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Speaker 1: Welcome to today's edition of the Clay Travis and buck

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Speaker 1: Sexton Show podcast.

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Speaker 2: Welcome back an hour number two Clay Travis buck Sexton Show.

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Speaker 1: We welcome in now all of you.

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Speaker 2: Hope you're having a fantastic Wednesday wherever you may be

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Speaker 2: across the country. We're joined now by Nate Silver, who's

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Speaker 2: got a book out called On the Edge, which I

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Speaker 2: have finished and read and really enjoyed. I also liked

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Speaker 2: his book. I think I'm correct in this, Nate. Your

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Speaker 2: prior book was The Signal and the Noise, which was

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Speaker 2: about trying to use probability analysis. I don't know if

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Speaker 2: you remember this, but we have met in person out

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Speaker 2: in Vegas. You love playing poker. I have recognized that

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Speaker 2: I am not good enough at math in real time

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Speaker 2: to be very good at poker. I just don't have

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Speaker 2: that skill set like a lot of great poker players do.

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Speaker 2: But what I love about your work is you try

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Speaker 2: to use data and be rational in a world that

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Speaker 2: has become wildly irrational. It feels like every single day

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Speaker 2: I want to start with this. You became somewhat well known,

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Speaker 2: I would say, with your analysis and predictions of the

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Speaker 2: twenty twelve election but in the twelve years that you've

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Speaker 2: been a public figure, and to a large degree since that,

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Speaker 2: do you think national discourse has gotten better or worse?

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Speaker 2: And if you think it's gotten worse, which I think

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Speaker 2: most people would say that it has, are we starting

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Speaker 2: to get a little bit better now, or is the

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Speaker 2: discourse continuing to get worse? How would you assess the

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Speaker 2: broader picture of sort of the national character of debate

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Speaker 2: in this country as we set two months from an election.

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Speaker 3: Yeah, thank you, Clay. No, Look, I think over the

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Speaker 3: course of this gosh, sixteen years had been covering politics,

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Speaker 3: it got pretty consistently worse. From two thousand and eight

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Speaker 3: when I started to twenty twenty, in the kind of

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Speaker 3: pandemic year where we had the racial reckoning and COVID

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Speaker 3: all this stuff an election year. I think in the

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Speaker 3: past four years it might have gotten a little bit better.

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Speaker 3: I think people have a little bit more of a

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Speaker 3: sense of humor. Interesting seeing Twitter go from being the

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Speaker 3: slipper platform to more conservative frankly under Elon, you know

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Speaker 3: you see more independent content creators on platforms like substack,

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Speaker 3: where I have my newsletter s over Builton, for example.

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Speaker 3: So I feel a little bit more optimistic. I suppose

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Speaker 3: not being in a huge pandemic is helpful at least.

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Speaker 2: Okay, one of the big things that you write about

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Speaker 2: in your book is being willing to re examine your priors.

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Speaker 2: That is, things that you believe. If there's data that suggests, hey,

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Speaker 2: maybe I was wrong about this, going back and looking

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Speaker 2: at it and being honest in the way that you

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Speaker 2: analyze that data. I agree. I think that's super important.

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Speaker 2: What percentage of Americans do you think actually do that

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Speaker 2: in their day to day life?

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Speaker 3: Oh gosh, like you know, five percent or ten percent?

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Speaker 3: About ninety percent of people are going to vote for

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Speaker 3: one party or another every single time. And look, it's hard.

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Speaker 3: It's hard to error, right, It's hard for me to

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Speaker 3: do that too. The discipline of having a model or

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Speaker 3: a process is that you follow some type of discipline

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Speaker 3: in how you look at the world. But yeah, I

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Speaker 3: can just kind of ping back and forth based on

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Speaker 3: the most recent conversation you had, then you won't be

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Speaker 3: very well off. I mean, you can be driven crazy

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Speaker 3: by Twitter and social media. You know, when our forecast

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Speaker 3: had Biden trailing Trump, people are like, oh, it is

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Speaker 3: Nate like a MAGA supporter. Now right, people I think

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Speaker 3: can't separate out that some of us are actually curious

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Speaker 3: about trying to make forecasts or you know, myopilitical preferences.

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Speaker 3: Probably they don't match your audience that much, Clay, But

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Speaker 3: I'm trying to be at armslength and trying to be

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Speaker 3: as accurate as I can be.

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Speaker 2: Okay, So I've voted. I've said this before, I voted

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Speaker 2: Democrat in the past. I'm going to vote Trump in

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Speaker 2: twenty twenty four. I Buck and I have debated this

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Speaker 2: and talked about it quite a lot. How many people

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Speaker 2: do you think there are out there on a percentage

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Speaker 2: basis that are open to go from voting Trump sixteen

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Speaker 2: Biden twenty and then going back to Trump in twenty four?

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Speaker 2: In other words, how many people are they really competing for,

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Speaker 2: particularly in the battleground states, if you were trying to

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Speaker 2: assess what that number looks like, what are we basically

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Speaker 2: spending billion dollars to try to persuade How persuadable is

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Speaker 2: that audience?

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Speaker 3: Yeah, Look, it's maybe ten percent of the electorate in

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Speaker 3: twenty percent of the states or tention of the states

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Speaker 3: that matters. So yeah, this billion dollars is being spent

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Speaker 3: on one percent of the electorate. You've seen polarization increased

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Speaker 3: a lot. Although there is more split ticket voting. You

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Speaker 3: see it in Congress for example, people splitting between the

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Speaker 3: Democrat for Congress and maybe a Trump vote. But yeah, look,

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Speaker 3: I in my life, I've certainly there's more of a

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Speaker 3: permission structure to use a fancy term to vote for

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Speaker 3: Trump than in some groups Lakon Valley for example. But

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Speaker 3: we're talking about you know, ninety plus percent people have

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Speaker 3: made their mind up already.

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Speaker 2: So when you look, we're talking tonight, Silver's got a

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Speaker 2: great new book out called On the Edge. When you

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Speaker 2: look at Elon Musk, RFK Junior and Tulsea Gabbard, I

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Speaker 2: would say that that in many ways my political evolution

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Speaker 2: mirrors what they've done. I was maybe a few years

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Speaker 2: ahead of them, and COVID accelerated it. Do you think

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Speaker 2: that those three obviously very big name and Elon Musk

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Speaker 2: and given the fact that he owns Twitter, Tulci Gabbard,

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Speaker 2: RFK Junior, do they change anything in terms of your calculus.

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Speaker 2: I was reading in Axios that one way to connect

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Speaker 2: those three people, and I think it connects with sort

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Speaker 2: of my political worldview is that the First Amendment is

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Speaker 2: sort of the thing that matters the most to me,

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Speaker 2: and so I'm going to vote for whoever I think

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Speaker 2: is going to have the most robust, open policy for discussion,

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Speaker 2: whether I agree or disagree with them. I think that's

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Speaker 2: where a lot of the Trump support now is coming

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Speaker 2: in Silicon Valley. Does RFK Junior, does Elon Musk, and

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Speaker 2: does Tulci Gabbard? To your point, give any cover for

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Speaker 2: people who might be willing to consider Trump that haven't

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Speaker 2: done it before. What's their impact?

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Speaker 3: I mean, so RFK, we removed him from our model

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Speaker 3: this weekend. So far not much impact, but we're kind

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Speaker 3: of taking a weight and see attitude. You might have

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Speaker 3: Kamala Harris's convention bounce administrated by that though it was

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Speaker 3: only three four percent of the vote. I think the

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Speaker 3: broadest thing here is, like you know, it's now conservatism,

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Speaker 3: that's the anti establishment party, and in different flavors of it, right,

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Speaker 3: not just the Trumpian flavor, but you see in those

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Speaker 3: other you know, Elon Musk, for example, a guy that

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Speaker 3: you know, one of the greatest founders of our time.

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Speaker 3: And yeah, look I used to be again, I'm gonna

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Speaker 3: I'm not gonna vote for Trump. I'll vote for Harris probably,

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Speaker 3: but yeah, you know the free speech stuff.

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Speaker 2: Let me let me pause you there, because I'm actually

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Speaker 2: I think you'd overlap with me on the free speech stuff.

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Speaker 2: But so you said you're not going to vote for Trump,

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Speaker 2: You're going to vote for Harris? What is harrisden?

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Speaker 3: Because I thought it was very irresponsible to nominate somebody

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Speaker 3: who was that old and clearly not capable of being president.

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Speaker 4: For him, you.

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Speaker 2: Wouldn't you wouldn't have voted for Biden? Well, how would

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Speaker 2: you have voted if Biden Trump was going on?

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Speaker 3: Probably libertarian? Or see which other which other funky third

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Speaker 3: party candidates were on the ballot? Where in New York?

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Speaker 1: Okay?

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Speaker 4: So relevant vote?

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Speaker 2: But yeah, I get it. You live in New York, right,

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Speaker 2: so you're I'm in Tennessee. It doesn't really matter who

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Speaker 2: I vote for, uh in terms of the presidential election,

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Speaker 2: because my state's going to go for plus twenty.

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Speaker 1: What do you think Kamala Harris is going to do?

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Speaker 4: Well?

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Speaker 2: You thought Biden was too old? I think there's people

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Speaker 2: out there who would say, yeah, I agree with that.

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Speaker 2: Now you say, but you're willing to vote for Kamala,

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Speaker 2: She's going to be sixty years old. She's you know,

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Speaker 2: twenty two years some odd younger than Biden. What attracts

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Speaker 2: you to Kamala? What do you think she's going to

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Speaker 2: do well or what do you think she's done well

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Speaker 2: in the past? Or is it just you're not willing

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Speaker 2: to vote for Trump?

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Speaker 4: Yeah?

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Speaker 3: To me, some of the January sixth stuff was predisqualifying. Look,

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Speaker 3: I think she has shown some tendency to play for

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Speaker 3: the center. Yeah, there's something to be of flip flopping

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Speaker 3: there for sure. But Eric convention, the speech to talk

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Speaker 3: about how he wanted like a lethal military and how

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Speaker 3: we want to kick China's butt and things like that,

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Speaker 3: it was a very atypical speech for Democrats that spoke

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Speaker 3: to the center. And usually I watch speeches like at

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Speaker 3: arms like I'm like, this is not speaking to me.

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Speaker 3: I'm not a partisan Democrat whatever. But like she showed

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Speaker 3: some instincts there that I thought we're a little different.

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Speaker 3: And yeah, you're getting some flip flopping for sure, But

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Speaker 3: but look, I mean, you know, with the January sixth stuff,

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Speaker 3: that's kind of just an issue for me. In Biden's

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Speaker 3: age was also disqualifying. So now it's kind of process

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Speaker 3: of elimination.

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Speaker 5: I suppose.

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Speaker 2: Okay, I'm gonna give you a pass on that because

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Speaker 2: I don't see how anybody with a functional brain can

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Speaker 2: vote for Kamala, and you clearly have a functional brain.

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Speaker 2: But I want to dive into your modeling and the

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Speaker 2: battleground states. Here again talking to Nate Silver, who is

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Speaker 2: smart despite the fact that he says he's going to

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Speaker 2: vote for Kamala. All right, so you have an interesting read.

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Speaker 2: I was reading it North Carolina and Georgia to southern states.

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Speaker 2: They did not move in tandem all the time. They

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Speaker 2: went in different directions in twenty twenty. My thesis is

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Speaker 2: Trump's gonna win North Carolina and Georgia. I think that

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Speaker 2: he is. That's what the betting markets would show right now.

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Speaker 2: How connected are they in your analysis? Do you like

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Speaker 2: Kamala's chances in North Carolina more than Georgia vice versa.

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Speaker 2: How likely are they to move together in your mind?

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Speaker 3: Yeah, Look, we have Trump as a modest favorite about

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Speaker 3: sixty to forty in both states. She's actually polling better

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Speaker 3: in North Carolina, which might be a bit counterintuitive, but

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Speaker 3: keep in mind that in two thousand and eight brock

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Speaker 3: Obama one North Carolina and not Georgia. You have a

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Speaker 3: very popular GP governor in Georgia, although not one that

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Speaker 3: Trump gets along with terribly well. The guy identical. North

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Speaker 3: Carolina has a substantially larger white population, although they're both

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Speaker 3: quite diverse. We had seen Biden's numbers frankly collapse Creater

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Speaker 3: with younger voters of color, younger Black voters, younger Hispanic voters.

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Speaker 3: Harris has gotten about half of that back, So she's

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Speaker 3: competitive in these states. But you actually have some degree

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Speaker 3: of racial depolarization in the country, which, other things being equal,

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Speaker 3: I think is probably a healthy thing.

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Speaker 2: Okay, So the other states that I think are most

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Speaker 2: fascinating clearly what I call the Big ten states, Pennsylvania, Michigan, Wisconsin.

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Speaker 2: They all moved together in sixteen. Trump won them all,

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Speaker 2: Biden won them all in twenty twenty. What are the

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Speaker 2: chances that they move together in your mind in twenty

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Speaker 2: four and how would you assess them state by state

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Speaker 2: in terms of the odds that Trump would win. Because

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Speaker 2: if I'm right, and if your analysis is right, if

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Speaker 2: Trump wins North Carolina and he wins Georgia, the easiest

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Speaker 2: way for Trump to win the election is to win Pennsylvania,

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Speaker 2: and it's over. And it doesn't even matter what happens

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Speaker 2: in Nevada, Arizona, Michigan, or Wisconsin. He would have their

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Speaker 2: requisite electoral votes. So how would you assess those three

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Speaker 2: states chances they move together? Where does Trump have the

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Speaker 2: best chance? Where does Kamala have the best chance?

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Speaker 3: Yeah, and they do tend to vote together, as Hillary

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Speaker 3: Clinton learned the hard way in twenty sixteen. You know,

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Speaker 3: of the three, Pennsylvania's the one where I mean, look,

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Speaker 3: Tamo Harris has been a good in a good period

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Speaker 3: for the polls, but Pennsylvania has been still a toss up,

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Speaker 3: a photo finish. There's a chance to regret not picking

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Speaker 3: Josh Shapiro. Tim Wallace has been effective in some ways.

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Speaker 3: But look, I'm a maths guy, and I'm like, yeah,

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Speaker 3: I want with electoral votes if I were hear, probably

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Speaker 3: they are different tuctors in each state. You see in Michigan,

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Speaker 3: for example, has a larger Palestinian Arab American population. I

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Speaker 3: think Democrats are fortunate that the Gaza issue seems to

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Speaker 3: have been diffused quite a bit. You know, Wisconsin's more

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Speaker 3: world than the other two Pennsylvania is kind of northeastern

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Speaker 3: in the city, midwestern, so there are subtle, little differences,

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Speaker 3: but you know ninety percent of time they'll vote as

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Speaker 3: a block basically.

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Speaker 2: And which one do you like Trump's chances in the

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Speaker 2: best of those three based on your modeling?

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Speaker 3: I think Pennsylvania. I mean, you know, we've just seen

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Speaker 3: that consistently poll as a TSSA. You know, Harris plus one,

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Speaker 3: Trump plus one, whereas Harris been ahead recently in most

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Speaker 3: polls of Michigan Wisconsin.

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Speaker 2: If you had to pick one state that you could

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Speaker 2: know the exact results for and forecast based on that

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Speaker 2: to twenty twenty four, would.

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Speaker 1: It be Pennsylvania? Is that the one that you would

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Speaker 1: want to have the data for.

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Speaker 3: Yeah, Look, all you tell me is Harris went Pennsylvania

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Speaker 3: or Trump when Pennsylvania? Could you know predict with ninety

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Speaker 3: some percent accuracy it was going in the electoral college.

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Speaker 3: That's that's It's a lot of electoral votes. It's a

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Speaker 3: very democratic, democratic representative state. You got rural areas and

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Speaker 3: a big city in suburbs, et cetera decent sized black population.

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Speaker 3: So that's the biggest circle on the map.

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Speaker 2: Okay, you have it almost dead even I think right now,

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Speaker 2: fifty to fifty in your forecast, Trump was ahead, Kamal

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Speaker 2: has got a small lead right now. Is it fair

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Speaker 2: to say that based on what we've seen from the

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Speaker 2: polls so far? And my suspicion is that they're waiting

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Speaker 2: to give us a lot of poles after Labor Day

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Speaker 2: and so we're going to get an inflection of a

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Speaker 2: new data then. But is it fair to say that

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Speaker 2: we haven't seen any kind of real substantial Kamala Harris

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Speaker 2: convention bounce as would typically occur historic or are the

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Speaker 2: convention bounces more of something that isn't as much of

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Speaker 2: a function anymore because so many people have already made

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Speaker 2: up their mind.

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Speaker 3: Yeah, between the twenty four seven news cycle and partsnship,

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Speaker 3: they're less profoundly used to be. I mean, you know,

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Speaker 3: poles are showing, we're showing about it one and a

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Speaker 3: half or two point bounce so far. You're right, by

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Speaker 3: the way, Clay that like the timings weird. We have

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Speaker 3: Labor Day coming up, we had RFK. I'm sure some

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Speaker 3: people tossed out their polls because they tested RFK. He

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Speaker 3: won't officially be a catly some states look if you

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Speaker 3: have an election today, then Harris would be the favorite,

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Speaker 3: a big favorite. We have all seen elections where the

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Speaker 3: polls are off by quite a bit. But our model

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Speaker 3: thinks that when you get to November it'll tighten back

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Speaker 3: up again. Look, I think Trump has not been on

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Speaker 3: his a game recently, but you have a debate, you

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Speaker 3: have other things to shake up and learn from. It's

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Speaker 3: a bit like if you have the backup quarterback come

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Speaker 3: in and he's like a scrambler and not like a

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Speaker 3: downfield passer, and you need some adjust period. But but look,

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Speaker 3: we know that American elections are very close. We know

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Speaker 3: also that if the popular vote is a tie, then

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Speaker 3: probably Republicans win, So they have a couple of it's

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Speaker 3: a big ace in the hole. We're talking about strategy

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Speaker 3: down the road.

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Speaker 1: All right, last question for you, I'd encourage you to

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Speaker 1: check out the book.

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Speaker 2: I read it. I really enjoyed it. On The Edge,

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Speaker 2: Signal and the Noise also a really good read. If

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Speaker 2: you're looking for something and you're a data probabilistic guy

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Speaker 2: who is more rational sports fans for political junkies, who

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Speaker 2: is a more rational group?

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Speaker 3: Fans by far right, I mean you can see a

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Speaker 3: field goal like joint off the crossbar, or you know,

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Speaker 3: a sharply hit baseball that gets caught by the shortstop. Yeah,

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Speaker 3: there's not this grievance. I mean there's some. You know,

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Speaker 3: I'm kind of a New York Knicks fan now, so

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Speaker 3: you see some grievance of the Knicks fans. But I

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Speaker 3: think sports fans tend to have a sense of humor

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Speaker 3: that is often lacking in the political sphere.

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Speaker 2: Also, sports fans are constantly willing to re examine their

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Speaker 2: priors when it comes to a coach, when it comes

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Speaker 2: to a player. Sports fans innately embrace data, right. I mean,

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Speaker 2: if youve got Aaron Judge hitting fifty one home runs

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Speaker 2: in New York City and you didn't think he was

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Speaker 2: going to have a good year, I mean, the data

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Speaker 2: comes out and you have to adjust. It seems to

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Speaker 2: me that sports fans are, by the way, crazy, but

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Speaker 2: more rational than political diehards.

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Speaker 4: Yeah.

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Speaker 3: To be confronted with the reality check one hundred and

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Speaker 3: six two times a year in Major League Baseball or

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Speaker 3: seventeen times a year in the NFL instead of once

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Speaker 3: every four years, it just kind of makes you adjust

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Speaker 3: to reality a lot more.

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Speaker 4: Rapidly.

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Speaker 2: Nate, I appreciate the time. Again, the book on the edge,

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Speaker 2: good luck with it. Appreciate you giving us the time.

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Speaker 2: I think people will enjoy it, and I'd encourage signal

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Speaker 2: and the noise as well.

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Speaker 3: Of course, thank you, Clay.

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Speaker 2: I got lots of good things to say. You almost

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Speaker 2: killed them all by saying you're voting for Kamala by

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Speaker 2: the way, but I appreciate the time.

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Speaker 3: Thanks man.

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Speaker 2: It's Nate Silver, super smart guy. His forecast fun to

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Speaker 2: watch and look at. Among many other outlets out there,

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Speaker 2: I find that he is rational in a way that

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Speaker 2: few people are very often rational.

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Speaker 1: By the way, we'll take some of your calls. Eight

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Speaker 1: hundred and two two two eight a two.

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Speaker 2: We're going to be joined by Jim Jordan to talk

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Speaker 2: about this big Facebook story that much of the media

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Speaker 2: is ignoring. That letter from Mark Zuckerberg was addressed directly

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Speaker 2: to Congressman Jim Jordan of Ohio. We'll talk with him next.

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Speaker 2: But I want to tell you we've got a lot

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Speaker 2: of hunters out there in this audience. If you're one

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Speaker 2: talk about the performance quality but also the price points.

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Speaker 1: I'm going to be down in Miami in a few weeks.

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Speaker 2: We're gonna go out and try out many of their

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Speaker 2: arsenal and Bear Creek has eliminated the middleman. You get

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Speaker 2: the benefit of those savings. You can save even more

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Speaker 2: when you use my name Clay as the discount code

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Speaker 2: at bear creekarsenal dot com. Again, we're gonna spend a

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Speaker 2: day out on the range. I can't wait to check

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Speaker 2: all this stuff out and add it to my own

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Speaker 2: at bear Creek arsenal dot com. Remember discount code Clay.

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Speaker 2: That's Bearcreek Arsenal dot com. My name Clay. Ike in

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Speaker 2: California's fired up about the Nate Silver interview. Mike, what

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Speaker 2: you got for me?

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Speaker 4: Yeah? How you doing, Clay?

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Speaker 5: I don't know how you can even trust that guy.

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Speaker 5: He's a full on partisan. I know the book is

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Speaker 5: good and everything, but you know what.

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Speaker 4: All he did was say that he looked at facts

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Speaker 4: and numbers and everything.

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Speaker 5: Else, and then said he's gonna vote for Kamala Harrison.

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Speaker 5: He doesn't even know what she stands for.

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Speaker 2: But if you heard me, ask him, I will say,

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Speaker 2: right now, his forecast is fifty one point five percent

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Speaker 2: Kamala wins, forty eight point three percent Trump wins. And

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Speaker 2: of anybody out there in twenty twenty, he had the

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Speaker 2: highest probability that Trump would win. When everybody else was

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Speaker 2: running around saying ninety seven percent chance that Hillary was

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Speaker 2: going to win, he was saying there was about a

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Speaker 2: third of a chance that Trump was going to win.

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Speaker 1: So I think his forecast model.

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Speaker 5: They knew that ninety seven percent was fake. If anything,

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Speaker 5: it was maybe sixty forty. But everybody knew that she

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Speaker 5: wasn't going to get ninety seven percent of the vote.

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Speaker 2: No, no, no, ninety seven percent chance that she won, not

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Speaker 2: that she was going to get ninety seven percent of

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Speaker 2: the vote. That's what thanks for the call, by the way,

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Speaker 2: that's what the New York Times forecast said. I think

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Speaker 2: you have to challenge your priors. That's a big part

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Speaker 2: of his book. I would encourage you guys to read

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Speaker 2: it and when you consider other options, what I find

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00:18:01,040 --> 00:18:04,639
Speaker 2: is that your opinions end up stronger. This is why

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Speaker 2: I think that Ron DeSantis wiped the floor to such

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Speaker 2: an extent with Gavin Newsom, because Gavin Newsom didn't have

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Speaker 2: any actual arguments in his back pocket. Because the media

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00:18:16,200 --> 00:18:18,119
Speaker 2: is so biased in his favor. You got to challenge

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Speaker 2: your priors. That makes you stronger. We'll talk more about that,

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Speaker 2: but I want to tell you Pure Talk. My soon

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Speaker 2: one thousand dollars a year. Why wouldn't you trust them

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Speaker 2: Make the switch today to Puretalk. That is pound two

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Speaker 2: five zero Puretalk. Do it today. Made Silver on Twitter

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Speaker 2: getting a lot of hate for saying that he's going

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00:19:02,920 --> 00:19:06,119
Speaker 2: to vote for kamalas He's a smart guy making a

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Speaker 2: very bad choice. But where I'll give him credit is

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Speaker 2: I don't mind when people tell you what they're gonna do,

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00:19:14,080 --> 00:19:17,120
Speaker 2: and then you can assess how to weigh their opinion

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Speaker 2: based on that. What I am bothered by is Dana

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Speaker 2: Bash is going to sit down and interview Kamala Harris

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Speaker 2: and Tim Walls tomorrow, and after that interview, she's gonna say,

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Speaker 2: you know, I don't really have partisan leanings. I'm a journalist.

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Speaker 2: I'm right down the middle. I don't ever make decisions

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Speaker 2: based on what might be my own biases. I am

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Speaker 2: completely right down the middle. And it's a lie. I've

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Speaker 2: told people my whole career in media. When I started

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Speaker 2: writing an O four, I told people every election, Hey,

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Speaker 2: this is who I'm voting for. You don't have to

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Speaker 2: agree at all, but I think you should be able

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Speaker 2: to understand where I'm coming from. And if you think

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Speaker 2: I'm biased or you think I'm not being honest, all

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Speaker 2: I can do is tell you what I'm doing. And

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Speaker 2: I did the same thing during COVID. I'm like, hey,

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Speaker 2: I'm putting my kids back in school. My kids aren't

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Speaker 2: wearing masks, I'm not getting them shots. To me, what

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Speaker 2: you do in your own life tells us way more

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Speaker 2: than what you say. I didn't get the COVID shot.

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Speaker 2: I told everybody, and I think actually Nate Silver knows

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Speaker 2: that Kamala is a joke. I think because he works

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Speaker 2: in media and lives in New York City, he's not

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Speaker 2: willing to cross the bridge and vote for Trump. Because

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Speaker 2: I'll tell you, when I came out and said I

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Speaker 2: was voting for Trump in sports media, people said, you

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Speaker 2: just lit the bridge on fire behind you. You can't

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Speaker 2: work in sports media anymore. When twenty twenty, when I

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Speaker 2: did that, I was the only person. I think I'm

436
00:20:51,960 --> 00:20:55,199
Speaker 2: still the only person who talked about sports for a

437
00:20:55,240 --> 00:21:00,280
Speaker 2: living that publicly said I'm voting for Donald Trump. Jim

438
00:21:00,359 --> 00:21:04,119
Speaker 2: Jordan's with us now. Jim, I appreciate you coming on

439
00:21:04,240 --> 00:21:07,159
Speaker 2: with us. You went through some of those fires with me.

440
00:21:07,240 --> 00:21:09,280
Speaker 2: You would come on the Sports Show back in the day.

441
00:21:09,320 --> 00:21:13,399
Speaker 2: You're a huge sports fan. Isn't it incredible that not

442
00:21:13,560 --> 00:21:17,480
Speaker 2: one single person who works at ESPN has ever said, Hey,

443
00:21:17,480 --> 00:21:18,840
Speaker 2: I'm voting for Trump publicly.

444
00:21:19,840 --> 00:21:25,040
Speaker 4: No, that's today's sports world, today's media world. I think

445
00:21:25,080 --> 00:21:27,640
Speaker 4: what's interesting too, is that it's sticking with a sports analogy.

446
00:21:27,760 --> 00:21:30,359
Speaker 4: Is I think we're now getting past the pep rally

447
00:21:30,359 --> 00:21:32,520
Speaker 4: stage and we're getting into the game. You know, for

448
00:21:32,600 --> 00:21:35,680
Speaker 4: third thirty seven thirty eight days, it's been one giant

449
00:21:35,720 --> 00:21:38,200
Speaker 4: pep rally, all kicks and giggles, but I learned a

450
00:21:38,200 --> 00:21:39,720
Speaker 4: long time ago in the sport I was involved in.

451
00:21:39,800 --> 00:21:41,240
Speaker 4: The pep rally is great. At some point you got

452
00:21:41,240 --> 00:21:41,680
Speaker 4: to step.

453
00:21:41,520 --> 00:21:41,880
Speaker 5: On the mat.

454
00:21:41,920 --> 00:21:44,119
Speaker 4: You gotta prove it, yep. And now it's time to

455
00:21:44,320 --> 00:21:45,520
Speaker 4: get to the facts and the issues.

456
00:21:45,560 --> 00:21:46,120
Speaker 3: And we've got this.

457
00:21:46,119 --> 00:21:49,560
Speaker 4: Debate and we'll see what happens there. I have a

458
00:21:49,560 --> 00:21:51,560
Speaker 4: pretty good feeling that President Trump, who does great in

459
00:21:51,600 --> 00:21:54,200
Speaker 4: these debates, and Jade bands are going to win when

460
00:21:54,600 --> 00:21:57,040
Speaker 4: they go up against the Vice president and Governor Wallas.

461
00:21:57,440 --> 00:21:58,800
Speaker 2: I agree with you, and let me give you some

462
00:21:58,840 --> 00:22:01,199
Speaker 2: credit here. You and I, I believe, met for the

463
00:22:01,240 --> 00:22:04,840
Speaker 2: first time in twenty twenty one in person when I

464
00:22:04,960 --> 00:22:10,120
Speaker 2: came and spoke at a subcommittee hearing dealing with Yeah,

465
00:22:10,119 --> 00:22:12,600
Speaker 2: it was fun, but it was important too, dealing with

466
00:22:12,720 --> 00:22:15,600
Speaker 2: the way that Facebook can rig traffic. And I don't

467
00:22:15,640 --> 00:22:17,639
Speaker 2: know how well you remember that, but I certainly remember

468
00:22:17,680 --> 00:22:20,080
Speaker 2: it well because I lived it. With my business. We

469
00:22:20,119 --> 00:22:22,560
Speaker 2: wrote a bunch of positive things about Donald Trump after

470
00:22:22,600 --> 00:22:25,920
Speaker 2: he came on my show, and our site traffic vanished

471
00:22:25,960 --> 00:22:30,120
Speaker 2: on Facebook and it was an algorithmic move. They punished

472
00:22:30,200 --> 00:22:34,080
Speaker 2: us for writing too positively about Trump and what we're

473
00:22:34,080 --> 00:22:36,120
Speaker 2: now saying. I got to give you a minse credit here.

474
00:22:36,640 --> 00:22:40,640
Speaker 2: The letter that Mark Zuckerberg wrote to you that came

475
00:22:40,680 --> 00:22:44,600
Speaker 2: out what Monday afternoon is one of the most jaw

476
00:22:44,680 --> 00:22:47,879
Speaker 2: dropping admissions that I have ever seen anyone.

477
00:22:47,560 --> 00:22:48,560
Speaker 1: In big tech make.

478
00:22:49,160 --> 00:22:52,680
Speaker 2: And it was so incredible that The New York Times

479
00:22:52,760 --> 00:22:55,320
Speaker 2: is pretending it doesn't exist. I was talking to my

480
00:22:55,359 --> 00:22:58,040
Speaker 2: audience earlier. I read the New York Times this morning.

481
00:22:58,080 --> 00:23:00,280
Speaker 2: I read it yesterday print edition of the news paper

482
00:23:00,280 --> 00:23:02,359
Speaker 2: delivered to my house. They don't have a single article

483
00:23:02,480 --> 00:23:05,479
Speaker 2: about Zuckerberg admitting that he was lied to by the FBI,

484
00:23:05,880 --> 00:23:10,000
Speaker 2: about the Hunter Biden laptop, or about the censorship attempts

485
00:23:10,000 --> 00:23:12,440
Speaker 2: from the Biden White House. So my question, first of all,

486
00:23:12,480 --> 00:23:15,400
Speaker 2: incredible work by you to get that letter written. Why

487
00:23:15,400 --> 00:23:16,719
Speaker 2: did Mark Zuckerberg write it?

488
00:23:16,760 --> 00:23:18,320
Speaker 1: Now? What do you think his motivations were?

489
00:23:19,320 --> 00:23:22,560
Speaker 4: Well, because we've deposed over a dozen Facebook employees, we

490
00:23:22,560 --> 00:23:26,040
Speaker 4: had a year and a half investigation. We've deposed from deposeder,

491
00:23:26,080 --> 00:23:28,080
Speaker 4: interviewed line level people all the way up to Sir

492
00:23:28,200 --> 00:23:30,520
Speaker 4: Nick Clegg, the Number three guy, former Deputy Prime Minister,

493
00:23:30,600 --> 00:23:33,040
Speaker 4: Great Grid, number three guy Meta, and I think at

494
00:23:33,040 --> 00:23:35,240
Speaker 4: some point he just said, look, this needs to be

495
00:23:35,280 --> 00:23:37,240
Speaker 4: over with. We're gonna We're just gonna come clean and

496
00:23:37,640 --> 00:23:39,760
Speaker 4: save the fact. And if you've been that letter, there

497
00:23:39,760 --> 00:23:42,639
Speaker 4: were four key key things he pointed out. One Biden

498
00:23:42,680 --> 00:23:46,920
Speaker 4: Harris administration pressure Facebook to censor. Two they did censor.

499
00:23:47,080 --> 00:23:51,480
Speaker 4: Three they throttled back the Hunter Biden laptop story. And

500
00:23:51,520 --> 00:23:53,280
Speaker 4: then the fourth big point, which I think is really

501
00:23:53,320 --> 00:23:56,159
Speaker 4: important as well, he said, no more Zuckerbocks, no more

502
00:23:56,200 --> 00:23:58,560
Speaker 4: spending on these elections like we did back in twenty twenty.

503
00:23:59,760 --> 00:24:02,080
Speaker 4: But I think the reason they did is because our

504
00:24:02,119 --> 00:24:04,000
Speaker 4: team and it didn't. It wasn't just you know, it

505
00:24:04,040 --> 00:24:06,359
Speaker 4: was our committee, it was our staff. We worked hard

506
00:24:06,400 --> 00:24:08,440
Speaker 4: on this over the last year, and God bless Elon

507
00:24:08,520 --> 00:24:10,960
Speaker 4: Musk who came forward and provided sort of the catalyst

508
00:24:11,080 --> 00:24:12,639
Speaker 4: for this with the Twitter files, because then it was

509
00:24:12,640 --> 00:24:15,680
Speaker 4: the Facebook files, the YouTube files, all this stuff we did,

510
00:24:15,920 --> 00:24:19,040
Speaker 4: but specifically with Meta, it was we deposed over a

511
00:24:19,080 --> 00:24:22,680
Speaker 4: dozen people. They saw, we have the transcripts that point

512
00:24:22,720 --> 00:24:25,399
Speaker 4: all this out, how government was working with them, and

513
00:24:25,480 --> 00:24:26,879
Speaker 4: I think he just said, let's just put it in a

514
00:24:26,920 --> 00:24:28,200
Speaker 4: letter and hopefully get this thing over with.

515
00:24:29,240 --> 00:24:33,000
Speaker 2: FBI got the Hunter Biden laptop in December of twenty nineteen.

516
00:24:33,560 --> 00:24:37,840
Speaker 2: They were telling Twitter and Facebook in the late fall,

517
00:24:38,240 --> 00:24:41,199
Speaker 2: right before the twenty twenty election, that they thought this

518
00:24:41,320 --> 00:24:42,920
Speaker 2: was going to be Russian disinformation.

519
00:24:43,720 --> 00:24:44,880
Speaker 1: Yeah, Congressman, are.

520
00:24:44,800 --> 00:24:48,240
Speaker 2: We ever going to know who ordered that code red

521
00:24:48,400 --> 00:24:52,240
Speaker 2: inside of our government? And here's what I'm troubled by.

522
00:24:52,280 --> 00:24:54,040
Speaker 2: I mean, one that rigged the election. I think you

523
00:24:54,040 --> 00:24:56,560
Speaker 2: would probably agree with it because if the truth had

524
00:24:56,560 --> 00:24:59,960
Speaker 2: come out about the Hunter Biden laptop and Joe Biden's involvement,

525
00:25:00,440 --> 00:25:03,359
Speaker 2: all the different foreign millions of dollars pouring into the family,

526
00:25:03,680 --> 00:25:05,880
Speaker 2: Trump would have won. So I do think they rigged

527
00:25:05,920 --> 00:25:08,600
Speaker 2: the election. They are just based on that individual story.

528
00:25:09,040 --> 00:25:12,280
Speaker 2: But if that person or those individuals did that in

529
00:25:12,359 --> 00:25:15,400
Speaker 2: twenty twenty, why wouldn't they try something similar in twenty

530
00:25:15,400 --> 00:25:15,880
Speaker 2: twenty four.

531
00:25:16,280 --> 00:25:18,280
Speaker 4: Well that's why this the timing this I think is

532
00:25:18,320 --> 00:25:20,359
Speaker 4: also important because we don't know what they may be

533
00:25:20,480 --> 00:25:23,479
Speaker 4: up to now. You're right, that came out on October fourteenth,

534
00:25:23,480 --> 00:25:25,560
Speaker 4: the New York Post story. I mean, we're just what

535
00:25:25,840 --> 00:25:28,000
Speaker 4: twenty some days before the most important election we have

536
00:25:28,119 --> 00:25:30,720
Speaker 4: for you know, President of United States, and they had

537
00:25:30,760 --> 00:25:33,719
Speaker 4: prebunked it all along. I mean, Shallenberger writes about this

538
00:25:33,520 --> 00:25:37,080
Speaker 4: in Twitter files TYBI the whole thing. So they had

539
00:25:37,080 --> 00:25:38,840
Speaker 4: prebunked this. But one of the guys we did interview

540
00:25:38,880 --> 00:25:41,040
Speaker 4: is a guy named Brady Olsen with the FBI, and

541
00:25:41,119 --> 00:25:43,040
Speaker 4: he told us that there was a meeting the day

542
00:25:43,119 --> 00:25:45,560
Speaker 4: the New York Post story comes out. The government, our

543
00:25:45,560 --> 00:25:48,000
Speaker 4: government just happened to be meeting with big tech companies

544
00:25:48,000 --> 00:25:50,760
Speaker 4: and the meeting with Twitter. Someone from Twitter asked, hey,

545
00:25:50,840 --> 00:25:53,159
Speaker 4: is this true, this story because it didn't jibe with

546
00:25:53,200 --> 00:25:56,120
Speaker 4: what our government had been telling him that the whole

547
00:25:56,160 --> 00:25:59,159
Speaker 4: summer in early fall, And they said, is this true?

548
00:25:59,520 --> 00:26:02,480
Speaker 4: And someone said the laptop's reel That was quickly followed

549
00:26:02,520 --> 00:26:05,040
Speaker 4: up by another individual from the Justice Department says no

550
00:26:05,160 --> 00:26:07,800
Speaker 4: further comment. And then they got their stories straight and

551
00:26:07,840 --> 00:26:10,040
Speaker 4: that they put out a statement to all tech companies

552
00:26:10,280 --> 00:26:12,800
Speaker 4: basically saying we have the laptop and we're not going

553
00:26:12,840 --> 00:26:15,480
Speaker 4: to say one way or the other. But initially someone

554
00:26:15,600 --> 00:26:18,000
Speaker 4: spilled the beans and then they quickly tried to say,

555
00:26:18,000 --> 00:26:19,600
Speaker 4: oh no, no, we're not going to talk about that, and

556
00:26:19,640 --> 00:26:22,359
Speaker 4: that's exactly what they did. So yeah, they pre bunked it,

557
00:26:22,400 --> 00:26:25,560
Speaker 4: and then when it happened. That's why Facebook and Twitter,

558
00:26:25,600 --> 00:26:27,679
Speaker 4: and these folks kind of did the the censoring that

559
00:26:27,720 --> 00:26:29,399
Speaker 4: they did of the laptop story, and as you and

560
00:26:29,440 --> 00:26:32,080
Speaker 4: I believe that had an impact on the election. Probably

561
00:26:32,080 --> 00:26:34,679
Speaker 4: when you're talking about such a small margin for Joe Biden,

562
00:26:35,200 --> 00:26:37,040
Speaker 4: probably changed the course of the election.

563
00:26:37,640 --> 00:26:39,400
Speaker 2: I think Trump would be in office. I don't think

564
00:26:39,480 --> 00:26:42,200
Speaker 2: Ukraine would have gotten invaded. I don't think that Hamas

565
00:26:42,200 --> 00:26:44,320
Speaker 2: would have done what they did on October seventh. Certainly

566
00:26:44,320 --> 00:26:46,520
Speaker 2: we wouldn't have let over ten million people in the nation.

567
00:26:46,720 --> 00:26:49,159
Speaker 2: Inflation wouldn't have gone from one point four percent to

568
00:26:49,280 --> 00:26:52,800
Speaker 2: nine percent, all of those things. Jim, I think our

569
00:26:52,920 --> 00:26:55,479
Speaker 2: direct result of the lies that were told from inside

570
00:26:55,480 --> 00:26:57,040
Speaker 2: our government to the big tech companies.

571
00:26:57,040 --> 00:26:57,600
Speaker 1: And I know this.

572
00:26:58,359 --> 00:27:01,119
Speaker 2: I don't know what your personal relationship is like, if any,

573
00:27:01,320 --> 00:27:03,399
Speaker 2: with Mark Zuckerberg, but I've said on this show, and

574
00:27:03,440 --> 00:27:05,080
Speaker 2: I know you listened, so you may have heard it.

575
00:27:05,119 --> 00:27:08,639
Speaker 2: But it's easy to focus on the Zuckerbergs and the

576
00:27:08,760 --> 00:27:12,359
Speaker 2: Jack Dorsey's of the world at Twitter. But if the

577
00:27:12,480 --> 00:27:16,040
Speaker 2: FBI comes to you and says that Russian disinformation is coming,

578
00:27:16,480 --> 00:27:19,600
Speaker 2: and then a version of that comes out, it's natural

579
00:27:19,680 --> 00:27:23,160
Speaker 2: for them to have believed that it was Russian disinformation.

580
00:27:23,600 --> 00:27:25,720
Speaker 2: A lot of the blame shifting on them to me

581
00:27:26,040 --> 00:27:29,120
Speaker 2: should instead be back on our intelligence agents in our

582
00:27:29,280 --> 00:27:32,320
Speaker 2: interior government for what they did not the tech guys.

583
00:27:33,000 --> 00:27:37,199
Speaker 4: No, exactly right, And you know the biggest problem is

584
00:27:37,200 --> 00:27:39,040
Speaker 4: government government pressuring him.

585
00:27:39,080 --> 00:27:39,840
Speaker 3: That's the real city.

586
00:27:39,880 --> 00:27:41,720
Speaker 4: Now. They us succumb to it. And it wasn't just

587
00:27:41,800 --> 00:27:45,160
Speaker 4: the laptop. Remember they censored other things as well. And

588
00:27:46,000 --> 00:27:48,479
Speaker 4: remember too, did this. You had a tweet last week

589
00:27:48,520 --> 00:27:50,399
Speaker 4: that I thought was so good about RFK what he

590
00:27:50,440 --> 00:27:52,760
Speaker 4: said about President Trump and his endorsement. He made the

591
00:27:52,800 --> 00:27:54,760
Speaker 4: same statement in front of our committee because I invited

592
00:27:54,840 --> 00:27:56,639
Speaker 4: him in as a witness a year ago, and he

593
00:27:56,680 --> 00:27:59,560
Speaker 4: said the same thing. He said, the people who censor

594
00:28:00,080 --> 00:28:02,119
Speaker 4: usually aren't the good guys in history. I'm paraphrase. He

595
00:28:02,160 --> 00:28:04,600
Speaker 4: said it better than that, but he said that in

596
00:28:04,680 --> 00:28:07,439
Speaker 4: our hearing as well, because he came in. And the

597
00:28:07,480 --> 00:28:09,520
Speaker 4: reason he is so far up about President Trump is

598
00:28:09,560 --> 00:28:13,280
Speaker 4: this censorship industrial complex is ab and Tellenburger have called

599
00:28:13,320 --> 00:28:16,000
Speaker 4: it because they went after him. On day three of

600
00:28:16,040 --> 00:28:19,720
Speaker 4: the Biden Harris regime. There's an email from Clark Humphrey

601
00:28:19,960 --> 00:28:23,480
Speaker 4: to Twitter saying take down this tweet ASAP car company

602
00:28:23,480 --> 00:28:25,840
Speaker 4: working at the White House take down this tweet asap.

603
00:28:25,960 --> 00:28:28,280
Speaker 4: And it was a tweet from RFK Junior where he said,

604
00:28:28,320 --> 00:28:30,880
Speaker 4: Hank Aaron took the vaccine, Hank Karen passed away after

605
00:28:30,920 --> 00:28:33,320
Speaker 4: taking it. He was doing so to encourage black Americans

606
00:28:33,359 --> 00:28:36,119
Speaker 4: to get vaccinated. There's not one false statement in that tweet,

607
00:28:36,280 --> 00:28:37,840
Speaker 4: and yet they wanted him to take it down because

608
00:28:37,840 --> 00:28:39,760
Speaker 4: they didn't like what it was conveying. They didn't like

609
00:28:39,760 --> 00:28:43,040
Speaker 4: the context that this was this, you know, malinformation belowing

610
00:28:43,240 --> 00:28:45,960
Speaker 4: that our government was involved in. So this is big

611
00:28:46,080 --> 00:28:50,480
Speaker 4: stuff when our government is pressuring people to censor. Because,

612
00:28:50,520 --> 00:28:53,240
Speaker 4: as RFK Junior said, and you tweeted about it, the

613
00:28:53,520 --> 00:28:55,240
Speaker 4: good guys in history aren't the ones who are on

614
00:28:55,280 --> 00:28:57,320
Speaker 4: the censorship side, Jim.

615
00:28:57,680 --> 00:29:00,680
Speaker 2: When you look at the way that the New York Times,

616
00:29:00,680 --> 00:29:04,760
Speaker 2: the Washington Post, NPR, the usual suspects c in NMSNBC,

617
00:29:05,360 --> 00:29:08,720
Speaker 2: the way they covered a few ad bys by Russia

618
00:29:08,760 --> 00:29:12,360
Speaker 2: on Facebook, which in the grand scheme of things, is

619
00:29:12,440 --> 00:29:15,320
Speaker 2: completely inconsequential. And I'm saying that as a media figure

620
00:29:15,840 --> 00:29:17,720
Speaker 2: who's run a media company. We bought a lot of

621
00:29:17,720 --> 00:29:20,720
Speaker 2: Facebook ads over the years. You have to spend millions

622
00:29:20,760 --> 00:29:24,600
Speaker 2: and millions of dollars, tens of millions, even hundreds of millions,

623
00:29:24,680 --> 00:29:27,520
Speaker 2: in order for your message to really get out. Russia

624
00:29:27,560 --> 00:29:29,800
Speaker 2: was testing and spending a few hundred dollars, maybe one

625
00:29:29,840 --> 00:29:32,600
Speaker 2: thousand dollars here and there. It was covered as if

626
00:29:32,640 --> 00:29:34,840
Speaker 2: those ad bys put Trump in the White House in

627
00:29:34,880 --> 00:29:38,800
Speaker 2: twenty sixteen. We spent years on that allegation. Given what

628
00:29:38,880 --> 00:29:42,560
Speaker 2: Mark Zuckerberg just admitted to, How wild is it if

629
00:29:42,600 --> 00:29:45,240
Speaker 2: you compare the amount of attention given to that twenty

630
00:29:45,240 --> 00:29:48,760
Speaker 2: sixteen tiny ad buy with the censorship and also with

631
00:29:48,840 --> 00:29:51,520
Speaker 2: the government briefings that led to the censorship of the

632
00:29:51,640 --> 00:29:52,960
Speaker 2: Hunter Biden laptop story.

633
00:29:53,800 --> 00:29:57,440
Speaker 4: Not even close, not even close. And this is just

634
00:29:57,600 --> 00:30:00,400
Speaker 4: part of it. Remember three weeks ago, get two and

635
00:30:00,440 --> 00:30:05,280
Speaker 4: a half weeks ago, this organization GARM, This continues operation,

636
00:30:05,400 --> 00:30:08,960
Speaker 4: and this all stem from US doing an investigation. But

637
00:30:09,040 --> 00:30:11,280
Speaker 4: basically I really started when I when I was in

638
00:30:11,320 --> 00:30:14,520
Speaker 4: a meeting with Speaker McCarthy and Elon Musk, and I

639
00:30:14,520 --> 00:30:18,080
Speaker 4: remember Elon Musk, we were talking about garment, he said, and.

640
00:30:18,120 --> 00:30:20,200
Speaker 5: He was so right. We dug into this, but it

641
00:30:20,520 --> 00:30:21,160
Speaker 5: is so broad.

642
00:30:21,200 --> 00:30:22,840
Speaker 4: And yet they made a big deal out of a

643
00:30:22,880 --> 00:30:24,080
Speaker 4: few what Russian.

644
00:30:23,720 --> 00:30:26,720
Speaker 5: Spots doing whatever they were doing and something other back

645
00:30:26,720 --> 00:30:28,360
Speaker 5: in twenty sixteen, and it.

646
00:30:28,320 --> 00:30:32,520
Speaker 4: Was nothing compared to what they're doing, to what they

647
00:30:32,600 --> 00:30:35,640
Speaker 4: did to you and OutKick, what they did to Fox News,

648
00:30:35,840 --> 00:30:38,280
Speaker 4: the Federalists, what they did the Daily Wire, Ben Shapiro.

649
00:30:38,320 --> 00:30:40,360
Speaker 4: We had been Shapiro in a testified about a month

650
00:30:40,360 --> 00:30:42,880
Speaker 4: and a half ago, and as a result of that

651
00:30:42,440 --> 00:30:47,760
Speaker 4: that hearing group them the Big advertised they fired their guy.

652
00:30:47,800 --> 00:30:49,560
Speaker 4: This is all part of this garm and everything else

653
00:30:49,640 --> 00:30:52,120
Speaker 4: was going on. So yeah, this is so much bigger.

654
00:30:52,520 --> 00:30:55,640
Speaker 4: But as you point out, the mainstream the big media

655
00:30:55,680 --> 00:30:56,480
Speaker 4: doesn't really want to cover.

656
00:30:57,160 --> 00:30:59,280
Speaker 2: You're doing the Lord's work here. It's truly incredible to

657
00:30:59,280 --> 00:31:01,160
Speaker 2: see it all stacked together and how important it is

658
00:31:01,200 --> 00:31:04,680
Speaker 2: to fight the sensor censorship industrial complex inside a big

659
00:31:04,720 --> 00:31:08,160
Speaker 2: tech You talk to President Trump all the time. How

660
00:31:08,200 --> 00:31:10,160
Speaker 2: often I know you know JD. Vance well because he's

661
00:31:10,160 --> 00:31:13,920
Speaker 2: from Ohio where you are a congressman. How optimistic are

662
00:31:14,000 --> 00:31:16,120
Speaker 2: you about what you are seeing as we get close

663
00:31:16,160 --> 00:31:18,760
Speaker 2: to Labor Day weekend and the official kickoff in many

664
00:31:18,760 --> 00:31:22,160
Speaker 2: ways of the election season. What do you expect to see?

665
00:31:22,200 --> 00:31:24,440
Speaker 2: What is the president telling you? What should our audience know?

666
00:31:24,520 --> 00:31:27,120
Speaker 2: Based on your overview of the election.

667
00:31:27,920 --> 00:31:29,480
Speaker 4: Look, I listened to you and I and I actually

668
00:31:29,560 --> 00:31:31,880
Speaker 4: I think you you've nailed it. I'm optimistic like you are.

669
00:31:31,960 --> 00:31:34,680
Speaker 4: I think it is EBC, you know, economy, boarder crime.

670
00:31:35,040 --> 00:31:37,880
Speaker 4: I think I think we're now getting past the PEP

671
00:31:37,960 --> 00:31:40,280
Speaker 4: Rally's she and Tim Walls are going to do uhuh

672
00:31:40,400 --> 00:31:42,560
Speaker 4: the vice president Tim Wals is going to do an interview. Uh.

673
00:31:42,640 --> 00:31:45,880
Speaker 4: They're they're uh, they're going to have debates. You know

674
00:31:45,920 --> 00:31:48,080
Speaker 4: that there's a big difference between playing the game and

675
00:31:48,880 --> 00:31:51,240
Speaker 4: having the pep rally. I know at the pep rally

676
00:31:51,240 --> 00:31:52,720
Speaker 4: everyone's undefeated, but you've got.

677
00:31:52,600 --> 00:31:54,880
Speaker 5: To when we get to the facts, think about it.

678
00:31:55,080 --> 00:31:57,560
Speaker 5: In four years time, we went from a secure border

679
00:31:57,560 --> 00:32:00,000
Speaker 5: to no border. They just made the borders are their nominee.

680
00:32:00,200 --> 00:32:02,280
Speaker 5: We went from safe streets to record crime. They made

681
00:32:02,320 --> 00:32:04,160
Speaker 5: the lady who was saying, let's bail out the rioters

682
00:32:04,200 --> 00:32:06,920
Speaker 5: in twenty twenty, they made her the nominee. We went

683
00:32:06,960 --> 00:32:08,560
Speaker 5: from two dollars gas to four dollar gas.

684
00:32:08,600 --> 00:32:10,920
Speaker 4: They made the person who said I'm against cracking as

685
00:32:10,960 --> 00:32:14,040
Speaker 4: their nominee. And we went from stable prices to record inflation.

686
00:32:14,240 --> 00:32:18,200
Speaker 4: And the lady who was for the inflation so called

687
00:32:18,200 --> 00:32:21,440
Speaker 4: inflation Reduction Act, which actually called it caused inflation. Voted

688
00:32:21,440 --> 00:32:24,840
Speaker 4: for that, so that's their nominee. You can't run from

689
00:32:24,880 --> 00:32:28,560
Speaker 4: the facts and the issues, and at some point we're

690
00:32:28,560 --> 00:32:30,200
Speaker 4: going to get to that. I think it'll probably start

691
00:32:30,240 --> 00:32:33,440
Speaker 4: as a debate on the tent, but I feel good

692
00:32:33,480 --> 00:32:35,880
Speaker 4: about it. And the polling numbers, while they're close in

693
00:32:35,880 --> 00:32:38,520
Speaker 4: the seven swing states, they're much better now than they

694
00:32:38,520 --> 00:32:42,720
Speaker 4: were with Biden and McClinton. So I feel good. I

695
00:32:42,720 --> 00:32:44,040
Speaker 4: feel like the country is going to say, no, we

696
00:32:44,080 --> 00:32:45,840
Speaker 4: want to go back to the good policies we had

697
00:32:46,120 --> 00:32:46,960
Speaker 4: under President Trump.

698
00:32:47,480 --> 00:32:48,920
Speaker 1: Congressman, keep up the good work.

699
00:32:48,920 --> 00:32:50,760
Speaker 2: Look forward to meeting with you in person again at

700
00:32:50,760 --> 00:32:52,680
Speaker 2: some point, and anytime you want to come on, just

701
00:32:52,760 --> 00:32:53,160
Speaker 2: let us know.

702
00:32:53,880 --> 00:32:56,160
Speaker 4: All right, thanks, say take care, guys, we'll.

703
00:32:56,040 --> 00:32:59,560
Speaker 2: Do That's Congressman Jim Jordan of Ohio really doing incredible

704
00:32:59,560 --> 00:33:02,040
Speaker 2: work as it pertains to the impact of big tech,

705
00:33:02,080 --> 00:33:05,480
Speaker 2: censorship and more. The letter that Mark Zuckerberg wrote addressed

706
00:33:05,480 --> 00:33:08,440
Speaker 2: to him seismic and importance that we have spent a

707
00:33:08,480 --> 00:33:10,200
Speaker 2: lot of time talking on. It would not have happened

708
00:33:10,240 --> 00:33:12,040
Speaker 2: without the work of he as committee and his staff.

709
00:33:12,400 --> 00:33:15,080
Speaker 2: Look right now, it's the end of summer. I am

710
00:33:15,080 --> 00:33:18,120
Speaker 2: headed down on Friday evening to go to the beach,

711
00:33:18,600 --> 00:33:22,080
Speaker 2: last trip to the beach with my family before the

712
00:33:22,200 --> 00:33:25,560
Speaker 2: fall officially gets underway. You're gonna take a bunch of pictures,

713
00:33:25,600 --> 00:33:27,640
Speaker 2: probably going to relive a lot of memories we've been

714
00:33:27,680 --> 00:33:29,200
Speaker 2: having with our family. I bet a lot of you

715
00:33:29,240 --> 00:33:30,680
Speaker 2: are going to do the same as you get ready

716
00:33:30,680 --> 00:33:32,800
Speaker 2: for this Labor Day weekend. You're gonna go out to

717
00:33:32,800 --> 00:33:34,880
Speaker 2: the lake. You're gonna have barbecues. You're gonna hang out

718
00:33:34,880 --> 00:33:37,239
Speaker 2: with your friends, your family, people that you hold and

719
00:33:37,320 --> 00:33:39,640
Speaker 2: cherish the most. How many times have you done that

720
00:33:39,640 --> 00:33:41,480
Speaker 2: in the past, and how many of those memories that

721
00:33:41,520 --> 00:33:44,720
Speaker 2: you created on those days the end of summer during

722
00:33:44,760 --> 00:33:47,760
Speaker 2: the course of the summer are old photos, old family

723
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Speaker 2: VHS tapes, old reels. Now's the time to preserve those

724
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Speaker 2: forever in a digital fashion. That's what Legacy Box does now.

725
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Speaker 2: In my mom's hometown at Chattanooga, Tennessee, where I spend

726
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Speaker 2: a lot of time over the years, these guys do

727
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729
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730
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Speaker 2: hours of entertainment in your family's future and just preserving

731
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Speaker 2: your family's history and what that can mean. At legacybox

732
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733
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Speaker 2: a legacybox dot com slash clay fifty five percent off.

734
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Speaker 2: Summer's coming to an end, but make sure that you

735
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Speaker 2: preserve your summer memories forever at legacybox dot com slash

736
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Speaker 2: clay for fifty five percent off. My thanks for Jim

737
00:34:42,640 --> 00:34:47,520
Speaker 2: Jordan and how great he was just there and for

738
00:34:47,560 --> 00:34:50,520
Speaker 2: the work he's been doing. Because a lot of times

739
00:34:50,560 --> 00:34:54,080
Speaker 2: people say, Okay, what is the impact of let's say,

740
00:34:54,080 --> 00:34:56,759
Speaker 2: a Republican control of the House. I don't think a

741
00:34:56,760 --> 00:34:59,360
Speaker 2: lot of this information comes out if Republicans don't control

742
00:34:59,400 --> 00:35:02,759
Speaker 2: the House. And the decision of Mark Zuckerberg not to

743
00:35:02,800 --> 00:35:06,920
Speaker 2: spend money in twenty twenty four and for him to

744
00:35:06,960 --> 00:35:10,560
Speaker 2: say publicly, hey, I'm not going to endorse in the

745
00:35:10,560 --> 00:35:13,759
Speaker 2: twenty twenty four race is actually a huge win for

746
00:35:13,800 --> 00:35:16,960
Speaker 2: Trump because I'll tell you this, if people say I'm

747
00:35:17,000 --> 00:35:20,400
Speaker 2: not endorsing, that often means I'm for Trump.

748
00:35:21,400 --> 00:35:22,920
Speaker 1: They are just a lot of people who are.

749
00:35:22,800 --> 00:35:27,080
Speaker 2: Not willing to actually take the next step and say yeah,

750
00:35:27,120 --> 00:35:30,759
Speaker 2: I'm voting for Trump. Patrick Mahomes, a quarterback of the

751
00:35:30,840 --> 00:35:32,719
Speaker 2: Kansasity Chiefs, did this recently. I don't know how many

752
00:35:32,760 --> 00:35:36,680
Speaker 2: of you saw. His wife, Brittany liked a Trump post

753
00:35:36,760 --> 00:35:41,120
Speaker 2: on Instagram and got absolutely savaged for it. She refused

754
00:35:41,160 --> 00:35:45,600
Speaker 2: to apologize, but Patrick Mahomes came out and said, I'm

755
00:35:45,600 --> 00:35:48,480
Speaker 2: not going to endorse in the twenty twenty four race. Well,

756
00:35:48,560 --> 00:35:51,920
Speaker 2: I would just submit to you. I know some couples

757
00:35:52,000 --> 00:35:54,640
Speaker 2: vote different ways. You may have a husband or a

758
00:35:54,640 --> 00:35:57,520
Speaker 2: wife that votes it differently. But if Brittany Mahomes is

759
00:35:57,560 --> 00:36:01,920
Speaker 2: going on Instagram and liking Donald Trump up posts, I

760
00:36:01,960 --> 00:36:05,680
Speaker 2: don't think there's very many women voting Trump married to

761
00:36:05,840 --> 00:36:08,640
Speaker 2: men who are voting Kamala or Biden.

762
00:36:09,040 --> 00:36:10,400
Speaker 1: Would most of you agree with that?

763
00:36:10,440 --> 00:36:12,840
Speaker 2: I mean, the data reflects that men are voting for

764
00:36:12,920 --> 00:36:16,760
Speaker 2: Trump by a substantial margin. I think if your wife

765
00:36:16,840 --> 00:36:20,440
Speaker 2: is a Trump supporter, the odds that a Trump supporting

766
00:36:20,560 --> 00:36:23,440
Speaker 2: woman is married to a Kamala voter.

767
00:36:23,680 --> 00:36:27,080
Speaker 1: Are super slim. I'm just tossing that out there.

768
00:36:28,160 --> 00:36:30,319
Speaker 2: If you're one of the women out there that is

769
00:36:30,400 --> 00:36:34,120
Speaker 2: voting Trump and you've got a husband that's voting Kamala,

770
00:36:34,320 --> 00:36:36,560
Speaker 2: I'd actually love to hear from you right now eight

771
00:36:36,640 --> 00:36:39,200
Speaker 2: hundred and two two eight A two. I don't think

772
00:36:39,200 --> 00:36:41,759
Speaker 2: there are very many of those of those people. We

773
00:36:41,840 --> 00:36:43,840
Speaker 2: come back, we'll break down the absolute latest on the

774
00:36:43,880 --> 00:36:45,440
Speaker 2: Kamala interview and the polic