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Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics,

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Speaker 1: where we discussed the top political headlines with some of

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Speaker 1: today's best minds and Kevin McCarthy's role in leadership isn't

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Speaker 1: a lot of trouble. Today we have a show filled

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Speaker 1: with the ups and downs of democracy. First, we're joined

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Speaker 1: by the Daily Shows Jordan Klepper, who's going to discuss

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Speaker 1: his new special American follows Democracy and things he saw

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Speaker 1: attending mcgarelli's. Then we'll talk to Douglas Rushkoff, author of

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Speaker 1: the Survival of the Richest, about what the hell is

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Speaker 1: going on with Elon Musk and Twitter. And trust me,

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Speaker 1: you do not want to miss that because it's gonna

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Speaker 1: tell you a lot about our politics today. But first

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Speaker 1: discussed last night's election. We have the author of Lucky

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Speaker 1: How Joe Biden barely won the presidency NBC News Digitals.

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Speaker 1: John Allen, Welcome to Fast Politics. We are taping this

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Speaker 1: minute before you are listening to it, so it's very

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Speaker 1: Fast Politics. I have brought on my friend, be very sleepless,

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Speaker 1: John Allen. Welcome, John Allen. Hey, Molly John Fast. We're

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Speaker 1: so giddy and punchy because none of us have slept

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Speaker 1: very much. And Jesse is I think in a coma.

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Speaker 1: Are you glive? Um? So, if you're hearing this, it

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Speaker 1: means that Jesse was not in a coma. John Allen,

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Speaker 1: good night for democracy, bad night for Donald J. Trump

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Speaker 1: discuss certainly a bad night for Donald J. Trump. I mean,

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Speaker 1: he put up a bunch of candidates, a lot of

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Speaker 1: them lost, particularly election deniers, people that he might have

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Speaker 1: been able to put in place to help him try

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Speaker 1: to steal a future election. And then you know, the

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Speaker 1: sort of the cherry on top of the bad night

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Speaker 1: for Donald Trump, uh Sunday is Rod de Santis, his

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Speaker 1: main potential rival for the Republican presidential nomination, winning by

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Speaker 1: like twenty points in Florida. Rod Savus couldn't have looked

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Speaker 1: stronger last night. It's possible Donald Trump could have looked weaker,

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Speaker 1: but not much would. By the way, the other piece

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Speaker 1: of it, just a small sliver piece. It gets less

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Speaker 1: attention because it doesn't it doesn't say Trump next to it.

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Speaker 1: But Joe Biden walks out in better standing than he

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Speaker 1: did going into the night. Oh yeah, I mean I

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Speaker 1: was just reading Playbook this morning, and it was like

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Speaker 1: Joe Biden the comeback kid. Now there is a scenario

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Speaker 1: where Joe Biden this is just a punditry fail, right,

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Speaker 1: like the pundance and I and by pundance, I mean

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Speaker 1: you and I too. Everybody gets together and it's so,

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Speaker 1: you know, has a lot of thoughts and feelings, and

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Speaker 1: then the polling we know is complete. Hohoeie. Though one

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Speaker 1: of my poster friends wrote to me last night it

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Speaker 1: was like a lot of polls were right. I was like, okay, um,

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Speaker 1: but I mean the polls were wrong. Yeah, I mean

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Speaker 1: as a general matter, yes, and certainly like the thing

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Speaker 1: the thing that we do is is to like aggregate

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Speaker 1: the polls and then strapolate the aggregation. Like the further

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Speaker 1: you get from actual polling, uh, the more likely you

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Speaker 1: are to be often in some bad direction, you know.

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Speaker 1: Obviously there were factors that played in the election last

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Speaker 1: night that um that either weren't picked up by the

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Speaker 1: polling or were ignored by polsters and the people who

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Speaker 1: read polsters, including people like you and me. Yeah, it's

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Speaker 1: like one of the I am waking up this morning

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Speaker 1: very happy to be wrong, but a lot of people

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Speaker 1: are waking up this morning very mad. Which let's just

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Speaker 1: have a moment to it in our sleeplessness and let

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Speaker 1: us have a moment to enjoy. For example, does Dr

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Speaker 1: Oz move back to New Jersey? I mean you're assuming

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Speaker 1: that he ever moved Pennsylvania. I mean, what do you

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Speaker 1: think are the top lines? Do I think is going

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Speaker 1: to be a Pennsylvania residents six months from now? Like?

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Speaker 1: Absolutely not. I will be surprised if is there Pennsylvania

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Speaker 1: residents six months from now, six days from an how

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Speaker 1: or six hours from now. So here's a question for you.

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Speaker 1: What are you What are the things that you really

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Speaker 1: saw like I mean, I mean the things that like

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Speaker 1: I okay, great, let's go abortion. Like we saw for

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Speaker 1: months and months and months, all this polling that suggested

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Speaker 1: that yes, abortion was among the issues that the voters

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Speaker 1: cared about a lot. And but but it was far

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Speaker 1: behind the economy, inflation, other things that are similar to

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Speaker 1: those things, jobs, um, and and so I think what

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Speaker 1: we saw last night, and this bears out from the

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Speaker 1: exit polling. In fact, when I saw the exit polling

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Speaker 1: the first time last night, I was like, Wow, our

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Speaker 1: exit polling might be off because it was so foreign,

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Speaker 1: odds with what would have made sense for what the

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Speaker 1: polling on everything else shown, and I think it in

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Speaker 1: fact turned out that the polling on that issue was

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Speaker 1: much more accurate and uh, you know, in the exit

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Speaker 1: polls and should have been like a good indication of

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Speaker 1: where things were headed last night. That basically all that

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Speaker 1: energy that we anticipated there being, um, you know, after

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Speaker 1: it became clear the Supreme Court was going to overturn

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Speaker 1: versus Way. Uh, that energy actually appeared on election day,

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Speaker 1: even though for a while it wasn't happening. So really

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Speaker 1: the Supreme Court and the jobs decision really did motivate voters. Yeah,

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Speaker 1: I think the Supreme Court decision, you know, as much

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Speaker 1: as anything else killed the red wave, killed the traditional

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Speaker 1: mid term advantage that the out of power party has,

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Speaker 1: and you know, I think to the extent that it

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Speaker 1: continues to be a right that is in jeopardy. You

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Speaker 1: may see, um, you know, similar outcomes. This is and well,

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Speaker 1: by the way, look, Republicans knew in a sort of

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Speaker 1: abstract way that this was a problem for them, um,

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Speaker 1: and went ahead and pushed forward anymore like they thought

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Speaker 1: it was the right thing. You do. Put conservative justices

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Speaker 1: on this report, have them overturned o versus Way, and

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Speaker 1: then deal with the consequences of that when this consequence

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Speaker 1: has come. But I think, you know, throughout that process,

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Speaker 1: I think at least smart Republican strategist knew that there

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Speaker 1: was a risk that if they got the thing they

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Speaker 1: wanted policy was um, then it would end up being

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Speaker 1: a political albatross. You know. What's funny is like I

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Speaker 1: keep thinking about that moment when they overturned Dobbs and

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Speaker 1: they talked to Trump and Trump was like, oh, you know,

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Speaker 1: like he knew. I mean, and I don't want to

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Speaker 1: give Trump any critic because he's absolute, abject more on,

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Speaker 1: but speaking of Trump, I feel like this is the

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Speaker 1: moment where Republicans start to distance themselves. But you may

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Speaker 1: you may be right. But the reason I laugh at

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Speaker 1: that is we say that what's the moment where the

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Speaker 1: republic existence themselves from Domaldrup. So you may actually be right.

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Speaker 1: I mean, there is there may could be an alternative

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Speaker 1: Roger Santis. If rod De Santis decides that he's going

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Speaker 1: to run for president, there may be an alternative. And

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Speaker 1: if Roda Santis says, uh, Donald Trump did some terrible

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Speaker 1: stuff as president or was like really bad at getting

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Speaker 1: things done, or um, whatever the set of things you right,

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Speaker 1: run against someone essentially run into his right, Like, yes,

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Speaker 1: the Republicans may like jump onto some other some other guy,

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Speaker 1: but for the moment, I would say, always be a

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Speaker 1: little bit wary of the copy idea that Republicans are

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Speaker 1: going to distance themselves from Trump because about thirty five

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Speaker 1: percent of them love him and the rest of them

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Speaker 1: will fall in live right right, But they're but they

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Speaker 1: are making their parties smaller and smaller, you know again,

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Speaker 1: like this is the thing they go to, you know,

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Speaker 1: Republicans of a situation where they are stuck, which they

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Speaker 1: did that, you know, they held themselves hostage and now

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Speaker 1: they continue to throw away the keys to the handcuffs.

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Speaker 1: And I love to see it. Boy, I'm, I'm. That's

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Speaker 1: like the most lovely mixed metaphor what they're holding themselves hostage.

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Speaker 1: But listen, man, I got not much that is art.

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Speaker 1: I think like the things that I was the most

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Speaker 1: worried about as a you know, are these, like, um,

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Speaker 1: the seats where the sort of vote counter would be

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Speaker 1: an election denialist, the people where Trump was putting them

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Speaker 1: in office for his race. Those people have either all

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Speaker 1: lost or about to lose. Can we go through those Yeah,

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Speaker 1: it's like a big suite, right, Like, I mean, I

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Speaker 1: think the ones that, uh that you would look at

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Speaker 1: Tutor Dixon, everyone's favorite actress. Tutor Dixon loses, um, you know,

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Speaker 1: Carrylas having trouble on your Zona, some secretary states and

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Speaker 1: there in these very states around probably of course the

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Speaker 1: one interesting place where the Republicans did very well. Uh

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Speaker 1: in Georgia you have Governor Brian Kemp winning re election,

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Speaker 1: which makes a lot of Democrats unhappy. But Brian Kemp

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Speaker 1: did stand up to Donald Trump last time round, and

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Speaker 1: Brad Rathisburger winning re election after standing up to Trump.

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Speaker 1: So you know, I mean, I think it was a

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Speaker 1: bad night for an election deniers and a good night

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Speaker 1: for people who say that we should have free and

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Speaker 1: fair elections and then, which is honestly quite good. So

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Speaker 1: let's talk about the places where Democrats really got Jesse

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Speaker 1: wants us to talk about the places in which Democrats

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Speaker 1: got the did not do well. Um, Florida is the

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Speaker 1: most glaring, Like Florida is no longer a purple state. Yeah,

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Speaker 1: I mean, it's like one might even argue that there

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Speaker 1: was a red wave it just washed over Florida and

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Speaker 1: didn't hit the rest of the country. UM, So Florida,

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Speaker 1: I would say vance. I mean Ryan ran a really

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Speaker 1: good campaign. I mean it wasn't I think an unwell

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Speaker 1: winnable seat, but like Ryan did really show how Democrat

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Speaker 1: can run in those states. Maybe running to lose, I

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Speaker 1: mean I don't know. I mean shared Brown can show

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Speaker 1: you how to run and yeah, that's right. So I

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Speaker 1: don't think I don't think Tim Ryan's is good at

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Speaker 1: a candidate Brown, it's like just fought out. Um and

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Speaker 1: and the Democrats have of weight been able to fall

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Speaker 1: in love with candidates who can come kind of like

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Speaker 1: within a few points statewide, UM, in various states Texas, Georgia, Ohio, UM,

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Speaker 1: its stead of the ones that can win. And you know,

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Speaker 1: there are Democratic candidates that can win competitive states Steve Fetterman,

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Speaker 1: Toma John you know, like so I think even even

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Speaker 1: Iver's I mean in Wisconsin he won again. He got

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Speaker 1: re elected, you know, and that was supposed to be

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Speaker 1: a tough race. Yeah, and that was good. I mean,

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Speaker 1: I we did have the state party Democratic state Party

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Speaker 1: chair from Wisconsin on the podcast, Ben Whickler, and um,

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Speaker 1: they've worked really hard at their state party. I wonder

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Speaker 1: how much like I know Florida has had some real

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Speaker 1: problems with their state party and Nevada has also had

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Speaker 1: some state party problems. So I wonder if this is

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Speaker 1: connected to you know, I think this is an important

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Speaker 1: point here. Yeah, I think it's um. It's absolutely true

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Speaker 1: that having a party organization is helpful to winning elections.

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Speaker 1: I mean, what are elections? What our elections by getting

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Speaker 1: people to vote, and if you have a disorganized party

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Speaker 1: makes a lot harder to do that. If you have

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Speaker 1: an organized party, it makes it easier to do that.

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Speaker 1: Of course, you still have to have a good product

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Speaker 1: to sell, right. Sometimes maybe you sell OUTBO and people

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Speaker 1: buy it. But for the most part, if you're going

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Speaker 1: to um, you know, if you're gonna win racist, you

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Speaker 1: need sort of both things to be true. You need

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Speaker 1: a good product to your candidate and also a good

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Speaker 1: party organization or non party organization that functions in that

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Speaker 1: same way. And uh and you know can a network

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Speaker 1: for you so they can get people out of the polls.

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Speaker 1: I mean that said Val Demmings was a great candidate

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Speaker 1: and Charlie Chris was not, and Charlie Chris lost by

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Speaker 1: twenty points, and Charlie Chris is lost now as an

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Speaker 1: independent Republican and a Democrat. Congratulations, maybe it's time to retire.

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Speaker 1: But the other thing that I want to, like take

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Speaker 1: a second to talk about is that there was towards

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Speaker 1: the end of this pundentry cycle, before everyone was proved

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Speaker 1: wrong to varying degrees. Uh, they we did see a

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Speaker 1: lot of chunky, gop leaning polls which may have goosed

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Speaker 1: the averages. Can you talk about that for a minute. Yeah,

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Speaker 1: I mean it's a pretty simple, uh strategy, right. Uh,

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Speaker 1: if you put out a bunch of bogus poles or

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Speaker 1: poles that lean in one direction, you'll affect what the

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Speaker 1: averages for the pollsters that do straight averages, And a

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Speaker 1: lot of people use the real clear politics average of poles, um,

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Speaker 1: something I've long used. What you can see is actually,

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Speaker 1: you know, the very easy potential for manipulation of the

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Speaker 1: average of polls by you know, bad faith actors. I

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Speaker 1: would be reluctant to point to a specific case and

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Speaker 1: say this was a bad faith poll. What I would

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Speaker 1: say in general is that, you know, we saw that

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Speaker 1: average point move towards the end, and a bunch of

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Speaker 1: these races and moved to places where it wasn't actually true.

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Speaker 1: So interesting, all right, So let's just we're gonna go

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Speaker 1: through before we both go back to bed. The GOP

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Speaker 1: gubernatorial candidates who are reflecting election deniers who lost. Okay,

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Speaker 1: you ready, these are the people who turned on democracy.

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Speaker 1: Lee's Eldon, Everyone's favorite little buddy in New York State,

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Speaker 1: Dan Cox in Maryland, Darren Bailey in Illinois, Doug Mastriano

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Speaker 1: in Pennsylvania, Tutor Dixon in Michigan, and Tim Michael's in Wisconsin.

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Speaker 1: And then we're still waiting on Carry Lake in Arizona.

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Speaker 1: Isn't l losing and we'll probably lose. Bad night for

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Speaker 1: election deniers, good night for democracy. Absolutely a bad night

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Speaker 1: for election deniers. I would say it's a good night

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Speaker 1: for democracy anytime people are at the vote and there

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Speaker 1: aren't huge fights over over the truth. And what we

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Speaker 1: didn't see a lot of is is people claiming that

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Speaker 1: they had won when they lost. So that's kind of democracy.

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Speaker 1: Uh So, Still, so how does it end now? John

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Speaker 1: Allen tell us the future. It's still seems like McCarthy

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Speaker 1: will eke out the speakership, right, I think we're way

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Speaker 1: too early to tell how mcar speakership. No I mean,

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Speaker 1: the Republicans will eke out winning. Right, It seems very

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Speaker 1: like that the Republicans will have the House and maybe

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Speaker 1: even have some pad um in terms of their numbers,

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Speaker 1: But it's not gonna be a situation where they're you know,

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Speaker 1: they've got like a majority. So you know, this will

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Speaker 1: be very difficult for Kevin McCarthy to make himself speaker

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Speaker 1: in the first place, and if he gets to be

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Speaker 1: speakers to actually manage that confidence. It's I love to

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Speaker 1: see it, and there's gonna be and you think they'll

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Speaker 1: definitely be a leadership kerfuffle at best. I think it

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Speaker 1: will be painful for Kevin McCarthy to get the votes

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Speaker 1: that he needs to become speaker. Not I'm not saying

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Speaker 1: you can't do it. I think it'll be painful. Mary, Mary.

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Speaker 1: We're gonna leave it with this quote from Ben Collins,

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Speaker 1: the Monitor of the Many Maladies of the Art. All right,

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Speaker 1: this is true. Trump cannot fail in the bat in universe.

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Speaker 1: He can only be failed. I'm not even sure what

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Speaker 1: that needs. There you go. Jordan Clipper is a correspondent

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Speaker 1: on the Daily Show and the host of an American

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Speaker 1: follows Democracy. Welcome to Fast politics Jordan Clepper, Well, thanks

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Speaker 1: for having me. Oh my god, I just watched your special,

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Speaker 1: which is wait, tell us what this specialist? God, did

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Speaker 1: it make you happy? To give you hope? What the

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Speaker 1: what the funk is happening out there? It's the feel

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Speaker 1: good special of the year. It's it's a weird time. Yeah,

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Speaker 1: you have. People's brains are melting across the country and

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Speaker 1: luckily the only thing we have to stop it is

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Speaker 1: a referendum where we have to utilize those brains. So

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Speaker 1: it's an interesting time to stay the least. I feel

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Speaker 1: like another title for your special could be democracy dies

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Speaker 1: in stupid. That's not bad. You should have been around

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Speaker 1: a couple of weeks ago. We went with America unfollowed

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Speaker 1: democracy because you know, we're trying to get the kids.

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Speaker 1: We're trying to get them. We want to get all

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Speaker 1: the young kids on their social media is But I

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Speaker 1: think you're You're not wrong. You could feel our democracy

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Speaker 1: quickly slipping away, and if we had a little bit

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Speaker 1: more engagement, a little better information, we might be in

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Speaker 1: a better place. But wish is where horses beggars would ride, right,

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Speaker 1: I don't know. There were so many destinations and so

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Speaker 1: many people you interviewed, and all of them seem to

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Speaker 1: have the medical diagnosis is brain worms. My favorite were

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Speaker 1: the pole watchers. Sure I did not know that Mark Fincham,

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Speaker 1: who is running for Secretary of State and could very

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Speaker 1: well win, was actually an oathkeeper. Yeah, Fincham's got quite

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Speaker 1: the backstory. I think that was part of what we

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Speaker 1: wanted to jump into, is those Secretary of State races.

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Speaker 1: It shows you how desperate things are when Comedy Central

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Speaker 1: and comedians are covering Secretary of State races during the

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Speaker 1: mid term election. It's this is all heads on deck everyone. Um.

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Speaker 1: I actually want to push into that a little bit

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Speaker 1: because so you're a comedian, but you're covering what's actually

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Speaker 1: happening in a way And I actually was thinking about

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Speaker 1: that when I was watching. In a way that seems

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Speaker 1: like almost a little more accurate than a sort of

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Speaker 1: more straight news. Like straight news will come into that

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Speaker 1: and be like, well, Mark Fincham is an oathkeeper. But

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Speaker 1: Democrats want to, you know, give view free glasses. So

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Speaker 1: basically they're the same. The question is are you better

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Speaker 1: suited to cover the state of American decay than a

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Speaker 1: more traditional news program because you don't have to two sides.

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Speaker 1: What seems like the death of democracy. It feels like

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Speaker 1: we're living in a force. So you see where comedians

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Speaker 1: serve a purpose. You understand where we're coming from. I've

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Speaker 1: always said that this idea that there's any kind of

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Speaker 1: unbiased information that we get is in and of itself

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Speaker 1: a illusion. And so when people would come to the

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Speaker 1: Daily Show, they would understand, like I understand, this is

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Speaker 1: a comedy show. It has the bias of a comedy.

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Speaker 1: Hopefully the comedy bias is looking for bs, having your

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Speaker 1: own point of view and bringing that to something. I

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Speaker 1: think that allows us to go into the field. And yeah,

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Speaker 1: it's we bring logic into the field. We we we

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Speaker 1: try to counteract some of these narratives and try to

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Speaker 1: get down to why these people believe certain things, or

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Speaker 1: just try to see what the word on the ground is.

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Speaker 1: And more often than that, you're wasting time and you

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Speaker 1: yourself are full of bs if you're both sidering so

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Speaker 1: wild stuff like should we fight for democracy or not?

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Speaker 1: Or should votes count or not? Or should we trust

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Speaker 1: an election that has been adjudicated or not? And yeah,

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Speaker 1: I think I think we don't waste time trying to

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Speaker 1: put on this veil of an unbiased perspective where in

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Speaker 1: many cases the biases just coming through the facts of

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Speaker 1: the realities that we're in. And so that gives us

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Speaker 1: a good spot as a comedian, as a comedy show

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Speaker 1: of approaching these things. I'm curious it seems like this

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Speaker 1: Denish Just Susan movie has had a huge influence on

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Speaker 1: a lot of these people. Can you talk about that

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Speaker 1: for sure? You know what I found is we go

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Speaker 1: out into the field and we try to sense what

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Speaker 1: narrative people are holding onto. How do they push back

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Speaker 1: against some some pretty incontrovertible evidence. Something like January six

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Speaker 1: was one where you quickly saw there didn't have to

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Speaker 1: be a cohesive narrative for people to push back against

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Speaker 1: wanting to accept the reality of January six. People would

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Speaker 1: say it was Antifa, some people would say it didn't happen.

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Speaker 1: Some people would say it was a good thing. There

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Speaker 1: wasn't a coherent narrative, but you didn't need one. And

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Speaker 1: then you fast forward to this election and there isn't

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Speaker 1: a good coherent narrative for people to bat down the

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Speaker 1: fact that Donald Trump lost so many lawsuits that there

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Speaker 1: hasn't been really clear, incontrovertible evidence of election fraud. And

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Speaker 1: then Denisia SUSA does this documentary which is cleverly titled

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Speaker 1: It's Got Scary Music. It's called two thousand Mules, and

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Speaker 1: you don't even have to watch it to be given

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Speaker 1: a tool to push back against someone. And so it's

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Speaker 1: not as if we even get into the details of

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Speaker 1: what is claimed in Denisia SUSS two thousand mules, which

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Speaker 1: has been knocked down by many of folks, many including

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Speaker 1: those inside the Trump administration, but it does give you

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Speaker 1: a catch phrase. And so this last time out, I've

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Speaker 1: had discussions with people about what happened this past election,

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Speaker 1: and people didn't have great answers. Now they can easily

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Speaker 1: just say two thousand mules, and then you give them information,

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Speaker 1: they're like, well, two thousand mules, and so that's almost

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Speaker 1: all you need. What is the catch phrase? What is

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Speaker 1: the simple idea that we can boil down to it,

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Speaker 1: something that's clean that you can put in the face

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Speaker 1: of uncomfortable information. So I have to move on to

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Speaker 1: something else, then what I just wait, stop for one second?

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Speaker 1: Are there two thousand mules? Are there two thousand Where

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Speaker 1: does that even come from I did not see the movie.

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Speaker 1: You got it downloaded. For God's sakes. Yeah, I want to.

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Speaker 1: I want to give Janesse disus of my money boat somehow.

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Speaker 1: I mean, I think the basic theory around it is

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Speaker 1: there are two thousand people at least I believe, is

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Speaker 1: what he claims, traveling around their voting districts, dumping piles

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Speaker 1: and piles of votes in drop boxes. He uses cell

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Speaker 1: phone technology to place certain people in different places, which

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Speaker 1: has been pushed back upon because the cell phone technology

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Speaker 1: is nowhere near as precise. Is what he claims. He

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Speaker 1: has anecdotal evidence, He has greeny footage and sort of

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Speaker 1: this narrative that we have cell phone technology, again a

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Speaker 1: loose technology, but it sounds fancy if you're watching quickly,

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Speaker 1: that puts certain people in locations of drop boxes. Hilariously,

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Speaker 1: guess where drop boxes tend to be located around people,

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Speaker 1: so you can see where there might be overlap in

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Speaker 1: this conspiracy. But with a little bit of footage of

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Speaker 1: people near drop boxes, it shows this doubt. People are wondering,

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Speaker 1: okay exactly and when I go and I talked to

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Speaker 1: the oathkeepers in Arizona who are armed outside of ballot boxes.

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Speaker 1: They reference this, They references images of people carrying fifty

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Speaker 1: to a hundred ballots in their hands, and that's what

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Speaker 1: they're on the lookout for. I used to go to

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Speaker 1: Seapack all the time before they would like just come

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Speaker 1: over and scream at me. I found it very valuable

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Speaker 1: and super fascinating because what they're doing here is kind

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Speaker 1: of outside of the purview of many of our like

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Speaker 1: ideas about reality, which is what you see when you

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Speaker 1: do these interviews. Sometimes they're like weirdly proud, and sometimes

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Speaker 1: they're mad at. You do find them mad at. You

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Speaker 1: do have people screaming at you. Are now at rallies,

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Speaker 1: I get my fair share of people who will scream

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Speaker 1: at me, partially because from the Daily Show, but also

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Speaker 1: just because I have a camera with me and the

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Speaker 1: media are bad news. I went to see Pack and

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Speaker 1: I had the opposite reaction. Though I had dozens of

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Speaker 1: folks wanting to take selfies with me. I had young

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Speaker 1: Republican candidates eager to do interviews, handing me their business

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Speaker 1: cards waiting in line. And I had super Trump fans

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Speaker 1: trying to get selfies and pictures and talk to me,

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Speaker 1: which sort of melted my brain because it was a

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Speaker 1: mix of thirsty Republicans who don't care about ideology and

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Speaker 1: are just want to talk about the game of politics.

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Speaker 1: There were assistance to prominent Republicans who were just like,

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Speaker 1: oh yeah, we love your videos around the office and

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Speaker 1: like oh interesting. And then there were people who are

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Speaker 1: just the Trump fanboys who saw me as part of

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Speaker 1: the MAGA universe. And even if they didn't necessary like

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Speaker 1: some of the things that I put on television, the

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Speaker 1: fact that I was part of the narrative that they

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Speaker 1: were so invested in was exciting to them. I don't

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Speaker 1: wonder how much of this is about fame too, Like

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Speaker 1: Trump got so famous speak because he was famous already, right,

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Speaker 1: I mean they loved him, Like do they sort of

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Speaker 1: love you because you're famous? If that makes any sense. Yes,

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Speaker 1: it is at sea Pack. It was all just a game.

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Speaker 1: Seapack was nauseating because there's no discussion of governing. It

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Speaker 1: was the show and this is show business. The more

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Speaker 1: the deeper I go into it, I see the show

429
00:24:20,080 --> 00:24:22,320
Speaker 1: business of it all. I only see the show business

430
00:24:22,320 --> 00:24:24,280
Speaker 1: of it all. And the fact that Marjorie Taylor Green

431
00:24:24,400 --> 00:24:27,600
Speaker 1: is walking through Seapack and she has twenty people behind

432
00:24:27,640 --> 00:24:30,119
Speaker 1: her with cell phone footage trying to just get pictures

433
00:24:30,160 --> 00:24:32,880
Speaker 1: of her because she's famous because she says the wildest things.

434
00:24:32,920 --> 00:24:36,040
Speaker 1: And then I'm talking to moderate Republicans who can't get

435
00:24:36,040 --> 00:24:38,360
Speaker 1: anybody to pay attention to them because they're not famous,

436
00:24:38,400 --> 00:24:41,000
Speaker 1: because they don't say wild things that travel. And they're

437
00:24:41,000 --> 00:24:43,560
Speaker 1: talking to me because I'm part of this narrative. I'm

438
00:24:43,600 --> 00:24:46,080
Speaker 1: the bad guy, I'm the heel, I'm the villain, and

439
00:24:46,160 --> 00:24:48,119
Speaker 1: therefore they want a selfie with me there too, And

440
00:24:48,200 --> 00:24:52,119
Speaker 1: so it is just this big show, and and the

441
00:24:52,160 --> 00:24:54,480
Speaker 1: deeper farther I go into, it's like, oh, this is

442
00:24:54,520 --> 00:24:56,880
Speaker 1: the only way to compete in this is get your fame,

443
00:24:56,920 --> 00:24:59,119
Speaker 1: get your brand, get as high as you can, and

444
00:24:59,160 --> 00:25:01,000
Speaker 1: then rile up the true who want to turn on

445
00:25:01,040 --> 00:25:04,600
Speaker 1: and watch the most exciting television show they can. It's

446
00:25:04,600 --> 00:25:08,360
Speaker 1: funny because you are in a situation where there are

447
00:25:08,520 --> 00:25:11,160
Speaker 1: people who really believe this stuff. Who are the people

448
00:25:11,200 --> 00:25:14,520
Speaker 1: in the special right you interview them, they believe it,

449
00:25:14,680 --> 00:25:18,400
Speaker 1: but the Sea Pack folks are are really just using them, right,

450
00:25:18,680 --> 00:25:21,800
Speaker 1: Oh yes, And Seepack had a mix of the super fans,

451
00:25:21,800 --> 00:25:24,040
Speaker 1: the super fans who do believe it, but again believing

452
00:25:24,080 --> 00:25:26,439
Speaker 1: it is it's let's it's not the issues. The Trump

453
00:25:26,440 --> 00:25:28,679
Speaker 1: fans are just Trump fans. They're a fan of the fame,

454
00:25:29,080 --> 00:25:31,520
Speaker 1: the brand, and they want it to be it's it's

455
00:25:31,560 --> 00:25:34,760
Speaker 1: about win loss. They identify with Donald Trump and therefore

456
00:25:34,800 --> 00:25:36,800
Speaker 1: they want him to win at all costs. That's what

457
00:25:36,800 --> 00:25:39,399
Speaker 1: they're excited about. And then you see the second tier

458
00:25:40,000 --> 00:25:42,800
Speaker 1: of people in the MAGA universe who do know better

459
00:25:43,200 --> 00:25:46,080
Speaker 1: and see this and just see the only way to

460
00:25:46,200 --> 00:25:49,920
Speaker 1: find success in that platform is to utilize this to

461
00:25:50,080 --> 00:25:51,919
Speaker 1: play it up. You know, I go out and I

462
00:25:51,920 --> 00:25:54,480
Speaker 1: see Carrie Lake utilizing this. I see all of these

463
00:25:54,520 --> 00:25:59,200
Speaker 1: major candidates utilizing this narrative. Okay, I'll use election denialism

464
00:25:59,240 --> 00:26:01,520
Speaker 1: as a way to to people, to rile them up,

465
00:26:01,560 --> 00:26:04,040
Speaker 1: to get them excited, and from that I will find

466
00:26:04,040 --> 00:26:07,160
Speaker 1: my own success. So you know this group pretty wow?

467
00:26:07,400 --> 00:26:10,119
Speaker 1: Where do you think they go? Now? They follow everything

468
00:26:10,160 --> 00:26:12,320
Speaker 1: that Trump does, and so it's easy for them to

469
00:26:12,400 --> 00:26:13,960
Speaker 1: jump on board. And Trump's going to give them a

470
00:26:14,000 --> 00:26:16,639
Speaker 1: destination in a week. It sounds like social media is

471
00:26:16,640 --> 00:26:19,520
Speaker 1: where they're going to converse, and so whoever can make

472
00:26:19,560 --> 00:26:22,800
Speaker 1: the most noise there they will follow. I don't see

473
00:26:22,840 --> 00:26:27,080
Speaker 1: other candidates that have the same as Donald Trump did

474
00:26:27,119 --> 00:26:30,119
Speaker 1: if he extricates himself from the conversation. I went to

475
00:26:30,160 --> 00:26:37,680
Speaker 1: a Dug Mastriano rally in Pennsylvania and jazz it was like, oh,

476
00:26:37,800 --> 00:26:42,160
Speaker 1: here is Maga is m without Donald Trump, And it's

477
00:26:42,200 --> 00:26:45,080
Speaker 1: that people are talking, talking to talk, but they're not

478
00:26:45,119 --> 00:26:48,040
Speaker 1: showing up. Mastriano is not as compelling, and like you said,

479
00:26:48,119 --> 00:26:50,280
Speaker 1: he's not as famous. He's not a big famous guy

480
00:26:50,280 --> 00:26:51,800
Speaker 1: that they want to take a selfie with, And so

481
00:26:51,920 --> 00:26:55,360
Speaker 1: even though the ideas are there, the energy isn't. So

482
00:26:55,560 --> 00:26:58,200
Speaker 1: it's very curious. I think Trump can have little Maga,

483
00:26:58,200 --> 00:27:01,480
Speaker 1: it's that grow up underneath him. But I've definitely seen

484
00:27:01,560 --> 00:27:04,040
Speaker 1: failures of that and failures of charisma that start to

485
00:27:04,080 --> 00:27:06,800
Speaker 1: populate the Hoplican Party. So I would say, like the

486
00:27:07,240 --> 00:27:11,359
Speaker 1: sort of errors to Trump is um are the carry

487
00:27:11,480 --> 00:27:16,479
Speaker 1: legs right and the de Santis is though I mean again,

488
00:27:16,520 --> 00:27:18,760
Speaker 1: there's this possibility that there's going to be a de

489
00:27:18,840 --> 00:27:22,520
Speaker 1: Santis Trump face off, right, m M. Yeah. When I

490
00:27:22,680 --> 00:27:25,200
Speaker 1: talked to people out there, these rallies, people are showing

491
00:27:25,280 --> 00:27:27,600
Speaker 1: up sea Donald Trump. Sometimes they can't even name the

492
00:27:27,640 --> 00:27:32,720
Speaker 1: other people who the rallies are for that that's more

493
00:27:32,760 --> 00:27:35,159
Speaker 1: often than that, and they really have They seem to

494
00:27:35,200 --> 00:27:37,960
Speaker 1: have no idea who. Then Tutor Dixon is now even

495
00:27:38,000 --> 00:27:39,920
Speaker 1: the even the Trump fans, she's like she was their

496
00:27:40,000 --> 00:27:44,320
Speaker 1: third choice. But it doesn't matter. Trump is gonna be there.

497
00:27:44,359 --> 00:27:46,520
Speaker 1: So they show up. And then De Santis his name

498
00:27:46,560 --> 00:27:49,600
Speaker 1: pops up because he loves to make noise. He's becoming

499
00:27:49,600 --> 00:27:52,639
Speaker 1: more famous in that world, and he's openly cruel just

500
00:27:52,720 --> 00:27:56,639
Speaker 1: snanctimonius as we call him now, and that's yes, the

501
00:27:56,720 --> 00:28:00,480
Speaker 1: sanctimonius as he now is. So I think I think

502
00:28:00,480 --> 00:28:03,000
Speaker 1: there are people like him who if he runs against Trump,

503
00:28:03,080 --> 00:28:04,760
Speaker 1: I don't think there's a space for him. But if

504
00:28:04,760 --> 00:28:08,040
Speaker 1: Trump leaves an opening, he has the playbook. He has

505
00:28:08,080 --> 00:28:13,040
Speaker 1: the actionable cruel moves that people get excited about. And sadly,

506
00:28:13,040 --> 00:28:14,600
Speaker 1: that is something that is out there that is is

507
00:28:14,640 --> 00:28:17,400
Speaker 1: not talked enough about, is the cruelty and the excitement

508
00:28:17,400 --> 00:28:21,280
Speaker 1: around the cruelty. There is palpable and can be weaponized,

509
00:28:21,480 --> 00:28:24,480
Speaker 1: and I see a couple of those big stars they're

510
00:28:24,840 --> 00:28:26,680
Speaker 1: figuring out how to do it. One of the things

511
00:28:26,720 --> 00:28:32,000
Speaker 1: I think that we in the sane media industrial complex

512
00:28:32,040 --> 00:28:34,840
Speaker 1: whatever that means, have always sort of made a mistake

513
00:28:34,920 --> 00:28:36,960
Speaker 1: with Trump is that there's a lot of us feel

514
00:28:36,960 --> 00:28:39,080
Speaker 1: like there's a moment where Trump will be like, oh

515
00:28:39,160 --> 00:28:42,760
Speaker 1: this I can't deal with anymore. That's never gonna happen, right,

516
00:28:42,760 --> 00:28:45,480
Speaker 1: I mean, he just keeps going until he hits the

517
00:28:45,800 --> 00:28:49,000
Speaker 1: you know, until he hits sort of someone who will

518
00:28:49,040 --> 00:28:52,880
Speaker 1: stop him, which is no one. So he is he

519
00:28:53,040 --> 00:28:56,360
Speaker 1: is attempting to flush our democracy down the toilet because

520
00:28:56,360 --> 00:28:58,760
Speaker 1: he was embarrassed he lost an election. He is willing

521
00:28:58,800 --> 00:29:01,600
Speaker 1: to do that. Yeah, it goes no further than that

522
00:29:01,600 --> 00:29:04,800
Speaker 1: that comment, I'm embarrassed that I lost, So what do

523
00:29:04,840 --> 00:29:07,440
Speaker 1: I need to do? We'll burn it all down. So

524
00:29:07,560 --> 00:29:10,560
Speaker 1: will he go away quietly? No, He'll never go away.

525
00:29:10,640 --> 00:29:13,760
Speaker 1: If there's an ounce of embarrassment that the narrative around

526
00:29:13,840 --> 00:29:17,040
Speaker 1: him is turning, he will do everything in his power

527
00:29:17,120 --> 00:29:20,480
Speaker 1: to continue to push whatever lies make him a hero

528
00:29:20,640 --> 00:29:23,400
Speaker 1: and people will buy that. And he's already set up

529
00:29:23,400 --> 00:29:25,440
Speaker 1: the blueprint for it all. And that's where like even

530
00:29:25,560 --> 00:29:27,680
Speaker 1: these mid terms and what's happened in the future, people

531
00:29:27,680 --> 00:29:30,720
Speaker 1: are already don't believe the results. They haven't happened yet,

532
00:29:30,800 --> 00:29:32,760
Speaker 1: and people don't believe it. They don't believe it. People

533
00:29:32,800 --> 00:29:36,280
Speaker 1: already don't believe what happened in four And I think

534
00:29:36,480 --> 00:29:38,560
Speaker 1: the ship is in the pool. It's hard to get

535
00:29:38,560 --> 00:29:40,400
Speaker 1: the ship back out of the pool. And I think

536
00:29:40,640 --> 00:29:42,640
Speaker 1: as long as there's ship in the pool, that's the

537
00:29:42,640 --> 00:29:45,640
Speaker 1: pool that Donald Trump feels most comfortable. And that's both

538
00:29:45,680 --> 00:29:50,440
Speaker 1: disgusting and also yeah, yeah, yeah, so I I I

539
00:29:50,560 --> 00:29:52,880
Speaker 1: don't see a clean pool in the near future. And

540
00:29:52,960 --> 00:29:55,640
Speaker 1: I do think I don't see a world where he's

541
00:29:55,680 --> 00:29:57,960
Speaker 1: not a part of the conversation, if not attempting to

542
00:29:57,960 --> 00:30:00,760
Speaker 1: make himself the central focus. What is stuff you saw

543
00:30:00,800 --> 00:30:04,480
Speaker 1: that you couldn't put in the show because you were like, oh,

544
00:30:04,520 --> 00:30:07,040
Speaker 1: we can't put that in. Sadly, some of the stuff

545
00:30:07,040 --> 00:30:11,200
Speaker 1: goes darker, stranger and Saturday, Yeah, I think we we.

546
00:30:11,560 --> 00:30:14,760
Speaker 1: In Michigan, I ran into a woman who was openly

547
00:30:14,800 --> 00:30:19,080
Speaker 1: anti Semitic and talked about the Rothschild's and the banking system.

548
00:30:20,560 --> 00:30:22,480
Speaker 1: She was really forceful that it was there. There was

549
00:30:22,520 --> 00:30:24,400
Speaker 1: a glimpse of her there, but you could tell there's

550
00:30:24,560 --> 00:30:25,960
Speaker 1: there was There was a lot of hate that was

551
00:30:26,000 --> 00:30:28,520
Speaker 1: out there. There was another man who went into depth

552
00:30:28,600 --> 00:30:32,120
Speaker 1: about Nancy Pelosi and drinking the blood. I think there's

553
00:30:32,120 --> 00:30:34,240
Speaker 1: also a little clip of him, but it went to

554
00:30:34,280 --> 00:30:37,920
Speaker 1: a very dark place, and sadly, Mike Lindell kept popping

555
00:30:37,960 --> 00:30:41,120
Speaker 1: up at rallies trying to interrupt our interviews and and

556
00:30:41,200 --> 00:30:43,880
Speaker 1: frankly we were like, Mike, we just don't have the time, man,

557
00:30:43,920 --> 00:30:46,760
Speaker 1: I'm sorry. So there's sadly no Mike Lindell in this,

558
00:30:46,960 --> 00:30:50,040
Speaker 1: and not without him trying. By the way, Mike Gondell,

559
00:30:50,400 --> 00:30:53,600
Speaker 1: he has really worked hard to like mimic Trump is um,

560
00:30:53,640 --> 00:30:57,880
Speaker 1: but he can't do it because he's just too uncharismatic.

561
00:30:58,240 --> 00:31:02,200
Speaker 1: He is a celebrities rallies it is. He shows up early.

562
00:31:02,600 --> 00:31:06,360
Speaker 1: There was I don't think I don't think he's technically

563
00:31:06,400 --> 00:31:10,520
Speaker 1: invited to some of the inside events, right I saw

564
00:31:10,560 --> 00:31:15,360
Speaker 1: a video of him outdoors, right, Yes, he's And two

565
00:31:15,360 --> 00:31:17,880
Speaker 1: of the events we went to, he would show up

566
00:31:18,240 --> 00:31:22,160
Speaker 1: literally seven hours before Trump spoke, and he would speak

567
00:31:22,240 --> 00:31:26,560
Speaker 1: on a ramshackle stage outdoors for the people who are waiting.

568
00:31:26,760 --> 00:31:29,880
Speaker 1: And there's part of it like he might not technically

569
00:31:29,960 --> 00:31:32,160
Speaker 1: be invited, but they're like, if you want to stand

570
00:31:32,160 --> 00:31:34,840
Speaker 1: on the stage in between us playing Y M C

571
00:31:35,000 --> 00:31:37,280
Speaker 1: A by the village people for the hundredth time, you

572
00:31:37,320 --> 00:31:40,160
Speaker 1: can get up there and say whatever you want and

573
00:31:40,200 --> 00:31:43,280
Speaker 1: people will take selfies. And frankly, he travels. He gets

574
00:31:43,280 --> 00:31:45,920
Speaker 1: in his big car and he drives to that stage

575
00:31:45,920 --> 00:31:50,440
Speaker 1: and he likes to talk. Is the brick wall guy

576
00:31:50,560 --> 00:31:53,560
Speaker 1: still there, the guy in the brick wall suit. Have

577
00:31:53,680 --> 00:31:57,320
Speaker 1: you seen him or now I haven't seen brick wall Guy. No, Sadly,

578
00:31:58,200 --> 00:32:01,760
Speaker 1: I hope, I hope he's all right. Although some of

579
00:32:01,760 --> 00:32:04,400
Speaker 1: them are already up front. I mean they get there

580
00:32:04,880 --> 00:32:07,080
Speaker 1: sometimes days ahead of time, So there's a chance either

581
00:32:07,120 --> 00:32:08,840
Speaker 1: he's no longer part of the movement or he's so

582
00:32:09,000 --> 00:32:11,080
Speaker 1: part of the movement he's way ahead of even where

583
00:32:11,080 --> 00:32:13,640
Speaker 1: we have access to him. I have one last question,

584
00:32:13,640 --> 00:32:16,640
Speaker 1: which is jfk Jr Guy. This is a guy who

585
00:32:16,680 --> 00:32:20,920
Speaker 1: claims he's the reincarnation. No, he's he's actually jfk Jr.

586
00:32:20,920 --> 00:32:24,200
Speaker 1: Who faked his own death. This guy looks nothing like

587
00:32:25,120 --> 00:32:28,840
Speaker 1: one thing. He's several inches shorter than jfk Jr. Is

588
00:32:28,880 --> 00:32:33,120
Speaker 1: that guy still around? I saw him at Yeah, he's

589
00:32:33,160 --> 00:32:36,400
Speaker 1: handing out cards at sea pack. He's it's all a show.

590
00:32:36,600 --> 00:32:40,840
Speaker 1: It's all the show guy right there, the guy who

591
00:32:40,880 --> 00:32:44,760
Speaker 1: the Internet randomly said is actually jfk Jr. Was like,

592
00:32:45,280 --> 00:32:48,840
Speaker 1: I can use this and frankly, he did. He's using it.

593
00:32:48,920 --> 00:32:51,560
Speaker 1: He's taking pictures with people who may or may not

594
00:32:51,640 --> 00:32:53,920
Speaker 1: think he's JFK Jr. But he's he's part of the

595
00:32:53,960 --> 00:32:58,000
Speaker 1: model world. Congratulations. It's so grim because it's like it

596
00:32:58,440 --> 00:33:01,840
Speaker 1: is hilarious and it's so stupid, but it actually is.

597
00:33:01,920 --> 00:33:05,960
Speaker 1: These people do really believe what is happening, and they

598
00:33:06,000 --> 00:33:09,400
Speaker 1: really are being ruined by it. You know what breaks

599
00:33:09,440 --> 00:33:11,440
Speaker 1: my heart about that? And it's true, the JFK Junior

600
00:33:11,480 --> 00:33:15,520
Speaker 1: one specifically, because it's so absurd, but it's gaining momentum

601
00:33:15,680 --> 00:33:17,720
Speaker 1: to the point that I was in Wisconsin a few

602
00:33:17,720 --> 00:33:20,280
Speaker 1: months ago and I'm talking to the sweet old Midwestern

603
00:33:20,360 --> 00:33:22,120
Speaker 1: ladies and I'm from the Midwest, and so I see

604
00:33:22,120 --> 00:33:24,120
Speaker 1: these sweet old ladies. They look like relatives of mine,

605
00:33:24,360 --> 00:33:27,000
Speaker 1: and we're talking about Roe v. Wade, and out of nowhere,

606
00:33:27,000 --> 00:33:30,160
Speaker 1: they bring up this JFK Junior conspiracy and they're consumed

607
00:33:30,200 --> 00:33:32,400
Speaker 1: by it. And I saw them as people who don't

608
00:33:32,440 --> 00:33:34,880
Speaker 1: need to be consumed by this. They don't have to

609
00:33:34,920 --> 00:33:38,080
Speaker 1: waste their time and their energy and their anger on

610
00:33:38,240 --> 00:33:41,680
Speaker 1: this Bonker's idea that the Internet has shoved in their faces.

611
00:33:41,720 --> 00:33:45,000
Speaker 1: But yet here we are and now they're paranoid beyond

612
00:33:45,040 --> 00:33:49,360
Speaker 1: belief about these absurdities, and it's pulling them and it's

613
00:33:49,360 --> 00:33:52,520
Speaker 1: being manipulated by other people who know how to manipulate them. Jesus.

614
00:33:52,920 --> 00:33:57,600
Speaker 1: So hilarious, but also incredibly soul crushing, Lee, sad and depression.

615
00:33:58,120 --> 00:34:03,400
Speaker 1: That's comedy. Two baby, Thanks so much, Jordan, Thanks for

616
00:34:03,440 --> 00:34:07,920
Speaker 1: having me. Douglas Rushcoff is the author of Survival of

617
00:34:07,960 --> 00:34:13,920
Speaker 1: the Richest. Welcome to Fast Politics. Doug Rushcoff, What a week?

618
00:34:14,520 --> 00:34:17,480
Speaker 1: What a week? So? I feel like, is this the

619
00:34:17,600 --> 00:34:22,560
Speaker 1: dystopian end times that I've read about before or now? Well,

620
00:34:22,920 --> 00:34:25,840
Speaker 1: if we're over the lip of the event horizon, for sure.

621
00:34:25,920 --> 00:34:29,600
Speaker 1: But this is the easy slope part, the easy slope down.

622
00:34:29,800 --> 00:34:33,200
Speaker 1: This is not you know, we're not in the cannibalistic

623
00:34:33,280 --> 00:34:40,239
Speaker 1: humanoid underground dwellers haven't emerged yet. We're still good. We're

624
00:34:40,280 --> 00:34:43,840
Speaker 1: still good. What do you think about what we're seeing

625
00:34:43,920 --> 00:34:50,080
Speaker 1: here with this? Elon musk bies Twitter go to cut

626
00:34:50,120 --> 00:34:53,160
Speaker 1: to the chase. I mean, the punchline of this for

627
00:34:53,200 --> 00:34:57,160
Speaker 1: me is I feel like this is the third level

628
00:34:57,200 --> 00:35:02,040
Speaker 1: of something that started with Charlie Sheen. Whatever that means.

629
00:35:02,160 --> 00:35:06,160
Speaker 1: Let's go. That sounds great. Well, remember when Charlie Sheen

630
00:35:06,360 --> 00:35:09,399
Speaker 1: got in trouble or fired from Six and a half

631
00:35:09,400 --> 00:35:12,680
Speaker 1: Men or whatever, he was on for something, and then

632
00:35:12,680 --> 00:35:14,879
Speaker 1: he told people that he had aids that he didn't

633
00:35:14,920 --> 00:35:20,960
Speaker 1: tell people whatever something right, then he went tiger blood right.

634
00:35:21,000 --> 00:35:24,320
Speaker 1: So Charlie Sheen was, for my money, the first person

635
00:35:24,600 --> 00:35:29,880
Speaker 1: who kind of jumped into the cultural standing wave created

636
00:35:29,920 --> 00:35:33,400
Speaker 1: by Twitter, and he just embodied. He wrote it for everything.

637
00:35:33,440 --> 00:35:37,160
Speaker 1: It was worth right, winning, winning, and and and we

638
00:35:37,320 --> 00:35:40,960
Speaker 1: saw what happens to someone when they jump into that

639
00:35:41,040 --> 00:35:44,879
Speaker 1: standing wave is it kills them right, finally destroys them.

640
00:35:45,400 --> 00:35:47,880
Speaker 1: So he was the first test case. And then I

641
00:35:47,920 --> 00:35:50,400
Speaker 1: feel like Donald Trump was the second. He goes I

642
00:35:50,440 --> 00:35:52,840
Speaker 1: can do it, and he jumps in there and decides

643
00:35:52,840 --> 00:35:55,319
Speaker 1: to look, Charlie Sheen, he just used the standing wave

644
00:35:55,400 --> 00:35:57,840
Speaker 1: to maybe get laid and get some attention. I'm going

645
00:35:57,920 --> 00:36:01,040
Speaker 1: to use it to become president. So he work and

646
00:36:01,120 --> 00:36:04,680
Speaker 1: it worked, right, It worked. That was one thing real quick.

647
00:36:04,719 --> 00:36:06,399
Speaker 1: So I can get a little bit more clarity here.

648
00:36:06,400 --> 00:36:09,239
Speaker 1: What is a standing wave? A standing wave is like

649
00:36:09,520 --> 00:36:12,239
Speaker 1: when you see the water in the ocean and there's

650
00:36:12,239 --> 00:36:15,480
Speaker 1: a big wave coming at you. There's two things about

651
00:36:15,520 --> 00:36:18,960
Speaker 1: that wave. There's the water in the wave, right, the

652
00:36:19,000 --> 00:36:23,600
Speaker 1: molecules of water. But then there's like the wave itself,

653
00:36:23,880 --> 00:36:28,839
Speaker 1: that thing, that dynamical system of of energy that's just

654
00:36:29,080 --> 00:36:31,839
Speaker 1: moving right, so you can sort of jump in that

655
00:36:31,880 --> 00:36:35,239
Speaker 1: wave and become part of that wave. So I look

656
00:36:35,239 --> 00:36:38,520
Speaker 1: at culture as that it's this big wave moving forward,

657
00:36:38,719 --> 00:36:41,600
Speaker 1: and you've got guys like Bannon who can say, ah,

658
00:36:41,640 --> 00:36:45,600
Speaker 1: I am the wizard moving these wig waves around. It's like, no, no,

659
00:36:45,760 --> 00:36:49,600
Speaker 1: you're just riding these waves like that rays about the crash.

660
00:36:49,880 --> 00:36:55,280
Speaker 1: So but these folks, these almost like uh dominator masochists,

661
00:36:55,480 --> 00:36:57,640
Speaker 1: kind of jump into the wave to ride it for

662
00:36:57,680 --> 00:37:01,239
Speaker 1: everything it's worth and try to maintain some uh controller

663
00:37:01,280 --> 00:37:05,120
Speaker 1: coherence over it. But it's I feel like what Donald

664
00:37:05,160 --> 00:37:10,920
Speaker 1: Trump was to Charlie Sheen, Alon Musk is to Trump.

665
00:37:11,480 --> 00:37:15,000
Speaker 1: He's like the next level of this. So he's using

666
00:37:15,040 --> 00:37:21,160
Speaker 1: the same Trumpian trolling techniques on Twitter, but it's bigger.

667
00:37:21,200 --> 00:37:24,560
Speaker 1: He's a bigger troll than Trump because he owns the

668
00:37:24,640 --> 00:37:29,120
Speaker 1: platform itself, and I feel like he's showing that, yeah, yeah,

669
00:37:29,160 --> 00:37:32,000
Speaker 1: you did this, Trump, but your politics that was just

670
00:37:32,200 --> 00:37:35,920
Speaker 1: kind of a a sub category, a little subset of

671
00:37:35,960 --> 00:37:39,440
Speaker 1: the digital society that I own. You know, if if

672
00:37:39,440 --> 00:37:44,239
Speaker 1: Trump has a real competitor in the social attention landscape,

673
00:37:44,480 --> 00:37:49,440
Speaker 1: it is not you know, Hillary or Biden, it is Musk, right.

674
00:37:49,520 --> 00:37:52,640
Speaker 1: And then Musk even does it like when the advertisers

675
00:37:52,680 --> 00:37:55,680
Speaker 1: start start jumping ship it's so TRUMPI and he goes, oh,

676
00:37:55,800 --> 00:37:57,960
Speaker 1: Twitter had this, this is called Twitter had a massive

677
00:37:58,040 --> 00:38:01,680
Speaker 1: drop in revenue, you know, because of activist groups pressuring

678
00:38:01,840 --> 00:38:05,640
Speaker 1: advertisers even though nothing changed. And then he says, what

679
00:38:05,680 --> 00:38:10,440
Speaker 1: does he say, I'm gonna do thermonuclear name and shame

680
00:38:10,680 --> 00:38:13,680
Speaker 1: on brands that drop out of Twitter? What is that?

681
00:38:13,680 --> 00:38:16,799
Speaker 1: That is Trump? So when a politician says, I think

682
00:38:16,840 --> 00:38:20,440
Speaker 1: Biden really won the election, You're gonna get Thermo nuclear shade.

683
00:38:20,680 --> 00:38:24,440
Speaker 1: So now what Musk is doing is taking that Trump technique.

684
00:38:24,719 --> 00:38:27,480
Speaker 1: I'm going to use my swarm of Twitter people to

685
00:38:28,040 --> 00:38:31,080
Speaker 1: ruin your career, ruin your political career, to to to

686
00:38:31,120 --> 00:38:35,160
Speaker 1: bring you down. Now Musk is using that same kind

687
00:38:35,200 --> 00:38:38,120
Speaker 1: of of power, and what it's showing, I think, more

688
00:38:38,160 --> 00:38:42,239
Speaker 1: than anything, there's been a drive among a lot of

689
00:38:42,280 --> 00:38:45,759
Speaker 1: the big tech bros, like Musk and Teal towards what

690
00:38:45,800 --> 00:38:49,759
Speaker 1: they're calling techno monarchism. You know, it's like they're sort

691
00:38:49,760 --> 00:38:52,239
Speaker 1: of brand of fascism. They believe that, you know, the

692
00:38:52,280 --> 00:38:54,920
Speaker 1: world should just be run by a really smart tech

693
00:38:54,960 --> 00:38:58,920
Speaker 1: bro ceo and now we don't need voting or anything,

694
00:38:58,960 --> 00:39:01,400
Speaker 1: but get a good one like Deal or like Musk,

695
00:39:01,520 --> 00:39:03,839
Speaker 1: and they can just run things. And I think what

696
00:39:03,840 --> 00:39:08,239
Speaker 1: we're getting is a preview of what actually look like.

697
00:39:09,239 --> 00:39:13,799
Speaker 1: This is a very bad advertisement for the techno solutionist

698
00:39:13,880 --> 00:39:17,680
Speaker 1: monarchy running our society. God, I think you're absolutely right.

699
00:39:17,719 --> 00:39:20,239
Speaker 1: I mean, everything you're saying makes total sense. I want

700
00:39:20,239 --> 00:39:25,000
Speaker 1: to know what you how you think Ellen Twitter drama

701
00:39:25,040 --> 00:39:28,000
Speaker 1: plays out. I mean, it seems like to me from

702
00:39:28,040 --> 00:39:31,200
Speaker 1: what I'm seeing as a person who's very online, is

703
00:39:31,280 --> 00:39:37,040
Speaker 1: that he's just decompensating rapidly. Right, But I could be wrong.

704
00:39:37,080 --> 00:39:39,359
Speaker 1: I mean again, do you think there's some I mean,

705
00:39:39,360 --> 00:39:43,600
Speaker 1: do you think this? You know he's super leveraged, but

706
00:39:43,960 --> 00:39:46,680
Speaker 1: is there some way that he wins at this? I mean,

707
00:39:46,680 --> 00:39:49,960
Speaker 1: there's some way he wins at it. I mean, but

708
00:39:50,080 --> 00:39:53,160
Speaker 1: I think then everybody loses if that happens. I Mean.

709
00:39:53,400 --> 00:39:55,799
Speaker 1: The weird thing is we know that Elon Musk had

710
00:39:55,960 --> 00:39:58,560
Speaker 1: time to think about what he wanted to do. He

711
00:39:58,640 --> 00:40:01,040
Speaker 1: really had time. They were weeks and weeks leading up

712
00:40:01,040 --> 00:40:03,160
Speaker 1: to this, and now we see that when he gets

713
00:40:03,200 --> 00:40:07,840
Speaker 1: in the seat, his decision making process is is imperious,

714
00:40:08,440 --> 00:40:11,719
Speaker 1: right like this, like this strange little child king. It's

715
00:40:11,840 --> 00:40:16,040
Speaker 1: it's whimsical, and that he improvises minute to minute, like

716
00:40:16,120 --> 00:40:18,799
Speaker 1: he's careening around in a game of you know, deathmatch

717
00:40:18,880 --> 00:40:24,560
Speaker 1: or something. He's got no strategy and seemingly no awareness

718
00:40:24,640 --> 00:40:27,200
Speaker 1: of the kind of second or third order effects of

719
00:40:27,239 --> 00:40:29,880
Speaker 1: any decision he makes. He's like, all right, I'm gonna

720
00:40:29,880 --> 00:40:33,200
Speaker 1: fire everybody, and then oh there's nobody, all right, hire

721
00:40:33,239 --> 00:40:35,800
Speaker 1: them all back. It's like, yeah, when you fire people,

722
00:40:35,920 --> 00:40:38,839
Speaker 1: the thing stops working. You gotta It's like and with

723
00:40:38,880 --> 00:40:41,960
Speaker 1: his decision, he's got no reluctance to externalize harm to

724
00:40:42,000 --> 00:40:45,200
Speaker 1: other people, just to fire and hire at will. He's

725
00:40:45,239 --> 00:40:49,160
Speaker 1: doing it in front of everybody, almost like performance art

726
00:40:49,360 --> 00:40:54,080
Speaker 1: or reality television. And he's gathered this sort of group

727
00:40:54,160 --> 00:40:56,799
Speaker 1: of of sick of fants, you know, who used to

728
00:40:56,840 --> 00:40:59,840
Speaker 1: be somewhat respected tech investors who are now just these

729
00:41:00,400 --> 00:41:06,600
Speaker 1: sycathantic hangars on this entourage, tweeting suggestions around him like

730
00:41:06,719 --> 00:41:10,560
Speaker 1: the would be first circle around the new cult leader.

731
00:41:11,080 --> 00:41:14,239
Speaker 1: And he's so willing, it's so trump he's so willing

732
00:41:14,280 --> 00:41:19,120
Speaker 1: just to destroy norms in order to yield this vision

733
00:41:19,360 --> 00:41:23,080
Speaker 1: of a world of total equality, you know, where you

734
00:41:23,200 --> 00:41:27,200
Speaker 1: use this kind of scorched earth tactic to wipe away

735
00:41:27,280 --> 00:41:32,879
Speaker 1: every institutional governance or or hesitation or roadblock to this.

736
00:41:33,120 --> 00:41:36,279
Speaker 1: Not even anarchy, it's something. It's something worse. And the

737
00:41:36,320 --> 00:41:38,160
Speaker 1: only people I knew who used to talk about this,

738
00:41:38,239 --> 00:41:42,759
Speaker 1: let's wipe away all regulations, all restrictions were libertarians who

739
00:41:42,800 --> 00:41:45,919
Speaker 1: happened to be starting with wealth. It's like, yeah, if

740
00:41:45,920 --> 00:41:49,600
Speaker 1: you've got a billion dollars take away, I'm gonna do

741
00:41:49,800 --> 00:41:53,360
Speaker 1: just a yeah, sure you are, you know, And that

742
00:41:53,600 --> 00:41:56,200
Speaker 1: he was so out of the gate with fake news

743
00:41:56,640 --> 00:42:01,280
Speaker 1: showed that he really just wants a tench. He's trying

744
00:42:01,320 --> 00:42:07,560
Speaker 1: to get attention while demonstrating that this kind of rambunctious, chaotic,

745
00:42:08,160 --> 00:42:14,319
Speaker 1: free form, impulsive explosion will just accelerate us to this

746
00:42:14,440 --> 00:42:19,479
Speaker 1: next phase of totally free, total self expression, perfect self

747
00:42:19,520 --> 00:42:23,120
Speaker 1: sovereignty without restriction. The thing I've been like stripped by

748
00:42:23,200 --> 00:42:25,400
Speaker 1: is like, and maybe this is because they want us

749
00:42:25,440 --> 00:42:27,480
Speaker 1: to be struck by this, but like how much it

750
00:42:27,520 --> 00:42:32,040
Speaker 1: feels like ancient Greece, you know, like a white guy

751
00:42:32,160 --> 00:42:36,560
Speaker 1: ruling over, you know, the sort of lesser women and manure,

752
00:42:36,760 --> 00:42:40,040
Speaker 1: you know, like we're sort of not we're you know,

753
00:42:40,239 --> 00:42:42,359
Speaker 1: not this sort of thing. You know. They have their

754
00:42:42,400 --> 00:42:44,680
Speaker 1: sort of inner circle and they I mean, you don't

755
00:42:44,680 --> 00:42:47,719
Speaker 1: see any women in this inner circle and just men.

756
00:42:48,239 --> 00:42:51,279
Speaker 1: It's just tech guys. They're all white. I mean, you

757
00:42:51,320 --> 00:42:53,600
Speaker 1: know what I'm saying, I do. And the odd thing

758
00:42:53,719 --> 00:42:58,399
Speaker 1: is they keep talking about no censorship, no rules, no regulation.

759
00:42:59,040 --> 00:43:02,360
Speaker 1: Yet when you do that, at least in this landscape,

760
00:43:02,360 --> 00:43:06,919
Speaker 1: you end up with just wealthy, white tech bros. Now

761
00:43:07,000 --> 00:43:11,200
Speaker 1: why is that? It's because the landscape itself is tilted

762
00:43:11,320 --> 00:43:15,719
Speaker 1: toward them, right. This is Twitter is the loving environment

763
00:43:15,840 --> 00:43:19,560
Speaker 1: for trolls, right, and it's gonna favor the troll and

764
00:43:19,800 --> 00:43:25,960
Speaker 1: the wealthy troll. And uh, it's uh. I'm hoping, although

765
00:43:26,080 --> 00:43:30,960
Speaker 1: you know, I always overestimate people's awareness, but I was

766
00:43:31,120 --> 00:43:36,640
Speaker 1: hoping that this raw Twitter thing, this this Charlie Sheen

767
00:43:36,800 --> 00:43:42,000
Speaker 1: like public meltdown of the world's richest man, would somehow

768
00:43:42,400 --> 00:43:46,600
Speaker 1: make this more transparent, would make people go, oh, this

769
00:43:46,680 --> 00:43:48,840
Speaker 1: doesn't work. I Mean, the whole reason I wrote the

770
00:43:48,920 --> 00:43:50,799
Speaker 1: last book I did, the Survival of the Richest Book,

771
00:43:50,840 --> 00:43:53,080
Speaker 1: was I thought I'm gonna make these tech bros look

772
00:43:53,200 --> 00:43:56,000
Speaker 1: so silly in an almost Swiftian way that people are

773
00:43:56,000 --> 00:44:00,520
Speaker 1: gonna laugh them and then discard it. Yet, people who

774
00:44:00,560 --> 00:44:02,640
Speaker 1: interviewed me about the book, we're like, okay, so where

775
00:44:02,640 --> 00:44:11,360
Speaker 1: should I put my bunker? And what I've heard people say, well,

776
00:44:11,520 --> 00:44:15,600
Speaker 1: Ellen thinks that he can scale back, make it profitable,

777
00:44:15,880 --> 00:44:18,879
Speaker 1: and take a public next year. That in my mind

778
00:44:18,920 --> 00:44:24,000
Speaker 1: seems completely improbable. Not just improbable, but it's the opposite

779
00:44:24,080 --> 00:44:29,200
Speaker 1: of what is in theory philosophically behind what he's doing anyway.

780
00:44:29,320 --> 00:44:32,920
Speaker 1: You know, what he's in theory, what he is trying

781
00:44:32,960 --> 00:44:37,200
Speaker 1: to do is to build the open protocol system through

782
00:44:37,239 --> 00:44:42,600
Speaker 1: which human civilization organizes itself in the next century. You

783
00:44:42,640 --> 00:44:46,680
Speaker 1: know if there is pretty ambitious. Yeah, but it's it's

784
00:44:46,760 --> 00:44:49,279
Speaker 1: that's what he's that's the thing. So you start with

785
00:44:49,320 --> 00:44:56,280
Speaker 1: a social network and then you turn that into a PayPal, venmo, banking, crypto,

786
00:44:56,719 --> 00:45:02,239
Speaker 1: blockchain thing. You know, it's Twitter is Musk's way of

787
00:45:02,280 --> 00:45:05,880
Speaker 1: trying to do meta. And you're right, the guy behind meta,

788
00:45:05,920 --> 00:45:08,640
Speaker 1: Mark Zuckerberg, what does he model He models himself after

789
00:45:08,680 --> 00:45:12,040
Speaker 1: Augustus Caesar, Right, so I guess Musk could do Caligula.

790
00:45:12,400 --> 00:45:19,960
Speaker 1: You know, and then Bezos can go for good old Julius, right, yeah, yeah,

791
00:45:20,000 --> 00:45:22,960
Speaker 1: But I feel like a more likely scenario with Muskie

792
00:45:23,040 --> 00:45:27,040
Speaker 1: is that Tesla stock continues to go down, Lender's calling

793
00:45:27,160 --> 00:45:32,040
Speaker 1: the dead, and the bank owns Twitter in two years. Yeah,

794
00:45:32,120 --> 00:45:37,560
Speaker 1: because the problem for him is Musk's crowd is different

795
00:45:37,640 --> 00:45:42,359
Speaker 1: than Bannon's crowd. You know, if Musk really had like

796
00:45:43,000 --> 00:45:47,080
Speaker 1: the Trump world behind him, then there'd be enough, there'd

797
00:45:47,080 --> 00:45:49,920
Speaker 1: be a different Tesla buying audience, right, and all the

798
00:45:49,960 --> 00:45:52,800
Speaker 1: red state maga you get maga Tesla or whatever, it

799
00:45:52,840 --> 00:45:55,759
Speaker 1: could be a whole thing, but the Maga people, they're

800
00:45:55,800 --> 00:45:59,080
Speaker 1: actually they're the people who are most afraid of these

801
00:45:59,120 --> 00:46:01,719
Speaker 1: tech guys. Know you listen to Steve Bann, and they're

802
00:46:01,760 --> 00:46:05,400
Speaker 1: afraid of the techno elite and the techno solutionists and

803
00:46:05,520 --> 00:46:09,839
Speaker 1: Bill Gates putting nanobots in their vaccines and there they

804
00:46:09,960 --> 00:46:13,319
Speaker 1: look at all this as taking us away from you know,

805
00:46:13,480 --> 00:46:18,600
Speaker 1: good oil, piston whatever is that those horrible So I

806
00:46:18,640 --> 00:46:21,160
Speaker 1: couldn't imagine any of my friends buying a Tesla at

807
00:46:21,160 --> 00:46:23,520
Speaker 1: this point, no matter how good they are for the environment,

808
00:46:23,560 --> 00:46:26,280
Speaker 1: even if they were, just because it's like, you're gonna

809
00:46:26,280 --> 00:46:27,960
Speaker 1: do that it's like, you know, it's kind of like

810
00:46:28,080 --> 00:46:30,600
Speaker 1: buying a Ford right after we published Protocols of the

811
00:46:30,640 --> 00:46:35,480
Speaker 1: Elders of Zion's not a not a good moment for

812
00:46:35,520 --> 00:46:38,240
Speaker 1: that brand. There are the kids, you know, the gamer

813
00:46:38,320 --> 00:46:40,680
Speaker 1: Gate kind of kids, and the kids are gonna do

814
00:46:40,840 --> 00:46:43,440
Speaker 1: meme stocks and some of the some of the crypto

815
00:46:43,520 --> 00:46:48,239
Speaker 1: people who do think that Musk is realizing Jack Dorsey's

816
00:46:48,360 --> 00:46:51,640
Speaker 1: vision When they talked about this thing called Blue Sky,

817
00:46:51,760 --> 00:46:54,120
Speaker 1: can you explain to us what blue Sky is? Because

818
00:46:54,120 --> 00:46:56,880
Speaker 1: there's been a lot of subjecture about it, but it

819
00:46:56,920 --> 00:46:59,520
Speaker 1: doesn't seem like people really know. I mean, I don't

820
00:46:59,560 --> 00:47:03,960
Speaker 1: think Blue Guys is so real. That seems like an

821
00:47:04,000 --> 00:47:07,360
Speaker 1: important data point. It's kind of like blue Sky is

822
00:47:07,440 --> 00:47:11,719
Speaker 1: trying to imagine what if we did social networks the

823
00:47:11,840 --> 00:47:16,680
Speaker 1: way that the original Internet worked, in this wonderful decentralized

824
00:47:16,719 --> 00:47:20,399
Speaker 1: way with open standards, and it's not a no monopolist

825
00:47:20,440 --> 00:47:23,239
Speaker 1: can come and own the platform, you know, and then

826
00:47:23,320 --> 00:47:26,560
Speaker 1: create all these choke points that prevent people from leaving.

827
00:47:26,600 --> 00:47:30,640
Speaker 1: We're getting a big interoperable thing. So isn't that Mastodon? Yes,

828
00:47:31,360 --> 00:47:33,960
Speaker 1: right now, that is Mastodon, And it is interesting. I

829
00:47:33,960 --> 00:47:37,640
Speaker 1: feel like that things are bad enough in the monopoly

830
00:47:37,840 --> 00:47:42,880
Speaker 1: Internet for people to finally learn like the twenty minutes

831
00:47:43,080 --> 00:47:45,279
Speaker 1: of stuff that you have to learn in order to

832
00:47:45,360 --> 00:47:50,799
Speaker 1: use the Internet properly barrier to entry, you have to

833
00:47:50,840 --> 00:47:54,839
Speaker 1: read on this works, Yeah, exactly. I mean that's my

834
00:47:54,960 --> 00:47:57,920
Speaker 1: feeling is like I funked around with Masodon. I was like,

835
00:47:57,960 --> 00:47:59,759
Speaker 1: this doesn't make any sense. It doesn't make any sense,

836
00:47:59,840 --> 00:48:03,080
Speaker 1: and and I just like, you know, Adam Davidson, who

837
00:48:03,160 --> 00:48:05,239
Speaker 1: who used to write for The New Yorker and is

838
00:48:05,320 --> 00:48:08,600
Speaker 1: very tech savvy, started a server. So I just went

839
00:48:08,640 --> 00:48:12,080
Speaker 1: on that and it was pretty easy, right, it actually is. Yeah.

840
00:48:12,239 --> 00:48:14,680
Speaker 1: The thing is it's easier than you think. All it

841
00:48:14,760 --> 00:48:17,719
Speaker 1: really is is it works like email. Right, you're on

842
00:48:17,880 --> 00:48:20,799
Speaker 1: you might be on Gmail. I might be on proton Mail.

843
00:48:20,880 --> 00:48:23,960
Speaker 1: He might be on a o L. But magically I

844
00:48:24,000 --> 00:48:26,279
Speaker 1: can send an email to you, and and a o

845
00:48:26,400 --> 00:48:28,640
Speaker 1: L knows how to read the same protocol of email.

846
00:48:28,880 --> 00:48:31,239
Speaker 1: That's all it is. You create certain standards so that

847
00:48:31,280 --> 00:48:33,960
Speaker 1: you can have little servers all over the place. You

848
00:48:34,000 --> 00:48:36,840
Speaker 1: belong to a server, you do all your social media

849
00:48:37,000 --> 00:48:39,480
Speaker 1: functioning through that server, and if you want to leave

850
00:48:39,520 --> 00:48:41,759
Speaker 1: that server, you move to another one, take all your

851
00:48:41,840 --> 00:48:44,000
Speaker 1: data with you. And all your friends and all your

852
00:48:44,040 --> 00:48:46,400
Speaker 1: links and all your everything, and now you're there, and

853
00:48:46,440 --> 00:48:49,439
Speaker 1: so there's no big one place. Everybody has their own

854
00:48:49,760 --> 00:48:52,280
Speaker 1: little servers with their own little rules and it it's

855
00:48:52,840 --> 00:48:57,000
Speaker 1: you know, it's genuinely decentralized. What Musk would need to

856
00:48:57,040 --> 00:48:59,520
Speaker 1: do if he wants to make money off Twitter, and

857
00:48:59,560 --> 00:49:03,880
Speaker 1: I know he he knows this is Twitter, right is

858
00:49:03,880 --> 00:49:06,080
Speaker 1: turn it into and everything is to look at the

859
00:49:06,160 --> 00:49:09,120
Speaker 1: Chinese and you think about like we chat, so you

860
00:49:09,239 --> 00:49:11,839
Speaker 1: do everything on that. You do your banking on there,

861
00:49:11,840 --> 00:49:15,480
Speaker 1: and your socializing and your credit score and your education

862
00:49:15,760 --> 00:49:19,680
Speaker 1: and your linked in qualifications. That everything's there. Your your

863
00:49:19,920 --> 00:49:23,960
Speaker 1: identity which is on some blockchain, it's all right there.

864
00:49:24,280 --> 00:49:26,960
Speaker 1: But why would you do it right there on a

865
00:49:27,040 --> 00:49:30,200
Speaker 1: platform where, in his first week of running it, the

866
00:49:30,239 --> 00:49:32,680
Speaker 1: guy who runs the platform says, if you take your

867
00:49:32,719 --> 00:49:35,759
Speaker 1: ads off this, I'm gonna have all my followers do

868
00:49:35,960 --> 00:49:40,759
Speaker 1: Thermo nuclear shame on your company. It's like, uh, that's

869
00:49:40,800 --> 00:49:43,320
Speaker 1: worse than I mean, it's worse than China. At that point,

870
00:49:43,360 --> 00:49:45,560
Speaker 1: it's just like, oh my god. I mean, That's what

871
00:49:45,680 --> 00:49:49,200
Speaker 1: I don't understand too, is like, what do you think

872
00:49:49,239 --> 00:49:51,960
Speaker 1: the thinking there is that he's so powerful. He doesn't

873
00:49:51,960 --> 00:49:55,359
Speaker 1: need advertisers or that. I mean, because like, if if

874
00:49:55,400 --> 00:49:58,719
Speaker 1: we've learned anything from the my Pillow guy, it's that

875
00:49:59,000 --> 00:50:02,360
Speaker 1: there's not enough off of this sort of non advertisers,

876
00:50:02,680 --> 00:50:06,600
Speaker 1: the non woke advertisers, so to speak, Well, he thinks

877
00:50:06,640 --> 00:50:09,319
Speaker 1: he's got the power to de woke ify them. In

878
00:50:09,320 --> 00:50:13,319
Speaker 1: other words, are you really willing to turn against me

879
00:50:13,600 --> 00:50:15,960
Speaker 1: and Twitter? I don't think it's a bluff, but I

880
00:50:15,960 --> 00:50:18,359
Speaker 1: don't think it works. I don't think he does have

881
00:50:18,480 --> 00:50:21,279
Speaker 1: that power because all he could do, I mean, yes,

882
00:50:21,360 --> 00:50:25,480
Speaker 1: he could get you know, maybe himself for Jason Callicanis

883
00:50:25,800 --> 00:50:29,840
Speaker 1: and you know, you know a hundred thousand, two hundred thousand,

884
00:50:30,160 --> 00:50:33,440
Speaker 1: you know, doche coin people too, then turn against Nike

885
00:50:33,880 --> 00:50:37,319
Speaker 1: because they left Twitter, and it's like, come on, this

886
00:50:37,400 --> 00:50:39,799
Speaker 1: is not about free speech or anything else. It's about

887
00:50:39,840 --> 00:50:43,080
Speaker 1: do I want my ads? Vengeance were right? For him,

888
00:50:43,080 --> 00:50:45,759
Speaker 1: it's vengeance and for the company's leaving. It's not that

889
00:50:45,800 --> 00:50:48,520
Speaker 1: they're against free speech. It's that Wait a minute. On

890
00:50:48,560 --> 00:50:51,759
Speaker 1: the first day, you said that Nancy Pelosi's husband got

891
00:50:51,840 --> 00:50:53,640
Speaker 1: hit in the head by a gay lover or something,

892
00:50:53,680 --> 00:50:55,799
Speaker 1: and it's part of it. Really, do you really want

893
00:50:55,800 --> 00:50:57,920
Speaker 1: to do that on your first day as CEO of

894
00:50:57,920 --> 00:51:01,280
Speaker 1: the what you're trying to call where a public square

895
00:51:01,880 --> 00:51:07,040
Speaker 1: most truthful. Yeah, there's so much talk about algorithms and

896
00:51:07,680 --> 00:51:11,600
Speaker 1: anxiety almost about algorithms, like what do they do and

897
00:51:11,640 --> 00:51:14,279
Speaker 1: how do they work? And you know, the right is

898
00:51:14,320 --> 00:51:17,279
Speaker 1: always like the algorithms are against us big tech is

899
00:51:17,520 --> 00:51:21,759
Speaker 1: can you just explain a little bit about what Elon

900
00:51:22,000 --> 00:51:25,200
Speaker 1: can do with the algorithm? I mean, can he really

901
00:51:25,400 --> 00:51:28,040
Speaker 1: sort of shadow band people? Is that a real thing?

902
00:51:28,719 --> 00:51:31,400
Speaker 1: And it's sort of a little bit of an algorithm

903
00:51:31,400 --> 00:51:37,600
Speaker 1: explainer for us. Yeah, algorithms simply choose what stories and

904
00:51:37,760 --> 00:51:41,400
Speaker 1: ideas and tweets and things are put in front of

905
00:51:41,440 --> 00:51:46,600
Speaker 1: you based on what you've done before. So a benevolent

906
00:51:46,920 --> 00:51:49,839
Speaker 1: algorithm might just keep trying to bring you stuff that

907
00:51:49,880 --> 00:51:53,160
Speaker 1: you're going to read and like and engage with. Very often,

908
00:51:53,239 --> 00:51:55,440
Speaker 1: even if it just does that, what it brings you

909
00:51:55,520 --> 00:51:57,600
Speaker 1: is going to get more and more salacious because we

910
00:51:57,680 --> 00:52:02,600
Speaker 1: can't help but click on worse up. But uh, algorithm

911
00:52:02,600 --> 00:52:06,359
Speaker 1: can also decide, oh, I'm gonna amplify these kinds of

912
00:52:06,360 --> 00:52:09,320
Speaker 1: messages to make you I'm going to amplify news stories

913
00:52:09,360 --> 00:52:11,480
Speaker 1: about crime to get you more scared of crime in

914
00:52:11,520 --> 00:52:14,280
Speaker 1: the streets, so you're more likely to vote, you know, Republican,

915
00:52:14,400 --> 00:52:16,480
Speaker 1: which we found as those people who are afraid of

916
00:52:16,520 --> 00:52:18,960
Speaker 1: crime in cities vote Republican. Or we're gonna show you

917
00:52:19,000 --> 00:52:20,680
Speaker 1: more of this or more of that, so you can

918
00:52:21,120 --> 00:52:24,319
Speaker 1: tweak it. Where it gets the most interesting and the

919
00:52:24,360 --> 00:52:28,160
Speaker 1: most dangerous, though, is when the folks who are tuning

920
00:52:28,200 --> 00:52:32,760
Speaker 1: the algorithms are also getting deeply entrenched in what's called

921
00:52:32,960 --> 00:52:37,200
Speaker 1: behavioral finance. Where behavioral finance is sort of a psychological

922
00:52:37,200 --> 00:52:39,200
Speaker 1: study of how do you get people to act against

923
00:52:39,239 --> 00:52:41,319
Speaker 1: their own best interests. It's the kind of stuff that

924
00:52:41,360 --> 00:52:43,839
Speaker 1: you know, credit card companies figure out when they're giving

925
00:52:43,880 --> 00:52:46,120
Speaker 1: you offers to try to get you to pick things

926
00:52:46,120 --> 00:52:49,319
Speaker 1: that where you'll pay more interest and and and get

927
00:52:49,680 --> 00:52:54,839
Speaker 1: worser worser conditions. So when you have algorithms using behavioral

928
00:52:54,840 --> 00:53:00,960
Speaker 1: finance techniques basically psychological ninja to get people to think differently,

929
00:53:01,320 --> 00:53:04,440
Speaker 1: it's kind of sad. It's kind of dangerous. And I

930
00:53:04,440 --> 00:53:06,680
Speaker 1: don't trust people at Facebook either, But the people at

931
00:53:06,760 --> 00:53:11,160
Speaker 1: any for profit private company looking to establish a monopoly,

932
00:53:11,440 --> 00:53:13,920
Speaker 1: they're gonna do first whatever they can to get you

933
00:53:13,960 --> 00:53:18,200
Speaker 1: dependent on their on their platform. But if they are political,

934
00:53:18,320 --> 00:53:21,879
Speaker 1: which is what Musk is basically saying. He is right.

935
00:53:21,920 --> 00:53:25,200
Speaker 1: If they are political, you've got more control over the

936
00:53:25,520 --> 00:53:28,440
Speaker 1: over the national psyche than the people who are producing

937
00:53:28,640 --> 00:53:32,200
Speaker 1: reality television shows, you know. And that's really what we're

938
00:53:32,200 --> 00:53:36,560
Speaker 1: looking at. We're looking at Musk as trying to establish

939
00:53:36,680 --> 00:53:41,280
Speaker 1: himself as the true dictator. It's not that little reality

940
00:53:41,360 --> 00:53:46,480
Speaker 1: star President Trump. It's the person who owns the platform

941
00:53:46,920 --> 00:53:51,440
Speaker 1: capitalism right and can figure out where you spend your money.

942
00:53:51,600 --> 00:53:55,719
Speaker 1: It's so interesting that this was so interesting. I ran

943
00:53:55,800 --> 00:53:58,960
Speaker 1: too long. Jesse is mad at me. Please please please

944
00:53:59,040 --> 00:54:08,920
Speaker 1: come back, Molly John Fast, Jesse Cannon. So Trump's doing

945
00:54:08,960 --> 00:54:13,480
Speaker 1: this like mega media tour today. He's hitting all the networks.

946
00:54:13,520 --> 00:54:18,040
Speaker 1: I'm shocked he's not cuddling Mike Wilendell right now. Networks.

947
00:54:18,320 --> 00:54:21,960
Speaker 1: I think we should use the word networks and quote yes, yes,

948
00:54:22,000 --> 00:54:25,760
Speaker 1: that's that's true. Rag tag operations. Trump told the News

949
00:54:25,840 --> 00:54:28,680
Speaker 1: Nation you may have heard of News Nation, but you

950
00:54:28,760 --> 00:54:32,239
Speaker 1: also may not this, which is like a sort of

951
00:54:32,280 --> 00:54:35,799
Speaker 1: Trump and fortune cookie. About tonight's results, well, I think

952
00:54:35,800 --> 00:54:38,120
Speaker 1: if they win, I should get all the credit. If

953
00:54:38,120 --> 00:54:41,839
Speaker 1: they lose, I should not be blamed at all. Very

954
00:54:41,880 --> 00:54:45,440
Speaker 1: trumpy and for that, Donald Trump, you continue to be

955
00:54:46,360 --> 00:54:51,520
Speaker 1: our moment of that's it for this episode of Fast Politics.

956
00:54:51,880 --> 00:54:54,799
Speaker 1: Tune in every Monday, Wednesday, and Friday to her the

957
00:54:54,880 --> 00:54:58,200
Speaker 1: best minds in politics, makes sense of all this chaos.

958
00:54:58,560 --> 00:55:01,160
Speaker 1: If you enjoyed what you've heard, please send it to

959
00:55:01,160 --> 00:55:04,520
Speaker 1: a friend and keep the conversation going. And again, thanks

960
00:55:04,600 --> 00:55:09,040
Speaker 1: for listening. Mm hm.