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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 Donald Trump says he never said

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Speaker 1: locker up about Hillary Clinton. He did, in fact, stay

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Speaker 1: locker up about Hillary Clinton. We have such a great

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Speaker 1: show for you today talking Feds. Harry Littman is here

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Speaker 1: to talk Donald Trump convictions. Then we'll talk to Semaphores

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Speaker 1: Ben Smith about the media's coverage of the election. But

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Speaker 1: first we have the host of the Enemy's List, the

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Speaker 1: Lincoln Project's own Rick Wilson. Welcome back to Fast Politics,

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Speaker 1: Rick Wilson.

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Speaker 2: Ollie John Fast, and what a beautiful thing it is

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Speaker 2: to talk to you once again, as I do every day.

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Speaker 1: Ah, it is yet another day of justice. Alito telling

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Speaker 1: Democrats to go fuck themselves discuss.

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Speaker 2: I do believe that we have a system where the

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Speaker 2: three branches of are separate, our tripartite system, to use

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Speaker 2: a good three dollar word. But I also believe that

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Speaker 2: doesn't mean that he's not being a dick, and that

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Speaker 2: there's not a honestly incredibly troubling and dark underpinning to

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Speaker 2: this which he's basically saying to America, fuck you. I'm

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Speaker 2: gonna do what I can to help my boy Donald.

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Speaker 1: So let's talk about the second, because there's an oversight

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Speaker 1: question here, right. There are members in the Senate who

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Speaker 1: have oversight over.

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Speaker 2: This, yeah, over the courts.

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Speaker 1: Right, Maybe not quite the way they would like, but

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Speaker 1: they certainly do. Let's pretend for a minute that they

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Speaker 1: was reversed and it was a Democrat.

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Speaker 2: Let's do Katanji Brown Jackson puts up a Black Lives

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Speaker 2: Matter flag? What do you think would happen with Fox?

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Speaker 2: What do you think would happened with the MAGA universe?

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Speaker 1: And say Republicans controlled the judiciary? H what do you

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Speaker 1: think would happen?

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Speaker 2: Take those hypotheticals for a moment, and I will describe

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Speaker 2: it to you in the technical term of public affairs

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Speaker 2: and politics, of a screaming, gigantic shit show of the

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Speaker 2: highest possible order. There would be nothing but chaos. And

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Speaker 2: oh my god, the American Republic is dying. How dare

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Speaker 2: she engage in this outrageous, unbelievably undignified behavior that is

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Speaker 2: reducing America's dignity in the courts? And I could almost

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Speaker 2: write the story for you of just how crappy they

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Speaker 2: would be about it. And you know what, it's not

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Speaker 2: like we don't know what they're going to say. They

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Speaker 2: are nothing if not utterly predictable.

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Speaker 1: Yeah, so Alito said he's not going to recuse. Robert said,

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Speaker 1: go fuck yourself. What do you think Dick Durbin should do? Now?

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Speaker 1: What would you do if you were Dick Durbin's chief

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Speaker 1: of staff?

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Speaker 2: Dick Durbin, He's in the horns of a slight dilemma.

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Speaker 1: Yes, save American democracy or be a good guy.

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Speaker 2: Well, that's exactly it. It's like, is anybody still pretending that

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Speaker 2: American institutions are robust and the guardrails are fully intact?

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Speaker 2: Because if you are, I have a bridge to sell

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Speaker 2: you or several, because that is no longer a viable

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Speaker 2: position to have in the world today. No one for

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Speaker 2: a moment should think that we live in a world

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Speaker 2: where the institutions will hold, where the rules will be sustained,

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Speaker 2: where nothing could go wrong, because America is just too strong. Look,

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Speaker 2: and I've been calling the Supreme Court the red Court

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Speaker 2: for a while now. First, with this whole thing about

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Speaker 2: giving Trump all the time he needed to delay the

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Speaker 2: Jack Smith case on the immunity question, and the rest

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Speaker 2: of their actions as an overtly political branch of the

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Speaker 2: MAGA movement have sort of added up. I think it's

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Speaker 2: hard for Americans to kind of admit it to themselves.

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Speaker 2: There are even conservatives who are like, yeah, you know

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Speaker 2: what if the Democrats did this. There is a little

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Speaker 2: bit of hesitation on this, not a lot, but there's

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Speaker 2: a little bit of like a sense of like, is

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Speaker 2: this really how we're going to end this thing? Because

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Speaker 2: right now it's kind of how we're going to end

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Speaker 2: this thing.

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Speaker 1: Honestly, American democracy.

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Speaker 2: Yeah, it feels like we're not in a great spot

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Speaker 2: as a country. You know. Everybody keeps asking me today,

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Speaker 2: like tell me something cheerful, give me some good news.

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Speaker 2: I'm like, I'm a barrel of freaking laughs.

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Speaker 1: Yeah. Yeah. Well, but let's pause for a second because

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Speaker 1: I want to talk about this. There are a lot

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Speaker 1: of people who want everyone who listens to this podcast

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Speaker 1: to despair and to not bother and to tell you

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Speaker 1: that Trump has it, But in fact, Trump doesn't have it.

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Speaker 1: And I'm going to point to a political twenty twenty

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Speaker 1: four Dave Wasserman tweet twenty twenty four is a tale

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Speaker 1: of two electorates, and again this is something I've written

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Speaker 1: about ad nauseum. Same Biden leads forty nine forty five

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Speaker 1: among the most reliable voters, betrayals Trump forty one fifty

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Speaker 1: one among low mid engagement voters. So the point is

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Speaker 1: low mid engagement voters are likelier than high engagement peers.

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Speaker 1: They may not turn out. And if you look at

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Speaker 1: the bottom of the ticket, Democrats are brushing the Republican

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Speaker 1: in those Senate seeds and the House seats.

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Speaker 2: Oh listen, I will give people some good news. The

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Speaker 2: Senate races look increasingly optimistic. I think initially it felt

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Speaker 2: a little bit like Arizona might be a tie ballgame.

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Speaker 2: But now Ruben is starting to open up real numbers.

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Speaker 2: I think there are a number of arguments to be

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Speaker 2: made that the House number could end up being much

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Speaker 2: larger than we even think right now. And I think

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Speaker 2: that there's a weird reverse coattail effect here that we

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Speaker 2: need to consider. It may end up in some of

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Speaker 2: these states that the Democratic candidates North Carolina being one

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Speaker 2: of them, end up pulling Joe Biden over the line,

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Speaker 2: and it's normally the other way around. But I'll take it.

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Speaker 2: I will happily take it. At this point.

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Speaker 1: So reverse cotails is an idea that I first heard

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Speaker 1: from Amanda Lippman, who does Run for Something and if

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Speaker 1: you are despairing, Run for Something is an awesome organization

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Speaker 1: that helps candidates everyone from the dog walkers to the

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Speaker 1: state legislators to the House of Delegates.

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Speaker 2: They're concouraging up and down the ballot.

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Speaker 1: And her theory of the case was that young local

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Speaker 1: voters would energize the electorate in a way that an

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Speaker 1: eighty one year old incumbent might not look.

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Speaker 2: I think there are going to be a lot of

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Speaker 2: voters out there this year that take a long damn

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Speaker 2: time to get home to the reality that this really

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Speaker 2: is not Donald Trump versus Gavin Newsom, or Donald Trump

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Speaker 2: versus Gretch Whitmer or somebody else. They're going to realize finally,

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Speaker 2: it's just Donald Trump. If you're going to vote for

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Speaker 2: Donald Trump as an eighteen to thirty year old, I

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Speaker 2: am not buying it. I'll be perfectly can that I'm

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Speaker 2: not buying that argument at this point. I think that

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Speaker 2: we are going to see people come home it. Maybe later,

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Speaker 2: but you know, I mean obviously, and as I always

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Speaker 2: tell people, there's no excuse for not every day, getting

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Speaker 2: up to beat the bad guys in the face. But

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Speaker 2: this is not a Donald Trump from twenty sixteen. This

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Speaker 2: is not a Donald Trump from twenty twenty. This is

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Speaker 2: a diminished and weird and less mentally capable and frankly,

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Speaker 2: I think I have no evidence of this, but I

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Speaker 2: feel this sense with Trump that there's a physical diminishment,

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Speaker 2: that there may be something wrong with Trump more than

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Speaker 2: we know, is what I'm trying to say. It feels

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Speaker 2: like he's.

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Speaker 1: Sick, right, I mean, people have been saying that for

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Speaker 1: like a million years.

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Speaker 2: It feels increasingly like this is a guy who's like

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Speaker 2: faking it to get through the.

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Speaker 1: Day, a man who knows. But I mean, I don't

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Speaker 1: want to speculate about his medical health. Only Republicans do

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Speaker 1: that about Biden. By the way, one of the things

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Speaker 1: I love so State of the Union, so Trump always

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Speaker 1: says Biden is dementias, dementia, he's sww, senile, he's diminished.

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Speaker 1: He gives all these speeches, but nobody watches them because

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Speaker 1: nobody gets verified news. The only clips that ever make

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Speaker 1: it onto social are you know, crazy lists that right,

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Speaker 1: or that are selectively edited. But I'll be curious after

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Speaker 1: this debate when Biden does really well, because though he

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Speaker 1: is not a gifted orator, he is not a complete lunatic,

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Speaker 1: so he will kill I think he'll do really well

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Speaker 1: in these debates, and Trump will say insane things, and

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Speaker 1: I think what will happen then is Trump World will

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Speaker 1: again say that he's on drugs, which, by the way,

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Speaker 1: if there was a drug that made demented people not demented,

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Speaker 1: I would give it to my mom. Like, this is

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Speaker 1: not a thing, right.

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Speaker 2: This is an imaginary thing. It is to my mind,

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Speaker 2: one of the great telling points of how broken the

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Speaker 2: Republican Party is right now is that they continue to

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Speaker 2: have this bifurcated view of Joe Biden. Either he is

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Speaker 2: a drooling, senile idiot who can barely stay awake, or

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Speaker 2: he's a devious mastermind running a program throughout all the

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Speaker 2: American courts and legal system to destroy Donald Trump. It's

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Speaker 2: got to be one of the other. Folks. Pick a side,

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Speaker 2: pick a theme.

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Speaker 1: I'm going to ask you a question here that you're

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Speaker 1: not prepared for, and it's something I've been thinking a lot.

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Speaker 1: I think Gaza is hurting Biden.

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Speaker 2: You know, I'm going to disagree with you for two reasons.

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Speaker 1: By the way, I've been told my Biden world that

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Speaker 1: that's not true. I've been told that people don't vote

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Speaker 1: on foreign policy. But I think that it's hurting him

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Speaker 1: with voters.

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Speaker 2: It's hurting him with young voters who are not voters.

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Speaker 1: Those are the voters that Trump is winning. Right.

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Speaker 2: Let's put it this way. If I was to demographically

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Speaker 2: describe the average Trump voter, it's a sixty five year old,

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Speaker 2: pissed off white dude who drives a four out two fifty.

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Speaker 2: It is not a young African American looking at Donald

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Speaker 2: Trump at long last as a as a solution to

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Speaker 2: the political stasis in this country. That's not the real world.

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Speaker 2: And young voters historically aren't voters. You know, just I've

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Speaker 2: lived in politics too long and seen it hyped too

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Speaker 2: many times. You know, George Bush and Mike Dukakis got

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Speaker 2: a higher percentage of young voters than Barack Obama and

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Speaker 2: John McCain, and Barack Obama was supposedly the singular motivator

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Speaker 2: of young voters in American history. It just isn't real.

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Speaker 2: And those kids you're watching in the college came campuses

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Speaker 2: scream about Gaza, they're not going to vote. I know

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Speaker 2: people want to believe that they're all motivated in democracy.

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Speaker 2: It doesn't matter. They're not going to vote. They are

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Speaker 2: low propensity voters. They are one to two voters. And

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Speaker 2: if somebody had the secret sauce rock the vote wouldn't

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Speaker 2: still be in business thirty five years later. No one

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Speaker 2: knows how to reliably turn out young voters. They just

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Speaker 2: don't know how to do it. It's a mechanical process

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Speaker 2: with a hard target, and it's much easier to go

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Speaker 2: out and swap out. And even if you accept the

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Speaker 2: theory right and you say, oh, okay, we're hurting with

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Speaker 2: young voters, Okay, go turn out some more white union members,

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Speaker 2: Go turn out some more African American women in North Carolina.

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Speaker 2: Go turn out people you know how to turn out.

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Speaker 1: See. I don't think you're right, but I'm going to

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Speaker 1: agree to disagree with you here. But I am curious.

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Speaker 1: Why do you think there is so little movement in

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Speaker 1: the Middle East? Why can't Biden and the war tomorrow?

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Speaker 2: It is a hard choice. You could say, Okay, we

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Speaker 2: don't like Babe, and nobody likes Boebe Byebe's a global pariah.

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Speaker 2: At this point, as somebody who tried to defeat BB

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Speaker 2: in twenty twenty one with a secular center right candidate,

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Speaker 2: I can tell you how hard it is. It's not

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Speaker 2: easy to get rid of the guy. But even if

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Speaker 2: you said that, you said, Okay, we're cutting off all

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Speaker 2: aid to Israel until they stop doing X. You have

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Speaker 2: not solved the problem. You have not solved the settler problem.

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Speaker 2: You have not solved the massive problem that Iran has

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Speaker 2: its finger in the pie here. You have not solved

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Speaker 2: any number of the other externalities that are real and

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Speaker 2: that you know. Look, if BB died tomorrow, you're not

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Speaker 2: gonna have Kumbaya in Gaza. Hamas still believes that Israelis

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Speaker 2: should be stacked like Cordwood and killed.

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Speaker 1: What state do you think Democrats would be focused on

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Speaker 1: right now?

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Speaker 2: I'm a big fan of pushing Wisconsin and Michigan. Right now,

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Speaker 2: I'm feeling a little better about Pennsylvania, feeling a little

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Speaker 2: worse about Arizona, but not like dramatically, so, not like catastrophically,

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Speaker 2: so I think it's still gettable, winnable, fixable. But you know,

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Speaker 2: I would not take the bait on Minnesota that's going

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Speaker 2: to be It's not a thing.

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Speaker 1: Yeah, that's Trump's favorite thing that they're going to put Minnesota.

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Speaker 2: I'm play I know, so I'm trying to make Minnesota

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Speaker 2: a thing. Don it's not a thing?

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Speaker 1: Yeah yeah, But what about like Florida. If you have

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Speaker 1: a choice between Florida and North Carolina.

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Speaker 2: I'd go to North Carolina. It's a cheaper state to win.

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Speaker 2: Florida is a very expensive state to try to win.

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Speaker 2: I think the numbers in North Carolina, especially with Robinson

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Speaker 2: at the top of the of the state ticket, are

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Speaker 2: really I think. I think You're going to end up

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Speaker 2: with a guy who is so weird and so cuckoo

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Speaker 2: that it gives Democrats a lift there that they did

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Speaker 2: not otherwise anticipate.

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Speaker 1: They would have, right, explain that story a little more so.

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Speaker 2: This is a guy who believes that women shouldn't vote.

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Speaker 1: He's black, he was the lieutenant governor, and he's now

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Speaker 1: running for governor, and he's running against someone.

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Speaker 2: Who is pretty moderate.

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Speaker 1: Yeah, okay, so go on.

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Speaker 2: But this is a guy who literally says things like,

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Speaker 2: you know, women are up at they and they shouldn't

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Speaker 2: be allowed to vote. I will say this, On the

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Speaker 2: one hand, it's proof, thank God that our country is

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Speaker 2: now color blind enough to accept insane people of all

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Speaker 2: races on the Republican side of the ticket. But I

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Speaker 2: know national Republicans, including the Trump campaign, are very nervous

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Speaker 2: that this guy is gonna draw down a sufficient number

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Speaker 2: of Republicans and leave them either on the bench like

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Speaker 2: eg not voting at all, or drive them into the

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Speaker 2: Democrat's arms. And I think if you're a person like

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Speaker 2: that in a state like North Carolina. And again the

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Speaker 2: reason I argue for North Carolina is it is about

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Speaker 2: a third man maybe forty percent of the cost of

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Speaker 2: operating in Florida. You've got five media markets versus ten.

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Speaker 2: You've got operational cost per week. If you're really doing

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Speaker 2: a campaign there per week in Florida, you're going to

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Speaker 2: spend five million dollars now every week. In North Carolina

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Speaker 2: you could probably do that for a million six somewhere

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Speaker 2: in that member million five And those are back of

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Speaker 2: the envelope math. But it's a legitimate question of how

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Speaker 2: you handle the money assignments in a national campaign. That's

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Speaker 2: where that kind of thing comes into play. Do you

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Speaker 2: go to Nevada instead of North Carolina? As of now, No,

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Speaker 2: but maybe Nevada's a really fluid state. It's a really

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Speaker 2: weird state. So there's a lot here, Molly In we

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Speaker 2: have one hundred and fifty nine days. Trump is going

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Speaker 2: to absolutely fucking immolate himself any number of times between

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Speaker 2: now and the end of the election. Sometimes it'll matter,

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Speaker 2: sometimes it won't. The trick is to add up all

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Speaker 2: the times it matters for voters who are still either

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Speaker 2: undecided or uncomfortable voting for Trump.

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Speaker 1: People say voters have their minds decided. I think that's

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Speaker 1: true only of a small group. I think that the

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Speaker 1: group that has gotten so excited about Trump, from what

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Speaker 1: we've seen with the polling, that group actually is very

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Speaker 1: low information. So aren't those actually hearts and minds that

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Speaker 1: you could change.

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Speaker 2: I'm much more inclined, by both experience and inclination, to

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Speaker 2: try to go after the votes of those software Republicans,

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Speaker 2: those conservative Democrats, those independent leaning conservatives that at Lincoln

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Speaker 2: we were pretty good at talking to and we're pretty

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Speaker 2: good at focusing on, because those folks are a gettable demo.

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Speaker 2: They are not dug in on Trump and trump Ism

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Speaker 2: at the same level. That's where I'd go.

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Speaker 1: Rick Wilson, thank you.

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Speaker 2: You are quite welcome talk to you soon.

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Speaker 1: Spring is here, and I bet you are trying to

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Speaker 1: look fashionable, So why not pick up some fashionable all

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Speaker 1: Harry Littman is a former US attorney and host of

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Speaker 1: the podcast Talking Feds. We're doing a little Fast Politics

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Speaker 1: Talking Fed mashup Harry.

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Speaker 3: Letman Moly junk Fest. What a day we chose for

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Speaker 3: our latest Molly mass out.

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Speaker 1: How much sleep did you get last night?

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Speaker 3: It's a fair question. I'm four or five hours. Was

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Speaker 3: a late night, and I had stuff tell the early morning,

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Speaker 3: and it just kind of folded up right on a

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Speaker 3: beautiful New York day, So I could have slept in

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Speaker 3: a little more. But some kind of residual excitement or

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Speaker 3: something for the day before.

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Speaker 1: So let's talk about what happened. We are talking about

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Speaker 1: the Trump verdict, which came at four point fifteen on

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Speaker 1: a Thursday, when everyone thought that the jury was about

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Speaker 1: to be sent home. First of all, let's just quarterback

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Speaker 1: on ourselves. You and I both have been talking about

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Speaker 1: this on television, but you much more than me, because

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Speaker 1: you obviously are a lawyer and know how to do this.

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Speaker 1: What did you think, I mean, did you think this

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Speaker 1: was going to happen? Were you surprised? And more broadly,

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Speaker 1: I want you to sort of talk to us about

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Speaker 1: what you thought when it came in and what you

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Speaker 1: thought when you heard it.

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Speaker 3: I was not surprised, it was They had told us

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Speaker 3: they were going over the day, so I would have

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Speaker 3: expected a verdict on Friday. But it is the verdict

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Speaker 3: I expected. It's the verdict that at the time or

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Speaker 3: maybe even in retrospect, seemed inexorable from the evidence. But

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Speaker 3: really props to the DA. They really took the tangled

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Speaker 3: story that could have been played as a hush money

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Speaker 3: case and constructed a narrative of election interference that just

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Speaker 3: had a through line from Pecker to Cohen and seemed

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Speaker 3: really cogent and featured a few witnesses that could be

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Speaker 3: assailed like so many witnesses in a criminal case in

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Speaker 3: New York. Not just Cohen, but you know, Pecker is

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Speaker 3: quite the character Stormy Daniels, but each of them. This

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Speaker 3: was the great thing about being in the court roomly

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Speaker 3: jurys make credibility determinations based on the overall sense, and

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Speaker 3: you know, yeah, it's a really human thing to kind

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Speaker 3: of take the measure of a witness and are they

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Speaker 3: telling the truth? And I think they were, and that

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Speaker 3: that came across, and so I wasn't surprised at the verdict,

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Speaker 3: including that they ran the table. I do want to

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Speaker 3: say though, at the DEA, I won't say they made

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Speaker 3: it look easy, but the preparation gap was just enormous.

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Speaker 3: Steinlass got up and he went too long shore, but

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Speaker 3: knew every he had practiced it from the overall contours

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Speaker 3: to the sentences and had pauses and drama and coherence,

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Speaker 3: and really the defense who I think you may know

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Speaker 3: this beread than I. There's some kind of backstory of

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Speaker 3: real bickering among them. They were just didn't have any

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Speaker 3: kind of narrative, and there was part based on the evidence,

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Speaker 3: and part I think they just didn't really ever bear

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Speaker 3: down on a story they could present. That's a shorthand verse.

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Speaker 1: What I thought, as a non lawyer was the same

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Speaker 1: thing as what you just said, which is and again

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Speaker 1: I am not a lawyer. I don't even have a

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Speaker 1: college degree. I have a Master's of Fine Arts. So

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Speaker 1: few people are less confident. Not true way in the

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Speaker 1: URA system. But what I noticed, what I was surprised by,

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Speaker 1: was this was a case where a lot of us

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Speaker 1: in the media industrial complex had misgivings. And then when

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Speaker 1: they brought out David Pecker, I was like, Wow, this

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Speaker 1: guy really just has Trump dead to rights. And then

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Speaker 1: you had Hope Picks weeping on the stand because she

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Speaker 1: knew she had him dead to rights. And then you

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Speaker 1: had Madeline Westerhouse on the stand going like this is

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Speaker 1: what this is, and everyone being like wow. And then

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Speaker 1: you had Trump's lawyer attacking Storey Daniels and opening the

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Speaker 1: door for a whole thing that didn't even have anything

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Speaker 1: to do with the case. And so I was the same.

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Speaker 1: I was shocked at what a good job the prosecution did.

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Speaker 1: Nut shocked, but I was impressed. And then I was

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Speaker 1: really shocked at what a bad job. I mean, Trump

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Speaker 1: is spending hundreds of millions of dollars on legal fees.

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Speaker 1: And what was clear, and Todd Blanche said this on

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Speaker 1: television later, was that in fact, Trump had basically told

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Speaker 1: him everything he had to do.

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Speaker 3: Everyone noted what a big problem you had who didn't

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Speaker 3: just meander in his closing. They went after if you noticed,

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Speaker 3: their big point was these records are not false. Michael

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Speaker 3: Cohen actually performed these legal services. Are you leidding me?

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Speaker 2: You know?

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Speaker 3: And just so just in general, not only the lack

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Speaker 3: sort of pop and kind of overall it's less the

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Speaker 3: lawyers on a storyteller's art, but they made so many

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Speaker 3: bad choices. Let me talk to you about the verdict

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Speaker 3: from your sort of vantage point, because you know, you

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Speaker 3: wrote in your bed Trump sash money, gully vertics, someone

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Speaker 3: going to make him more dangerous that in the aftermath

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Speaker 3: we knew was coming, and has everything to do with

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Speaker 3: his habit of lying for his political gain. That he

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Speaker 3: was always going to Trump was always going to try

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Speaker 3: to use candidate Trump to help defending Trump. Surely true,

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Speaker 3: and any verdict was destined to be molded into his

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Speaker 3: brand and narrative of persecution which hunts in a fight

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Speaker 3: against the political system, also clearly true. But do you

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Speaker 3: think in other instances it's things that seemed so meaningful

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Speaker 3: and pivotal just kind of receded. Do you actually think

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Speaker 3: that he's going to leverage the verdict to fight against

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Speaker 3: the political system and he'll be railing about this in September,

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Speaker 3: when he could just hope that time would put it

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Speaker 3: enough in the distance that he could do. As the

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Speaker 3: rest of his playbook.

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Speaker 1: Trump World desperately wants you to believe that, right, this

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Speaker 1: is a guy and I'm going to quote Will Hurd

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Speaker 1: here he announced a week after the midterms because he

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Speaker 1: hoped that it would keep him from being indicted. So

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Speaker 1: this has always been a play about Trump not going

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Speaker 1: to jail. So what Trump desperately wants, what Trump World

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Speaker 1: desperately wants is for you, you and the justice system and

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Speaker 1: the three other cases which are waiting to go forward

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Speaker 1: and may never go forward because Trump has been able

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Speaker 1: to use all this donor money to punt these cases.

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Speaker 1: What Trump desperately wants is for people in the pundit

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Speaker 1: complex to say it's baked in. Voters know they don't

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Speaker 1: care that he's done crimes. That's what Trump wants, and

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Speaker 1: that's what Mitch McConnell wants, and that's even what Susan

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Speaker 1: Collins wants. Concerned moderate Susan Collins also wants you to

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Speaker 1: not believe that any of this matters, because nihilism helps

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Speaker 1: autocracy and Trump is an autocrat.

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Speaker 3: Nicely put, we need a button for that.

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Speaker 1: Okay, Trump is an autocrat, and all Trump wants is

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Speaker 1: for people to say doesn't matter, don't care. And the

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Speaker 1: reality is we have seen and again I fucking hate poles,

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Speaker 1: but we have seen polls that say that swing voters

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Speaker 1: in Wisconsin don't want to vote for a criminal and

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Speaker 1: they don't like this. And the look, Trump has only

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Speaker 1: won one election twenty sixteen. Since then, he has lost

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Speaker 1: every election. He has never tried to grow the base.

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Speaker 1: Like if you just think of this, like the idea

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00:23:12,960 --> 00:23:16,040
Speaker 1: that Trump is constantly trying to get pundits to think

436
00:23:16,480 --> 00:23:21,720
Speaker 1: in three dimensional chests is really so fucking stupid. That's

437
00:23:21,760 --> 00:23:22,960
Speaker 1: all I could say. I was going to make a

438
00:23:23,040 --> 00:23:25,679
Speaker 1: larger case about how autocrats work, but the truth is

439
00:23:26,080 --> 00:23:27,080
Speaker 1: it's fucking stupid.

440
00:23:27,320 --> 00:23:30,160
Speaker 3: Okay, And how does that translate? So right, So what's

441
00:23:30,160 --> 00:23:33,679
Speaker 3: a canard? Great, give me the conviction. But so do

442
00:23:33,720 --> 00:23:36,320
Speaker 3: you think that means, in practical terms, Molly, that he

443
00:23:36,480 --> 00:23:38,959
Speaker 3: will do a pivot from this and try to ignore it,

444
00:23:39,080 --> 00:23:41,520
Speaker 3: or do you think he'll be beating his breasts about

445
00:23:41,520 --> 00:23:43,560
Speaker 3: this the whole time? That's a strategy?

446
00:23:43,680 --> 00:23:46,800
Speaker 1: Yeah, Because first of all, Remember, Trump has for this

447
00:23:47,080 --> 00:23:49,639
Speaker 1: entire time thought that being a defendant helps him, and

448
00:23:49,680 --> 00:23:52,760
Speaker 1: he's thought that. I mean, I really do think Trump.

449
00:23:52,880 --> 00:23:56,080
Speaker 1: There's so much bullshit here and so many conservatives lying

450
00:23:56,119 --> 00:23:59,560
Speaker 1: about stuff, but I actually do believe that Trump believes

451
00:23:59,760 --> 00:24:01,880
Speaker 1: be being a defendant helps him well.

452
00:24:01,880 --> 00:24:03,800
Speaker 3: And his poles went up during the trough, So you

453
00:24:03,840 --> 00:24:04,959
Speaker 3: think that's my foot ships.

454
00:24:05,040 --> 00:24:07,600
Speaker 1: In turn, that's not true. His poles did not go

455
00:24:07,760 --> 00:24:10,280
Speaker 1: up during the trial. His polls were pretty much static.

456
00:24:10,440 --> 00:24:16,080
Speaker 1: And during the incredible primaries where Trump ran against many

457
00:24:16,200 --> 00:24:19,399
Speaker 1: versions of him who had no other leg to stand

458
00:24:19,440 --> 00:24:22,240
Speaker 1: on except he's our guy and we're not as interesting

459
00:24:22,280 --> 00:24:24,879
Speaker 1: as he is, then he did well. But that is

460
00:24:24,920 --> 00:24:28,760
Speaker 1: the Republican base, which is again a brain worms contingent

461
00:24:28,880 --> 00:24:30,000
Speaker 1: of the American public.

462
00:24:30,200 --> 00:24:32,639
Speaker 3: Yeah all right, but so you're saying he wants me

463
00:24:32,760 --> 00:24:36,919
Speaker 3: to but this will actually be a feature, not something

464
00:24:36,960 --> 00:24:39,280
Speaker 3: to put to the side of the campaign. He'll beat

465
00:24:39,280 --> 00:24:41,639
Speaker 3: his breast all the way through about New York and

466
00:24:41,720 --> 00:24:43,600
Speaker 3: Biden and I'm your martyr.

467
00:24:43,800 --> 00:24:49,240
Speaker 1: Trump ran against American democracy. You'll remember, even when he

468
00:24:49,320 --> 00:24:52,040
Speaker 1: lost in twenty twenty. It's a fixed system, it's a

469
00:24:52,119 --> 00:24:54,760
Speaker 1: rigged system. He was saying that even before he won

470
00:24:54,800 --> 00:24:58,560
Speaker 1: in twenty sixteen. This guy is an autocrat. He wants

471
00:24:58,640 --> 00:25:02,800
Speaker 1: to degrade and attack American institutions, that's what. So he's

472
00:25:02,840 --> 00:25:04,800
Speaker 1: going to run against the courts. But you know, a

473
00:25:04,800 --> 00:25:07,879
Speaker 1: person with that much legal trouble needs to run against

474
00:25:07,920 --> 00:25:11,000
Speaker 1: the courts. And remember, for him, this all works out

475
00:25:11,040 --> 00:25:16,040
Speaker 1: great because either way he uses that campaign money for lawyers. Right,

476
00:25:16,080 --> 00:25:19,080
Speaker 1: he doesn't have to pay one hundred plus million dollars

477
00:25:19,119 --> 00:25:22,120
Speaker 1: in legal fees not coming out of the Trump family

478
00:25:22,640 --> 00:25:25,920
Speaker 1: irrevocable trusts. And I think that that is a really

479
00:25:25,920 --> 00:25:26,679
Speaker 1: important point.

480
00:25:26,840 --> 00:25:28,960
Speaker 3: By the way, I wouldn't say great in the sense

481
00:25:29,000 --> 00:25:32,040
Speaker 3: that the endgame might be if he doesn't win, you know,

482
00:25:32,600 --> 00:25:35,320
Speaker 3: confinement for the rest of his natural life. But you

483
00:25:35,359 --> 00:25:36,240
Speaker 3: know it's a real risk.

484
00:25:36,720 --> 00:25:39,560
Speaker 1: No, but it's still he was going to have that confinement.

485
00:25:39,760 --> 00:25:42,880
Speaker 1: I see their way. So running for office just gave

486
00:25:43,000 --> 00:25:45,560
Speaker 1: him free money for lawyers. And I would say that

487
00:25:45,760 --> 00:25:48,439
Speaker 1: I would be shocked if he didn't try to run

488
00:25:48,560 --> 00:25:52,240
Speaker 1: in twenty twenty eight. Wow, why wouldn't he There's no

489
00:25:52,400 --> 00:25:55,920
Speaker 1: It's like, there's no incentive not to. If you can

490
00:25:56,040 --> 00:25:58,440
Speaker 1: use this money for lawyers, why wouldn't you just keep

491
00:25:58,560 --> 00:25:59,880
Speaker 1: running until you die.

492
00:26:00,240 --> 00:26:03,720
Speaker 3: So we keep asking is there trump Ism after trum

493
00:26:03,720 --> 00:26:07,000
Speaker 3: but instead there's Trump after Trump? Holy cal I.

494
00:26:06,960 --> 00:26:09,640
Speaker 1: Mean I do think there's trump Ism after Trump.

495
00:26:09,400 --> 00:26:12,680
Speaker 3: And it's named Donald Trump. Okay, what you got? Bring it?

496
00:26:12,800 --> 00:26:14,719
Speaker 1: How are they going to keep this Joey safe?

497
00:26:15,000 --> 00:26:19,560
Speaker 3: Yeah, there's no great answer from the criminal justice system.

498
00:26:19,600 --> 00:26:22,760
Speaker 3: They are not going to go into witness protection program,

499
00:26:22,840 --> 00:26:25,360
Speaker 3: et cetera. You know, I have to think, are they

500
00:26:25,560 --> 00:26:31,160
Speaker 3: really the villains in the eyes of the maga faithful,

501
00:26:31,200 --> 00:26:33,600
Speaker 3: even the crazy maga faithful, the kind of you know,

502
00:26:34,000 --> 00:26:37,359
Speaker 3: Loan Wlos who went in and shot up the Cincinnati FBI.

503
00:26:37,800 --> 00:26:40,919
Speaker 3: You know, I wouldn't be surprised. I was half the

504
00:26:41,080 --> 00:26:44,240
Speaker 3: kind of subtext, wasn't it, of don't put him in jail,

505
00:26:44,800 --> 00:26:48,120
Speaker 3: that maybe they come after you. It's folly to think

506
00:26:48,160 --> 00:26:51,240
Speaker 3: that their identities won't be known by people who want

507
00:26:51,240 --> 00:26:54,239
Speaker 3: to diligently do it. So the short answer this used

508
00:26:54,280 --> 00:26:56,840
Speaker 3: to I come up. Well, you know, in terrorism age,

509
00:26:56,880 --> 00:26:59,760
Speaker 3: I was a US attorney and judges would say, what

510
00:26:59,760 --> 00:27:03,280
Speaker 3: happens if some crazy person wants to come and tear

511
00:27:03,400 --> 00:27:06,160
Speaker 3: us and blow us up? And the answer really was, well,

512
00:27:06,160 --> 00:27:08,040
Speaker 3: then they'll blow you up. You know, I think it

513
00:27:08,080 --> 00:27:13,080
Speaker 3: will reduce to a marginal risk. I don't see why

514
00:27:13,119 --> 00:27:16,520
Speaker 3: they would be the target, but there's no way to

515
00:27:16,760 --> 00:27:21,560
Speaker 3: forget criminal justice. The entire justice system can one percent

516
00:27:21,800 --> 00:27:24,520
Speaker 3: prevent and now, of course they could one hundred percent

517
00:27:24,560 --> 00:27:27,440
Speaker 3: go after it would be very serious crime. So it's

518
00:27:27,480 --> 00:27:30,160
Speaker 3: the same kind of disincentive as how do you get

519
00:27:30,240 --> 00:27:33,320
Speaker 3: keep a judge or a US attorney. You know, we

520
00:27:33,400 --> 00:27:36,240
Speaker 3: used to have Marshall protections, et cetera. But it's it's

521
00:27:36,480 --> 00:27:37,920
Speaker 3: I have no good answer for you.

522
00:27:38,040 --> 00:27:40,800
Speaker 1: They can't send them home with security.

523
00:27:41,160 --> 00:27:43,399
Speaker 3: Sure, Oh no, no, no. When I say good answer, I

524
00:27:43,400 --> 00:27:45,720
Speaker 3: mean fool. How are they going to keep them safe?

525
00:27:45,720 --> 00:27:49,160
Speaker 3: I mean literally, if Trump world they're what they want

526
00:27:49,240 --> 00:27:49,800
Speaker 3: to do.

527
00:27:49,840 --> 00:27:52,000
Speaker 1: Is starts boxing them or something.

528
00:27:52,400 --> 00:27:54,840
Speaker 3: That's all I'm saying. Of course, they're the equivalent of

529
00:27:54,840 --> 00:27:58,160
Speaker 3: double locks on the door and burglar alarms and buzzers

530
00:27:58,200 --> 00:28:00,840
Speaker 3: and stuff. But I think that the literal answer to

531
00:28:00,840 --> 00:28:03,240
Speaker 3: your question is they can't. They can just they can

532
00:28:03,280 --> 00:28:05,840
Speaker 3: reduce the risk, and you know, these guys could leave

533
00:28:05,920 --> 00:28:08,919
Speaker 3: New York, but no one's hopefully gonna do that. I

534
00:28:09,119 --> 00:28:12,199
Speaker 3: just think that during these few weeks, there's been a

535
00:28:12,200 --> 00:28:14,639
Speaker 3: few of our peers who have been distinguished by the

536
00:28:14,760 --> 00:28:18,399
Speaker 3: glowers they seem to draw personally from Trump and the

537
00:28:18,560 --> 00:28:22,240
Speaker 3: personal messages. You know, it's kind of a resume builder.

538
00:28:22,640 --> 00:28:26,400
Speaker 3: But there's been real questions among our are we in danger?

539
00:28:26,520 --> 00:28:28,960
Speaker 3: I don't put myself in that category. And the short

540
00:28:29,000 --> 00:28:32,920
Speaker 3: answer is, you know, look, if some crazy person hears

541
00:28:32,960 --> 00:28:37,399
Speaker 3: from channels with aluminum foil the voice of Donald Trump saying.

542
00:28:37,160 --> 00:28:40,760
Speaker 1: Go, got that out, we don't need to tempt fade here.

543
00:28:40,920 --> 00:28:42,920
Speaker 1: You go, Okay, ask me a question.

544
00:28:43,160 --> 00:28:47,120
Speaker 3: Go so, I'm really intrigued. You said, you fucking hate poles,

545
00:28:47,160 --> 00:28:50,320
Speaker 3: and I do too, But I also think I but

546
00:28:50,520 --> 00:28:55,280
Speaker 3: we as really don't fucking understand poles, and we're gonna

547
00:28:55,520 --> 00:28:58,480
Speaker 3: make so we're gonna learn a lot about it, just

548
00:28:58,560 --> 00:29:01,960
Speaker 3: social scientists over the next several years, but especially this

549
00:29:02,000 --> 00:29:04,880
Speaker 3: whole problem that it seems like all the action for

550
00:29:04,960 --> 00:29:07,840
Speaker 3: Trump is at the level of social media and things

551
00:29:07,840 --> 00:29:12,440
Speaker 3: that just don't actually register in the typical polls. But

552
00:29:12,520 --> 00:29:15,840
Speaker 3: you said, I'm talking now about your piece, really great piece,

553
00:29:15,920 --> 00:29:18,600
Speaker 3: by the way, the Vanny Fair piece. Everybody checked. It's

554
00:29:18,600 --> 00:29:21,680
Speaker 3: out the panic of the polls. But anyway, you said, look,

555
00:29:21,880 --> 00:29:25,200
Speaker 3: Nikki Hagey's zombie campaign still getting more thin ten percent

556
00:29:25,320 --> 00:29:28,080
Speaker 3: of the vote. Biden seems to be pulling better among

557
00:29:28,120 --> 00:29:31,280
Speaker 3: those who are likely to actually show up. But just

558
00:29:31,520 --> 00:29:33,320
Speaker 3: I want to zero in on the point that the

559
00:29:33,400 --> 00:29:37,920
Speaker 3: polling industry just doesn't seem to have the real precision

560
00:29:38,160 --> 00:29:42,600
Speaker 3: of access to, as you put a people's political persuasions

561
00:29:42,640 --> 00:29:47,600
Speaker 3: and habits that tech companies do. So, you know, are

562
00:29:47,680 --> 00:29:52,960
Speaker 3: these are the pollsters you think overestimating mega members commitment

563
00:29:53,120 --> 00:29:56,200
Speaker 3: actually shown up for their guy? You know? Is does

564
00:29:56,240 --> 00:29:59,440
Speaker 3: that lesson? I don't think we can calibrate it yet,

565
00:29:59,520 --> 00:30:02,320
Speaker 3: but the do you think in general it points toward

566
00:30:02,800 --> 00:30:04,480
Speaker 3: hidden good news for Biden.

567
00:30:04,800 --> 00:30:07,959
Speaker 1: Yeah, so this is what I would say, Yes, pollsters

568
00:30:08,000 --> 00:30:08,920
Speaker 1: have a big problem.

569
00:30:09,120 --> 00:30:10,320
Speaker 3: It's actually a good question.

570
00:30:10,400 --> 00:30:12,320
Speaker 4: You were going, yeah, no, Well.

571
00:30:12,200 --> 00:30:14,760
Speaker 1: It's actually something I spent a lot of time thinking about.

572
00:30:14,880 --> 00:30:17,920
Speaker 1: So posters don't have a great pulse on what's happening,

573
00:30:17,960 --> 00:30:21,760
Speaker 1: but what's happening here is actually something different. So in

574
00:30:21,840 --> 00:30:25,480
Speaker 1: twenty sixteen, and again, posters will tell you they weren't wrong,

575
00:30:25,560 --> 00:30:28,160
Speaker 1: but they were wrong. They were really, really really wrong.

576
00:30:28,320 --> 00:30:32,080
Speaker 1: They were wrong. And from that they extrapolated Hillary Quinn

577
00:30:32,080 --> 00:30:34,880
Speaker 1: as a ninety nine point nine percent or an eighty

578
00:30:34,960 --> 00:30:36,880
Speaker 1: nine percent chance of winning.

579
00:30:36,560 --> 00:30:40,000
Speaker 3: And then during the evening eighty seventy you watched it

580
00:30:40,080 --> 00:30:42,400
Speaker 3: like the oxygen level dropping it.

581
00:30:42,720 --> 00:30:46,040
Speaker 1: Yeah, so that was a real polling error. And since

582
00:30:46,080 --> 00:30:48,280
Speaker 1: then what they've done and part of the reason that

583
00:30:48,400 --> 00:30:50,680
Speaker 1: was a polling error, not to make excuses for them,

584
00:30:50,680 --> 00:30:53,240
Speaker 1: but this is actually was not their fault. Was that

585
00:30:53,680 --> 00:30:58,120
Speaker 1: Trump voters were a group of low frequency voters who

586
00:30:58,480 --> 00:31:01,400
Speaker 1: you couldn't pull because he didn't think they existed. And

587
00:31:01,440 --> 00:31:04,080
Speaker 1: that was what Kelly and Conway said about these hidden

588
00:31:04,080 --> 00:31:07,040
Speaker 1: Trump voters. They were hidden because they weren't voters. They

589
00:31:07,040 --> 00:31:10,280
Speaker 1: were just people who usually didn't vote. But Trump had

590
00:31:10,280 --> 00:31:14,200
Speaker 1: this populist message which they responded to, and that happened

591
00:31:14,200 --> 00:31:16,959
Speaker 1: in twenty sixteen, and that also happened in twenty twenty.

592
00:31:17,120 --> 00:31:19,240
Speaker 1: But it doesn't happen in the midterms. Those people don't

593
00:31:19,280 --> 00:31:21,680
Speaker 1: come out for anyone else. Trump Ism does not scale.

594
00:31:21,760 --> 00:31:24,680
Speaker 1: So you know, maybe you win a primary, but like

595
00:31:24,760 --> 00:31:28,200
Speaker 1: a really good example is Jade Vance in Ohio, Ruby

596
00:31:28,240 --> 00:31:32,280
Speaker 1: red state running you know, trumpy Trump and trump Asaurus

597
00:31:32,600 --> 00:31:37,760
Speaker 1: running ten points behind the partisan lean because ultimately it

598
00:31:37,800 --> 00:31:40,640
Speaker 1: doesn't work for anyone else. It only works for Trump.

599
00:31:40,760 --> 00:31:43,320
Speaker 3: Yeah, I mean in Ohio, the guy who was just

600
00:31:43,360 --> 00:31:46,440
Speaker 3: a friend of mine lost the U to the wine

601
00:31:46,920 --> 00:31:49,880
Speaker 3: had more votes than anybody who's ever had, but the

602
00:31:49,960 --> 00:31:53,680
Speaker 3: wind somehow, these hidden Trump voters came out and just

603
00:31:53,840 --> 00:31:55,560
Speaker 3: new voters put the wine over the top.

604
00:31:55,640 --> 00:31:59,280
Speaker 1: Same thing with Vans, right, but usually that doesn't happen, right,

605
00:31:59,320 --> 00:32:03,320
Speaker 1: and Vans still ran behind the partisan lane. So what

606
00:32:03,400 --> 00:32:06,440
Speaker 1: I would say is that my guess is what these

607
00:32:06,720 --> 00:32:09,600
Speaker 1: posters are doing. And Nate Cohen wrote very smartly about

608
00:32:09,600 --> 00:32:13,840
Speaker 1: this other times, they're trying to estimate whether or not

609
00:32:14,000 --> 00:32:16,680
Speaker 1: this group comes out. I think they will come out.

610
00:32:16,800 --> 00:32:20,120
Speaker 1: But the question is because you know, remember a million

611
00:32:20,760 --> 00:32:24,360
Speaker 1: Americans died of COVID, and even if I know, that

612
00:32:24,560 --> 00:32:27,240
Speaker 1: was three years ago, so no one remembers, but that

613
00:32:27,320 --> 00:32:29,800
Speaker 1: was a million people, and so maybe a lot of

614
00:32:29,800 --> 00:32:33,200
Speaker 1: those people were older. It doesn't matter. If your grandmother

615
00:32:33,760 --> 00:32:36,920
Speaker 1: refuse to get vaccinated and then died of COVID, do

616
00:32:37,120 --> 00:32:40,120
Speaker 1: her five grandchildren run out and vote for Trump. It's

617
00:32:40,120 --> 00:32:43,920
Speaker 1: an open question. Maybe they think COVID is a you know,

618
00:32:44,120 --> 00:32:46,680
Speaker 1: racist can I mean, maybe they have some racist way

619
00:32:47,160 --> 00:32:50,080
Speaker 1: to make it work for them, but you could see

620
00:32:50,160 --> 00:32:53,280
Speaker 1: that that could actually theoretically hurt him. But the other

621
00:32:53,400 --> 00:32:57,680
Speaker 1: thing that I think is really still possible. It's just

622
00:32:57,800 --> 00:33:01,800
Speaker 1: really hard to square what the unpredictable voters will do.

623
00:33:02,480 --> 00:33:04,600
Speaker 1: And if you look at the bottom of the ticket,

624
00:33:05,040 --> 00:33:08,560
Speaker 1: which I think is really where the action is happening,

625
00:33:08,840 --> 00:33:13,480
Speaker 1: these Democratic senators are running way ahead, like I saw

626
00:33:13,800 --> 00:33:17,880
Speaker 1: Tammy Baldwin running twelve points ahead and swing state Wisconsin,

627
00:33:18,120 --> 00:33:21,840
Speaker 1: so I think there's some sorcery going on and trying

628
00:33:21,880 --> 00:33:24,280
Speaker 1: to measure these Trump voters. I have a lot of

629
00:33:24,320 --> 00:33:28,360
Speaker 1: trouble imagining a voter coming out voting for Tammy Baldwin

630
00:33:28,400 --> 00:33:32,760
Speaker 1: and then voting for Trump. Not impossible, but feels like

631
00:33:32,800 --> 00:33:33,320
Speaker 1: a stretch.

632
00:33:33,680 --> 00:33:36,480
Speaker 3: Yeah, you know, Mollie, like well as always, I think

633
00:33:36,480 --> 00:33:39,240
Speaker 3: we cover from the what you cover, from the sort

634
00:33:39,240 --> 00:33:43,320
Speaker 3: of daily to the more profound national trends and really

635
00:33:43,360 --> 00:33:46,600
Speaker 3: that you know, the things that are growth. Sorcery is

636
00:33:47,160 --> 00:33:50,160
Speaker 3: is such a perfect word for what seems to be

637
00:33:50,200 --> 00:33:54,000
Speaker 3: going on. You know, presumably the magic will reveal itself

638
00:33:54,040 --> 00:33:56,840
Speaker 3: not to be magic, but right now I'm not sure

639
00:33:56,920 --> 00:34:00,000
Speaker 3: how and why. So that'll keep me biting my name

640
00:34:00,040 --> 00:34:01,280
Speaker 3: else for the next six months.

641
00:34:01,400 --> 00:34:04,080
Speaker 1: And you and I both agree that we don't know

642
00:34:04,120 --> 00:34:07,240
Speaker 1: what we don't know, and we'll know a lot more in.

643
00:34:07,200 --> 00:34:11,520
Speaker 3: November, there's an end. Look forward to the next Molly mashup.

644
00:34:11,560 --> 00:34:13,479
Speaker 3: Thanks a lot and a big week.

645
00:34:13,719 --> 00:34:19,520
Speaker 1: Thank you, thank you, thank you. Ben Smith is the

646
00:34:19,640 --> 00:34:23,840
Speaker 1: editor of Semaphore. Welcome back to Fast Politics, Ben.

647
00:34:23,680 --> 00:34:25,960
Speaker 4: Smith, thank you so much for having me back.

648
00:34:26,400 --> 00:34:29,319
Speaker 1: You're here ostensibly to promote a podcast, but I'm going

649
00:34:29,360 --> 00:34:31,280
Speaker 1: to ask you lots of out the questions, but first

650
00:34:31,400 --> 00:34:34,120
Speaker 1: you can tell us about the podcast. It's a media podcast.

651
00:34:34,320 --> 00:34:36,640
Speaker 4: Yeah, the people seem to care a lot about media.

652
00:34:37,160 --> 00:34:41,520
Speaker 1: The last gasps of a dying industry. I it's like

653
00:34:41,600 --> 00:34:43,040
Speaker 1: a snuff film.

654
00:34:43,200 --> 00:34:46,839
Speaker 5: Oh god right, Molly, No, it's I like, I kind

655
00:34:46,840 --> 00:34:50,880
Speaker 5: of hate that whole like colagulating journalism thing. No, I mean,

656
00:34:50,920 --> 00:34:52,520
Speaker 5: I think the thing about media is that it is

657
00:34:52,880 --> 00:34:55,000
Speaker 5: both grim and boring if you think of it as

658
00:34:55,000 --> 00:34:57,000
Speaker 5: a sort of small, dying industry. But it's in a

659
00:34:57,080 --> 00:34:59,160
Speaker 5: huge story if you think about it, as it cuts

660
00:34:59,160 --> 00:35:03,520
Speaker 5: into politics, into culture, into technology, and it lends on

661
00:35:03,600 --> 00:35:06,640
Speaker 5: all of those stories. And so we're launching a show

662
00:35:06,680 --> 00:35:09,680
Speaker 5: called Mixed Signals. Naima Raza and I will be co

663
00:35:09,760 --> 00:35:13,320
Speaker 5: hosting it some help from Max Tani, Samaphore's great media editor,

664
00:35:13,520 --> 00:35:16,400
Speaker 5: to sort of tell those stories about what is happening

665
00:35:16,480 --> 00:35:19,439
Speaker 5: behind the scenes, as it affects things that actually matter,

666
00:35:19,520 --> 00:35:22,680
Speaker 5: as it affects national politics, as it affects Hollywood and

667
00:35:22,719 --> 00:35:24,759
Speaker 5: the culture and things we really care about. And I

668
00:35:24,760 --> 00:35:27,000
Speaker 5: think that's you know that to me, that's really one

669
00:35:27,000 --> 00:35:29,640
Speaker 5: of one of the biggest stories of this media age,

670
00:35:29,760 --> 00:35:31,680
Speaker 5: and so we're excited about it. And I think there

671
00:35:31,760 --> 00:35:33,800
Speaker 5: is this sort of lingering sense that I think everybody

672
00:35:33,840 --> 00:35:36,480
Speaker 5: has that there's some kind of conspiracy that media is

673
00:35:36,480 --> 00:35:38,840
Speaker 5: a bit of a conspiracy, that people are making decisions

674
00:35:38,920 --> 00:35:41,440
Speaker 5: that are in some sense manipulative, or that something's happening

675
00:35:41,480 --> 00:35:43,600
Speaker 5: behind the scenes. I think what's so interesting about covering

676
00:35:43,680 --> 00:35:45,960
Speaker 5: it is that in some ways media is a conspiracy,

677
00:35:45,960 --> 00:35:49,200
Speaker 5: but often it's particularly a conspiracy to make money and

678
00:35:49,239 --> 00:35:52,160
Speaker 5: not a very good one, and actually understanding how decisions

679
00:35:52,160 --> 00:35:54,000
Speaker 5: are being made is often very interesting and often very

680
00:35:54,000 --> 00:35:55,920
Speaker 5: different from what you would think from the outside.

681
00:35:56,120 --> 00:35:59,480
Speaker 1: I listened to on the Media that podcast from w

682
00:35:59,680 --> 00:36:03,880
Speaker 1: Andy See, which is really really smart. They had a

683
00:36:03,920 --> 00:36:06,840
Speaker 1: podcast yesterday, but I think it was like two weeks

684
00:36:06,840 --> 00:36:09,520
Speaker 1: ago or whatever, because you know, the timing of when

685
00:36:09,520 --> 00:36:12,000
Speaker 1: these drop is not, you know, it doesn't totally line

686
00:36:12,080 --> 00:36:15,000
Speaker 1: up and they talked about how much a lot of

687
00:36:15,040 --> 00:36:21,880
Speaker 1: tech journalism has been really celebrating advancement without providing a

688
00:36:21,920 --> 00:36:25,759
Speaker 1: lot of pushback, and then most AI coverage seems to

689
00:36:25,840 --> 00:36:28,760
Speaker 1: do a similar like AA is going to change our lives.

690
00:36:28,880 --> 00:36:33,800
Speaker 1: AI is fire and not been critical enough. That strikes

691
00:36:33,840 --> 00:36:36,799
Speaker 1: me as like maybe a larger problem too when it

692
00:36:36,840 --> 00:36:38,600
Speaker 1: comes to coverage.

693
00:36:38,800 --> 00:36:42,120
Speaker 5: That strikes me as a pretty broad generalization and maybe

694
00:36:42,120 --> 00:36:44,200
Speaker 5: a story that was true fifteen years ago. I mean,

695
00:36:44,200 --> 00:36:46,000
Speaker 5: the sort of interesting story about tech coverage, I think

696
00:36:46,080 --> 00:36:48,839
Speaker 5: is how hard it turned against the technology industry kind

697
00:36:48,840 --> 00:36:51,000
Speaker 5: of during the social media era and during the Trump era,

698
00:36:51,160 --> 00:36:53,400
Speaker 5: and kind of how hostile the industry has become to

699
00:36:53,440 --> 00:36:55,359
Speaker 5: the people who cover it. I mean, I agree with you,

700
00:36:55,400 --> 00:36:57,719
Speaker 5: there is a certain amount of and honestly a certain

701
00:36:57,760 --> 00:37:01,000
Speaker 5: amount of justified like, hey, this shure is coverage of AI,

702
00:37:01,160 --> 00:37:03,360
Speaker 5: because you know, at the core of a lot of

703
00:37:03,400 --> 00:37:07,000
Speaker 5: technology coverage is and ought to be an excitement about technology,

704
00:37:07,320 --> 00:37:08,880
Speaker 5: and a lot of tech reporters have that. But I

705
00:37:08,880 --> 00:37:12,880
Speaker 5: actually find that we have sometimes imported the criticisms of

706
00:37:12,880 --> 00:37:16,320
Speaker 5: social media into coverage of AI in a way that

707
00:37:16,400 --> 00:37:18,080
Speaker 5: doesn't make all that much sense, Like they're just very

708
00:37:18,080 --> 00:37:20,600
Speaker 5: different the threats posed b AI are quite different.

709
00:37:20,400 --> 00:37:22,080
Speaker 4: So i'd actually I think I don't buy that.

710
00:37:22,280 --> 00:37:23,880
Speaker 5: In fact, it's like one of the great things is

711
00:37:23,880 --> 00:37:26,520
Speaker 5: that you can just have an avatar go on podcasts

712
00:37:26,560 --> 00:37:28,160
Speaker 5: so you don't have to. I'm really a big fan

713
00:37:28,200 --> 00:37:30,280
Speaker 5: of AI, and particularly for that use case.

714
00:37:30,440 --> 00:37:32,360
Speaker 1: I think you're kidding, but I can't.

715
00:37:32,160 --> 00:37:34,000
Speaker 4: Tell I'm kidding. I'm kidding, I'm kidding.

716
00:37:34,000 --> 00:37:37,960
Speaker 1: Sorry, Okay, how do you think that's unfair? A pushback

717
00:37:38,040 --> 00:37:40,200
Speaker 1: on something that none of us really know what it's

718
00:37:40,239 --> 00:37:43,640
Speaker 1: going to do. I mean, I'm just curious, Like I

719
00:37:43,800 --> 00:37:47,120
Speaker 1: love when people come on my podcast and I'm happy

720
00:37:47,160 --> 00:37:49,360
Speaker 1: to have you and not your AI version.

721
00:37:50,040 --> 00:37:53,040
Speaker 5: Yeah, that's the thing. Podcasting is the last use case

722
00:37:53,080 --> 00:37:53,640
Speaker 5: for humans.

723
00:37:53,680 --> 00:37:56,920
Speaker 1: Actually, again, like that would make AI kind of bad.

724
00:37:57,560 --> 00:38:00,600
Speaker 1: I mean maybe I am just not as smart as

725
00:38:00,640 --> 00:38:04,480
Speaker 1: you are, but I just assume that being replaced. I mean,

726
00:38:04,520 --> 00:38:08,080
Speaker 1: we're seeing these summations now of pieces written. You know,

727
00:38:08,160 --> 00:38:10,680
Speaker 1: now there are summaries of pieces. I mean, doesn't that

728
00:38:10,760 --> 00:38:13,200
Speaker 1: strike you as like the beginning of the end.

729
00:38:13,440 --> 00:38:13,880
Speaker 2: You do, know?

730
00:38:14,080 --> 00:38:16,200
Speaker 5: I mean I think I guess from my perspective when

731
00:38:16,200 --> 00:38:18,680
Speaker 5: it comes to journalism, particularly like again, I think there

732
00:38:18,680 --> 00:38:20,880
Speaker 5: are lots of really scary things. I think some of

733
00:38:20,880 --> 00:38:24,840
Speaker 5: the kind of really growth tesque sexual harassment and defix

734
00:38:24,880 --> 00:38:27,440
Speaker 5: that AI can do are just incredibly disturbing. The sort

735
00:38:27,440 --> 00:38:28,800
Speaker 5: of you know, I think there's a sort of general

736
00:38:28,880 --> 00:38:32,360
Speaker 5: undermining people's broad shared idea of truth that is incredibly disturbing.

737
00:38:32,400 --> 00:38:34,120
Speaker 5: I think there are there are really negative things. But

738
00:38:34,160 --> 00:38:37,560
Speaker 5: also when it comes to journalism in particular, journalists you know,

739
00:38:37,600 --> 00:38:39,680
Speaker 5: have been complaining about the birth the growth of new

740
00:38:39,719 --> 00:38:42,880
Speaker 5: technology since you know, probably the printing press. But the

741
00:38:42,880 --> 00:38:46,200
Speaker 5: main thing I use AFI for every day is transcribing interviews.

742
00:38:46,480 --> 00:38:49,399
Speaker 5: I suspect you guys too, Like my least favorite part

743
00:38:49,400 --> 00:38:51,360
Speaker 5: of journalism was doing an interview for an hour and

744
00:38:51,360 --> 00:38:53,359
Speaker 5: that's spent two hours typing it up. I don't think

745
00:38:53,400 --> 00:38:55,880
Speaker 5: anybody feels that that is a great loss to the

746
00:38:55,880 --> 00:38:58,840
Speaker 5: profession that you're not doing that. I suspect there's editing

747
00:38:58,880 --> 00:38:59,920
Speaker 5: of this show that gets you.

748
00:39:00,480 --> 00:39:03,400
Speaker 4: And actually like the way in which AI is actually.

749
00:39:03,200 --> 00:39:06,239
Speaker 5: Manifesting itself in newsrooms, I mean sort of more sophisticated

750
00:39:06,320 --> 00:39:09,080
Speaker 5: spell checking. Like I don't think anybody thinks Grammarly is

751
00:39:09,120 --> 00:39:11,200
Speaker 5: going to be on the street corners murdering people. But

752
00:39:11,480 --> 00:39:13,960
Speaker 5: you probably did reduce the number of copy editors in

753
00:39:14,000 --> 00:39:14,520
Speaker 5: the business.

754
00:39:14,840 --> 00:39:15,839
Speaker 4: Right right now, the.

755
00:39:15,760 --> 00:39:18,759
Speaker 5: Way these tools are being used in newsrooms is really

756
00:39:18,760 --> 00:39:20,400
Speaker 5: helpful and allows us to do more of the kind

757
00:39:20,400 --> 00:39:22,160
Speaker 5: of stuff we want to do, which is trying to

758
00:39:22,160 --> 00:39:24,800
Speaker 5: figure out what's really happening, talking to other human beings

759
00:39:25,040 --> 00:39:27,239
Speaker 5: and leaving the boring stuff to the AI. And I

760
00:39:27,280 --> 00:39:30,279
Speaker 5: guess I don't really think that either your job of

761
00:39:30,560 --> 00:39:33,120
Speaker 5: helping people figure out the world and explaining it to

762
00:39:33,160 --> 00:39:35,800
Speaker 5: them in a nuanced and human way, or my job

763
00:39:35,920 --> 00:39:38,319
Speaker 5: of trying to get people to tell me information are

764
00:39:38,360 --> 00:39:40,520
Speaker 5: really at risk. And maybe that's sort of self centered,

765
00:39:40,520 --> 00:39:41,839
Speaker 5: but that is how I see it.

766
00:39:42,080 --> 00:39:47,000
Speaker 1: No I agree, and Jase uses in AI software to

767
00:39:47,160 --> 00:39:50,160
Speaker 1: edit this and to get rid of noise, and I

768
00:39:50,280 --> 00:39:54,960
Speaker 1: certainly agree. As a dyslexic who is violently very dyslexic,

769
00:39:55,360 --> 00:39:58,440
Speaker 1: the improvement in spellcheck has been humongous for me, so

770
00:39:59,080 --> 00:40:00,000
Speaker 1: I definitely agree with that.

771
00:40:00,320 --> 00:40:02,480
Speaker 4: So you also welcome our new overlords.

772
00:40:02,480 --> 00:40:06,080
Speaker 1: Basically, my question is more like, should we be pushing

773
00:40:06,160 --> 00:40:09,719
Speaker 1: back more all the time? For example, I had on

774
00:40:09,760 --> 00:40:13,760
Speaker 1: this podcast Jim Hines, who I really like and who

775
00:40:14,000 --> 00:40:17,120
Speaker 1: is a Democrat and who is very smart, and who

776
00:40:17,239 --> 00:40:21,200
Speaker 1: comes from Connecticut and lives in coscop And I said

777
00:40:21,200 --> 00:40:24,359
Speaker 1: to him, you know, you guys don't regulate anything, right,

778
00:40:24,360 --> 00:40:27,200
Speaker 1: you didn't regulate any technology. And you know when you

779
00:40:27,239 --> 00:40:29,440
Speaker 1: say that to a member of Congress, they always say, well,

780
00:40:29,520 --> 00:40:32,040
Speaker 1: the First Amendment. And I'd say, the First Amendment doesn't

781
00:40:32,040 --> 00:40:34,000
Speaker 1: have anything to do with regulating technology.

782
00:40:34,239 --> 00:40:35,800
Speaker 4: You say, what about the Second Amendment?

783
00:40:36,000 --> 00:40:36,239
Speaker 2: Right?

784
00:40:36,400 --> 00:40:37,160
Speaker 1: Right? Exactly?

785
00:40:37,280 --> 00:40:37,360
Speaker 2: No.

786
00:40:37,480 --> 00:40:40,840
Speaker 1: But I mean the reality is Congress gave up on

787
00:40:40,920 --> 00:40:43,600
Speaker 1: trying to regulate technology. And they did that because they

788
00:40:43,600 --> 00:40:46,480
Speaker 1: were like low key, they didn't want to. It was

789
00:40:46,560 --> 00:40:49,160
Speaker 1: too many problems. If you look at Europe, like there's

790
00:40:49,480 --> 00:40:53,000
Speaker 1: actual fullsome regulations, they haven't stopped some of the problems.

791
00:40:53,040 --> 00:40:56,279
Speaker 1: And look, Europe is a much different regulatory environment. But

792
00:40:56,360 --> 00:41:00,279
Speaker 1: like to a certain extent, Congress has fallen as sleep

793
00:41:00,280 --> 00:41:01,560
Speaker 1: on the switch in certain ways.

794
00:41:01,640 --> 00:41:02,920
Speaker 4: It's a strange story.

795
00:41:03,280 --> 00:41:07,359
Speaker 1: Imagine a world where technology started. You know, we went

796
00:41:07,440 --> 00:41:11,680
Speaker 1: onto the internet, we lost newspapers, and Congress said, okay,

797
00:41:12,400 --> 00:41:17,560
Speaker 1: two percent of all pre tax earning or even post

798
00:41:17,600 --> 00:41:21,520
Speaker 1: tax earning that tech companies make must be put in

799
00:41:21,800 --> 00:41:26,880
Speaker 1: nonprofit funds for local news like something, you know, one percent,

800
00:41:26,920 --> 00:41:31,240
Speaker 1: zero point five percent of the five hundred billion dollars

801
00:41:31,280 --> 00:41:34,040
Speaker 1: that Elon Musk made. I mean, because remember Elon Musk

802
00:41:34,080 --> 00:41:37,400
Speaker 1: made all of this money on these sort of regulatory

803
00:41:37,600 --> 00:41:43,040
Speaker 1: tax bonuses for doing environmental stuff right like he has.

804
00:41:43,400 --> 00:41:46,400
Speaker 1: He is like a guy who got government handouts. But

805
00:41:46,440 --> 00:41:49,719
Speaker 1: the government handouts went to oil companies and went to

806
00:41:49,880 --> 00:41:52,480
Speaker 1: people like Elon and didn't go to people like the

807
00:41:52,560 --> 00:41:56,359
Speaker 1: main daily reporter. I'm just saying, like Congress had an

808
00:41:56,360 --> 00:42:00,919
Speaker 1: opportunity to not crush the news industry, right, and they

809
00:42:00,960 --> 00:42:01,640
Speaker 1: didn't take it.

810
00:42:01,880 --> 00:42:03,960
Speaker 5: I hate to say this as a journalist, but I'm

811
00:42:04,000 --> 00:42:06,200
Speaker 5: not sure this is totally about us in the news industry.

812
00:42:06,239 --> 00:42:07,920
Speaker 5: And to me, the core deal, which is a pretty

813
00:42:07,920 --> 00:42:11,759
Speaker 5: weird deal, is that we both got all of the companies,

814
00:42:11,840 --> 00:42:13,960
Speaker 5: all of the jobs, all of the growth, and all

815
00:42:14,000 --> 00:42:17,239
Speaker 5: of the wealth created, and we got to be the

816
00:42:17,440 --> 00:42:20,359
Speaker 5: like laboratory where all the damage got done. And then

817
00:42:20,480 --> 00:42:22,840
Speaker 5: the Europeans came in got, by the way, none of

818
00:42:22,880 --> 00:42:24,960
Speaker 5: the wealth, none of the great job growth, none of

819
00:42:24,960 --> 00:42:27,680
Speaker 5: the companies. But also they get to write they regulate

820
00:42:27,800 --> 00:42:30,919
Speaker 5: us and themselves, and that is essentially the unspoken deal

821
00:42:30,960 --> 00:42:32,520
Speaker 5: is that we get we get the business and the

822
00:42:32,520 --> 00:42:35,160
Speaker 5: companies and the damage and they get neither, I think,

823
00:42:35,239 --> 00:42:37,520
Speaker 5: and I do think that's fundamentally the deal that Congress

824
00:42:37,520 --> 00:42:40,800
Speaker 5: made with not regulating these companies. It has costs and benefits,

825
00:42:41,000 --> 00:42:43,440
Speaker 5: and I think they're both pretty substantial.

826
00:42:43,680 --> 00:42:47,319
Speaker 1: So let's talk about the election coverage right now. You know,

827
00:42:47,320 --> 00:42:50,080
Speaker 1: we're a country of three hundred million plus, right, three

828
00:42:50,160 --> 00:42:52,640
Speaker 1: hundred and fifty million, three hundred and thirty million. It's

829
00:42:52,680 --> 00:42:56,000
Speaker 1: a lot of people, like maybe at most twenty million

830
00:42:56,000 --> 00:42:59,840
Speaker 1: people read in newspapers, magazines, maybe a little more than

831
00:43:00,200 --> 00:43:02,960
Speaker 1: watch cable news, you know, maybe a little more than

832
00:43:02,960 --> 00:43:05,520
Speaker 1: that watch network news. So do you think we have

833
00:43:05,680 --> 00:43:07,239
Speaker 1: any influence on them anymore?

834
00:43:07,440 --> 00:43:10,080
Speaker 5: It's such a strange question this election. It's interesting because

835
00:43:10,040 --> 00:43:12,360
Speaker 5: I'm also obsessed with this and for this first episode

836
00:43:12,360 --> 00:43:14,239
Speaker 5: of Mixed Signals, I just just this morning went down

837
00:43:14,280 --> 00:43:16,279
Speaker 5: to the courthouse just a couple of blocks from here,

838
00:43:16,320 --> 00:43:18,799
Speaker 5: where there's a massive, you know, classic trial of the

839
00:43:18,840 --> 00:43:21,600
Speaker 5: century media circus with you know, dozens and dozens of

840
00:43:21,600 --> 00:43:25,000
Speaker 5: cameras and dozens of fifteen satellite trucks. And we went

841
00:43:25,040 --> 00:43:27,000
Speaker 5: and I went and found our old friend Maggie Haberman,

842
00:43:27,040 --> 00:43:30,000
Speaker 5: who's standing online, you know, at seven in the morning,

843
00:43:30,360 --> 00:43:32,840
Speaker 5: task through that like you know, is anybody learning anything

844
00:43:32,880 --> 00:43:34,479
Speaker 5: you go into this court and you cover this trial,

845
00:43:34,480 --> 00:43:37,040
Speaker 5: and then and then what right is there anything new?

846
00:43:37,360 --> 00:43:39,160
Speaker 4: Is it change? Is anybody's mind being changed? On our

847
00:43:39,200 --> 00:43:40,480
Speaker 4: own mind being changed?

848
00:43:40,760 --> 00:43:43,480
Speaker 5: Or does everybody in America basically feel one way or

849
00:43:43,520 --> 00:43:45,080
Speaker 5: the other that you get who these people are, you

850
00:43:45,080 --> 00:43:47,440
Speaker 5: get who Donald Trump is, and so you're not paying

851
00:43:47,440 --> 00:43:52,000
Speaker 5: that much attention to this incredible, historic, dramatic spectacles. Very

852
00:43:52,200 --> 00:43:54,120
Speaker 5: I agree, it's an utterly strange situation.

853
00:43:54,600 --> 00:43:57,040
Speaker 1: So how do you think these people are getting their news?

854
00:43:57,200 --> 00:44:00,520
Speaker 1: Which people it's three hundred and forty five million people

855
00:44:00,560 --> 00:44:02,880
Speaker 1: on the storytry who don't read the New York Times.

856
00:44:03,000 --> 00:44:04,520
Speaker 1: How do you think they're getting their news?

857
00:44:04,640 --> 00:44:07,359
Speaker 5: That is a harder question to answer than it has

858
00:44:07,440 --> 00:44:11,319
Speaker 5: been since the nineteenth century because they're basically from the

859
00:44:11,400 --> 00:44:14,840
Speaker 5: kind of proliferation of newspapers, penny dailies and things in

860
00:44:14,880 --> 00:44:18,399
Speaker 5: the second half of the nineteenth century through not that long ago.

861
00:44:18,640 --> 00:44:21,680
Speaker 5: The big story was consolidation, was more and more people

862
00:44:21,719 --> 00:44:24,239
Speaker 5: getting their information from fewer and fewer places. You know,

863
00:44:24,360 --> 00:44:28,600
Speaker 5: big national television networks replacing local ones, big national newspapers

864
00:44:28,719 --> 00:44:31,880
Speaker 5: displacing local ones, and then big social media companies and

865
00:44:31,920 --> 00:44:35,040
Speaker 5: big centralized platforms replacing everything. And then the story of

866
00:44:35,040 --> 00:44:37,680
Speaker 5: the last few years has been this crazy fragmentation and

867
00:44:37,719 --> 00:44:39,400
Speaker 5: people moving out to.

868
00:44:39,520 --> 00:44:40,880
Speaker 4: Smaller and smaller places.

869
00:44:40,880 --> 00:44:43,319
Speaker 5: I mean audio is actually the most interesting example of that,

870
00:44:43,400 --> 00:44:46,880
Speaker 5: where tens of thousands of people listened to an array

871
00:44:46,960 --> 00:44:49,879
Speaker 5: of shows where they're each you know, finding people who

872
00:44:49,880 --> 00:44:53,240
Speaker 5: they really find valuable and helpful, but there's no dominant player.

873
00:44:53,280 --> 00:44:55,520
Speaker 5: In fact, there was a Pew study that said they

874
00:44:55,520 --> 00:44:57,920
Speaker 5: asked people, who's your favorite podcast? Among people who have

875
00:44:57,920 --> 00:44:59,600
Speaker 5: a favorite podcast, what's your favorite podcast?

876
00:45:00,000 --> 00:45:01,560
Speaker 4: Pictively? The answer was Joe Rogan.

877
00:45:01,880 --> 00:45:03,640
Speaker 5: But the most interesting thing about that was it was

878
00:45:03,719 --> 00:45:05,799
Speaker 5: Joe Rogan at five percent. So you got a market

879
00:45:05,800 --> 00:45:08,640
Speaker 5: where the number one player is five percent and everything

880
00:45:08,640 --> 00:45:10,680
Speaker 5: else is smaller. And that's just a situation which you

881
00:45:10,760 --> 00:45:12,480
Speaker 5: get on the subway and you have no idea what

882
00:45:12,560 --> 00:45:14,400
Speaker 5: the person sitting across from you has in their ears,

883
00:45:14,560 --> 00:45:17,160
Speaker 5: whereas five years ago you get in the subway and like,

884
00:45:17,200 --> 00:45:19,640
Speaker 5: it's Facebook, right, and so they're ten years ago. And

885
00:45:19,719 --> 00:45:22,400
Speaker 5: so there has I think been this big shift that

886
00:45:22,520 --> 00:45:25,319
Speaker 5: makes the landscape way harder to understand because you just

887
00:45:25,480 --> 00:45:26,640
Speaker 5: it's it's splintered.

888
00:45:26,840 --> 00:45:29,160
Speaker 1: So how do you think people get their news. I mean,

889
00:45:29,280 --> 00:45:31,960
Speaker 1: Joe Rogan is millions of people, right, just.

890
00:45:31,960 --> 00:45:35,279
Speaker 5: So like Fox News and the big broadcast networks you know,

891
00:45:35,360 --> 00:45:38,600
Speaker 5: will reach between maybe three and ten million a night,

892
00:45:38,640 --> 00:45:42,080
Speaker 5: and that's that's something Rogan, you know, maybe a vaguely

893
00:45:42,120 --> 00:45:44,440
Speaker 5: similar number. Lots of people a bit fewer than that.

894
00:45:44,800 --> 00:45:47,520
Speaker 1: So say, Joe Rogan is twenty, Fox is four.

895
00:45:47,560 --> 00:45:50,000
Speaker 5: Right, But these listens, I'm just listens, Like what gets

896
00:45:50,080 --> 00:45:53,880
Speaker 5: counted as a view on YouTube is much much smaller.

897
00:45:53,560 --> 00:45:55,600
Speaker 1: Okay, Howard Stern is eighteen.

898
00:45:55,920 --> 00:45:58,319
Speaker 5: Those are much smaller numbers than what gets counted as

899
00:45:58,360 --> 00:46:00,840
Speaker 5: a view on television. So there's it's hard these numbers

900
00:46:00,840 --> 00:46:02,960
Speaker 5: don't really make sense to either. But nobody is anywhere

901
00:46:03,000 --> 00:46:05,799
Speaker 5: close to the scale of what CBS News had twenty

902
00:46:05,880 --> 00:46:07,880
Speaker 5: years ago. Everything is smaller.

903
00:46:07,640 --> 00:46:11,440
Speaker 1: Right, So there definitely are millions of people on TikTok.

904
00:46:11,719 --> 00:46:13,680
Speaker 5: Enormous numbers of people on TikTok, but again being kind

905
00:46:13,719 --> 00:46:16,040
Speaker 5: of micro targeted with the thing that they're looking for

906
00:46:16,160 --> 00:46:18,520
Speaker 5: and having the news reflected to them as what TikTok

907
00:46:18,600 --> 00:46:21,000
Speaker 5: is great at telling them exactly what they want.

908
00:46:21,239 --> 00:46:23,000
Speaker 1: By the Chinese government.

909
00:46:22,640 --> 00:46:24,760
Speaker 4: Yes, by our friends that by dents, yes.

910
00:46:25,320 --> 00:46:27,680
Speaker 1: Which is owned by the Chinese government.

911
00:46:27,840 --> 00:46:30,279
Speaker 5: No, mostly it is just holding a mirror up to

912
00:46:30,440 --> 00:46:33,560
Speaker 5: you and telling you exactly and giving you exactly what

913
00:46:33,600 --> 00:46:33,919
Speaker 5: you want.

914
00:46:33,960 --> 00:46:35,279
Speaker 4: That's what that machine does.

915
00:46:35,320 --> 00:46:37,600
Speaker 5: Maybe it hasn't been proven the Chinese government puts its

916
00:46:37,600 --> 00:46:40,400
Speaker 5: thumb on the scale would not particularly surprise me. But

917
00:46:40,440 --> 00:46:42,600
Speaker 5: if young people are being told that the Palestinian side

918
00:46:42,640 --> 00:46:45,040
Speaker 5: is correct, it's mostly because that's what they want to hear.

919
00:46:45,239 --> 00:46:46,919
Speaker 5: And I don't really think the idea that tip people

920
00:46:46,920 --> 00:46:49,359
Speaker 5: are being brainwashed on there. We're all kind of the

921
00:46:49,400 --> 00:46:51,160
Speaker 5: beauty of social media as we get to wash our

922
00:46:51,200 --> 00:46:51,760
Speaker 5: own brands.

923
00:46:51,920 --> 00:46:55,000
Speaker 1: I don't think anyone thinks that people are being brainwashed.

924
00:46:55,040 --> 00:46:56,880
Speaker 1: I mean, I don't know. Maybe there are people who

925
00:46:56,920 --> 00:46:58,640
Speaker 1: think that, but I mean, with all of this, it's

926
00:46:58,680 --> 00:47:02,480
Speaker 1: not just TikTok, it's also Instagram and Facebook. I mean,

927
00:47:02,800 --> 00:47:07,200
Speaker 1: there are opportunities for transparency with the algorithm that we

928
00:47:07,320 --> 00:47:08,439
Speaker 1: have not been given. Right.

929
00:47:08,719 --> 00:47:11,520
Speaker 5: No thinks have been getting less transparent for as long

930
00:47:11,520 --> 00:47:13,759
Speaker 5: as I've covered these companies. In twenty twelve, there was

931
00:47:13,760 --> 00:47:16,720
Speaker 5: a site called your open Book where you could search

932
00:47:16,760 --> 00:47:18,600
Speaker 5: Facebook and see what people were talking about on there.

933
00:47:18,640 --> 00:47:19,879
Speaker 4: And Facebook cut that.

934
00:47:19,840 --> 00:47:23,400
Speaker 5: Off from the API and progressively, all of these companies

935
00:47:23,440 --> 00:47:25,719
Speaker 5: have released less and less and less data because they

936
00:47:25,760 --> 00:47:28,520
Speaker 5: don't like the media coverage of what is actually happening

937
00:47:28,560 --> 00:47:28,960
Speaker 5: on them.

938
00:47:29,160 --> 00:47:31,799
Speaker 1: Right, we have so many people in this country, So

939
00:47:31,840 --> 00:47:34,319
Speaker 1: say there are three hundred million people who are just

940
00:47:34,560 --> 00:47:38,600
Speaker 1: not interacting with traditional news at all. I mean at

941
00:47:38,719 --> 00:47:41,960
Speaker 1: least one hundred and fifty million of those people will vote.

942
00:47:42,040 --> 00:47:44,479
Speaker 5: Yeah, but the biggest sources of sort of non big

943
00:47:44,520 --> 00:47:49,000
Speaker 5: mainstream media are the local television news, local radio. I mean,

944
00:47:49,080 --> 00:47:52,200
Speaker 5: voters are disproportionately older and being reached by these older forms.

945
00:47:52,239 --> 00:47:54,920
Speaker 5: So like, among the biggest of these small and medium

946
00:47:54,960 --> 00:47:59,520
Speaker 5: sized things are still, you know, along with deranged streams

947
00:47:59,600 --> 00:48:04,920
Speaker 5: on rumble or YouTube, are a lot of fairly normy stuff.

948
00:48:04,640 --> 00:48:07,040
Speaker 4: You know, a lot of targeting stuff.

949
00:48:06,800 --> 00:48:09,359
Speaker 5: Moms on Instagram, a lot of it's just you know,

950
00:48:09,600 --> 00:48:13,240
Speaker 5: it's splintering without I think the social media age really

951
00:48:13,280 --> 00:48:16,400
Speaker 5: elevated hyperpartisan voices, and that was what worked for the

952
00:48:16,440 --> 00:48:18,960
Speaker 5: sort of engagement metrics of Facebook. I think that's less

953
00:48:18,960 --> 00:48:21,680
Speaker 5: true enough. I think it's just a legitimate sprit splintering

954
00:48:21,719 --> 00:48:25,799
Speaker 5: that's spreading further probably further right, further left, but also

955
00:48:25,920 --> 00:48:27,759
Speaker 5: all along the middle of that spectrum in a way

956
00:48:27,760 --> 00:48:30,000
Speaker 5: that if you're writing about it, you are sort of

957
00:48:30,080 --> 00:48:32,839
Speaker 5: operating in the dark in a way that is disorienting

958
00:48:32,880 --> 00:48:33,479
Speaker 5: for me a list.

959
00:48:33,719 --> 00:48:36,600
Speaker 1: Do you feel that the traditional media though we have

960
00:48:36,920 --> 00:48:40,480
Speaker 1: become such an incredibly small piece of the pie, right

961
00:48:40,560 --> 00:48:42,600
Speaker 1: and we have such a small reach.

962
00:48:42,880 --> 00:48:45,520
Speaker 5: It's funny that we're saying we you a podcaster, me

963
00:48:45,640 --> 00:48:46,799
Speaker 5: running a new website.

964
00:48:47,160 --> 00:48:49,920
Speaker 1: Well, I am an editor of Fanny Fair. I'm a

965
00:48:50,000 --> 00:48:52,719
Speaker 1: true official correspondent at a Condie Nast magazine.

966
00:48:52,960 --> 00:48:54,160
Speaker 4: You got to foot in both worlds.

967
00:48:54,320 --> 00:48:59,120
Speaker 1: My podcast is from a podcast company called iHeartRadio, So

968
00:48:59,280 --> 00:49:01,880
Speaker 1: I mean, I don't think that's true. Is not that?

969
00:49:02,360 --> 00:49:04,520
Speaker 1: And you worked at the New York Times for many

970
00:49:04,600 --> 00:49:08,719
Speaker 1: years and have a media startup called Temophore. So if

971
00:49:08,760 --> 00:49:11,280
Speaker 1: we're not the mainstream media, I don't know who is, man.

972
00:49:11,440 --> 00:49:12,480
Speaker 4: I think it's the New York Times.

973
00:49:12,600 --> 00:49:14,239
Speaker 5: Molick, No, But I'm just saying I think there's a

974
00:49:14,320 --> 00:49:16,120
Speaker 5: there's sort of a wide spectrum and a lot of

975
00:49:16,160 --> 00:49:19,040
Speaker 5: and even the question of what is mainstream is is

976
00:49:19,120 --> 00:49:21,600
Speaker 5: kind of disputed. Like you know, the like iHeart is

977
00:49:21,640 --> 00:49:23,839
Speaker 5: a great example, Like that's a company that does a

978
00:49:23,880 --> 00:49:26,920
Speaker 5: bunch of great podcasts that built around the House Stuff

979
00:49:26,960 --> 00:49:29,680
Speaker 5: Works podcast. That's sort of what built that company at first,

980
00:49:29,719 --> 00:49:31,439
Speaker 5: and where the sort of people running it come from,

981
00:49:31,520 --> 00:49:33,360
Speaker 5: which I think I don't really think of a mainstream

982
00:49:33,440 --> 00:49:35,880
Speaker 5: or political right. That's just sort of a great popular

983
00:49:35,920 --> 00:49:38,680
Speaker 5: show that I hope is running promos for this podcast

984
00:49:38,760 --> 00:49:41,319
Speaker 5: on its big network, and I think the lines have

985
00:49:41,400 --> 00:49:45,040
Speaker 5: really blurred, actually quite a bit between what's what's considered

986
00:49:45,120 --> 00:49:47,520
Speaker 5: traditional media and what's new media, making all of this

987
00:49:47,520 --> 00:49:48,200
Speaker 5: more confusing.

988
00:49:48,400 --> 00:49:50,480
Speaker 4: Thank you, Ben Smith, Yeah, thank you.

989
00:49:53,080 --> 00:49:59,360
Speaker 1: No moment Jesse Cannon by John Fast the reaction to

990
00:49:59,360 --> 00:50:03,319
Speaker 1: the trumpert you predicted it hot. I mean, you don't

991
00:50:03,360 --> 00:50:06,680
Speaker 1: have to be a psychic to know exactly how this

992
00:50:06,760 --> 00:50:10,479
Speaker 1: is going to go. Donald Trump will degrade the rule

993
00:50:10,520 --> 00:50:14,960
Speaker 1: of law, rail against the judiciary, say the judge was corrupt,

994
00:50:15,560 --> 00:50:18,279
Speaker 1: and in fact he did all of the things we

995
00:50:18,440 --> 00:50:23,640
Speaker 1: thought he would because Donald Trump has about four plays

996
00:50:23,680 --> 00:50:26,600
Speaker 1: and he always does pretty much the same thing, no

997
00:50:26,680 --> 00:50:31,719
Speaker 1: matter what the situation is. And that, my friends, is

998
00:50:31,760 --> 00:50:35,759
Speaker 1: our moment of Fuckeray. That's it for this episode of

999
00:50:35,800 --> 00:50:39,600
Speaker 1: Fast Politics. Tune in every Monday, Wednesday and Friday to

1000
00:50:39,640 --> 00:50:42,520
Speaker 1: hear the best minds in politics makes sense of all

1001
00:50:42,640 --> 00:50:46,120
Speaker 1: this chaos. If you enjoyed what you've heard, please send

1002
00:50:46,120 --> 00:50:48,840
Speaker 1: it to a friend and keep the conversation going. And

1003
00:50:48,920 --> 00:50:50,400
Speaker 1: again thanks for listening.