:26:29,960 --> 00:26:31,840
Speaker 9: Were you leaning towards the former president coming.

569
00:26:31,720 --> 00:26:33,360
Speaker 3: In to night, probably?

570
00:26:33,640 --> 00:26:35,399
Speaker 9: And did you a for him in twenty sixteen or

571
00:26:35,440 --> 00:26:36,959
Speaker 9: twenty twenty, I did so.

572
00:26:37,080 --> 00:26:38,720
Speaker 6: She had been a previous Trump voter.

573
00:26:39,000 --> 00:26:42,560
Speaker 1: She was leaning towards him but undecided on this CNN

574
00:26:42,640 --> 00:26:45,160
Speaker 1: voter panel. So you have, on the one hand, overwhelmingly

575
00:26:45,200 --> 00:26:48,120
Speaker 1: people like Gomulo one, but at least one voter raising

576
00:26:48,200 --> 00:26:52,199
Speaker 1: some concerns about the question Trump should have made at

577
00:26:52,240 --> 00:26:54,480
Speaker 1: the core of his debate performance, which is, are you

578
00:26:54,520 --> 00:26:55,920
Speaker 1: better off now than you were forty Yeah?

579
00:26:55,920 --> 00:26:58,320
Speaker 2: And this is a debate I have with Trump people

580
00:26:58,359 --> 00:27:00,800
Speaker 2: all the time. It's always like, let Trump beat Trump

581
00:27:00,840 --> 00:27:02,600
Speaker 2: and all of that, and I go, well, look, Trump

582
00:27:02,600 --> 00:27:06,399
Speaker 2: has been Trump, and it sometimes works and sometimes doesn't.

583
00:27:06,480 --> 00:27:07,320
Speaker 3: Overwhelmingly.

584
00:27:07,520 --> 00:27:09,439
Speaker 2: I mean, this is part of the issue is you

585
00:27:09,560 --> 00:27:12,000
Speaker 2: do hear from a lot of people like that woman

586
00:27:12,280 --> 00:27:13,879
Speaker 2: who are like, I like the policies.

587
00:27:14,119 --> 00:27:15,120
Speaker 3: He pisses me off.

588
00:27:15,359 --> 00:27:18,800
Speaker 2: Someone described him to me as an ugly plumber with

589
00:27:18,920 --> 00:27:21,840
Speaker 2: a massive ass crack, but a guy who does a

590
00:27:21,880 --> 00:27:24,119
Speaker 2: really good job, and so you just have to look away.

591
00:27:24,440 --> 00:27:26,480
Speaker 2: You got to look away from the crack and you

592
00:27:26,560 --> 00:27:28,879
Speaker 2: just let the man fix the pipes. And I was like,

593
00:27:28,920 --> 00:27:31,600
Speaker 2: you know, that is a very act way that a

594
00:27:31,800 --> 00:27:34,000
Speaker 2: lot of people think about Donald Trump.

595
00:27:34,800 --> 00:27:36,720
Speaker 3: That one stuck with me for a long time.

596
00:27:37,680 --> 00:27:41,920
Speaker 2: Yeah, and I think that that what Trump can do

597
00:27:42,359 --> 00:27:44,720
Speaker 2: sometimes is like I remember his very first date of

598
00:27:44,760 --> 00:27:46,280
Speaker 2: the Union, he was acting presidential.

599
00:27:46,480 --> 00:27:48,960
Speaker 3: Every once in a while he'll do I think it

600
00:27:49,000 --> 00:27:50,520
Speaker 3: was when George H. W. Bush died.

601
00:27:50,520 --> 00:27:52,600
Speaker 2: You know, he was like shockingly silent for like two

602
00:27:52,640 --> 00:27:55,560
Speaker 2: weeks and everyone couldn't believe it. Every once in a

603
00:27:55,560 --> 00:27:57,560
Speaker 2: while when he does that, his brutal rating actually does

604
00:27:57,600 --> 00:27:58,640
Speaker 2: take up quite a bit.

605
00:27:58,800 --> 00:28:00,240
Speaker 3: People have a lot of funness for.

606
00:28:00,240 --> 00:28:03,280
Speaker 2: The Trump era, at least in terms of their personal finances,

607
00:28:03,320 --> 00:28:04,880
Speaker 2: and the more that he could remind them of that

608
00:28:05,200 --> 00:28:07,639
Speaker 2: and not of chaos, he'd be better. One piece of

609
00:28:07,680 --> 00:28:10,560
Speaker 2: analysis I saw, which was really spot on, is and

610
00:28:10,600 --> 00:28:12,840
Speaker 2: I'm wondering if you agree, did not seem like Trump

611
00:28:12,920 --> 00:28:15,720
Speaker 2: was the incumbent in that debate. Everything was about him

612
00:28:16,000 --> 00:28:18,680
Speaker 2: or things that were controversies around him. It's like he's

613
00:28:18,720 --> 00:28:21,040
Speaker 2: been out of office for four years. Yeah, the best

614
00:28:21,040 --> 00:28:22,440
Speaker 2: he could have done was to be like, no, no, no,

615
00:28:22,640 --> 00:28:24,960
Speaker 2: I'm not in office. I'm running to get back into

616
00:28:25,000 --> 00:28:29,040
Speaker 2: office to run against the chaos of the Kamala Biden administration.

617
00:28:29,119 --> 00:28:31,359
Speaker 2: And so that's another thing where I think he really

618
00:28:31,560 --> 00:28:34,439
Speaker 2: screwed up and missed the opportunity to win over people

619
00:28:34,600 --> 00:28:36,840
Speaker 2: like her, because there's a lot more others like her

620
00:28:36,880 --> 00:28:38,160
Speaker 2: who probably went the other way.

621
00:28:38,280 --> 00:28:38,680
Speaker 3: You know, I.

622
00:28:38,720 --> 00:28:43,520
Speaker 1: Rewatched some of the debate, and the first fifteen minutes

623
00:28:43,640 --> 00:28:46,320
Speaker 1: or so before she really gets under his.

624
00:28:46,320 --> 00:28:47,960
Speaker 6: Skin, he's pretty unmissage.

625
00:28:48,080 --> 00:28:48,400
Speaker 3: He was good.

626
00:28:48,440 --> 00:28:51,320
Speaker 1: You know, he's getting the better of the of the exchanges.

627
00:28:51,400 --> 00:28:54,719
Speaker 1: She's a little nervous, clearly at the very beginning. And

628
00:28:54,760 --> 00:28:57,760
Speaker 1: then once she said that thing about his rallies, it

629
00:28:57,800 --> 00:29:01,560
Speaker 1: was over. Like the mind was like wiped clean, and

630
00:29:01,680 --> 00:29:04,520
Speaker 1: everything that he had practiced and planned to do in

631
00:29:04,520 --> 00:29:07,960
Speaker 1: the talk of it was gone. And then she was

632
00:29:08,000 --> 00:29:12,120
Speaker 1: calling every single shot. So yeah, I mean, he utterly

633
00:29:12,120 --> 00:29:15,000
Speaker 1: failed to try to land the points that he wanted

634
00:29:15,000 --> 00:29:18,240
Speaker 1: to land. That doesn't change some of the underlying dynamics

635
00:29:18,320 --> 00:29:21,680
Speaker 1: of the race ultimately. But you know, if we can

636
00:29:21,720 --> 00:29:25,280
Speaker 1: put up on the screen the CNN flash poll, which

637
00:29:25,320 --> 00:29:27,320
Speaker 1: was pretty consistent, I mean it was it was pretty

638
00:29:27,320 --> 00:29:32,520
Speaker 1: overwhelming that Harris outperformed Trump in this debate. Pre debate,

639
00:29:32,640 --> 00:29:36,000
Speaker 1: you had this same group of watchers split down the

640
00:29:36,040 --> 00:29:38,880
Speaker 1: middle as to who they expected to do better. So

641
00:29:38,920 --> 00:29:40,800
Speaker 1: it shows you it was like pretty evenly divided, going

642
00:29:40,840 --> 00:29:43,040
Speaker 1: and you had half say and they think Kamala would win.

643
00:29:43,120 --> 00:29:44,480
Speaker 6: Half say, and I thought Trump would win.

644
00:29:45,080 --> 00:29:49,080
Speaker 1: Post debate, sixty three percent said that Kamala Harris won

645
00:29:49,160 --> 00:29:51,040
Speaker 1: and thirty seven percent say that Trump won.

646
00:29:51,360 --> 00:29:53,560
Speaker 6: And maybe maybe most notable to me.

647
00:29:53,760 --> 00:29:56,760
Speaker 1: Was the fact that some thirty percent even of Republicans

648
00:29:56,800 --> 00:29:59,320
Speaker 1: were like, Yeah, she got him, she got him on

649
00:29:59,320 --> 00:30:01,960
Speaker 1: this one. There was a you know, it was a

650
00:30:02,080 --> 00:30:05,320
Speaker 1: very clear consensus that she got the better of him

651
00:30:06,000 --> 00:30:09,880
Speaker 1: that night. So, you know, is it ultimately going to matter?

652
00:30:10,480 --> 00:30:13,520
Speaker 1: I think the thing for part of why I think

653
00:30:13,560 --> 00:30:16,400
Speaker 1: this will be consequential is that Trump has such a

654
00:30:16,440 --> 00:30:22,560
Speaker 1: mystique around him as this showman, as teflon Dawn, someone

655
00:30:22,560 --> 00:30:25,600
Speaker 1: who just can you know, charge his way through anything

656
00:30:26,240 --> 00:30:29,360
Speaker 1: and you know, gets the better of anyone.

657
00:30:28,960 --> 00:30:31,520
Speaker 6: Who he goes toe to toe with. And here you

658
00:30:31,600 --> 00:30:32,400
Speaker 6: have this woman.

659
00:30:32,200 --> 00:30:35,080
Speaker 1: Who has been you know, very caricatured and at times

660
00:30:35,200 --> 00:30:38,480
Speaker 1: rightfully so, for being kind of incompetent and verbally incontinent

661
00:30:38,520 --> 00:30:40,920
Speaker 1: and all over the place, et cetera, et cetera. And

662
00:30:40,960 --> 00:30:44,240
Speaker 1: the fact that she was able to make him look

663
00:30:44,360 --> 00:30:48,080
Speaker 1: small and to really dominate him in the sense of

664
00:30:48,480 --> 00:30:51,080
Speaker 1: causing him to have to react to what she wanted

665
00:30:51,120 --> 00:30:54,560
Speaker 1: to talk about. You know, it really does puncture some

666
00:30:54,640 --> 00:30:58,680
Speaker 1: of the aura and some of the mystique around Donald Trump.

667
00:30:59,000 --> 00:31:01,680
Speaker 1: And that's part and you know, her whole strategy has

668
00:31:01,760 --> 00:31:04,320
Speaker 1: been different than the Hillary strategy, different than the Biden

669
00:31:04,400 --> 00:31:07,520
Speaker 1: strategy of trying to make him look small. So while

670
00:31:07,520 --> 00:31:11,360
Speaker 1: he is up there glowering and furious and seeing red

671
00:31:11,480 --> 00:31:13,840
Speaker 1: over you know, the rallies or whatever else she was

672
00:31:13,880 --> 00:31:17,680
Speaker 1: goading him on, she is laughing, she's above the fraight,

673
00:31:17,880 --> 00:31:21,400
Speaker 1: she's enjoying it. She's having a good time. And so's

674
00:31:21,560 --> 00:31:24,560
Speaker 1: that's part of why I do think that this debate

675
00:31:24,640 --> 00:31:28,240
Speaker 1: will end up being pretty consequential, is because it really

676
00:31:28,320 --> 00:31:31,560
Speaker 1: damaged a core part of his appeal and his mystique

677
00:31:31,560 --> 00:31:34,600
Speaker 1: in his aura that had made him so powerful in

678
00:31:34,640 --> 00:31:37,200
Speaker 1: American politics. And it's also part of why I think

679
00:31:37,240 --> 00:31:40,840
Speaker 1: this strategy that they have landed on, of the you know,

680
00:31:40,960 --> 00:31:44,320
Speaker 1: sort of dismissive like roll your eyes, not taking his bait,

681
00:31:44,560 --> 00:31:48,560
Speaker 1: has been so much more effective than when previously, you know,

682
00:31:48,640 --> 00:31:51,000
Speaker 1: whatever he threw out there, they were just chasing after

683
00:31:51,040 --> 00:31:54,000
Speaker 1: the ball, endlessly moralizing about it, et cetera, et cetera.

684
00:31:54,160 --> 00:31:55,760
Speaker 2: Yeah, and look, there's a lot of evidence to say

685
00:31:55,800 --> 00:31:57,440
Speaker 2: that this could be. Let's put the next one, please

686
00:31:57,520 --> 00:32:00,360
Speaker 2: up on the screen. What we see in terms of

687
00:32:00,440 --> 00:32:03,080
Speaker 2: the snap polling from Yugov who won the debate, Harris

688
00:32:03,120 --> 00:32:06,200
Speaker 2: forty three, Trump twenty eight, unsure thirty. I mean, the

689
00:32:06,280 --> 00:32:08,880
Speaker 2: unsure number is still, you know, relatively high, so it's

690
00:32:08,920 --> 00:32:13,280
Speaker 2: not like it was overwhelming, but it's still good. Yeah,

691
00:32:13,320 --> 00:32:16,360
Speaker 2: it's not good, all right. And then actually the next one.

692
00:32:16,600 --> 00:32:19,280
Speaker 2: This convinced me more than anything. And it's always funny

693
00:32:19,280 --> 00:32:21,640
Speaker 2: because I have to remind myself what I always sometimes

694
00:32:21,720 --> 00:32:24,000
Speaker 2: think is the most impactful part of the debate, that's

695
00:32:24,000 --> 00:32:26,840
Speaker 2: not what most people are The number one search topic

696
00:32:26,960 --> 00:32:31,360
Speaker 2: during the debate by state was abortion. The only state

697
00:32:31,360 --> 00:32:34,080
Speaker 2: where it was different was Ohio, and that was immigration,

698
00:32:34,560 --> 00:32:37,120
Speaker 2: maybe because they were searching things about Haitian migrants, if

699
00:32:37,120 --> 00:32:37,720
Speaker 2: I had to guess.

700
00:32:37,720 --> 00:32:38,920
Speaker 3: But my point.

701
00:32:38,680 --> 00:32:40,560
Speaker 2: Is just that if you look at all fifty states

702
00:32:40,800 --> 00:32:43,480
Speaker 2: in the country, forty nine out of fifty, abortion is

703
00:32:43,520 --> 00:32:46,800
Speaker 2: a number one search topic during that debate, possibly even

704
00:32:46,880 --> 00:32:49,959
Speaker 2: looking at the various positions on abortion, then that's a

705
00:32:50,000 --> 00:32:52,120
Speaker 2: massive win for the Democrats in my book.

706
00:32:51,920 --> 00:32:54,360
Speaker 1: That's right, And this almost doesn't matter what they're searching

707
00:32:54,360 --> 00:32:54,959
Speaker 1: about abortion.

708
00:32:55,000 --> 00:32:57,280
Speaker 2: I didn't get to challenge a Vita on this. We

709
00:32:57,320 --> 00:32:59,600
Speaker 2: have a segment coming up with her, but I guess

710
00:32:59,600 --> 00:33:00,000
Speaker 2: I'll pre.

711
00:33:02,600 --> 00:33:04,320
Speaker 3: Anyway just for scheduling purposes.

712
00:33:04,520 --> 00:33:07,240
Speaker 2: She she was bringing up late term abortion and all

713
00:33:07,280 --> 00:33:09,920
Speaker 2: of that, and I'm like, listen, whether you morally object

714
00:33:09,960 --> 00:33:12,600
Speaker 2: to late term abortion or not, on a polling level,

715
00:33:12,800 --> 00:33:15,400
Speaker 2: people don't care about it. At least they don't care

716
00:33:15,480 --> 00:33:18,600
Speaker 2: enough when presented with the converse of a six week

717
00:33:18,680 --> 00:33:22,960
Speaker 2: ban Republicans tried all throughout twenty twenty two. Democrats are

718
00:33:22,960 --> 00:33:25,200
Speaker 2: the real radicals on abortion. Democrats are the real radicals

719
00:33:25,200 --> 00:33:27,320
Speaker 2: on abortion. People don't care. They care a lot more

720
00:33:27,360 --> 00:33:30,160
Speaker 2: about the six to twelve weeks of the Row consensus

721
00:33:30,280 --> 00:33:33,600
Speaker 2: than they do about educationes of abortion that are after

722
00:33:33,720 --> 00:33:35,880
Speaker 2: what the sixth, between the six and the ninth month

723
00:33:36,160 --> 00:33:39,440
Speaker 2: of pregnancy. And I'm not even saying that I'm cool

724
00:33:39,440 --> 00:33:42,040
Speaker 2: with that. I'm or like, empirically, it's not about what

725
00:33:42,080 --> 00:33:44,960
Speaker 2: I think. It's about how people have responded to voting patterns.

726
00:33:44,960 --> 00:33:48,360
Speaker 2: And Trump trotted out all the twenty twenty two classics. Yeah,

727
00:33:48,360 --> 00:33:50,800
Speaker 2: they're the real radicals. It's about late term abortion. That's

728
00:33:50,800 --> 00:33:53,040
Speaker 2: why I'm not going to vote for Amendment four. Look

729
00:33:53,080 --> 00:33:55,840
Speaker 2: at the polling on Amendment four, it's overwhelmingly.

730
00:33:56,400 --> 00:33:57,320
Speaker 6: The Florida amendment.

731
00:33:58,240 --> 00:34:00,400
Speaker 1: First seemed to indicate he was going to side with

732
00:34:00,480 --> 00:34:02,640
Speaker 1: the pro choice side. Then there was a huge freak

733
00:34:02,680 --> 00:34:05,320
Speaker 1: out among the pro life community, flip flops within like

734
00:34:05,360 --> 00:34:07,880
Speaker 1: twenty four hours and says no, I am gonna exactly.

735
00:34:08,120 --> 00:34:09,960
Speaker 2: And actually, if you look at the polling, people have

736
00:34:10,040 --> 00:34:13,680
Speaker 2: become even more permissive in America of abortion restrictions past

737
00:34:13,800 --> 00:34:17,239
Speaker 2: the row versus way consents. So if anything, it's actually.

738
00:34:16,840 --> 00:34:18,799
Speaker 3: Backfired against the Republicans.

739
00:34:18,840 --> 00:34:21,800
Speaker 2: So in general, that talking point, again, you may be

740
00:34:21,840 --> 00:34:24,800
Speaker 2: a conservative Christian and God bless you, but most Americas

741
00:34:24,840 --> 00:34:27,360
Speaker 2: don't agree with you, period, especially whenever it comes to

742
00:34:27,400 --> 00:34:30,279
Speaker 2: the ballot box. So in terms of his answer, where

743
00:34:30,320 --> 00:34:31,640
Speaker 2: he landed, it doesn't work.

744
00:34:32,239 --> 00:34:33,320
Speaker 3: There's euro evidence.

745
00:34:33,360 --> 00:34:35,840
Speaker 1: But also where did he even land Because that's the

746
00:34:35,880 --> 00:34:39,480
Speaker 1: other piece is you know, as much as Kamala rightfully

747
00:34:39,520 --> 00:34:41,480
Speaker 1: gets pegged as a flip flopper, and that is true,

748
00:34:41,640 --> 00:34:44,000
Speaker 1: and you know that's another area where he just failed.

749
00:34:44,400 --> 00:34:46,919
Speaker 1: She baited him on something about his dad's net worth

750
00:34:46,960 --> 00:34:49,520
Speaker 1: and again he just chased that down the rabbit hole.

751
00:34:49,560 --> 00:34:51,759
Speaker 1: And let her you know, skate in terms of some

752
00:34:51,800 --> 00:34:54,560
Speaker 1: of the different positions she's taken. But you know, moving

753
00:34:54,600 --> 00:34:58,080
Speaker 1: back to abortion, he gets asked this question, Okay, your

754
00:34:58,160 --> 00:35:00,759
Speaker 1: running mate says, you would veto and ash abortion, pan

755
00:35:01,040 --> 00:35:03,680
Speaker 1: would you? And he's like, oh, I don't really talk

756
00:35:03,719 --> 00:35:05,520
Speaker 1: to that guy, so I don't know what he's talking.

757
00:35:05,600 --> 00:35:11,120
Speaker 1: He won't commit, and clearly he sa really felt the

758
00:35:11,160 --> 00:35:15,920
Speaker 1: heat from pro life first when he tried to actually

759
00:35:15,960 --> 00:35:18,759
Speaker 1: on a policy level moderate on abortion. You heard that

760
00:35:18,800 --> 00:35:20,839
Speaker 1: from Avida. That was the one thing that she was well,

761
00:35:20,880 --> 00:35:24,920
Speaker 1: you're going to hear from Vida. You will hear from Avida,

762
00:35:25,000 --> 00:35:28,359
Speaker 1: our young Republican woman who we had on to talk

763
00:35:28,360 --> 00:35:31,960
Speaker 1: about some of these issues. And she criticizes him about

764
00:35:32,080 --> 00:35:34,400
Speaker 1: how he, you know, moved to the pro choice side.

765
00:35:34,480 --> 00:35:38,360
Speaker 1: So even though you know, he didn't really have to

766
00:35:38,440 --> 00:35:41,480
Speaker 1: run in a primary, you know, he's sort of just

767
00:35:41,480 --> 00:35:43,600
Speaker 1: stayed out of it and participating in the debates or whatever.

768
00:35:43,840 --> 00:35:45,960
Speaker 6: Even though he has really tried to.

769
00:35:45,960 --> 00:35:47,880
Speaker 1: Lean into this rhetoric about just leave it up to

770
00:35:47,880 --> 00:35:50,160
Speaker 1: the states, et cetera, et cetera, this is still a

771
00:35:50,160 --> 00:35:52,840
Speaker 1: powerful coalition that really has a hold on him. So

772
00:35:53,040 --> 00:35:55,719
Speaker 1: much so that even though he recognizes this issue as

773
00:35:55,760 --> 00:35:58,880
Speaker 1: a major problem from him. He cannot bring himself to

774
00:35:59,040 --> 00:36:01,520
Speaker 1: break in a hard away from them. And I don't

775
00:36:01,560 --> 00:36:03,879
Speaker 1: know that it would really benefit him if even if

776
00:36:03,920 --> 00:36:07,839
Speaker 1: he did, because the bottom line is, Kama Harris can

777
00:36:07,880 --> 00:36:10,640
Speaker 1: always say you, as she did in the debate, you

778
00:36:10,920 --> 00:36:14,440
Speaker 1: hand picked these three Supreme Court justices to put on

779
00:36:14,560 --> 00:36:16,880
Speaker 1: the bench and overturn Row.

780
00:36:16,960 --> 00:36:18,520
Speaker 6: And then when she framed it.

781
00:36:18,400 --> 00:36:19,880
Speaker 1: As because he tries to say, oh, well, this is

782
00:36:19,880 --> 00:36:22,919
Speaker 1: what people wanted, and she flips it back and says, no,

783
00:36:23,400 --> 00:36:26,240
Speaker 1: the women who are bleeding out in a pot parking lot,

784
00:36:26,560 --> 00:36:30,120
Speaker 1: the twelve year old girls who were raped by their stepdads, No,

785
00:36:30,239 --> 00:36:34,080
Speaker 1: they they did not want this. And it continues to be,

786
00:36:34,719 --> 00:36:37,560
Speaker 1: you know, as evidenced by that search data, it continues

787
00:36:37,600 --> 00:36:40,560
Speaker 1: to be very powerful. When we picked our top moments

788
00:36:40,800 --> 00:36:43,400
Speaker 1: from the debate, there were a lot of candidates the

789
00:36:43,440 --> 00:36:48,920
Speaker 1: pets thing. I mean, that's just like the iconic, you know, memorable, ridiculous, absurd,

790
00:36:49,200 --> 00:36:51,200
Speaker 1: like the revealing.

791
00:36:51,120 --> 00:36:54,120
Speaker 6: Moment from the debate. But actually picked the abortion one.

792
00:36:54,000 --> 00:36:58,160
Speaker 1: Because it was such because it's such an important issue,

793
00:36:58,160 --> 00:37:00,920
Speaker 1: and because he still is so bad on it and

794
00:37:00,960 --> 00:37:04,480
Speaker 1: still has no idea what to say, and I continue

795
00:37:04,520 --> 00:37:06,920
Speaker 1: to think like, there isn't really an answer here that

796
00:37:07,480 --> 00:37:10,880
Speaker 1: can work for him, regardless of where he tries to

797
00:37:10,960 --> 00:37:13,520
Speaker 1: land or what talking points he tries to try sultimate.

798
00:37:13,920 --> 00:37:15,280
Speaker 3: I mean, do you don't want to know how insane

799
00:37:15,320 --> 00:37:17,600
Speaker 3: this is? With a congratulations?

800
00:37:17,600 --> 00:37:19,640
Speaker 2: By the way, pro life ers, you got Trump to

801
00:37:19,800 --> 00:37:23,360
Speaker 2: disavow a position that eighty percent of the American people

802
00:37:23,440 --> 00:37:26,960
Speaker 2: are against eighty percent. Even people who are pro life,

803
00:37:27,320 --> 00:37:29,640
Speaker 2: who are like in terms of the row versus way,

804
00:37:29,880 --> 00:37:32,880
Speaker 2: they don't even support a national ban on abortion.

805
00:37:33,320 --> 00:37:36,799
Speaker 3: If you look at it, three quarters even three quarters.

806
00:37:36,480 --> 00:37:38,640
Speaker 2: Of Americans people who are pro life say that there

807
00:37:38,680 --> 00:37:41,640
Speaker 2: should not be a federal law banning abortion at six weeks,

808
00:37:41,800 --> 00:37:45,799
Speaker 2: some sixty percent at fifteen weeks. So the rogue consensus

809
00:37:45,880 --> 00:37:49,120
Speaker 2: has literally never been more popular than ever. And JD,

810
00:37:49,400 --> 00:37:52,799
Speaker 2: who is a practicing pro life Catholic, gave you the

811
00:37:52,800 --> 00:37:56,120
Speaker 2: political layup of yeah, he would ban abord, he would

812
00:37:56,160 --> 00:37:59,520
Speaker 2: not sign the national abortion ban, he would veto it,

813
00:37:59,760 --> 00:38:01,840
Speaker 2: and he still decided to go along.

814
00:38:01,920 --> 00:38:02,600
Speaker 3: What is he doing?

815
00:38:02,680 --> 00:38:05,400
Speaker 2: I mean, this is where I look at Trump's calculus

816
00:38:05,400 --> 00:38:07,760
Speaker 2: sometimes and I just have to remind you, like, honestly,

817
00:38:07,800 --> 00:38:10,520
Speaker 2: sometimes he is just an idiot, or he's somebody who

818
00:38:10,680 --> 00:38:15,080
Speaker 2: his political his political like dialed in nature is not

819
00:38:15,160 --> 00:38:19,080
Speaker 2: there where previously. I really believe that twenty sixteen Trump,

820
00:38:19,239 --> 00:38:22,200
Speaker 2: if he were presented with that same landscape, absolutely would

821
00:38:22,239 --> 00:38:24,520
Speaker 2: not have done this. This is actually him a case

822
00:38:24,640 --> 00:38:27,839
Speaker 2: being too immersed in the traditional GOP where there are

823
00:38:27,880 --> 00:38:30,000
Speaker 2: a lot of people who probably support something like that

824
00:38:30,160 --> 00:38:32,640
Speaker 2: and who are in his ear convincing him that this

825
00:38:32,719 --> 00:38:35,440
Speaker 2: is a bigger constituency. If he was truly dialed in

826
00:38:35,480 --> 00:38:37,800
Speaker 2: the way that he has been in the past, there's

827
00:38:37,840 --> 00:38:39,600
Speaker 2: no way that you're going to come out on a

828
00:38:39,640 --> 00:38:40,840
Speaker 2: twenty percent issue.

829
00:38:40,960 --> 00:38:41,600
Speaker 3: Impossible.

830
00:38:41,840 --> 00:38:44,920
Speaker 1: So I've been sort of like workshopping a monologue in

831
00:38:45,000 --> 00:38:47,120
Speaker 1: my head that I may do for next week about

832
00:38:47,200 --> 00:38:52,720
Speaker 1: Trump's political evolution and how what current bubble.

833
00:38:52,600 --> 00:38:53,239
Speaker 6: He is in.

834
00:38:53,400 --> 00:38:57,040
Speaker 1: So in twenty sixteen he really comes out of the

835
00:38:57,080 --> 00:39:00,560
Speaker 1: New York tabloids. Yeah, right, he's like New York that's

836
00:39:00,640 --> 00:39:04,719
Speaker 1: kind of his that's his thing, right. So it's sensationalist,

837
00:39:05,000 --> 00:39:08,680
Speaker 1: it's crass, but there is some like connection to the

838
00:39:08,760 --> 00:39:11,200
Speaker 1: reality of like how people view issues and what normal

839
00:39:11,239 --> 00:39:14,200
Speaker 1: people think about various things that are happening in the country.

840
00:39:14,560 --> 00:39:16,440
Speaker 1: And you know, it's sort of like where Piers Morgan

841
00:39:16,560 --> 00:39:20,400
Speaker 1: is right, it's tabloid. Then he's really swimming in the

842
00:39:20,400 --> 00:39:24,120
Speaker 1: Fox News world, right, and Kyle calls him fox News

843
00:39:24,120 --> 00:39:26,359
Speaker 1: Grandpa in twenty twenty, and I think that's right.

844
00:39:26,480 --> 00:39:27,919
Speaker 6: You know, he's just totally.

845
00:39:27,600 --> 00:39:33,400
Speaker 1: Immersed in this like conservative but mainstream conservative echo chamber.

846
00:39:33,520 --> 00:39:35,560
Speaker 1: So already a lot of the populist elements from twenty

847
00:39:35,640 --> 00:39:37,960
Speaker 1: sixteen are stripped down in twenty twenty, and he's this

848
00:39:38,040 --> 00:39:43,560
Speaker 1: like Fox News Grandpa. Now it's like truth Social person,

849
00:39:44,520 --> 00:39:47,319
Speaker 1: psycho whatever. And that's where you get the pets, right,

850
00:39:47,360 --> 00:39:48,120
Speaker 1: he's hanging out.

851
00:39:47,960 --> 00:39:48,840
Speaker 6: With Laura Loomer.

852
00:39:49,000 --> 00:39:52,880
Speaker 1: I mean, it's just I think after Fox News calls

853
00:39:52,920 --> 00:39:55,520
Speaker 1: the election for Biden, and it is actually the first

854
00:39:55,880 --> 00:39:59,960
Speaker 1: to call Arizona for Biden, there's a rupture with Fox News.

855
00:40:00,040 --> 00:40:03,279
Speaker 1: At the same time he's you know, he gets kicked

856
00:40:03,280 --> 00:40:05,480
Speaker 1: off of Twitter. So now he's on truth Social, which

857
00:40:05,520 --> 00:40:10,920
Speaker 1: is truly this very niche fringe ecosystem, and those are

858
00:40:10,920 --> 00:40:14,040
Speaker 1: the waters he's swimming in. And that really comes out

859
00:40:14,400 --> 00:40:17,040
Speaker 1: in his politics and in how he's talking about issues

860
00:40:17,040 --> 00:40:19,000
Speaker 1: and how he's responding to issues, and how he's thinking

861
00:40:19,000 --> 00:40:21,799
Speaker 1: about things and what he chooses to highlight and so,

862
00:40:22,680 --> 00:40:24,400
Speaker 1: you know, I think a lot of the evolution of

863
00:40:24,440 --> 00:40:27,040
Speaker 1: Trump can be explained because he is such a media

864
00:40:27,160 --> 00:40:30,680
Speaker 1: creature that that's an important way to understand who he

865
00:40:30,840 --> 00:40:33,480
Speaker 1: is at that present moment. And I think that accounts

866
00:40:33,560 --> 00:40:37,359
Speaker 1: for a lot of the differences between the twenty sixteen Trump,

867
00:40:37,400 --> 00:40:39,680
Speaker 1: who has his finger on the pulse of something, in

868
00:40:39,719 --> 00:40:41,600
Speaker 1: the twenty twenty four of Trump where you're like, what

869
00:40:42,080 --> 00:40:45,040
Speaker 1: the hell are you even thinking right now? What world

870
00:40:45,120 --> 00:40:47,399
Speaker 1: do you live in? Where you thought that this would

871
00:40:47,440 --> 00:40:51,040
Speaker 1: be a good idea. So, you know, I think that

872
00:40:51,040 --> 00:40:53,400
Speaker 1: that changed media diet has.

873
00:40:53,320 --> 00:40:54,600
Speaker 6: Really done a number on him.

874
00:40:54,640 --> 00:40:56,520
Speaker 1: Not to mention just being in the bubble of being

875
00:40:56,560 --> 00:40:59,120
Speaker 1: a president of post president, surrounding yourself constantly with sick

876
00:40:59,120 --> 00:41:01,759
Speaker 1: of fans, et cetera, et cetera. To get back to

877
00:41:01,800 --> 00:41:04,480
Speaker 1: some of the we have one more. Washington Post did

878
00:41:04,520 --> 00:41:08,120
Speaker 1: an undecided voter panel as well. There's was pretty overwhelming

879
00:41:08,200 --> 00:41:10,239
Speaker 1: to put this up on the screen. So out of

880
00:41:10,280 --> 00:41:15,560
Speaker 1: twenty five people, twenty three said they thought that Harris.

881
00:41:16,200 --> 00:41:19,360
Speaker 1: Two said that they thought Trump performed better. You know,

882
00:41:19,360 --> 00:41:24,720
Speaker 1: they also interviewed these individuals throughout the debate, and even

883
00:41:24,760 --> 00:41:28,200
Speaker 1: the ones who said Harris performed better. There were you know,

884
00:41:28,280 --> 00:41:30,799
Speaker 1: there was some movement towards her also in terms of

885
00:41:30,840 --> 00:41:33,240
Speaker 1: which way people were planning on voting, which is obviously

886
00:41:33,280 --> 00:41:36,239
Speaker 1: really significant. There were still some questions raised about like hey,

887
00:41:36,280 --> 00:41:38,840
Speaker 1: I need to know more about her. But probably the

888
00:41:38,880 --> 00:41:42,040
Speaker 1: thing that I took most note of, which we commented

889
00:41:42,080 --> 00:41:45,080
Speaker 1: on on Debate Night as well, is with regard to

890
00:41:45,120 --> 00:41:49,279
Speaker 1: this like pets Haitian migrants situation, there was just deep

891
00:41:49,320 --> 00:41:51,960
Speaker 1: confusion about what the hell he was even talking about,

892
00:41:52,040 --> 00:41:54,799
Speaker 1: which again gets to the truth social bubble that he's

893
00:41:54,840 --> 00:41:58,200
Speaker 1: existing in where his conspiracies are so outlandish and so

894
00:41:58,280 --> 00:42:01,719
Speaker 1: fringe people like normal people just don't even comprehend what

895
00:42:01,760 --> 00:42:03,720
Speaker 1: your point you're even trying to get across.

896
00:42:04,160 --> 00:42:05,919
Speaker 6: So I thought that was interesting as.

897
00:42:05,840 --> 00:42:07,719
Speaker 3: Yeah, that happened a lot in twenty twenty. Actually are

898
00:42:07,880 --> 00:42:08,120
Speaker 3: it did.

899
00:42:08,160 --> 00:42:11,439
Speaker 2: Yeah, the Trump would be saying something where I'm like, dude,

900
00:42:11,560 --> 00:42:13,600
Speaker 2: I only know what you're talking about because I do

901
00:42:13,680 --> 00:42:15,759
Speaker 2: this for a living. Yeah, I know way too many

902
00:42:15,840 --> 00:42:19,160
Speaker 2: people who are like what, Like what is he saying?

903
00:42:19,239 --> 00:42:22,200
Speaker 2: And that simplicity. Look, this is what made him actually

904
00:42:22,200 --> 00:42:24,680
Speaker 2: a strong candidate in twenty sixteen. It wasn't about being

905
00:42:24,719 --> 00:42:28,719
Speaker 2: online he intuited a basic fact about America. He said,

906
00:42:28,760 --> 00:42:31,319
Speaker 2: Americans don't like the way that things are going. That

907
00:42:31,360 --> 00:42:34,239
Speaker 2: was actually a very counterintuitive in politics at that time.

908
00:42:34,320 --> 00:42:37,000
Speaker 2: American Carnage still one of the most important speeches in

909
00:42:37,040 --> 00:42:39,959
Speaker 2: modern American history because it was counter to the way

910
00:42:40,000 --> 00:42:43,160
Speaker 2: that most people in the elite circles thought about the

911
00:42:43,200 --> 00:42:45,480
Speaker 2: direction of the country this time and it was simple.

912
00:42:45,640 --> 00:42:48,320
Speaker 2: It was about immigration, and it was simply really about

913
00:42:48,440 --> 00:42:52,120
Speaker 2: rolling back the Obama era consensus. This time around, it's mishmash.

914
00:42:52,160 --> 00:42:52,880
Speaker 3: There's an amalgam.

915
00:42:52,960 --> 00:42:54,680
Speaker 2: Now, that doesn't mean he still can't win because a

916
00:42:54,719 --> 00:42:58,600
Speaker 2: lot of Biden is very unpopular and economic tends, trends,

917
00:42:58,600 --> 00:43:01,040
Speaker 2: et cetera. But he's not doing his best, I think,

918
00:43:01,239 --> 00:43:03,680
Speaker 2: to convince people to vote for him. And actually, you know,

919
00:43:03,760 --> 00:43:05,440
Speaker 2: you do see a lot of that, like, for example,

920
00:43:05,480 --> 00:43:07,960
Speaker 2: can we put B five on the screen? A lot

921
00:43:07,960 --> 00:43:11,680
Speaker 2: of these a lot of these swing state voters who

922
00:43:11,760 --> 00:43:16,440
Speaker 2: were asked about from the Washington Post specifically about how

923
00:43:16,680 --> 00:43:19,040
Speaker 2: they thought Trump and Harris performed twenty three out of

924
00:43:19,080 --> 00:43:22,040
Speaker 2: twenty five, said Kamala Harris did. And this is not

925
00:43:22,160 --> 00:43:23,839
Speaker 2: you know, it's not just a bunch of liberals who

926
00:43:23,920 --> 00:43:26,480
Speaker 2: are in this group. You even look at a lot

927
00:43:26,560 --> 00:43:28,800
Speaker 2: of the people who were saying the Harris performed better,

928
00:43:28,840 --> 00:43:30,839
Speaker 2: and they're like, look, I'm frustrated with the status quo.

929
00:43:31,080 --> 00:43:33,799
Speaker 2: We deserve better. I appreciate a strump, you know, I'm

930
00:43:33,800 --> 00:43:35,880
Speaker 2: reading from one. I appreciate Trump has strong views and

931
00:43:35,880 --> 00:43:38,239
Speaker 2: trust his ability to make strong decisions. I worry that

932
00:43:38,280 --> 00:43:41,280
Speaker 2: Harris may lack clear convictions or follow through. Still thinks

933
00:43:41,480 --> 00:43:44,520
Speaker 2: that Kamala had done better and has a preference now

934
00:43:44,680 --> 00:43:47,680
Speaker 2: for Harris, Like these are not people who are brainwashed

935
00:43:47,719 --> 00:43:50,840
Speaker 2: or any a deeply partisan in any way. And I

936
00:43:50,840 --> 00:43:54,080
Speaker 2: think that, you know, thinking about trying to intuit that

937
00:43:54,239 --> 00:43:57,640
Speaker 2: part of the country right there where the overall trend

938
00:43:57,800 --> 00:44:00,399
Speaker 2: is in general, he's not as die in.

939
00:44:00,360 --> 00:44:02,200
Speaker 3: As he was back in twenty sixteen. I think that's

940
00:44:02,239 --> 00:44:02,960
Speaker 3: generally a mistake.

941
00:44:03,120 --> 00:44:03,400
Speaker 6: Yeah.

942
00:44:03,440 --> 00:44:06,879
Speaker 1: And they asked those same individuals before the debate, Okay,

943
00:44:06,880 --> 00:44:07,279
Speaker 1: who are.

944
00:44:07,200 --> 00:44:09,800
Speaker 6: You leaning towards right? If you have a lean and.

945
00:44:10,080 --> 00:44:13,360
Speaker 1: Before the debate, twelve said Harris and ten said Trump.

946
00:44:13,760 --> 00:44:17,279
Speaker 1: Three were others were undecided, like totally unsure or they

947
00:44:17,280 --> 00:44:18,560
Speaker 1: said neither actually is.

948
00:44:18,520 --> 00:44:21,440
Speaker 6: What they said. Afterwards there were still three that said neither.

949
00:44:22,120 --> 00:44:25,520
Speaker 1: But the Harris number moved from twelve to fifteen, and

950
00:44:26,120 --> 00:44:29,520
Speaker 1: she now had five people that said they were definitely

951
00:44:29,600 --> 00:44:31,720
Speaker 1: voting for her, whereas before they were on the fence.

952
00:44:32,040 --> 00:44:34,680
Speaker 1: Trump's number went down from ten to six, so he

953
00:44:34,800 --> 00:44:37,640
Speaker 1: lost four voters, and no one moved from the probably

954
00:44:37,680 --> 00:44:39,880
Speaker 1: Trump to the definitely Trump column.

955
00:44:39,960 --> 00:44:41,160
Speaker 6: So at least listen.

956
00:44:41,200 --> 00:44:45,040
Speaker 1: It's a small a sample, obviously not statistically significant, et cetera,

957
00:44:45,040 --> 00:44:47,000
Speaker 1: et cetera, but still, if you're just looking at how

958
00:44:47,080 --> 00:44:50,640
Speaker 1: ordinary people process this debate in real time, it shows

959
00:44:50,680 --> 00:44:53,520
Speaker 1: you something something happened here for Trump, and I do

960
00:44:53,560 --> 00:44:57,360
Speaker 1: think part of it was just she did a perfect

961
00:44:57,440 --> 00:45:00,680
Speaker 1: job of queueing him up, and he took debate in

962
00:45:00,840 --> 00:45:04,960
Speaker 1: a way that her top advisors could never have possibly

963
00:45:05,440 --> 00:45:09,880
Speaker 1: dreamed in their wildest dreams, how much he would chase

964
00:45:10,000 --> 00:45:12,759
Speaker 1: every rabbit down the hole every time she tempted him

965
00:45:12,760 --> 00:45:17,680
Speaker 1: to do so. So one of the voters who apparently

966
00:45:17,719 --> 00:45:20,279
Speaker 1: was not overly impressed with Donald Trump's performance was his

967
00:45:20,360 --> 00:45:24,680
Speaker 1: new endorser and transition team member RFK Junior, who was

968
00:45:25,120 --> 00:45:28,480
Speaker 1: fairly fairly honest about how he thought this performance went

969
00:45:28,480 --> 00:45:29,840
Speaker 1: for Donald Trump. Let's take a listen.

970
00:45:30,160 --> 00:45:33,759
Speaker 11: Vice President Harris clearly won the debate in terms of

971
00:45:33,800 --> 00:45:41,200
Speaker 11: her delivery, her polish, her organization, and her preparation. I

972
00:45:41,239 --> 00:45:46,520
Speaker 11: think on the substance, President Trump wins in terms of

973
00:45:46,640 --> 00:45:52,120
Speaker 11: his governance, but he didn't tell that story. In fact,

974
00:45:52,840 --> 00:45:57,719
Speaker 11: the first question was an extraordinary lost opportunity because there's

975
00:45:57,760 --> 00:45:59,799
Speaker 11: a question, and I think Doris pointed out that she

976
00:45:59,800 --> 00:46:03,840
Speaker 11: never answered, which is our Americans better four years later?

977
00:46:04,840 --> 00:46:08,200
Speaker 11: And there's really no argument for saying that they are

978
00:46:08,360 --> 00:46:12,359
Speaker 11: by all the dish by which you measure social deterioration.

979
00:46:13,040 --> 00:46:15,560
Speaker 11: We have double the inflation rate, the housing rates have

980
00:46:15,640 --> 00:46:19,960
Speaker 11: doubled in the last four years. There's fifteen million more

981
00:46:20,040 --> 00:46:24,760
Speaker 11: people and Americans living in poverty. We have these enormous

982
00:46:24,800 --> 00:46:32,880
Speaker 11: suicide rates, enormous overdose rates, and we're in two wars.

983
00:46:32,920 --> 00:46:35,560
Speaker 11: And all of these things can be attributed to the

984
00:46:35,680 --> 00:46:41,360
Speaker 11: poor leadership from Biden Harry's team. And that's the argument

985
00:46:41,400 --> 00:46:44,359
Speaker 11: that he should have been making, but he got distracted.

986
00:46:45,360 --> 00:46:49,319
Speaker 11: And I think it's unfortunate because he you know, I

987
00:46:49,320 --> 00:46:53,440
Speaker 11: think he really had an airy argument for his presidency,

988
00:46:54,360 --> 00:46:57,000
Speaker 11: but he was not able to make that case to

989
00:46:57,040 --> 00:46:58,040
Speaker 11: the American public.

990
00:46:59,400 --> 00:47:01,320
Speaker 12: He said, and it's come in a couple of times

991
00:47:01,360 --> 00:47:04,279
Speaker 12: today that he won the debate, and all the polls.

992
00:47:04,000 --> 00:47:05,080
Speaker 7: Show that he won the debate.

993
00:47:05,160 --> 00:47:08,040
Speaker 3: I hadn't seen a single one to show that. What

994
00:47:08,080 --> 00:47:09,240
Speaker 3: did you think of that reaction?

995
00:47:12,000 --> 00:47:15,200
Speaker 11: Well, I have seen those polls. You know, they're not

996
00:47:16,840 --> 00:47:22,160
Speaker 11: There are polls that you see on uh, you know,

997
00:47:22,280 --> 00:47:26,200
Speaker 11: on the on the internet, and and a lot of

998
00:47:26,239 --> 00:47:31,960
Speaker 11: them probably have have a statistical problem.

999
00:47:32,760 --> 00:47:35,960
Speaker 1: He laughing says that, like, yeah, it's probably like a

1000
00:47:36,000 --> 00:47:38,040
Speaker 1: cat turn poll that I don't know that I'm going

1001
00:47:38,120 --> 00:47:38,880
Speaker 1: to take too seriously.

1002
00:47:38,920 --> 00:47:40,720
Speaker 6: What'd you make of those comments? I was kind of surprised.

1003
00:47:40,760 --> 00:47:41,319
Speaker 3: I think he should.

1004
00:47:41,360 --> 00:47:44,319
Speaker 2: I mean, look at the very least RFK every once

1005
00:47:44,320 --> 00:47:46,360
Speaker 2: in a while, I'm surprised you and he'll say, you

1006
00:47:46,400 --> 00:47:47,600
Speaker 2: know what he thinks. I think that's what a lot

1007
00:47:47,600 --> 00:47:50,400
Speaker 2: of people appreciated about him whenever he was running for president,

1008
00:47:50,440 --> 00:47:52,040
Speaker 2: and look at least give him credit.

1009
00:47:52,040 --> 00:47:53,560
Speaker 3: He's like, yeah, I don't think he did well at all.

1010
00:47:53,880 --> 00:47:56,239
Speaker 2: And that he's he honestly has the most at stake

1011
00:47:56,280 --> 00:47:58,759
Speaker 2: here right now, because not only did he steak his

1012
00:47:58,880 --> 00:48:01,800
Speaker 2: entire reputation, is his whole coalition on a Trump victory.

1013
00:48:01,880 --> 00:48:05,120
Speaker 2: He's claiming that Trump is going to appoint either him

1014
00:48:05,239 --> 00:48:07,920
Speaker 2: or people who are aligned with him to office whenever

1015
00:48:07,960 --> 00:48:10,640
Speaker 2: he is elected. So at the very least, like if

1016
00:48:10,680 --> 00:48:13,160
Speaker 2: you expect anybody to spend it really would be him.

1017
00:48:13,160 --> 00:48:15,319
Speaker 2: I honestly think he has the most on the line

1018
00:48:15,320 --> 00:48:16,600
Speaker 2: out of anybody in.

1019
00:48:16,560 --> 00:48:20,480
Speaker 1: The Coalitionink about it, your social circle and everything.

1020
00:48:21,280 --> 00:48:22,680
Speaker 3: Nicole Shanahan, I'm like, good luck.

1021
00:48:22,680 --> 00:48:25,080
Speaker 2: If Trump doesn't win, people are screwed, you know, in

1022
00:48:25,160 --> 00:48:28,360
Speaker 2: terms of just think about it, in terms of coalitional support,

1023
00:48:28,680 --> 00:48:31,879
Speaker 2: In terms of overall impact of your campaign which you're

1024
00:48:31,920 --> 00:48:35,839
Speaker 2: hoping to leverage into power, No Democrat or will ever

1025
00:48:35,880 --> 00:48:38,600
Speaker 2: speak to you like ever again, after you've gone this hard.

1026
00:48:38,600 --> 00:48:41,319
Speaker 2: They bet the farm really on the Trump campaign. So

1027
00:48:41,360 --> 00:48:44,600
Speaker 2: for their sake, the whole family came out against him,

1028
00:48:44,600 --> 00:48:46,480
Speaker 2: which I guess isn't all that new, but you know,

1029
00:48:46,520 --> 00:48:48,880
Speaker 2: when he the family name is now more in the

1030
00:48:48,920 --> 00:48:51,839
Speaker 2: discourse than ever before. Shanahan, Like I said, I mean,

1031
00:48:52,000 --> 00:48:54,400
Speaker 2: you shouldn't underestimate like the social circles and all these

1032
00:48:54,480 --> 00:48:56,600
Speaker 2: things that those people run in. If she doesn't have

1033
00:48:56,640 --> 00:48:58,360
Speaker 2: any power, like, good luck to you, the knives are

1034
00:48:58,400 --> 00:49:00,920
Speaker 2: going to come out from all her like billionaire neighbors

1035
00:49:00,960 --> 00:49:03,960
Speaker 2: and olive ours. My only point being that they probably

1036
00:49:04,000 --> 00:49:07,080
Speaker 2: have more incentive to spin than anybody else, and so

1037
00:49:07,239 --> 00:49:09,600
Speaker 2: whenever they have to tell the truth about what's going

1038
00:49:09,640 --> 00:49:11,759
Speaker 2: on here, that just tells you that things didn't go

1039
00:49:11,840 --> 00:49:14,640
Speaker 2: that well. And that's the problem that I find is

1040
00:49:14,680 --> 00:49:16,640
Speaker 2: that there's a deep amount of hackery and a lot

1041
00:49:16,640 --> 00:49:19,080
Speaker 2: of commentary. By the way, I did call it on

1042
00:49:19,120 --> 00:49:20,920
Speaker 2: our show afterwards, I was like, can't wait to see

1043
00:49:20,960 --> 00:49:24,080
Speaker 2: Jesse Waters bitch about the moderators. And that's immediately what

1044
00:49:24,200 --> 00:49:27,160
Speaker 2: happened when it was on the show, Because that's why

1045
00:49:27,200 --> 00:49:29,160
Speaker 2: if you notice the beginning of our show, what was

1046
00:49:29,200 --> 00:49:32,440
Speaker 2: Trump saying, He's like, Jesse, he really got it, he really.

1047
00:49:32,280 --> 00:49:34,040
Speaker 3: Understood what was going on.

1048
00:49:34,840 --> 00:49:37,640
Speaker 2: You know, at a certain point too, for those people,

1049
00:49:37,800 --> 00:49:41,279
Speaker 2: they're not being honest enough with their audience or preparing them,

1050
00:49:41,400 --> 00:49:43,880
Speaker 2: which will probably just prime stop the steal.

1051
00:49:43,640 --> 00:49:45,719
Speaker 3: Two point oh absolutely, Whereas with RFK on.

1052
00:49:45,800 --> 00:49:47,920
Speaker 2: The release, he's like, yeah, it didn't do well. It's like,

1053
00:49:48,000 --> 00:49:50,120
Speaker 2: that's that's what being honest with people actually looks like.

1054
00:49:50,160 --> 00:49:52,200
Speaker 1: But you know what, like Trump is not going to

1055
00:49:52,320 --> 00:49:54,640
Speaker 1: like that answer. Yeah, and he watches and I think

1056
00:49:54,680 --> 00:49:57,560
Speaker 1: he was out truth thing about Neil Cavudo, So you know,

1057
00:49:57,719 --> 00:50:02,440
Speaker 1: he watches this coverage on Cable New religiously and if

1058
00:50:02,440 --> 00:50:06,360
Speaker 1: you are anything but Jesse Waters level sycophantic ass kissing

1059
00:50:06,560 --> 00:50:08,880
Speaker 1: like it is not enough because Jesse not only was

1060
00:50:08,880 --> 00:50:10,840
Speaker 1: bitching about the moderators, which was kind of like the

1061
00:50:10,880 --> 00:50:14,720
Speaker 1: total go to play, but he also was like Trump

1062
00:50:14,719 --> 00:50:17,399
Speaker 1: had all the most memorable lines like when he said

1063
00:50:17,680 --> 00:50:19,880
Speaker 1: one spa run or when he said they had like

1064
00:50:19,960 --> 00:50:23,520
Speaker 1: four things that he rattled off and tried to not

1065
00:50:23,760 --> 00:50:26,800
Speaker 1: just say, oh, well, the moderators are the reason he lost,

1066
00:50:26,880 --> 00:50:29,799
Speaker 1: but also tried to say actively, actually, I thought Trump

1067
00:50:29,920 --> 00:50:32,880
Speaker 1: was way better than Kamwa Harris, which again, no, you

1068
00:50:32,920 --> 00:50:35,840
Speaker 1: don't believe that. You don't like you're just lying to

1069
00:50:35,880 --> 00:50:37,880
Speaker 1: your audience. You're saying the thing that you know is

1070
00:50:37,920 --> 00:50:41,279
Speaker 1: gonna make Trump happy. And this is why, because I'm

1071
00:50:41,280 --> 00:50:45,439
Speaker 1: sure like whatever slot RFK was promised on the transition team,

1072
00:50:45,560 --> 00:50:48,040
Speaker 1: you know, you can still technically get that slot, but

1073
00:50:48,120 --> 00:50:50,560
Speaker 1: that doesn't mean that you're going to have any influence

1074
00:50:50,640 --> 00:50:53,359
Speaker 1: whatsoever going for in a Trump administration. It doesn't mean

1075
00:50:53,400 --> 00:50:55,279
Speaker 1: he's ever gonna call you again, or you're gonna be

1076
00:50:55,400 --> 00:50:57,439
Speaker 1: on Air Force one or have a meeting with him

1077
00:50:57,600 --> 00:51:00,560
Speaker 1: or whatever because you made such an important point on

1078
00:51:00,640 --> 00:51:03,840
Speaker 1: debate night. After something like this, Trump is going to

1079
00:51:03,920 --> 00:51:08,520
Speaker 1: go and search out his most sycophantic aids, advisors, supporters, etc.

1080
00:51:08,960 --> 00:51:10,560
Speaker 6: That's why he's hanging out with Laura Lemmer.

1081
00:51:11,280 --> 00:51:14,279
Speaker 1: She's safe for him because she'll tell him whatever he

1082
00:51:14,320 --> 00:51:18,160
Speaker 1: wants to hear and have zero qualms about it. So

1083
00:51:18,280 --> 00:51:21,040
Speaker 1: if you are even willing to be honest enough, and

1084
00:51:21,160 --> 00:51:22,959
Speaker 1: you know, there was obviously spin there and what RFK

1085
00:51:23,080 --> 00:51:24,680
Speaker 1: Junior said too of being like, well, I think he

1086
00:51:24,719 --> 00:51:26,680
Speaker 1: had air tight case and here's why he's so much

1087
00:51:26,680 --> 00:51:28,120
Speaker 1: about her, and he just failed to make it and

1088
00:51:28,120 --> 00:51:30,640
Speaker 1: that's unfortunate. But the fact he led any truth in

1089
00:51:30,680 --> 00:51:32,840
Speaker 1: there at all means he is going to be persona

1090
00:51:32,920 --> 00:51:34,760
Speaker 1: non grotto with Donald Trump.

1091
00:51:34,880 --> 00:51:36,799
Speaker 6: And you know it was that was quick.

1092
00:51:36,840 --> 00:51:39,960
Speaker 1: I also wonder how Tulci Gabbard is going to standing,

1093
00:51:40,040 --> 00:51:44,160
Speaker 1: is gonna fair within the Trump world, because he's certainly

1094
00:51:44,239 --> 00:51:48,200
Speaker 1: not going to blame himself Trump ever for his complete failure,

1095
00:51:48,320 --> 00:51:51,160
Speaker 1: even though there is no one to blame but himself.

1096
00:51:51,840 --> 00:51:54,360
Speaker 1: You know, whatever you think about the moderators, I was

1097
00:51:54,400 --> 00:51:57,439
Speaker 1: thinking about remember when Megan Kelly asked him that question

1098
00:51:57,480 --> 00:52:00,319
Speaker 1: about like Rosie o'donna or whatever, which he also thought

1099
00:52:00,360 --> 00:52:03,000
Speaker 1: was very unfair. He turned that into a great moment

1100
00:52:03,080 --> 00:52:06,280
Speaker 1: for himself. So if you are on top of your game,

1101
00:52:06,719 --> 00:52:09,719
Speaker 1: even if a moderator is like, you know, unfair in

1102
00:52:09,760 --> 00:52:13,359
Speaker 1: your view towards you, he used to have the capability

1103
00:52:13,360 --> 00:52:15,239
Speaker 1: to do that. So anyway, there is no one to

1104
00:52:15,239 --> 00:52:17,600
Speaker 1: blame but himself. But we know he will not blame himself.

1105
00:52:17,640 --> 00:52:22,440
Speaker 1: So who was prominently advising him and advertised as advising

1106
00:52:22,560 --> 00:52:25,080
Speaker 1: him going up to this debate, Tulcy Gabbard. So I

1107
00:52:25,120 --> 00:52:27,240
Speaker 1: wonder if she also is going to be on the ounce,

1108
00:52:27,680 --> 00:52:30,759
Speaker 1: if he's going to blame her for his inability to

1109
00:52:31,080 --> 00:52:34,680
Speaker 1: rise to the occasion and some basic ass political points

1110
00:52:34,680 --> 00:52:35,439
Speaker 1: against gom Layer.

1111
00:52:35,560 --> 00:52:38,600
Speaker 2: That's what Look, that's what he does. He always lashes

1112
00:52:38,640 --> 00:52:41,200
Speaker 2: out at the advisors. It's always the people who are

1113
00:52:41,239 --> 00:52:43,600
Speaker 2: around him. I guarantee you there's gonna be a lot

1114
00:52:43,600 --> 00:52:46,640
Speaker 2: of snakes in his Oh yeah, right now. Polymarket odds

1115
00:52:46,680 --> 00:52:49,960
Speaker 2: on whether Trump will fires campaign manager before the election.

1116
00:52:50,120 --> 00:52:53,600
Speaker 2: Let's see, is thirty percent spiked since the debate. So anyway,

1117
00:52:53,760 --> 00:52:55,960
Speaker 2: let's put that out interesting and let's see.

1118
00:52:55,800 --> 00:52:57,839
Speaker 6: And is it Lasa Vita is technically the firs.

1119
00:52:57,920 --> 00:52:59,560
Speaker 3: Las Avita is technically.

1120
00:52:59,360 --> 00:53:01,879
Speaker 6: Not Susie, she's sort of like number two, or she's

1121
00:53:01,960 --> 00:53:02,399
Speaker 6: number two.

1122
00:53:02,560 --> 00:53:05,919
Speaker 2: Also, Kamala actually is now leading Trump in the betting market,

1123
00:53:06,040 --> 00:53:09,680
Speaker 2: says presidential election winner is twenty twenty four fifty Kamala

1124
00:53:09,800 --> 00:53:11,920
Speaker 2: forty nine percent Trump, first time that she's led him

1125
00:53:11,920 --> 00:53:12,920
Speaker 2: in quite a long time.

1126
00:53:12,800 --> 00:53:15,040
Speaker 1: And before going into the debate, it was like fifty

1127
00:53:15,080 --> 00:53:15,759
Speaker 1: four Trump.

1128
00:53:15,520 --> 00:53:18,640
Speaker 2: Forty six Paris or some bread that was to Donald

1129
00:53:18,680 --> 00:53:21,920
Speaker 2: Trump tied after the debate and now actually as Kamala

1130
00:53:22,000 --> 00:53:24,080
Speaker 2: in the lead. I mean, look, it's fifty forty nine,

1131
00:53:24,120 --> 00:53:26,759
Speaker 2: so it's not it's not like overwhelming, but.

1132
00:53:26,960 --> 00:53:29,520
Speaker 6: It's also just you know, random people betting online.

1133
00:53:29,600 --> 00:53:31,719
Speaker 3: But it's a billion dollar market.

1134
00:53:31,760 --> 00:53:33,839
Speaker 2: There's nine hundred million dollars in this market, so yeah,

1135
00:53:33,840 --> 00:53:34,640
Speaker 2: you shouldn't discount.

1136
00:53:34,680 --> 00:53:38,480
Speaker 1: No, No, it's still indicative of at least the analysis of

1137
00:53:38,520 --> 00:53:41,279
Speaker 1: the general consensus about what happened in this debate and

1138
00:53:41,280 --> 00:53:42,840
Speaker 1: how it's likely to move things.

1139
00:53:42,880 --> 00:53:44,200
Speaker 6: You know, I don't people can be row.

1140
00:53:44,080 --> 00:53:46,719
Speaker 2: Betting markets, by the way, perfect, but there's a good

1141
00:53:46,880 --> 00:53:48,719
Speaker 2: you know, I read Nate Silver's book and he does

1142
00:53:48,800 --> 00:53:51,320
Speaker 2: make a good point that there's just a lot more.

1143
00:53:51,440 --> 00:53:53,920
Speaker 3: That does go into into it.

1144
00:53:53,920 --> 00:53:56,800
Speaker 2: When you genuinely have money and large sums of money

1145
00:53:56,880 --> 00:54:00,200
Speaker 2: on the line that sometimes can call things out in

1146
00:54:00,239 --> 00:54:04,719
Speaker 2: a better direction than pundits or like pundits and or

1147
00:54:04,800 --> 00:54:07,319
Speaker 2: any other like so called conventional wisdom, because it's true

1148
00:54:07,520 --> 00:54:10,480
Speaker 2: you have to factor in downside risk and upside and everything.

1149
00:54:10,480 --> 00:54:12,759
Speaker 2: And because they were so wrong last time around, people

1150
00:54:12,760 --> 00:54:15,600
Speaker 2: are always looking for opportunities this time to make it.

1151
00:54:15,640 --> 00:54:18,360
Speaker 2: And that's why Kamala being tied pretty significant.

1152
00:54:18,560 --> 00:54:19,600
Speaker 3: Yeah, with the overall.

1153
00:54:19,280 --> 00:54:21,560
Speaker 1: Polling average, Yeah, it's going to be really interesting to

1154
00:54:21,560 --> 00:54:24,799
Speaker 1: see the next few rounds of post debate polls that

1155
00:54:24,880 --> 00:54:26,080
Speaker 1: come out to see you.

1156
00:54:26,000 --> 00:54:28,600
Speaker 3: Know, is probably gets any Monday, actually.

1157
00:54:28,320 --> 00:54:30,359
Speaker 1: I would think though, Yeah, I would definitely think by

1158
00:54:30,400 --> 00:54:32,320
Speaker 1: Monday we'll have something to chew on there.

1159
00:54:32,160 --> 00:54:33,360
Speaker 6: So something to look forward to.

1160
00:54:33,480 --> 00:54:36,520
Speaker 1: All right, all right, we got a great panel lined

1161
00:54:36,600 --> 00:54:39,480
Speaker 1: up that we have now promoted several times because we

1162
00:54:39,560 --> 00:54:42,440
Speaker 1: so enjoyed the conversation that we recorded with these two

1163
00:54:42,520 --> 00:54:46,160
Speaker 1: individuals talking about the Taylor Swift endorsement and also talking

1164
00:54:46,160 --> 00:54:50,040
Speaker 1: about these emerging gender dynamics among gen Z, which are

1165
00:54:50,120 --> 00:54:54,640
Speaker 1: really fascting largest gender divide political gender divide of any

1166
00:54:54,760 --> 00:54:56,440
Speaker 1: generation is gen Z.

1167
00:54:56,560 --> 00:54:57,560
Speaker 6: So let's go ahead and get to that.

1168
00:55:00,920 --> 00:55:04,000
Speaker 1: Very excited to be joined this morning by our two

1169
00:55:04,000 --> 00:55:06,000
Speaker 1: panelists on the left and the right. We've got Parker

1170
00:55:06,000 --> 00:55:08,160
Speaker 1: Short who is the president of the Georgia Young Dems,

1171
00:55:08,160 --> 00:55:09,920
Speaker 1: and we also have a Vita Duffy, who is the

1172
00:55:09,920 --> 00:55:10,680
Speaker 1: host of.

1173
00:55:10,680 --> 00:55:12,120
Speaker 6: The Bond Gino Report.

1174
00:55:12,640 --> 00:55:14,920
Speaker 1: Got a couple topics we wanted to dive into with

1175
00:55:14,960 --> 00:55:16,680
Speaker 1: you guys. Welcome, glad to have you this morning.

1176
00:55:16,680 --> 00:55:18,759
Speaker 3: Good to see you guys, Thanks for having us.

1177
00:55:18,880 --> 00:55:19,520
Speaker 6: Yeah, of course.

1178
00:55:20,160 --> 00:55:23,480
Speaker 1: So we got big news the night after the debate,

1179
00:55:23,680 --> 00:55:26,759
Speaker 1: like that night during the you know, the post aftermath

1180
00:55:26,800 --> 00:55:29,319
Speaker 1: of the debate, that Taylor Swift had come out and

1181
00:55:29,440 --> 00:55:33,880
Speaker 1: endorsed Kamala Harris, saying that describing herself as a childless

1182
00:55:34,000 --> 00:55:37,360
Speaker 1: cat lady. Parker, let me start with you. I'm sure

1183
00:55:37,160 --> 00:55:39,640
Speaker 1: you're you're happy about this news. How significant do you

1184
00:55:39,719 --> 00:55:42,000
Speaker 1: think that this will be for the Kamala Harris campaign.

1185
00:55:43,040 --> 00:55:46,799
Speaker 13: I'm not particularly a Taylor Swift fan, but I do

1186
00:55:46,920 --> 00:55:50,360
Speaker 13: appreciate somebody who uses their platform for a good purpose.

1187
00:55:51,080 --> 00:55:53,120
Speaker 13: You know, I kind of did expect this because she

1188
00:55:53,239 --> 00:55:57,000
Speaker 13: has been very outspoken before. If you will go out

1189
00:55:57,040 --> 00:55:59,960
Speaker 13: there and endorse Phil Bretison and Tennessee in twenty eighteen,

1190
00:56:00,480 --> 00:56:02,719
Speaker 13: then you will go out there and get behind Kamala Harris,

1191
00:56:02,760 --> 00:56:05,800
Speaker 13: who's one of the most exciting nominees that the party

1192
00:56:05,840 --> 00:56:08,480
Speaker 13: has seen in a while. I will say I think

1193
00:56:08,480 --> 00:56:11,160
Speaker 13: the most impactful part of this is not Taylor Swift

1194
00:56:11,239 --> 00:56:15,200
Speaker 13: convincing voters that wouldn't vote or that we're going to

1195
00:56:15,280 --> 00:56:18,480
Speaker 13: vote for Trump. It is instead reaching out to new folks.

1196
00:56:18,840 --> 00:56:22,680
Speaker 13: Last year, when she promoted the voter registration link on

1197
00:56:22,920 --> 00:56:27,440
Speaker 13: National Voter Registration Day, it was a forty thousand people increase.

1198
00:56:27,640 --> 00:56:30,080
Speaker 13: It was much more than last year. That was the

1199
00:56:30,160 --> 00:56:33,719
Speaker 13: last highest voter registration bump before Calmly got in the race.

1200
00:56:33,920 --> 00:56:35,880
Speaker 13: And then last night we saw more than three hundred

1201
00:56:35,880 --> 00:56:38,040
Speaker 13: thousand people click on her link. I think the thing

1202
00:56:38,080 --> 00:56:40,720
Speaker 13: that's going to make the difference this election in states

1203
00:56:40,760 --> 00:56:44,800
Speaker 13: like North Carolina and Georgia, where I have both lived

1204
00:56:45,239 --> 00:56:48,759
Speaker 13: and is new voters coming in, people on college campuses,

1205
00:56:48,840 --> 00:56:51,840
Speaker 13: people getting engaged, that are being touched by people like

1206
00:56:51,880 --> 00:56:55,239
Speaker 13: Taylor Swift that are using their platform for what I

1207
00:56:55,239 --> 00:56:56,160
Speaker 13: think is a good reason.

1208
00:56:56,440 --> 00:56:59,000
Speaker 6: Avian. If I to bolster Parker's point here, let's go in.

1209
00:56:59,080 --> 00:57:01,480
Speaker 1: But the third element up on the screen, he mentioned

1210
00:57:01,480 --> 00:57:05,040
Speaker 1: the number of visitors that her voter registration link received

1211
00:57:05,120 --> 00:57:09,239
Speaker 1: in the hours after she endorsed, so her link to

1212
00:57:09,320 --> 00:57:13,080
Speaker 1: register to vote brought in three hundred and six thousand

1213
00:57:13,760 --> 00:57:18,280
Speaker 1: visitors to vote dot gov. So you know, obviously it

1214
00:57:18,360 --> 00:57:22,200
Speaker 1: did something in terms of people being interested in registering

1215
00:57:22,240 --> 00:57:25,640
Speaker 1: to vote, and presumably since she endorsed Kamala Harris, many

1216
00:57:25,680 --> 00:57:28,400
Speaker 1: of those people will plan to vote for Kamala Harris.

1217
00:57:28,560 --> 00:57:30,520
Speaker 1: What do you think of the significance of this or

1218
00:57:30,560 --> 00:57:32,480
Speaker 1: do you think that it's very significant?

1219
00:57:33,640 --> 00:57:35,560
Speaker 12: You know, I tend to actually agree with Parker that

1220
00:57:35,600 --> 00:57:40,160
Speaker 12: I'm not sure it's going to be wildly, wildly, you know,

1221
00:57:40,200 --> 00:57:43,320
Speaker 12: significant to this election. I think what was interesting is

1222
00:57:43,360 --> 00:57:46,080
Speaker 12: I didn't feel like her heart was really in it.

1223
00:57:46,160 --> 00:57:49,560
Speaker 12: I mean, there hasn't been a rally with Kamala and Taylor,

1224
00:57:49,680 --> 00:57:51,520
Speaker 12: she hasn't put on a concert for her.

1225
00:57:51,800 --> 00:57:53,320
Speaker 6: It was just an Instagram post.

1226
00:57:53,640 --> 00:57:55,360
Speaker 3: And in the Instagram.

1227
00:57:54,880 --> 00:57:58,480
Speaker 12: Post was also kind of flirting with both sides ism

1228
00:57:58,520 --> 00:58:00,680
Speaker 12: at the beginning. In the end she sorts and vote

1229
00:58:00,680 --> 00:58:03,480
Speaker 12: for who you think would make the best president. And

1230
00:58:03,520 --> 00:58:06,960
Speaker 12: so it was not as strong as I think people

1231
00:58:07,000 --> 00:58:09,120
Speaker 12: are making it out to be. And you have that

1232
00:58:09,240 --> 00:58:11,560
Speaker 12: on top of the fact that she said her reason

1233
00:58:12,160 --> 00:58:16,320
Speaker 12: for endorsing Kamala's because she wants to combat misinformation. And

1234
00:58:16,560 --> 00:58:19,120
Speaker 12: a lot of people know that during a NATO meeting

1235
00:58:19,160 --> 00:58:23,360
Speaker 12: four years ago, the Pentagon's Psychological Operations Unit floated this

1236
00:58:23,480 --> 00:58:27,080
Speaker 12: idea of turning Taylor Swift into an asset for combating

1237
00:58:27,440 --> 00:58:30,080
Speaker 12: so called misinformation. So I think there's something really interesting

1238
00:58:30,120 --> 00:58:30,640
Speaker 12: going on there.

1239
00:58:31,120 --> 00:58:36,000
Speaker 1: Thank Taylor is a DP state that wast Well has

1240
00:58:36,040 --> 00:58:39,360
Speaker 1: floated the idea, so I mean impossible, And she cited

1241
00:58:39,360 --> 00:58:42,520
Speaker 1: misinformation in her statement, which I think is really interesting.

1242
00:58:43,040 --> 00:58:44,520
Speaker 6: But the other thing I want to say is that

1243
00:58:44,880 --> 00:58:46,240
Speaker 6: I think that.

1244
00:58:46,480 --> 00:58:48,560
Speaker 12: Young people are a lot more animated with the real

1245
00:58:48,600 --> 00:58:53,200
Speaker 12: issues than just a celebrity endorsement. Trump forced Kamala last

1246
00:58:53,320 --> 00:58:56,680
Speaker 12: night to say that he that she loves Israel, she

1247
00:58:56,720 --> 00:58:59,720
Speaker 12: supports Israel, she stands with Israel, after he accused her

1248
00:59:00,080 --> 00:59:03,280
Speaker 12: of saying of saying she hates Israel. So that to

1249
00:59:03,400 --> 00:59:05,080
Speaker 12: young people is going to be really significant as we

1250
00:59:05,120 --> 00:59:06,640
Speaker 12: look at what's happening in Gaza, and a lot of

1251
00:59:06,880 --> 00:59:10,280
Speaker 12: especially socially conscious left wing people who I think Kamala

1252
00:59:10,280 --> 00:59:12,800
Speaker 12: would consider her base, are really going to be alienated

1253
00:59:12,800 --> 00:59:14,720
Speaker 12: by that kind of rhetoric. I think that's more important

1254
00:59:14,720 --> 00:59:16,280
Speaker 12: to young people than a Taylor Swift endorsement.

1255
00:59:16,320 --> 00:59:17,840
Speaker 2: Well, Parker, what do you make of that, because if

1256
00:59:17,880 --> 00:59:20,640
Speaker 2: Vita is just I'm assuming giving like a political analysis,

1257
00:59:20,880 --> 00:59:23,360
Speaker 2: I believe we'd spoken previously last time we saw you

1258
00:59:23,400 --> 00:59:26,040
Speaker 2: in Chicago, and that's something christ and Ryan have been

1259
00:59:26,080 --> 00:59:28,440
Speaker 2: talking a lot about as well. You know, if anything,

1260
00:59:28,520 --> 00:59:32,080
Speaker 2: she's probably as forceful on the issue, at least on

1261
00:59:32,160 --> 00:59:34,480
Speaker 2: the substance that Joe Biden is. Do you think that's

1262
00:59:34,480 --> 00:59:35,720
Speaker 2: going to be a problem for her, Like, what do

1263
00:59:35,760 --> 00:59:37,080
Speaker 2: you see with younger voters.

1264
00:59:37,640 --> 00:59:41,240
Speaker 13: Well, younger voters care a lot about different issues. And

1265
00:59:41,320 --> 00:59:43,680
Speaker 13: again I don't speak Faull for all younger voters, but

1266
00:59:43,760 --> 00:59:46,400
Speaker 13: I will agree with your other guys. She started off

1267
00:59:46,400 --> 00:59:48,080
Speaker 13: by agreeing with me, and I do think she has

1268
00:59:48,120 --> 00:59:50,960
Speaker 13: a point. It's more about the policy. I watched that

1269
00:59:51,080 --> 00:59:53,960
Speaker 13: debate last night, and I saw Kamala Harris finally talk

1270
00:59:54,000 --> 00:59:55,920
Speaker 13: about a lot of policy issues that matter to me.

1271
00:59:56,240 --> 00:59:59,600
Speaker 13: I liked the opportunity economy stuff, and I was frustrated

1272
01:00:00,560 --> 01:00:05,640
Speaker 13: her her pandering to parts of the American electorate through

1273
01:00:05,720 --> 01:00:10,680
Speaker 13: the way that she talks about the genocide that is

1274
01:00:10,720 --> 01:00:15,440
Speaker 13: occurring in Gaza and I think she has shown me

1275
01:00:15,520 --> 01:00:18,440
Speaker 13: some hope at points on that issue by meeting with

1276
01:00:18,480 --> 01:00:21,320
Speaker 13: the Uncommitted movement, by trying to reach out to young voters.

1277
01:00:21,800 --> 01:00:23,840
Speaker 13: It is clear to me there's one candidate on the

1278
01:00:23,880 --> 01:00:28,200
Speaker 13: ballot that cares about human rights and does have at

1279
01:00:28,320 --> 01:00:33,440
Speaker 13: least some care for the human lives that are being

1280
01:00:33,480 --> 01:00:36,840
Speaker 13: lost in Gaza. Donald Trump does not care. And yes

1281
01:00:36,880 --> 01:00:38,960
Speaker 13: he did force her and push her to that position,

1282
01:00:39,200 --> 01:00:41,560
Speaker 13: but I think that kind of shows the difficult position

1283
01:00:41,760 --> 01:00:45,920
Speaker 13: that our American politics is in where it is so

1284
01:00:47,320 --> 01:00:50,320
Speaker 13: Israel has such power to the point where you can't

1285
01:00:50,440 --> 01:00:54,080
Speaker 13: really go out there in mainstream politics and you know,

1286
01:00:54,120 --> 01:00:57,240
Speaker 13: be on a presidential debate stage because APAC really is scary.

1287
01:00:57,320 --> 01:01:00,680
Speaker 13: Ask Andy Levin, who is you know? I went to

1288
01:01:00,760 --> 01:01:03,080
Speaker 13: University of Michigan. He's Jewish. He stood up for what

1289
01:01:03,160 --> 01:01:07,200
Speaker 13: was right, and they elected somebody far more pro war

1290
01:01:07,320 --> 01:01:10,640
Speaker 13: to his seat. I think that she is trying her

1291
01:01:10,680 --> 01:01:13,840
Speaker 13: best on the issue, but her best is definitely not enough.

1292
01:01:13,880 --> 01:01:15,960
Speaker 13: There's a lot that I would like to see, and

1293
01:01:16,040 --> 01:01:19,760
Speaker 13: I think that debate was her attempt to kind of, yes,

1294
01:01:20,640 --> 01:01:23,960
Speaker 13: find that middle ground and debate with Trump, But I

1295
01:01:24,000 --> 01:01:27,400
Speaker 13: really I didn't appreciate it. But I will say as

1296
01:01:27,440 --> 01:01:29,600
Speaker 13: I said, there is one candidate that cares about human

1297
01:01:29,680 --> 01:01:32,080
Speaker 13: rights in the Middle East. And I'm very close with

1298
01:01:32,240 --> 01:01:34,400
Speaker 13: rue Roman and I think she would love to come

1299
01:01:34,440 --> 01:01:37,760
Speaker 13: on y'all's show. She is a state representative from Georgia.

1300
01:01:37,880 --> 01:01:40,440
Speaker 13: She's the only Palestinian elective official in the state of Georgia,

1301
01:01:40,760 --> 01:01:43,120
Speaker 13: and we talk about this issue a lot. And for her,

1302
01:01:43,280 --> 01:01:46,360
Speaker 13: a Trump presidency looks like more of her family dying

1303
01:01:47,280 --> 01:01:50,600
Speaker 13: overseas and it really is terrible and Kamalin needs to

1304
01:01:50,600 --> 01:01:52,400
Speaker 13: step up. And Rua should have been a speaker at

1305
01:01:52,440 --> 01:01:54,640
Speaker 13: the DNC. That was a missed opportunity for the DNC

1306
01:01:55,040 --> 01:01:58,160
Speaker 13: to say, hey, let's hear Palestinian voices. I appreciate that

1307
01:01:58,240 --> 01:02:01,120
Speaker 13: Kamala could sit down with the Youngmited movement and hear

1308
01:02:01,160 --> 01:02:03,320
Speaker 13: their voices, but there is a lot more than needs

1309
01:02:03,320 --> 01:02:06,400
Speaker 13: to be done. Whoever, in Georgia, our rights are under

1310
01:02:06,440 --> 01:02:09,800
Speaker 13: attack every single day. People are dying in Georgia hospitals

1311
01:02:09,840 --> 01:02:13,080
Speaker 13: because they don't have access to reproductive rights, and that

1312
01:02:13,240 --> 01:02:15,480
Speaker 13: is frontline for a lot of voters. And I know

1313
01:02:15,880 --> 01:02:19,960
Speaker 13: Rua and I are both dedicated to electing Kamala. As

1314
01:02:20,040 --> 01:02:22,640
Speaker 13: frustrating as her position on this issue qvit.

1315
01:02:23,040 --> 01:02:26,440
Speaker 2: That is a key point that Parker raises is that

1316
01:02:26,520 --> 01:02:28,880
Speaker 2: the issue of abortion, you know, I would say equally

1317
01:02:28,920 --> 01:02:33,120
Speaker 2: animating probably to younger voters. What do you think then,

1318
01:02:33,240 --> 01:02:36,680
Speaker 2: about Trump's answers on the issue of abortion during that debate,

1319
01:02:36,720 --> 01:02:39,320
Speaker 2: which arguably could be just as consequential in swinging voters

1320
01:02:39,320 --> 01:02:41,000
Speaker 2: as well.

1321
01:02:41,000 --> 01:02:44,360
Speaker 12: Well, you know, I've it's an interesting question because I've

1322
01:02:44,400 --> 01:02:46,720
Speaker 12: really been disillusioned with the way that Trump has handled

1323
01:02:46,840 --> 01:02:50,200
Speaker 12: the abortion question over the last month or two. He's

1324
01:02:50,240 --> 01:02:53,200
Speaker 12: really turned the GOP much more to the pro choice

1325
01:02:53,240 --> 01:02:55,760
Speaker 12: side than I would like as a pro life person.

1326
01:02:56,000 --> 01:02:59,400
Speaker 12: Parker said that Kamala is somebody who cares about human rights,

1327
01:02:59,400 --> 01:03:02,160
Speaker 12: and in my opinionion killing unborn children in the womb

1328
01:03:02,280 --> 01:03:06,240
Speaker 12: is the epitome of spitting on human rights, and I

1329
01:03:06,240 --> 01:03:09,160
Speaker 12: think that is something that is egregious. And I liked

1330
01:03:09,200 --> 01:03:12,360
Speaker 12: that Trump during the debate for the first time, really

1331
01:03:12,400 --> 01:03:15,960
Speaker 12: started to put some pressure on Kamala and how radical

1332
01:03:16,160 --> 01:03:19,600
Speaker 12: the Democrats are with the pro with their pro abortion stance.

1333
01:03:19,640 --> 01:03:22,680
Speaker 12: I think a lot of Americans think abortion is safe, legal, rare.

1334
01:03:22,680 --> 01:03:25,240
Speaker 12: It's said in the early stages, and the reality is

1335
01:03:25,280 --> 01:03:30,480
Speaker 12: that Democrats have in Democrat controlled states permitted abortion at

1336
01:03:30,480 --> 01:03:31,080
Speaker 12: all stages.

1337
01:03:31,200 --> 01:03:32,280
Speaker 6: That's just a fact.

1338
01:03:32,720 --> 01:03:34,520
Speaker 12: You had the fact checkers come in and say, oh,

1339
01:03:34,520 --> 01:03:37,439
Speaker 12: this isn't true. There's no abortions after a baby is born.

1340
01:03:37,520 --> 01:03:39,760
Speaker 12: That actually has happened. There are five instances of this

1341
01:03:39,840 --> 01:03:42,200
Speaker 12: happening on the record. You can go to the Daily Signal.

1342
01:03:42,200 --> 01:03:43,040
Speaker 3: They've reported it out.

1343
01:03:43,040 --> 01:03:46,080
Speaker 12: But the truth is that Democrat states, when they have

1344
01:03:46,240 --> 01:03:50,160
Speaker 12: that option, will allow abortion at all stages. And this

1345
01:03:50,200 --> 01:03:53,200
Speaker 12: is a radical stance that I think most Americans don't

1346
01:03:53,280 --> 01:03:55,520
Speaker 12: agree with. And the other thing that I'll mention about

1347
01:03:55,560 --> 01:03:57,919
Speaker 12: human rights is I'm not really sure and I don't

1348
01:03:57,960 --> 01:03:59,520
Speaker 12: really think a lot of young people are sure that

1349
01:03:59,600 --> 01:04:02,080
Speaker 12: Kamala is going to bring peace to the world that

1350
01:04:02,200 --> 01:04:06,040
Speaker 12: she's at office, because we have right now the carnage

1351
01:04:06,120 --> 01:04:09,280
Speaker 12: that Trump also mentioned during the debate in Ukraine, and

1352
01:04:09,360 --> 01:04:12,360
Speaker 12: Kamala is not committed to ending this war, She's not

1353
01:04:12,400 --> 01:04:15,160
Speaker 12: committed to a peace deal. There is suffering. I mean,

1354
01:04:15,600 --> 01:04:18,760
Speaker 12: hundreds of thousands of people are dying because America keeps

1355
01:04:18,840 --> 01:04:22,040
Speaker 12: us in this funding this battle. That even Zelenski has

1356
01:04:22,040 --> 01:04:23,920
Speaker 12: now recently come out and said, we need to take

1357
01:04:23,920 --> 01:04:26,600
Speaker 12: a step back, we need to start negotiating a peace

1358
01:04:26,640 --> 01:04:30,520
Speaker 12: deal of some kind. I think that's really short sighted.

1359
01:04:30,560 --> 01:04:33,080
Speaker 12: I think that shows a real disrespect for human life

1360
01:04:33,560 --> 01:04:37,560
Speaker 12: and perhaps a symbol that Kamala is in bed with

1361
01:04:37,600 --> 01:04:41,080
Speaker 12: the military industrial complex, which makes money off of human

1362
01:04:41,120 --> 01:04:42,080
Speaker 12: lives being lost.

1363
01:04:42,320 --> 01:04:45,040
Speaker 6: Parking your respond there, well.

1364
01:04:44,800 --> 01:04:47,200
Speaker 13: I really would like to talk about Ukraine, because I

1365
01:04:47,240 --> 01:04:49,440
Speaker 13: do think it is interesting. But first I'm going to

1366
01:04:49,480 --> 01:04:53,120
Speaker 13: talk about abortion, because this is not something that we

1367
01:04:53,160 --> 01:04:57,880
Speaker 13: should beat around the bush on. We talk about human rights,

1368
01:04:57,920 --> 01:05:00,480
Speaker 13: and I hear where your panelist is coming. This is

1369
01:05:00,520 --> 01:05:02,680
Speaker 13: personal for a lot of people, but again, it is

1370
01:05:02,720 --> 01:05:06,840
Speaker 13: a personal choice. And in Georgia abortion is banned six weeks,

1371
01:05:07,240 --> 01:05:09,440
Speaker 13: as it is in South Carolina, as it is in

1372
01:05:09,480 --> 01:05:12,360
Speaker 13: many places all over the country. And every single day

1373
01:05:12,960 --> 01:05:16,240
Speaker 13: women are dying in Georgia because they don't have access

1374
01:05:16,480 --> 01:05:20,800
Speaker 13: to proper reproductive care. And reproductive care and women's rights

1375
01:05:20,960 --> 01:05:25,320
Speaker 13: are human rights. And you know, I have a girlfriend,

1376
01:05:25,400 --> 01:05:27,320
Speaker 13: We're very serious. We talk about having a kid, and

1377
01:05:27,320 --> 01:05:30,120
Speaker 13: she's scared to be pregnant in Georgia because you can't

1378
01:05:30,160 --> 01:05:33,760
Speaker 13: get proper medical care. I think human rights as people

1379
01:05:33,800 --> 01:05:37,160
Speaker 13: being able to have self determination and the choice about

1380
01:05:37,160 --> 01:05:40,240
Speaker 13: their own bodies. And while I respect you and your opinions.

1381
01:05:40,440 --> 01:05:43,880
Speaker 13: You can make that decision about your body. We are

1382
01:05:43,920 --> 01:05:46,520
Speaker 13: at a point in our lives and in our country

1383
01:05:46,720 --> 01:05:50,800
Speaker 13: where Donald Trump and the Republican majority, Republican appointed majority

1384
01:05:50,840 --> 01:05:53,800
Speaker 13: on the Supreme Court has taken away rights from people

1385
01:05:53,880 --> 01:05:56,960
Speaker 13: all across the country. And the truth is that a

1386
01:05:57,000 --> 01:06:00,400
Speaker 13: majority of Americans actually want to restore those right. I

1387
01:06:00,480 --> 01:06:02,600
Speaker 13: know you said that a majority don't feel that way.

1388
01:06:02,640 --> 01:06:05,960
Speaker 13: It's not true. Because you look at Kansas, they voted

1389
01:06:06,000 --> 01:06:08,760
Speaker 13: to protect abortion rights. You look at Michigan, they voted

1390
01:06:08,760 --> 01:06:13,520
Speaker 13: to protect abortion rights on November fifth. In Florida, they

1391
01:06:13,520 --> 01:06:16,200
Speaker 13: have the opportunity to protect abortion rights because they're trying

1392
01:06:16,200 --> 01:06:18,880
Speaker 13: to do They have a six week ban, which most

1393
01:06:18,920 --> 01:06:21,400
Speaker 13: women don't know they're pregnant, ends up preventing people from

1394
01:06:21,400 --> 01:06:25,680
Speaker 13: getting reproductive care, criminalizes healthcare. Doctors are unable to provide

1395
01:06:25,680 --> 01:06:29,440
Speaker 13: the care that women need, and they often put the

1396
01:06:29,480 --> 01:06:31,960
Speaker 13: health of the mother at risk. That's going to be

1397
01:06:31,960 --> 01:06:33,760
Speaker 13: on the ballot. Donald Trump's not going to vote for it.

1398
01:06:34,680 --> 01:06:35,479
Speaker 6: This is actually probably.

1399
01:06:35,720 --> 01:06:37,880
Speaker 13: That is as clear as it can be. Also, marijuana

1400
01:06:37,960 --> 01:06:42,880
Speaker 13: is on the ballot in Florida's statewide and that one

1401
01:06:43,160 --> 01:06:46,800
Speaker 13: Kamala Harris came out in favor of marijuana legalization recently.

1402
01:06:49,320 --> 01:06:51,560
Speaker 1: The other piece we wanted to get from you guys,

1403
01:06:51,600 --> 01:06:56,560
Speaker 1: which is this really massive emerging gender gap among gen z.

1404
01:06:57,200 --> 01:06:59,440
Speaker 1: David Hogg actually tweeted something about this that we found

1405
01:06:59,480 --> 01:07:02,520
Speaker 1: interesting and put up on the screen about how Democrats

1406
01:07:02,560 --> 01:07:05,280
Speaker 1: are struggling with young men. Go ahead and put d

1407
01:07:05,440 --> 01:07:08,160
Speaker 1: one up guys on the screen so we can read

1408
01:07:08,200 --> 01:07:12,280
Speaker 1: from this. Sure it is I hope I'm wrong, but

1409
01:07:12,320 --> 01:07:14,440
Speaker 1: if we lose in November, I think the main reason

1410
01:07:14,720 --> 01:07:16,600
Speaker 1: why will be the number of young men of all

1411
01:07:16,680 --> 01:07:19,480
Speaker 1: races that are no longer Democrats. There's been a taboo

1412
01:07:19,800 --> 01:07:22,560
Speaker 1: about talking about this because we understandably are hesitant to

1413
01:07:22,560 --> 01:07:25,200
Speaker 1: make men a main point of conversation, given we have

1414
01:07:25,280 --> 01:07:27,240
Speaker 1: been for thousands of years. But we have a real

1415
01:07:27,280 --> 01:07:29,560
Speaker 1: problem to deal with at this point, with sixty days ago,

1416
01:07:29,560 --> 01:07:31,240
Speaker 1: there isn't much we can do to recover it other

1417
01:07:31,280 --> 01:07:34,000
Speaker 1: than turning out more young women and trying to slow

1418
01:07:34,040 --> 01:07:36,200
Speaker 1: the departure of young men. I think a lot of

1419
01:07:36,240 --> 01:07:38,400
Speaker 1: this is caused by COVID, the epidemic of male loneliness

1420
01:07:38,400 --> 01:07:41,800
Speaker 1: in this country meansuing commodification through social media of misogyny.

1421
01:07:42,080 --> 01:07:43,400
Speaker 1: Long term We've got a lot of work to do

1422
01:07:43,480 --> 01:07:46,640
Speaker 1: to provide positive examples of what actual masculinity looks like

1423
01:07:46,920 --> 01:07:49,440
Speaker 1: that is not defined by putting down women or other people,

1424
01:07:49,440 --> 01:07:53,400
Speaker 1: but by lifting others up and being a true leader. Parker,

1425
01:07:53,440 --> 01:07:56,520
Speaker 1: what is what's your reaction? Is that your experience among

1426
01:07:56,640 --> 01:07:59,400
Speaker 1: the young gentlemen that you that you know.

1427
01:08:01,280 --> 01:08:06,720
Speaker 13: That's I've met David. He's an interesting guy, and I

1428
01:08:06,800 --> 01:08:09,840
Speaker 13: will say I appreciate him coming out and speaking out,

1429
01:08:09,880 --> 01:08:12,320
Speaker 13: and I think more young young men and young women

1430
01:08:12,480 --> 01:08:15,640
Speaker 13: should speak out. I know a lot of young men

1431
01:08:15,680 --> 01:08:18,519
Speaker 13: that are Democrats that are very much involved for different reasons.

1432
01:08:18,560 --> 01:08:20,880
Speaker 13: I got involved because I lost my dad at a

1433
01:08:20,880 --> 01:08:24,160
Speaker 13: young age and I understood the impact of good governance.

1434
01:08:24,520 --> 01:08:26,920
Speaker 13: I have a public policy degree because I knew I

1435
01:08:26,920 --> 01:08:28,960
Speaker 13: wanted to make a difference. But I have friends that

1436
01:08:29,000 --> 01:08:32,640
Speaker 13: are involved for all different reasons. And I know so

1437
01:08:32,720 --> 01:08:36,040
Speaker 13: many young men that have stood up, stepped up and

1438
01:08:36,240 --> 01:08:38,880
Speaker 13: really trying to make a difference in their communities, whether

1439
01:08:38,880 --> 01:08:41,760
Speaker 13: that's on the city council or the county commission. You know,

1440
01:08:41,920 --> 01:08:45,040
Speaker 13: I've got a best friend who's a teacher named Bryce Berry.

1441
01:08:45,080 --> 01:08:47,559
Speaker 13: He's twenty three years old. He's running for Statehouse in Georgia,

1442
01:08:47,600 --> 01:08:49,519
Speaker 13: and he's gonna win. There's no way he doesn't win.

1443
01:08:50,040 --> 01:08:54,000
Speaker 13: And I really think that young men can set an example.

1444
01:08:54,280 --> 01:08:57,200
Speaker 13: And you know, I'm from Atlanta. I know so many

1445
01:08:57,280 --> 01:09:01,000
Speaker 13: amazing men that show me what good mass kilnity looks like.

1446
01:09:01,040 --> 01:09:04,160
Speaker 13: That show me what giving back to your community looks like.

1447
01:09:04,439 --> 01:09:06,280
Speaker 13: And I'll end on the point that I think a

1448
01:09:06,320 --> 01:09:09,760
Speaker 13: good man looks like Tim Waltz, Somebody who cares, somebody

1449
01:09:09,800 --> 01:09:12,800
Speaker 13: who you can trust, somebody if you had a flat tire,

1450
01:09:12,880 --> 01:09:15,360
Speaker 13: they pull over the side of road and help you

1451
01:09:15,439 --> 01:09:18,120
Speaker 13: change it. And I think that's more of what our

1452
01:09:18,160 --> 01:09:20,120
Speaker 13: party should be elevating. And I think the choice of

1453
01:09:20,120 --> 01:09:22,960
Speaker 13: Tim Waltz demonstrates that. And I do think young men

1454
01:09:23,000 --> 01:09:25,000
Speaker 13: are going to surprise a lot of people and may

1455
01:09:25,040 --> 01:09:28,000
Speaker 13: hopefully surprise David in November, because I think we're going

1456
01:09:28,040 --> 01:09:30,320
Speaker 13: to turn out and show out, and at least in Georgia,

1457
01:09:30,400 --> 01:09:34,320
Speaker 13: I know young men are very much aware of what

1458
01:09:34,439 --> 01:09:37,320
Speaker 13: is at stake and how women's rights have been taken away.

1459
01:09:37,400 --> 01:09:39,320
Speaker 2: I think that's possible, but there's a lot of data

1460
01:09:39,400 --> 01:09:40,160
Speaker 2: that shows otherwise.

1461
01:09:40,240 --> 01:09:42,280
Speaker 3: Just put this a D two please up on the screen.

1462
01:09:42,320 --> 01:09:44,280
Speaker 2: This is originally guys, what we were all going to

1463
01:09:44,320 --> 01:09:46,479
Speaker 2: talk about the last time that we wanted to see you.

1464
01:09:46,800 --> 01:09:49,000
Speaker 2: If you look at the gen Z gender gap here

1465
01:09:49,160 --> 01:09:52,920
Speaker 2: for younger men specifically eighteen to twenty nine, you have

1466
01:09:53,080 --> 01:09:56,479
Speaker 2: men both overall but also between eighteen to twenty nine

1467
01:09:56,479 --> 01:09:59,639
Speaker 2: who are supporting Trump and almost over a plus ten

1468
01:09:59,800 --> 01:10:03,360
Speaker 2: mar while you have women, younger women specifically, almost at

1469
01:10:03,360 --> 01:10:07,960
Speaker 2: a plus eighteen margin there for Democrats, specifically for Kamala Harris,

1470
01:10:08,040 --> 01:10:10,400
Speaker 2: the gender gap actually gets even bigger whenever you look

1471
01:10:10,439 --> 01:10:11,519
Speaker 2: at thirty to forty four.

1472
01:10:11,560 --> 01:10:12,720
Speaker 3: So, my fellow.

1473
01:10:12,600 --> 01:10:15,880
Speaker 2: Millennials, but Ivita, why do you think then it is

1474
01:10:15,960 --> 01:10:19,680
Speaker 2: that Democrats are winning so much with younger women, you know,

1475
01:10:20,200 --> 01:10:23,120
Speaker 2: Taylor Swift demographic, I guess, But that Donald Trump is

1476
01:10:23,320 --> 01:10:25,080
Speaker 2: doing so well with younger men.

1477
01:10:26,240 --> 01:10:28,160
Speaker 12: Well, let me just start by saying that I don't

1478
01:10:28,160 --> 01:10:31,000
Speaker 12: think Tim Walls is a masculine man. I don't think

1479
01:10:31,040 --> 01:10:33,040
Speaker 12: any young men are looking to him as a role model.

1480
01:10:33,040 --> 01:10:35,280
Speaker 12: This is the guy that put tampon's in the boy's

1481
01:10:35,360 --> 01:10:39,360
Speaker 12: bathroom and who's also charged with making a city safe

1482
01:10:39,360 --> 01:10:41,599
Speaker 12: for the state of Minnesota, and he let it burn

1483
01:10:41,680 --> 01:10:45,200
Speaker 12: to the ground during BLM riding. So that's something to

1484
01:10:46,040 --> 01:10:46,640
Speaker 12: take note of.

1485
01:10:46,920 --> 01:10:48,599
Speaker 6: But I want to get to like the real heart

1486
01:10:48,600 --> 01:10:48,840
Speaker 6: of this.

1487
01:10:49,040 --> 01:10:53,040
Speaker 12: I think that third wave feminism has really disrupted the

1488
01:10:53,160 --> 01:10:56,280
Speaker 12: natural order between men and women, and I think men

1489
01:10:56,360 --> 01:11:00,960
Speaker 12: feel really lost in modern society. This idea that the

1490
01:11:01,000 --> 01:11:05,200
Speaker 12: future is female. It's turned out to be female domination

1491
01:11:05,680 --> 01:11:10,720
Speaker 12: over the masculine. And we're experiencing this mass feminization of society,

1492
01:11:10,800 --> 01:11:14,160
Speaker 12: encouraging men to be more effeminate, telling them to cry more,

1493
01:11:14,360 --> 01:11:17,360
Speaker 12: don't be too masculine, don't be too assertive, don't sit

1494
01:11:17,439 --> 01:11:21,240
Speaker 12: a certain way, don't have male only spaces, and they're

1495
01:11:21,280 --> 01:11:24,439
Speaker 12: even being told that they're very nature right. Masculinity is toxic.

1496
01:11:25,200 --> 01:11:27,880
Speaker 12: And you have the system itself set up against them,

1497
01:11:27,880 --> 01:11:32,320
Speaker 12: with scholarships and college admissions being rigged against men, particularly

1498
01:11:32,360 --> 01:11:35,080
Speaker 12: white men, and you're seeing men just give up. You

1499
01:11:35,080 --> 01:11:37,920
Speaker 12: have male suicide, depression, anxiety. It's through the roof. Men

1500
01:11:37,920 --> 01:11:40,439
Speaker 12: make up eighty percent of suicides in America, and a

1501
01:11:40,439 --> 01:11:42,120
Speaker 12: lot of guys are just saying, you know, we need

1502
01:11:42,200 --> 01:11:45,559
Speaker 12: to return to normalcy, we need to return to order.

1503
01:11:45,920 --> 01:11:48,000
Speaker 6: And that's why I think the phrase make.

1504
01:11:47,840 --> 01:11:50,320
Speaker 12: America great again. Whether you think it's dumb or not,

1505
01:11:50,840 --> 01:11:53,680
Speaker 12: it has a real effect with men who feel like

1506
01:11:53,800 --> 01:11:56,840
Speaker 12: the culture does not respect them, and that their unique

1507
01:11:56,880 --> 01:12:00,320
Speaker 12: contributions to society as men are not respected either. To

1508
01:12:00,360 --> 01:12:03,639
Speaker 12: return to the past and the normalcy of times before.

1509
01:12:04,040 --> 01:12:05,200
Speaker 6: Go ahead, Parker, what do you think.

1510
01:12:06,000 --> 01:12:08,639
Speaker 13: I don't think make America great again and is dumb.

1511
01:12:09,040 --> 01:12:12,519
Speaker 13: I think it's dangerous. I think a lot of this

1512
01:12:12,680 --> 01:12:18,280
Speaker 13: idea of what Trump is pushing as masculinity is actually toxic.

1513
01:12:18,479 --> 01:12:21,400
Speaker 13: I've never had somebody tell me that, you know, me

1514
01:12:21,479 --> 01:12:23,960
Speaker 13: playing basketball or hang out my friends at a bar

1515
01:12:24,800 --> 01:12:28,360
Speaker 13: is too masculine. But you know, I truly think the

1516
01:12:28,400 --> 01:12:31,960
Speaker 13: ideals that Trump is pushing for what masculinity is is

1517
01:12:32,000 --> 01:12:34,880
Speaker 13: more about hating women. You look at him on all

1518
01:12:34,880 --> 01:12:40,000
Speaker 13: these right wing podcasts. They are misogynists. They're divisive, and honestly,

1519
01:12:40,439 --> 01:12:43,160
Speaker 13: I do think Tim Waltz is a masculine man. He's

1520
01:12:43,200 --> 01:12:48,000
Speaker 13: a state championship winning football coach, and you know, I

1521
01:12:48,120 --> 01:12:49,960
Speaker 13: look up to him and I see a lot of

1522
01:12:50,000 --> 01:12:51,760
Speaker 13: other good men in my life where you know what,

1523
01:12:51,880 --> 01:12:55,200
Speaker 13: it's not bad to be loving and caring and joyful,

1524
01:12:55,400 --> 01:12:57,680
Speaker 13: and I think to be masculine is yeah, to be

1525
01:12:57,720 --> 01:13:00,519
Speaker 13: strong and tough, but to also be a person. And

1526
01:13:00,640 --> 01:13:04,080
Speaker 13: the ideal of what Trump and the right and people

1527
01:13:04,120 --> 01:13:07,040
Speaker 13: like Aidan Ross are pushing for men to be is

1528
01:13:07,080 --> 01:13:11,439
Speaker 13: in fact toxic. And yes, our politics is so divided

1529
01:13:11,760 --> 01:13:14,720
Speaker 13: and it's really frustrating, and there is an epidemic young

1530
01:13:14,880 --> 01:13:17,479
Speaker 13: among young men. I see it, I see it, we

1531
01:13:17,520 --> 01:13:19,400
Speaker 13: all see it. I'm not going to pretend that the

1532
01:13:19,520 --> 01:13:23,640
Speaker 13: gender divide does not exist in politics. It is stronger,

1533
01:13:24,360 --> 01:13:27,280
Speaker 13: just about the strongest divie we have in America politics,

1534
01:13:27,600 --> 01:13:30,519
Speaker 13: and it is more stark among young men because of COVID,

1535
01:13:30,720 --> 01:13:34,120
Speaker 13: because people are disconnected and young men are going to

1536
01:13:34,120 --> 01:13:37,080
Speaker 13: college at way lesser rates. Anyone that watches this show

1537
01:13:37,120 --> 01:13:39,479
Speaker 13: knows that there is a crisis among young men. But

1538
01:13:39,560 --> 01:13:43,840
Speaker 13: I think then watching these far right podcasts only isolates

1539
01:13:43,880 --> 01:13:46,280
Speaker 13: them more. I was watching the PBS News Hour last

1540
01:13:46,360 --> 01:13:48,719
Speaker 13: night and they had a former long time bright Bart

1541
01:13:48,760 --> 01:13:51,840
Speaker 13: contributor on there, and she was talking about how much

1542
01:13:51,880 --> 01:13:55,120
Speaker 13: she regretted being a part of the far right ecosystem

1543
01:13:55,360 --> 01:13:58,000
Speaker 13: and how it just encouraged people to get more and

1544
01:13:58,040 --> 01:14:01,920
Speaker 13: more far to the right, to push more and more

1545
01:14:02,040 --> 01:14:05,760
Speaker 13: dangerous rhetoric, to get more attention, to boost each other's networks.

1546
01:14:06,000 --> 01:14:09,200
Speaker 13: And I think what Trump is doing is appealing to

1547
01:14:10,760 --> 01:14:14,559
Speaker 13: this problem among young men. This insecurity. He is using

1548
01:14:14,560 --> 01:14:17,320
Speaker 13: it and taking advantage of young men and trying to

1549
01:14:17,439 --> 01:14:20,680
Speaker 13: lean on their fears and their insecurities to say, hey,

1550
01:14:20,720 --> 01:14:22,760
Speaker 13: then we aren't heard, so we have to go back

1551
01:14:22,800 --> 01:14:25,639
Speaker 13: and make America great again. You know, some men see

1552
01:14:25,720 --> 01:14:27,280
Speaker 13: things as they are and ask why I want to

1553
01:14:27,320 --> 01:14:29,559
Speaker 13: go back. I want to dream of things as they

1554
01:14:29,600 --> 01:14:32,599
Speaker 13: could be. And I don't know how you can look

1555
01:14:32,680 --> 01:14:35,040
Speaker 13: at Tim Waltzon and think that's not what a good

1556
01:14:35,080 --> 01:14:37,000
Speaker 13: man looks like. I mean, you don't have to be,

1557
01:14:37,439 --> 01:14:39,679
Speaker 13: you know, the strongest guy in the world, the meanest,

1558
01:14:39,720 --> 01:14:42,639
Speaker 13: toughest guy. I think masculinity is being a good dad,

1559
01:14:42,680 --> 01:14:44,799
Speaker 13: a good neighbor, a good coach. I mean, I wouldn't

1560
01:14:44,840 --> 01:14:46,280
Speaker 13: trust Donald Trump or JD.

1561
01:14:46,360 --> 01:14:49,000
Speaker 2: Pans put yourself out of the Tim Walls discussion though,

1562
01:14:49,040 --> 01:14:51,680
Speaker 2: and you even have liberals who are talking about this.

1563
01:14:51,840 --> 01:14:55,080
Speaker 2: Let's put D three please up on the screen. Hassan

1564
01:14:55,600 --> 01:14:58,040
Speaker 2: actually put this out. I think he's absolutely correct. Every

1565
01:14:58,040 --> 01:15:01,040
Speaker 2: young male interest online, from game being, fitness to culture

1566
01:15:01,080 --> 01:15:03,679
Speaker 2: and self help is dominated by right wing red pilled

1567
01:15:03,680 --> 01:15:07,040
Speaker 2: manosphere commentary. He says they prey on the anxieties and

1568
01:15:07,040 --> 01:15:10,639
Speaker 2: insecurities of vulnerable young men. Take his last sentence out

1569
01:15:10,640 --> 01:15:12,479
Speaker 2: of there, and I think empirically he is correct, and

1570
01:15:12,479 --> 01:15:15,320
Speaker 2: I could say that from first hand experience. So Evita,

1571
01:15:15,439 --> 01:15:18,000
Speaker 2: I mean, do you think that that is a positive development.

1572
01:15:18,040 --> 01:15:20,960
Speaker 2: We've previously had people like Ali Betstucky here on the

1573
01:15:20,960 --> 01:15:24,080
Speaker 2: show to kind of speak out against Andrew Tate's you know,

1574
01:15:24,280 --> 01:15:28,720
Speaker 2: kind of influence amongst younger red pilled I guess men,

1575
01:15:28,840 --> 01:15:31,479
Speaker 2: So how do you see that dynamic specifically as a

1576
01:15:31,520 --> 01:15:32,400
Speaker 2: young woman on the right.

1577
01:15:33,640 --> 01:15:37,120
Speaker 12: Yeah, you know, Andrew Taite is a really interesting topic.

1578
01:15:37,400 --> 01:15:40,439
Speaker 12: I mean, I think as a conservative, as a Christian,

1579
01:15:40,840 --> 01:15:44,080
Speaker 12: the best role models for young men are their fathers,

1580
01:15:44,200 --> 01:15:46,080
Speaker 12: and I think also their father in heaven. I think

1581
01:15:46,120 --> 01:15:48,439
Speaker 12: you should. I think religion is something that's really lacking

1582
01:15:48,520 --> 01:15:50,960
Speaker 12: right now. But men have had to go to these

1583
01:15:51,160 --> 01:15:54,800
Speaker 12: surrogate fathers, right the Andrew Tates of the world, you know,

1584
01:15:55,240 --> 01:15:59,880
Speaker 12: even a Jordan Peterson. And you know, I don't They're imperfect,

1585
01:16:00,160 --> 01:16:03,640
Speaker 12: they can never be perfect. But I also think that

1586
01:16:03,720 --> 01:16:07,639
Speaker 12: it's a net positive on society to have men start

1587
01:16:07,720 --> 01:16:11,679
Speaker 12: to take back some agency instead of just playing video

1588
01:16:11,800 --> 01:16:15,720
Speaker 12: games and watching porn and becoming in cells. Like I

1589
01:16:15,760 --> 01:16:20,519
Speaker 12: think that there is a real spiritual and emotional uh,

1590
01:16:20,920 --> 01:16:24,920
Speaker 12: epidemic happening among young men, and to have them be assertive,

1591
01:16:25,280 --> 01:16:28,519
Speaker 12: embrace their masculinity, even if it's through an imperfect avenue

1592
01:16:28,560 --> 01:16:31,280
Speaker 12: like Andrew Tate, is a net positive on society. And

1593
01:16:31,320 --> 01:16:34,800
Speaker 12: I'll also say, you know, the figures that the left

1594
01:16:34,880 --> 01:16:38,879
Speaker 12: is elevating to young men are just objectively not not positive.

1595
01:16:38,920 --> 01:16:41,160
Speaker 12: I mean, we talked about Tim Wallashill also mentioned Doug

1596
01:16:41,240 --> 01:16:42,840
Speaker 12: m Hoff, right, they want him to be some sort

1597
01:16:42,840 --> 01:16:45,559
Speaker 12: of masculine figure in this election cycle. And this guy

1598
01:16:45,840 --> 01:16:48,840
Speaker 12: impregnated his kid's nanny, right, and he can't control his

1599
01:16:48,920 --> 01:16:49,760
Speaker 12: sexual appetites.

1600
01:16:49,800 --> 01:16:50,759
Speaker 6: That's very masculine.

1601
01:16:51,720 --> 01:16:53,479
Speaker 1: I have to cut you off there, because it's not

1602
01:16:53,560 --> 01:16:57,320
Speaker 1: like Donald Trump has had the most you know, by

1603
01:16:57,360 --> 01:16:58,040
Speaker 1: the book.

1604
01:16:58,240 --> 01:17:02,160
Speaker 6: Christian Family Life. No, no, no, I is he a

1605
01:17:02,200 --> 01:17:04,080
Speaker 6: good positive role model for young men?

1606
01:17:05,360 --> 01:17:05,479
Speaker 5: No?

1607
01:17:05,560 --> 01:17:06,400
Speaker 6: I agree with that.

1608
01:17:06,439 --> 01:17:07,960
Speaker 12: I mean that's why I said this. These these are

1609
01:17:07,960 --> 01:17:11,880
Speaker 12: imperfect avenues. But there was there's specifically been a push

1610
01:17:11,920 --> 01:17:13,880
Speaker 12: to have Doug m Hoff and Tim Willalls be the

1611
01:17:14,000 --> 01:17:17,320
Speaker 12: new new masculine figures that I'm speaking to.

1612
01:17:17,520 --> 01:17:20,280
Speaker 1: Obviously a big push to make Donald Trump the sort

1613
01:17:20,280 --> 01:17:23,400
Speaker 1: of epitome of the masculine ideal. But I also want

1614
01:17:23,400 --> 01:17:24,840
Speaker 1: to flip the script at you of Vita, because if

1615
01:17:24,880 --> 01:17:26,800
Speaker 1: we can put the numbers back on the screen D two,

1616
01:17:27,520 --> 01:17:30,400
Speaker 1: you know, it's the gender gap goes both ways, right.

1617
01:17:30,760 --> 01:17:33,519
Speaker 1: So you have young men who favor Trump by you know,

1618
01:17:33,560 --> 01:17:37,360
Speaker 1: in this polling somewhere around plus ten, young women favoring

1619
01:17:37,439 --> 01:17:44,720
Speaker 1: Kamala Harris by almost forty points. So what is your analysis,

1620
01:17:44,920 --> 01:17:50,360
Speaker 1: as a Republican woman of why so many young women

1621
01:17:50,760 --> 01:17:54,280
Speaker 1: are so turned off from the Republican Party and some

1622
01:17:54,360 --> 01:17:57,320
Speaker 1: of the rhetoric and ideology and positioning coming from there.

1623
01:17:58,360 --> 01:17:59,760
Speaker 12: Well, I can tell you I think that there are

1624
01:18:00,120 --> 01:18:02,240
Speaker 12: to the Democratic Party. I mean, I think that the

1625
01:18:02,280 --> 01:18:07,160
Speaker 12: abortion issue is in this abortion being the women's issue

1626
01:18:07,200 --> 01:18:09,960
Speaker 12: of our time has been really animating for a lot

1627
01:18:10,000 --> 01:18:11,720
Speaker 12: of young women. I personally don't agree with it, but

1628
01:18:11,760 --> 01:18:14,719
Speaker 12: I think the left has done a really effective job

1629
01:18:14,840 --> 01:18:17,360
Speaker 12: of making women feel like this is a right of

1630
01:18:17,400 --> 01:18:20,600
Speaker 12: theirs and that it is being taken away by Republicans.

1631
01:18:20,640 --> 01:18:22,720
Speaker 12: I think they've done a really effective job messaging that way.

1632
01:18:22,760 --> 01:18:24,960
Speaker 12: But what I will also say is that I know

1633
01:18:25,240 --> 01:18:26,879
Speaker 12: a lot. I mean, I went to college, I graduated

1634
01:18:26,880 --> 01:18:31,920
Speaker 12: not that long ago, and the left wing lifestyle has

1635
01:18:32,000 --> 01:18:34,559
Speaker 12: been really damaging on young women. I think a lot

1636
01:18:34,600 --> 01:18:36,719
Speaker 12: of women think that they want a Doug m Hoff

1637
01:18:37,040 --> 01:18:39,680
Speaker 12: to marry or be with, and the reality is that

1638
01:18:39,720 --> 01:18:42,600
Speaker 12: they don't. I think young women actually are attracted to

1639
01:18:42,720 --> 01:18:45,240
Speaker 12: masculine men. I think that this idea that they're going

1640
01:18:45,280 --> 01:18:48,000
Speaker 12: to be a boss babe, and you know, fill in this,

1641
01:18:48,000 --> 01:18:50,519
Speaker 12: this corpor climb, the corporate ladder. I think a lot

1642
01:18:50,600 --> 01:18:54,280
Speaker 12: of these things actually have left women deeply unhappy. The

1643
01:18:54,320 --> 01:18:59,080
Speaker 12: most unhappy demographic in America is single women. The most

1644
01:18:59,120 --> 01:19:03,360
Speaker 12: mentally ill graphic in America is liberal women. That means something,

1645
01:19:03,960 --> 01:19:06,720
Speaker 12: and so I think focusing on your love life. I

1646
01:19:06,720 --> 01:19:10,720
Speaker 12: think embracing tradition is the answer for a lot of

1647
01:19:10,720 --> 01:19:13,679
Speaker 12: young women who are also feeling disillusioned with life. It's

1648
01:19:13,680 --> 01:19:17,120
Speaker 12: not just young men, but unfortunately they continue to vote

1649
01:19:17,160 --> 01:19:21,280
Speaker 12: for people who are promoting an ideology that is making

1650
01:19:21,320 --> 01:19:22,520
Speaker 12: people deeply unhappy.

1651
01:19:23,120 --> 01:19:28,800
Speaker 14: Parker, last thoughts to you, Well, I will say they

1652
01:19:28,840 --> 01:19:33,800
Speaker 14: are not perfect figures, these false dads that you as.

1653
01:19:33,840 --> 01:19:36,400
Speaker 13: The way you described it, that the right has people

1654
01:19:36,439 --> 01:19:40,640
Speaker 13: like Andrew Tait and Donald Trump being a model for masculinity.

1655
01:19:41,200 --> 01:19:43,960
Speaker 13: I think, again, the gender divide in politics is a

1656
01:19:44,000 --> 01:19:48,519
Speaker 13: lot because the Right is exploiting young men, and I

1657
01:19:48,520 --> 01:19:51,760
Speaker 13: think young women are scared of people like Andrew Tate

1658
01:19:52,120 --> 01:19:55,440
Speaker 13: because he's a sexual predator. And when we are elevating

1659
01:19:55,600 --> 01:19:58,479
Speaker 13: people like that, people like Aiden Ross who say racist

1660
01:19:58,479 --> 01:20:01,040
Speaker 13: and misogynist things on stream, when Donald Trump is giving

1661
01:20:01,080 --> 01:20:04,639
Speaker 13: them a platform, appealing to them, pandering to those voters,

1662
01:20:04,920 --> 01:20:09,400
Speaker 13: it's disgusting. And he's taking advantage of the insecurities of

1663
01:20:09,439 --> 01:20:12,439
Speaker 13: young men. He's telling them that they aren't masculine enough.

1664
01:20:12,640 --> 01:20:17,360
Speaker 13: These right wing figures, these bloggers, they're manipulating people to,

1665
01:20:17,720 --> 01:20:21,320
Speaker 13: you know, say that there is this crazy, you know,

1666
01:20:22,120 --> 01:20:27,719
Speaker 13: masculinity issue, and when the truth is yall are obsessed

1667
01:20:27,720 --> 01:20:30,639
Speaker 13: with Doug m Hoff and Tim Walls being the ideal man.

1668
01:20:30,680 --> 01:20:33,559
Speaker 13: They're not who I look to for masculinity in my life.

1669
01:20:33,640 --> 01:20:37,320
Speaker 13: I'm not spending all day thinking about masculinity. I think

1670
01:20:37,880 --> 01:20:40,520
Speaker 13: I'm more thinking about how to do a good job

1671
01:20:40,640 --> 01:20:43,040
Speaker 13: and how to get things done. I just be a man.

1672
01:20:45,000 --> 01:20:49,360
Speaker 6: Oh no, no, oh, there we go. You cut off

1673
01:20:49,360 --> 01:20:52,040
Speaker 6: for a cashf But hey, both.

1674
01:20:51,840 --> 01:20:54,599
Speaker 1: Of you guys, I really appreciate Yeah, I really appreciate

1675
01:20:54,640 --> 01:20:57,920
Speaker 1: having both of you today and enjoyed the conversation a lot.

1676
01:20:58,000 --> 01:20:59,519
Speaker 1: So I hope we can do it again. Thank you

1677
01:20:59,560 --> 01:21:00,639
Speaker 1: so much for time this morning.

1678
01:21:00,640 --> 01:21:01,080
Speaker 3: Thanks guys.

1679
01:21:01,120 --> 01:21:03,680
Speaker 2: We'll have links to Vita's show in the bio and

1680
01:21:03,720 --> 01:21:05,759
Speaker 2: to Parker's Parker, what do you prefer Instagram?

1681
01:21:05,760 --> 01:21:06,959
Speaker 3: Should we put that on Instagram?

1682
01:21:06,960 --> 01:21:09,439
Speaker 13: All right, let's do that talking you Hey, Sorry, y'all.

1683
01:21:09,520 --> 01:21:11,720
Speaker 13: I really appreciate you having me and Avida. Thank you.

1684
01:21:11,880 --> 01:21:13,280
Speaker 13: It was really nice to talk to you. I thought

1685
01:21:13,280 --> 01:21:14,840
Speaker 13: this was a good conversation.

1686
01:21:15,040 --> 01:21:15,559
Speaker 3: I enjoyed it.

1687
01:21:15,600 --> 01:21:16,920
Speaker 6: We enjured to talk to you.

1688
01:21:17,760 --> 01:21:19,400
Speaker 3: Thanks guys.

1689
01:21:18,600 --> 01:21:18,960
Speaker 10: Pleasure.

1690
01:21:22,080 --> 01:21:23,760
Speaker 2: At the same time, we want to turn now to

1691
01:21:23,960 --> 01:21:26,920
Speaker 2: a major anti trust case against Google, of which the

1692
01:21:26,960 --> 01:21:28,200
Speaker 2: details are super interesting.

1693
01:21:28,280 --> 01:21:30,240
Speaker 3: Let's put this up there on the screen.

1694
01:21:30,400 --> 01:21:34,400
Speaker 2: Major blockbuster anti trust case again after a judge has

1695
01:21:34,479 --> 01:21:37,920
Speaker 2: ruled that the company stifled competition in search, and another

1696
01:21:38,000 --> 01:21:41,040
Speaker 2: trial opens on ad tech practices, So it's actually here

1697
01:21:41,320 --> 01:21:44,719
Speaker 2: in Northern Virginia, the company is facing a federal lawsuit

1698
01:21:44,760 --> 01:21:48,760
Speaker 2: alleging it illegally monopolizes the ad tech workplace, second such

1699
01:21:48,800 --> 01:21:52,000
Speaker 2: case in less than a year. Opening arguments were made

1700
01:21:52,000 --> 01:21:55,480
Speaker 2: in the case on Monday, saying Google has quote unlawful

1701
01:21:55,479 --> 01:21:58,439
Speaker 2: grip on the market for software used to buy and

1702
01:21:58,520 --> 01:22:02,200
Speaker 2: sell digital ads. The trial is going to last approximately

1703
01:22:02,280 --> 01:22:06,280
Speaker 2: a month in northern Virginia, and it could face major

1704
01:22:06,360 --> 01:22:09,160
Speaker 2: implications for Google in terms of how it would get

1705
01:22:09,160 --> 01:22:11,880
Speaker 2: broken up and what the overall marketplace would look like.

1706
01:22:11,960 --> 01:22:14,959
Speaker 2: In addition to its even inability to buy bigger companies

1707
01:22:15,200 --> 01:22:18,759
Speaker 2: in the future. The search case must now quote decide

1708
01:22:18,760 --> 01:22:22,599
Speaker 2: how to remedy Google's antitrust violations, which could mean limiting

1709
01:22:22,600 --> 01:22:26,000
Speaker 2: its ability to pay web browsers and phone manufacturers to

1710
01:22:26,080 --> 01:22:29,960
Speaker 2: be their default search engine. This includes a multi billion

1711
01:22:29,960 --> 01:22:33,320
Speaker 2: dollar deal with Apple for the iPhone with Samsung, of course,

1712
01:22:33,320 --> 01:22:36,880
Speaker 2: which runs the Android ecosystem, and it says if the

1713
01:22:36,880 --> 01:22:40,679
Speaker 2: company loses in Virginia, back to backblows would crimp company

1714
01:22:40,720 --> 01:22:43,360
Speaker 2: revenue at a time when it is pouring money into

1715
01:22:43,479 --> 01:22:47,680
Speaker 2: artificial intelligence to compete with Microsoft. So that actually might

1716
01:22:47,720 --> 01:22:51,200
Speaker 2: be possibly the most significant thing. Something that's happening right

1717
01:22:51,240 --> 01:22:53,040
Speaker 2: now with a lot of these big tech companies is

1718
01:22:53,040 --> 01:22:56,760
Speaker 2: that they're using ad monopoly or ad search dollars to

1719
01:22:56,960 --> 01:22:59,960
Speaker 2: fund what has now been a crapshoot so far in aim.

1720
01:23:00,920 --> 01:23:03,800
Speaker 2: It sounds stupid, but the theory for all of them

1721
01:23:04,000 --> 01:23:07,080
Speaker 2: is if you dump billions into this, whoever wins is

1722
01:23:07,120 --> 01:23:09,800
Speaker 2: going to roll up all the potential gains of AI.

1723
01:23:10,120 --> 01:23:12,840
Speaker 2: So the upside of the bet is so asymmetric that

1724
01:23:12,880 --> 01:23:15,960
Speaker 2: it's worth burning as much capital as you can right now.

1725
01:23:16,000 --> 01:23:18,040
Speaker 2: And it sounds like they're more profitable than God, so

1726
01:23:18,240 --> 01:23:20,759
Speaker 2: it almost doesn't really matter. They're still posting record returns

1727
01:23:20,760 --> 01:23:23,360
Speaker 2: and doing sheer buybacks. Yeah, but the problem is that

1728
01:23:23,400 --> 01:23:26,000
Speaker 2: if you turn off the spigot for Google, it would

1729
01:23:26,080 --> 01:23:29,240
Speaker 2: essentially wipe them out of the AI race. I actually

1730
01:23:29,240 --> 01:23:32,080
Speaker 2: support that because one of the things I've been really

1731
01:23:32,120 --> 01:23:35,400
Speaker 2: concerned about is that the sheer cost of AI and

1732
01:23:35,439 --> 01:23:39,760
Speaker 2: of data centers just means that the big actors are

1733
01:23:39,800 --> 01:23:42,400
Speaker 2: the ones who are most likely to win. You have

1734
01:23:42,479 --> 01:23:46,639
Speaker 2: to spend tens of billions crunching all of this data

1735
01:23:46,960 --> 01:23:50,040
Speaker 2: on energy costs, you know, all of the chips and video.

1736
01:23:50,160 --> 01:23:52,559
Speaker 3: Obviously that's why it's profits are up so much.

1737
01:23:52,760 --> 01:23:55,280
Speaker 2: But that just means that the barrier to entry is

1738
01:23:55,360 --> 01:23:58,880
Speaker 2: so high that big tech almost certainly is going to win.

1739
01:23:59,120 --> 01:24:01,960
Speaker 2: Or what's happened with open AI is you just sign

1740
01:24:02,000 --> 01:24:05,400
Speaker 2: a major, multi billion dollar deal with Microsoft and you

1741
01:24:05,520 --> 01:24:07,559
Speaker 2: let them, you know, sab size all that stuff and

1742
01:24:07,560 --> 01:24:09,400
Speaker 2: you fuse the two companies together.

1743
01:24:09,479 --> 01:24:10,439
Speaker 3: Yeah, it's really concerning.

1744
01:24:10,560 --> 01:24:12,800
Speaker 1: No that's that's absolutely the case, and I mean it

1745
01:24:12,800 --> 01:24:15,120
Speaker 1: looks like it's sort of it's already happening. You know,

1746
01:24:15,160 --> 01:24:17,720
Speaker 1: it is almost our no point done that it's going

1747
01:24:17,760 --> 01:24:20,320
Speaker 1: to be. You know, they the big players already, are

1748
01:24:20,360 --> 01:24:22,160
Speaker 1: the ones that are spending the money already, are the

1749
01:24:22,200 --> 01:24:24,760
Speaker 1: ones that are pushing the tech forward. Just to go

1750
01:24:24,840 --> 01:24:27,240
Speaker 1: back to some of the details here, because this is

1751
01:24:27,240 --> 01:24:29,960
Speaker 1: something of course Matt Stiller of It has big substack

1752
01:24:30,040 --> 01:24:34,120
Speaker 1: has been covering and it is extremely consequential. And it's

1753
01:24:34,160 --> 01:24:39,840
Speaker 1: important to understand. Google is facing anti trust issues on

1754
01:24:40,280 --> 01:24:44,760
Speaker 1: multiple fronts. So there was a civil case against them

1755
01:24:44,960 --> 01:24:47,960
Speaker 1: that under that had to do with their control of

1756
01:24:48,000 --> 01:24:51,320
Speaker 1: the Android App Store, was a fight with Epic Games.

1757
01:24:51,640 --> 01:24:54,479
Speaker 1: Then the second suit, the second case against them from

1758
01:24:54,479 --> 01:24:58,480
Speaker 1: the DJ that Saga reference was over its search monopoly.

1759
01:24:59,200 --> 01:25:03,880
Speaker 1: This is a third issue for Google regarding their ad

1760
01:25:04,040 --> 01:25:08,000
Speaker 1: sales and so effectively. And I'll just read a little

1761
01:25:08,000 --> 01:25:11,200
Speaker 1: bit of how Stoler describes what they did and why

1762
01:25:11,400 --> 01:25:14,400
Speaker 1: you know there's a legitimate case against them. He says

1763
01:25:14,400 --> 01:25:17,560
Speaker 1: they locked in both sides of a network engaged in surveillance,

1764
01:25:17,800 --> 01:25:21,519
Speaker 1: thwarted rivals through course of practices and unlawful acquisitions and

1765
01:25:21,520 --> 01:25:25,000
Speaker 1: then manipulated pricing across a host of markets. And that's

1766
01:25:25,040 --> 01:25:28,200
Speaker 1: the essence of the case. You know, they got into

1767
01:25:28,280 --> 01:25:32,559
Speaker 1: this ad space early. This is the original Google founders

1768
01:25:32,560 --> 01:25:35,840
Speaker 1: were actually very leery of pairing advertising research because of

1769
01:25:35,880 --> 01:25:38,240
Speaker 1: the way that obviously is going to impact your results. Right,

1770
01:25:38,280 --> 01:25:41,439
Speaker 1: if you've got companies that are buying ads, you're going

1771
01:25:41,520 --> 01:25:44,160
Speaker 1: to be incentivized to put results for those companies at

1772
01:25:44,240 --> 01:25:47,240
Speaker 1: the top of the page. Then the next layer is, Okay,

1773
01:25:47,320 --> 01:25:49,559
Speaker 1: now we're going to place these ads actually on your website,

1774
01:25:49,560 --> 01:25:51,519
Speaker 1: which makes it even more of a conflict of interest.

1775
01:25:51,720 --> 01:25:52,960
Speaker 6: Then the next phase is.

1776
01:25:53,160 --> 01:25:55,439
Speaker 1: Oh, we're going to use all this data we're able

1777
01:25:55,479 --> 01:25:58,599
Speaker 1: to collect from those companies' websites and around the web

1778
01:25:58,840 --> 01:26:02,320
Speaker 1: to make it so it's basically impossible to compete with

1779
01:26:02,520 --> 01:26:05,479
Speaker 1: our ad product because we have so much data on

1780
01:26:05,520 --> 01:26:08,360
Speaker 1: these consumers and what it is that they want. Now,

1781
01:26:08,400 --> 01:26:13,360
Speaker 1: the counter case is the Google lawyers will argue, Okay, yeah,

1782
01:26:13,520 --> 01:26:15,719
Speaker 1: we've you know, we sell a lot of ads. Sure,

1783
01:26:16,080 --> 01:26:18,519
Speaker 1: but there are other places you can buy ads. It

1784
01:26:18,600 --> 01:26:21,160
Speaker 1: is a competitive market. You can buy ads on Instagram,

1785
01:26:21,200 --> 01:26:24,320
Speaker 1: you can buy ads on TikTok, there are any number

1786
01:26:24,360 --> 01:26:28,320
Speaker 1: of other alternative avenues you have to place and secure

1787
01:26:28,640 --> 01:26:31,759
Speaker 1: AD space. So that's going to be the argument Stoler,

1788
01:26:31,800 --> 01:26:33,880
Speaker 1: and I just you know, I defer to him because

1789
01:26:33,920 --> 01:26:36,080
Speaker 1: he looks at these issues so much more closely. He

1790
01:26:36,200 --> 01:26:38,960
Speaker 1: seems to think that the government does have a pretty

1791
01:26:39,000 --> 01:26:41,360
Speaker 1: strong case against them, even though Google does have some

1792
01:26:41,439 --> 01:26:46,439
Speaker 1: reasonable counter arguments. And what makes this so consequential is, Okay,

1793
01:26:47,479 --> 01:26:50,080
Speaker 1: Google has already lost in the search case. Now if

1794
01:26:50,120 --> 01:26:53,880
Speaker 1: they lose in the AD case, what is what is

1795
01:26:53,880 --> 01:26:56,080
Speaker 1: the remedy going to be? What does that look like?

1796
01:26:56,280 --> 01:27:01,360
Speaker 1: Are we actually talking about breaking Google up to constituent parts?

1797
01:27:01,760 --> 01:27:06,240
Speaker 1: And that is at this point very plausible, very plausible

1798
01:27:06,280 --> 01:27:09,479
Speaker 1: that that could be the end point here where you know,

1799
01:27:09,520 --> 01:27:11,840
Speaker 1: Google will still be fine whatever, you know, core parts

1800
01:27:11,840 --> 01:27:14,960
Speaker 1: of the business they remain, They retain, the different pieces

1801
01:27:15,000 --> 01:27:17,439
Speaker 1: of the business are you know, profitable on their own.

1802
01:27:17,800 --> 01:27:19,599
Speaker 1: But it does really put a crump in their style,

1803
01:27:19,600 --> 01:27:22,160
Speaker 1: as you're pointing out Zager, in terms of having the

1804
01:27:22,360 --> 01:27:25,800
Speaker 1: mass amount of cash available to them in order to

1805
01:27:26,439 --> 01:27:29,400
Speaker 1: you know, continue investing in AI in the massive quantities

1806
01:27:29,400 --> 01:27:30,000
Speaker 1: that they have there.

1807
01:27:30,000 --> 01:27:32,519
Speaker 2: There's also a Kamala angle. Let's put this up there

1808
01:27:32,600 --> 01:27:35,479
Speaker 2: on the screen. This is very interesting, important and you know,

1809
01:27:35,560 --> 01:27:39,439
Speaker 2: potentially very consequential is that Karen Dunn, who is one

1810
01:27:39,479 --> 01:27:43,639
Speaker 2: of the people who was preparing Kamala Harris for her debate,

1811
01:27:43,960 --> 01:27:48,639
Speaker 2: is also defending Google from a lawsuit being brought by

1812
01:27:48,680 --> 01:27:52,280
Speaker 2: the Biden Harris administration. This is from the American Prospect,

1813
01:27:52,360 --> 01:27:55,800
Speaker 2: but multiple other outlets have also picked it up. She

1814
01:27:56,040 --> 01:27:59,960
Speaker 2: literally was prepping legal defense strategy in Alexandria, Virginia courtroom

1815
01:28:00,080 --> 01:28:04,519
Speaker 2: for Google while also working at a law firm and

1816
01:28:04,600 --> 01:28:08,439
Speaker 2: being in the room for Kamala Harris debate preparation. There's

1817
01:28:08,479 --> 01:28:13,559
Speaker 2: probably no better visual into Washington politics as lady quote.

1818
01:28:13,600 --> 01:28:15,519
Speaker 2: If that seems to you like blatant conflict of interest,

1819
01:28:15,600 --> 01:28:17,800
Speaker 2: the Harris team would like to recommend you focus your

1820
01:28:17,840 --> 01:28:18,759
Speaker 2: attention elsewhere.

1821
01:28:18,880 --> 01:28:20,680
Speaker 3: Nothing to see because even the.

1822
01:28:20,680 --> 01:28:23,600
Speaker 2: Trump campaign is now picked up on it and criticizing

1823
01:28:23,640 --> 01:28:27,520
Speaker 2: her for coziness with the big tech top lawyers especially

1824
01:28:27,640 --> 01:28:30,960
Speaker 2: is noteworthy to me because not only is this lawyer

1825
01:28:31,479 --> 01:28:35,320
Speaker 2: very close with institutional democratic circles. Yeah, Kamala already has

1826
01:28:35,400 --> 01:28:38,120
Speaker 2: the problem of Tony West, who is her brother in law.

1827
01:28:38,200 --> 01:28:40,439
Speaker 2: She can't control who her family is. But you know,

1828
01:28:40,439 --> 01:28:42,000
Speaker 2: I don't know if this is common. Do you always

1829
01:28:42,040 --> 01:28:43,960
Speaker 2: have your brother in law is a very close political

1830
01:28:44,000 --> 01:28:46,479
Speaker 2: advisor to you, especially when he's a top lawyer for

1831
01:28:46,640 --> 01:28:50,080
Speaker 2: Uber and is a longtime connect to Silicon Valley, and

1832
01:28:50,160 --> 01:28:52,720
Speaker 2: you were the senator from California and you have all

1833
01:28:52,760 --> 01:28:55,160
Speaker 2: these connections. So I do think that there is very

1834
01:28:55,240 --> 01:28:58,120
Speaker 2: legitimate concern for Matt Soul from the American Prospect and

1835
01:28:58,200 --> 01:29:01,000
Speaker 2: others that a lot of action on anti trust from

1836
01:29:01,000 --> 01:29:03,960
Speaker 2: the Biden administration could be ditched by Kamala just given

1837
01:29:04,040 --> 01:29:06,800
Speaker 2: who her circles are and who she's currently brought in

1838
01:29:06,840 --> 01:29:08,960
Speaker 2: around her for closest closest advisors.

1839
01:29:09,080 --> 01:29:11,240
Speaker 1: If we had a better politics. These are the sort

1840
01:29:11,280 --> 01:29:14,920
Speaker 1: of questions that obviously to be asked. And I mean that, yeah,

1841
01:29:15,400 --> 01:29:18,120
Speaker 1: this is such a key question about that I have

1842
01:29:18,360 --> 01:29:20,840
Speaker 1: and that people who care about you know, corporate power

1843
01:29:20,880 --> 01:29:24,519
Speaker 1: should have about what a Kamala Harris administration would look

1844
01:29:24,600 --> 01:29:27,000
Speaker 1: like and what a Trump administration.

1845
01:29:26,920 --> 01:29:27,600
Speaker 6: Would look like.

1846
01:29:28,280 --> 01:29:31,840
Speaker 1: But you know they're they're listen. On the one hand,

1847
01:29:31,880 --> 01:29:34,080
Speaker 1: I'm sympathetic to the moderation because you only get ninety

1848
01:29:34,080 --> 01:29:35,840
Speaker 1: minutes and there are these big too. You know, you're

1849
01:29:35,880 --> 01:29:37,559
Speaker 1: going to ask about abortion, You're going to ask about

1850
01:29:37,560 --> 01:29:39,120
Speaker 1: the economy, You're going to ask about immigration.

1851
01:29:39,280 --> 01:29:41,280
Speaker 6: But this is, you know, part of why we need

1852
01:29:41,320 --> 01:29:42,519
Speaker 6: more from these candidates.

1853
01:29:42,520 --> 01:29:44,800
Speaker 1: We need more time from these candidates so we can

1854
01:29:44,840 --> 01:29:49,559
Speaker 1: really understand what their plans are. Kamala Harris is kind

1855
01:29:49,600 --> 01:29:52,360
Speaker 1: of an empty vessel, right, so it makes all these

1856
01:29:52,360 --> 01:29:56,080
Speaker 1: people she surrounds herself with more consequential. That's why I was,

1857
01:29:56,400 --> 01:29:58,759
Speaker 1: you know, really excited about the pick of Tim Wowles

1858
01:29:58,840 --> 01:30:01,519
Speaker 1: because of what that indicat with regards to his agenda

1859
01:30:01,600 --> 01:30:04,000
Speaker 1: in the state of Minnesota. That's why it's very pleased

1860
01:30:04,000 --> 01:30:06,160
Speaker 1: to see Brian Deese, who has been one of the

1861
01:30:06,160 --> 01:30:09,040
Speaker 1: better economic advisors to Biden and has been you know,

1862
01:30:09,040 --> 01:30:11,040
Speaker 1: really crucial pushing forward some of the best parts of

1863
01:30:11,040 --> 01:30:13,360
Speaker 1: his agenda, and I think was probably involved in some

1864
01:30:13,400 --> 01:30:16,000
Speaker 1: of the better parts of Kamala's announced agenda, the price gouging,

1865
01:30:16,080 --> 01:30:18,479
Speaker 1: the housing piece, all of the child six thousand dollars

1866
01:30:18,520 --> 01:30:21,559
Speaker 1: child tax credit, all of those pieces and why. On

1867
01:30:21,640 --> 01:30:24,360
Speaker 1: the other hand, it's troubling to see the closeness of

1868
01:30:24,520 --> 01:30:28,080
Speaker 1: someone like Karen Dunn, who is a long time Democratic

1869
01:30:28,120 --> 01:30:30,280
Speaker 1: Party She helped I think Hillary prepare for her to

1870
01:30:30,360 --> 01:30:32,640
Speaker 1: dates which I don't know why you wrote whatever it

1871
01:30:32,680 --> 01:30:34,760
Speaker 1: worked out in this case, Kambloy obviously came in and

1872
01:30:34,800 --> 01:30:37,479
Speaker 1: did a good job. She helped prepare Merrek Garland for

1873
01:30:37,520 --> 01:30:43,840
Speaker 1: his confirmation hearings. She is this longtime Democratic Party associated operative,

1874
01:30:44,360 --> 01:30:47,360
Speaker 1: and you know the closeness with Kamala not just in

1875
01:30:47,439 --> 01:30:51,120
Speaker 1: terms of the debate prep but as a larger advisor. Yeah,

1876
01:30:51,320 --> 01:30:55,080
Speaker 1: that's that's troubling. And there's been this billionaire led push

1877
01:30:55,479 --> 01:30:59,519
Speaker 1: to get rid of Lena Kahan and get undercut the

1878
01:31:00,320 --> 01:31:04,759
Speaker 1: fantastic trust busting work that the Biden administration has been doing.

1879
01:31:05,240 --> 01:31:08,840
Speaker 1: And it still is a question mark where what the

1880
01:31:08,880 --> 01:31:11,639
Speaker 1: direction is. So that's why you know, this may seem

1881
01:31:11,680 --> 01:31:14,080
Speaker 1: like inside baseball. It is inside baseball. But that's why

1882
01:31:14,080 --> 01:31:15,760
Speaker 1: we have to pay attention to these things, because we

1883
01:31:15,760 --> 01:31:21,720
Speaker 1: aren't getting direct answers about what Kamma's priorities would be,

1884
01:31:21,800 --> 01:31:24,160
Speaker 1: specifically with regard to corporate power. So this is one

1885
01:31:24,200 --> 01:31:26,000
Speaker 1: of those things that has to be seen as troubling.

1886
01:31:26,040 --> 01:31:29,320
Speaker 1: It's also wildlif just inside DC story here if you

1887
01:31:29,360 --> 01:31:32,679
Speaker 1: read this whole American Prospect story, because this particular law

1888
01:31:32,720 --> 01:31:37,280
Speaker 1: firm is where Jonathan Canter, yes, who now heads the

1889
01:31:37,360 --> 01:31:40,200
Speaker 1: doj Anti Trust Division and is you know, leading these

1890
01:31:40,200 --> 01:31:43,839
Speaker 1: cases against Google and other you know, giants and various sectors.

1891
01:31:44,240 --> 01:31:48,040
Speaker 1: He used to work there, and Karen Dunn comes in

1892
01:31:48,200 --> 01:31:51,600
Speaker 1: and she's going to represent Google, and he actually was

1893
01:31:51,680 --> 01:31:54,439
Speaker 1: represent some of the some of the people who were

1894
01:31:54,439 --> 01:31:57,320
Speaker 1: opposing Google in this suit, and so they were going

1895
01:31:57,400 --> 01:31:59,400
Speaker 1: to make him give up his clients and then he

1896
01:31:59,479 --> 01:32:01,719
Speaker 1: ends up going to the DOJ. So now this law firms,

1897
01:32:01,920 --> 01:32:04,800
Speaker 1: So there's like a law firm conflict of interest there too,

1898
01:32:04,920 --> 01:32:08,240
Speaker 1: because if you can imagine, they're handling the other side

1899
01:32:08,240 --> 01:32:10,720
Speaker 1: of this case, so they're privy to all of the

1900
01:32:11,040 --> 01:32:13,439
Speaker 1: strategy and the you know, research and all of the

1901
01:32:13,479 --> 01:32:15,920
Speaker 1: planning that's going on on the other side of the case,

1902
01:32:16,360 --> 01:32:19,599
Speaker 1: and then you know, now they're representing Google. So the

1903
01:32:19,640 --> 01:32:22,439
Speaker 1: government was arguing like this is really they have this

1904
01:32:22,560 --> 01:32:24,800
Speaker 1: massive conflict of interest. It's a really it's really a

1905
01:32:24,880 --> 01:32:27,840
Speaker 1: huge problem. The judge took those arguments pretty seriously as well.

1906
01:32:27,960 --> 01:32:31,320
Speaker 1: So anyway, little insight into the swamp here and how

1907
01:32:31,360 --> 01:32:32,120
Speaker 1: all of this works.

1908
01:32:32,240 --> 01:32:34,200
Speaker 2: That's right, all right, Thank you guys so much for watching.

1909
01:32:34,240 --> 01:32:37,240
Speaker 2: We really appreciate you. We will have any breaking news,

1910
01:32:37,240 --> 01:32:45,240
Speaker 2: we'll cover it otherwise. We'll see you all on Monday.