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Speaker 1: Hi, Brian, Hi Katie. Guess what, it's our last podcast

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Speaker 1: of the year, and I'm kind of sad about that, Brian,

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Speaker 1: I know, I know. But we're recording this a little

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Speaker 1: bit ahead of the new year because you're going on

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Speaker 1: a special trip. Actually, yes, I'm going to see my

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Speaker 1: daughter who's studying abroad, So I'm going to Barry's studying Jackie. Jackie.

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Speaker 1: She is in Paris, so I'm heading there actually momentarily.

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Speaker 1: But I just want to say to you, Brian, I've

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Speaker 1: really really enjoyed doing our podcast this year. Thank you

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Speaker 1: so much for being a part of this and for

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Speaker 1: sharing your wit and most importantly your wisdom about politics.

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Speaker 1: Oh my god, well, thank you. Wait what just happened?

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Speaker 1: I was just weirdly sincere for a moment. Thank you

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Speaker 1: for allowing me to to blah brush aside greatness every

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Speaker 1: other week for about an hour or so. Thank you

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Speaker 1: and away. Well, of course, Donald Trump was the big story.

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Speaker 1: We spent a lot of time talking about Donald Trump

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Speaker 1: this year, so we wanted to spend the last podcast

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Speaker 1: of the year talking about something new and different, right, Brian, Yeah,

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Speaker 1: what is that, Katie? Donald Trump? And we're going to

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Speaker 1: do that with a journalist who really held his feet

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Speaker 1: to the fire during the campaign season. David Farrenhald is

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Speaker 1: a reporter with the Washington Post. Many people are saying

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Speaker 1: he might actually want a pulic surprise. I don't want

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Speaker 1: to jinxis. But remember that Access Hollywood tape, Remember all

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Speaker 1: the reporting on the Trump Foundation. Yes, I'm not mentioning

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Speaker 1: you know the word because I don't. I don't know.

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Speaker 1: I don't like that word, Brian, the p word that

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Speaker 1: Donald Trump used. The Yeah, I tried to avoid that.

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Speaker 1: Let's just say, yeah, he likes to grab him by

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Speaker 1: the kiddies. How about that? That sounds gross too, But anyway,

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Speaker 1: David Barrel broke these stories and reported fearlessly throughout the

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Speaker 1: course of the campaign. But David became kind of Woodward

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Speaker 1: and Bernstein all rolled into one at a time when

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Speaker 1: a lot of people were just commenting on the poles

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Speaker 1: and the horse race. He was really focused on trying

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Speaker 1: to figure out who Donald Trump is, as revealed by

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Speaker 1: his charitable contributions, his past comments, and really broke news

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Speaker 1: about stuff that matter. That's right. He wasn't talking about

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Speaker 1: the comment of the day, the news of the day.

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Speaker 1: He actually did some serious digging and digging using a

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Speaker 1: cool mix of twenty one century social media technology and

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Speaker 1: little fashion gum shoe reporting and yellow legal pad reporting.

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Speaker 1: David Farenthal from The Washington Post, thank you for joining

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Speaker 1: our podcast. We're very excited to talk to you. We're

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Speaker 1: going to be completely geeking out today. David. It's going

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Speaker 1: to be all Trump, all the time. I'm glad to

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Speaker 1: be here. First of all, I want to say congratulations

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Speaker 1: on your extraordinary reporting this campaign season. I know you've

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Speaker 1: been heralded for all your scoops, for many of your

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Speaker 1: pieces that really got to the heart of issues in

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Speaker 1: a in a era when people were covering really the

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Speaker 1: superficialities of the horse race. So congratulations. There there Pulitzer

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Speaker 1: rumors flying around. David. Um, you must be feeling pretty

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Speaker 1: good about yourself right now. You know, I was looking

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Speaker 1: at your bio by the way, and it occurred to

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Speaker 1: me it took you about sixteen years to become an

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Speaker 1: overnight sensation, So I think that's probably good to remember. Exactly. Yes,

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Speaker 1: this time last year was writing about Jim Gilmore. So

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Speaker 1: it's really been a pretty fast rise. Well, how are

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Speaker 1: you feeling? Well? Good? I mean, the job that I

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Speaker 1: thought was going to sort of be over on November eight,

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Speaker 1: now continues on in a much more comprehensive way. So

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Speaker 1: I'm trying to kind of get figure out what my

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Speaker 1: niche is gonna be covering Trump the president, So it'll

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Speaker 1: be some Trump charity stuff as well, but it's obviously

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Speaker 1: got to expand because it's you know, He's there's a

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Speaker 1: lot more to say, you know, before we talk about

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Speaker 1: moving forward, because that's a big area I think we

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Speaker 1: want to broach. How would you characterize covering this campaign?

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Speaker 1: What was sort of your your true north as you

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Speaker 1: set out to cover the Trump candidacy. Well, I felt

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Speaker 1: like the way to cover Trump is not to write

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Speaker 1: about his words, or not about his words only not

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Speaker 1: about what he says, but about what he does. Uh.

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Speaker 1: And the Trump is a unique candidate. And then he

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Speaker 1: spends all his time sort of throwing out these things

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Speaker 1: that I don't know if they're on, if it's purposeful

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Speaker 1: or not, but they serve as a good distraction. Every

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Speaker 1: day there's some new thing to chase, and the campaign,

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Speaker 1: it was sort of a new outrage every day. He

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Speaker 1: said and said things that would have killed off anybody

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Speaker 1: else's campaign. But the way he did it. He would

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Speaker 1: say them, and the next day, when we were sort

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Speaker 1: of getting spun up to cover the first outrageus thing,

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Speaker 1: he'd do another outrageous thing. We have just jumped onto that.

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Speaker 1: So I wanted to cover his actions, the results of

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Speaker 1: his actions, and in particular in this case, this really

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Speaker 1: sort of important moral dimension. This is a guy bragged

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Speaker 1: his whole life about how much he gave the charity,

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Speaker 1: how generous he was. Let's go and see if it

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Speaker 1: did he really keep those promises, and how much sort

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Speaker 1: of moral compunction did he feel to help. He really

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Speaker 1: put sort of a bull's eye on Trump's carrot. I mean,

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Speaker 1: I think in a way that any great journalists would

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Speaker 1: can use just reveal or review what you discovered in

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Speaker 1: the course of your reporting. The main takeaway from me

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Speaker 1: was that Trump understood that because he was rich, people

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Speaker 1: expected him to be generous. That that's what rich people

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Speaker 1: do have always been. That's a sort of part of

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Speaker 1: the lifestyle of a rich person is giving to charity,

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Speaker 1: and he wanted to have that appearance as much as

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Speaker 1: he could. He wanted to have the the sort of

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Speaker 1: the facade that he was a very generous person, but

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Speaker 1: he did as much as he could to avoid actually

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Speaker 1: doing the charitable work that that that illusion or that

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Speaker 1: facade required. So often he would use other people's money

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Speaker 1: as his own charities. He's find a way to convert

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Speaker 1: other people's money into what seemed like his own charitable gifts.

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Speaker 1: He would often promise gifts that he never gave. Even

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Speaker 1: his own charity, that Donald J. Trump Foundation, he would

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Speaker 1: use that in ways that basically where he was the

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Speaker 1: biggest beneficiary, it looked like he was helping charity, but

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Speaker 1: actually often he was helping himself. So that's that's what

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Speaker 1: I learned. He knew that people expected him to be generous,

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Speaker 1: and he need to meet those expectations on the surface,

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Speaker 1: but didn't. I often want to comply with the sort

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Speaker 1: of true moral dimension of charity, which is giving actually

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Speaker 1: something of yourself to help others. So what actually spurred

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Speaker 1: you to start looking into Trump's philanthropy or lack thereof,

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Speaker 1: and and how hard was it to track down the

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Speaker 1: contributions he did or didn't make. Well, it started actually

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Speaker 1: kind of by Shant, So I spent all covering basically

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Speaker 1: loser candidates George Pataki, Jim Gilmour, Rick Santorum, Rick Perry,

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Speaker 1: they were all gone by the time we got to

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Speaker 1: the Iowa caucuses where they were, they were all clearly

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Speaker 1: hitted out the door. So I had nothing to do

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Speaker 1: on Iowa caucus Day. And the report the editors said, Okay,

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Speaker 1: go to Iowa, follow Trump around. We're gonna see this possibility,

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Speaker 1: which didn't actually come to pass. This three time married guy,

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Speaker 1: sort of play famous playboy is gonna win, we thought,

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Speaker 1: the famously conservative and religious state of Iowa. So go

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Speaker 1: see what he does on caucus day. So I went

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Speaker 1: to this rally Trump did on caucus in Waterloo, Iowa.

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Speaker 1: And during the rally, he got up. It was up

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Speaker 1: on stage, and he stopped the rally and he said, okay,

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Speaker 1: now come to the stage. Uh, this local veterans group,

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Speaker 1: the this Waterloo Veterans Aid group. And in the middle

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Speaker 1: of the rally, he gives them a giant check like

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Speaker 1: one that they give to people who win win golf

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Speaker 1: Tournament's a really big oversized check that says Donald J.

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Speaker 1: Trump Foundation on the top, make America great Again on

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Speaker 1: the bottom for a hundred thousand dollars. It turned out

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Speaker 1: this was money that he had raised a few days ago.

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Speaker 1: Do you remember he skipped a Republican debate because he

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Speaker 1: was having a fight with Fox News and he had

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Speaker 1: this big televised fundraiser race. He raised six million dollars

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Speaker 1: for veterans, which included a million bucks out of his

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Speaker 1: own pocket. So this is he's stopping his political rally

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Speaker 1: to give away a check from his charity. Um, And

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Speaker 1: I thought, Okay, I know you can't do that. I

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Speaker 1: don't know much about charity law, but I know that's

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Speaker 1: against the law. You can't use you the money and

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Speaker 1: your charity to boost a political campaign, even if it's

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Speaker 1: your own. Uh. So I came back from having covered

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Speaker 1: Iowa and New Hampshire and I wanted to look into

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Speaker 1: that to see where they'd broken the law. And there

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Speaker 1: was another thing. He had said he'd raised six million dollars,

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Speaker 1: but he only actually given out about a million dollars

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Speaker 1: of these oversized checks and then he stopped. So I

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Speaker 1: wanted to know, Okay, where's the rest of the money.

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Speaker 1: And I thought at the time that question where's the

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Speaker 1: rest of the money would take a couple of days

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Speaker 1: to answer, because what political campaign would basically stiff veterans

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Speaker 1: in the you know, in the middle of a Republican primary.

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Speaker 1: Who would ever do that? The veterans contributions he made

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Speaker 1: to catch people up were as part of an event

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Speaker 1: to get out of a presidential debate that he didn't

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Speaker 1: want to be part of. He said, you know what,

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Speaker 1: instead of debating these you know losers, I'm gonna throw

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Speaker 1: a fundraiser for veterans to which I'm going to contribute

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Speaker 1: a million bucks of my own money. Basically, you're right.

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Speaker 1: So he got a huge political goodwill out of that

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Speaker 1: televised fundraiser and out of the idea that, look, isn't

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Speaker 1: he amazing? He can raise six million bucks for veterans

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Speaker 1: at the drop of a hat. He's so rich he

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Speaker 1: can give a million bucks of his own at the

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Speaker 1: drop of the hat. It's important to test and see

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Speaker 1: whether he really followed through in those promises, because he

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Speaker 1: got a lot of good will out of making so funny.

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Speaker 1: This seems like such a no you know, a no brainer,

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Speaker 1: doesn't it, David? I mean, you did something that most

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Speaker 1: journalists should be doing. You've actually followed up right. You

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Speaker 1: continued to cover the story and connect the dots. But

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Speaker 1: the dots were harder to connect than I thought. Because

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Speaker 1: I got back and said, okay, Trump, you know Trump campaign,

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Speaker 1: Where did the rest of the money go? And I

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Speaker 1: looked and looked at called the veterans organizations he named

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Speaker 1: his beneficiaries. I talked to them. After a month, I

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Speaker 1: could find about three million worth of the six million.

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Speaker 1: I didn't know where the other three million had gone.

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Speaker 1: And I kept looking, kept looking. This was kind of

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Speaker 1: in the background while I was writing some other stories.

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Speaker 1: Finally we get to May, so it's not been almost

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Speaker 1: four months since the original fundraiser. Something really odd happened.

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Speaker 1: Coral Lewandowski, who was Trump's campaign manager at the time,

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Speaker 1: called me and he said, Okay, I can tell you

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Speaker 1: what happened to that one million dollars the Trump said

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Speaker 1: he'd get give out of his own pocket to veterans groups.

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Speaker 1: He gave it away. But I can't tell you who

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Speaker 1: got it, or in what amounts, or when or anything.

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Speaker 1: It's a secret. But just know that million dollars has

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Speaker 1: been given. At that point where you at that point

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Speaker 1: where you also looking into Trump's other charitable giving. Had

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Speaker 1: you broadened out yet or were you just focused on

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Speaker 1: this one event. I was mostly focused. We've done some

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Speaker 1: other stuff, but I was mostly focused on the veterans thing.

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Speaker 1: Was there was such a concrete promise he had made

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Speaker 1: in the in the context of the presidential campaign on TV.

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Speaker 1: So I really wanted to focus on that um. And

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Speaker 1: so after Lendowski says that to me, I thought, well, okay,

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Speaker 1: I want to I want to see if he's right,

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Speaker 1: you know, if you will tell me any details. I

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Speaker 1: want to try to find some of this money on

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Speaker 1: my own. So I used Twitter, knowing that Trump was

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Speaker 1: on it, knowing that a lot of media were on it.

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Speaker 1: I started sending out queries on Twitter to different veterans. Well,

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Speaker 1: I thought, if in the old days before Twitter, you

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Speaker 1: would just have to send out, you know, call or

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Speaker 1: email a million you know, different veterans groups, and there's

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Speaker 1: thousands of them, and you never would be able to

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Speaker 1: reach them all. So, you know, the idea you could

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Speaker 1: prove a negative. It's no way you could prove the

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Speaker 1: negative dot Trump gave no money. But I thought, maybe

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Speaker 1: I'll prove the opposite. I'll find a little bit of

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Speaker 1: the money and we'll say, okay, well I found the

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Speaker 1: tip of the iceberg. This must have been a real thing. Uh.

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Speaker 1: And I sent us spend a day tweeting and nothing,

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Speaker 1: no response from anybody. I couldn't find any of the money,

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Speaker 1: and I thought at the end, yeah, I thought, okay,

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Speaker 1: I've just wasted a day. I felt like the oldest

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Speaker 1: man in the world. I was like, right, the Twitter

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Speaker 1: didn't work. Uh, what a waste of time? Uh So

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Speaker 1: what howen was Trump? Uh? He saw he was paying attention.

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Speaker 1: And that night, the night that I had been making

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Speaker 1: this search and not finding any of his million dollars,

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Speaker 1: that's when he gave the million dollars away. So when

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Speaker 1: Lewandowski told me he'd given it away, that was totally false.

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Speaker 1: It was still in Trump's pocket. So how did you

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Speaker 1: do the crowdsourcing? So you tried to Twitter and that

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Speaker 1: didn't work so much, although it actually did because Donald

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Speaker 1: Trump saw it. But tell us about crowdsourcing the report? Well,

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Speaker 1: that was my first experience with it, and I was

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Speaker 1: surprised at how well it worked. And then a bunch

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Speaker 1: of media, a bunch of you know, campaign reporters, other

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Speaker 1: reporters picked it up and started tweeting about it, spreading

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Speaker 1: the reach and Veterans Group started started tweeting about it,

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Speaker 1: and the idea had been okay, maybe I don't. I

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Speaker 1: won't be able to query every veterans group in the world.

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Speaker 1: But you know, somebody else watching this will say, well, hey,

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Speaker 1: you didn't ask me directly, but you know he did

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Speaker 1: give us this money, so and I could find it

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Speaker 1: kind of by word of mouth that way. That was

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Speaker 1: the idea, and it turned out and I thought, okay,

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Speaker 1: my backup plan is maybe Trump will just tell us

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Speaker 1: what he did. And it turned out to be that's

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Speaker 1: what happened. Because he hadn't given it. There was nothing

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Speaker 1: else to find. There was nothing to find at that point.

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Speaker 1: It was all in Trump's pocket. So you kind of

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Speaker 1: shamed you kind of shamed him into it in a way. Yeah,

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Speaker 1: I guess. So I asked him that night. He so

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Speaker 1: Trump called me. This is the last time he and

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Speaker 1: I spoke. He called me in late May to say, okay, yes,

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Speaker 1: I just gave the million dollars out. He gave it

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Speaker 1: all in one fell swoop to this particular charity run

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Speaker 1: by a friend of his, and I said, well, you know,

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Speaker 1: could you would you have given this money out if

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Speaker 1: I hadn't been asking about it? And he said, oh,

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Speaker 1: you're really a nasty guy. You're a very nasty guy.

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Speaker 1: That was his answer. Did he say sad with an

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Speaker 1: exclamation mark? I don't. I don't find that he says

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Speaker 1: that in conversation, but he does say nasty a lot.

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Speaker 1: It was a weird interview because he would say, you're

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Speaker 1: so nasty guy, and Hillary Clinton was a nasty woman. Yeah,

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Speaker 1: I'm not definitely not the only nasty person. We should

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Speaker 1: que Janet Jackson right now for some reason. But anyway,

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Speaker 1: and then you started looking more broadly at what the

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Speaker 1: Donald J. Trump Foundation has been doing or not doing,

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Speaker 1: and in a nutshell, the answer would be, well, the

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Speaker 1: Trump Foundation, he was giving money out of it, not

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Speaker 1: in very large amounts, to charities, often charities that did

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Speaker 1: business with him. You have to remember a lot of

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Speaker 1: Trump's business at marl Lago and Trump Tower is running

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Speaker 1: out ballrooms to charities who can pay like two it

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Speaker 1: and seventy five thousand dollars a night to rent out

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Speaker 1: Mara Lago. So they're his clients. So he was giving

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Speaker 1: donations to these clients. But the money we figured out

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Speaker 1: was not his, the money the Trump Foundation. He hadn't

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Speaker 1: given the Trump Foundation a dollar since two thousand and eight,

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Speaker 1: and had all been other people's money that he took

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Speaker 1: in and then gave out. I mean, the one extreme

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Speaker 1: example we found just amazed me. So okay. So there's

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Speaker 1: this charity in in Florida, the Palm Beach Police Foundation,

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Speaker 1: which is uh, they ran out Mara Lago for seventy

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Speaker 1: five thousand dollars a night. Trump wants to give them

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Speaker 1: a donation, but he doesn't use his money. He doesn't

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Speaker 1: even use the money. And the Trump Foundation. He calls

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Speaker 1: the foundation of a friend of his who has died

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Speaker 1: and asked the friends surviving relatives, hey, listen, I'm raising

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Speaker 1: money for the Palm Beach Police Foundation. Can you kick

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Speaker 1: in someone? I say sure, but he says, don't give

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Speaker 1: it straight to the Palmic Police Foundation. Give it to

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Speaker 1: me and I'll pass it on to them. So they

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Speaker 1: give him two hundred thousand dollars the Donald J. Trump Foundation.

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Speaker 1: He just takes their money and gives it on, adding

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Speaker 1: nothing of his own, gives it to the Palm Beach

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Speaker 1: Police Foundation, and then Trump is recognized as a philanthropist.

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Speaker 1: He gets a giant crystal palm tree as a reward

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Speaker 1: for his grate philanthropy. When was all somebody else's money

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Speaker 1: that's the sort of most director like Florence Johnson gave

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Speaker 1: the money, but he gets the credit credit. Yeah. One

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Speaker 1: of the strangest stories was the story of his portrait

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Speaker 1: four ft tall or six foot tall portrait that was

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Speaker 1: paid for by the foundation. Can you tell us the

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Speaker 1: story of that portrait? Sure? So I've been calling all

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Speaker 1: these charities the Trump Foundation had given money to just

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Speaker 1: to find out a had Trump given them any money

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Speaker 1: extra out of his own pocket? And also to find out, Okay,

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Speaker 1: what was the money when the Trump Foundation gave this donation.

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Speaker 1: Was it an exchange for anything or was it just

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Speaker 1: a straight up gift? Uh? And so this particular group,

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Speaker 1: this was I'd called three and five charities is choarity

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Speaker 1: number three five? And they said, yes, that the Trump

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Speaker 1: Foundation gave us twenty thou dollars. But it wasn't just

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Speaker 1: a straight gift. It was a purchase. A Trump had

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Speaker 1: gone to this event at Mara Lago, where the entertainment

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Speaker 1: was a guy who was a speed painter. He paints

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Speaker 1: portraits in like five minutes and then then auctions them off.

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Speaker 1: So he painted a picture of Trump he auctioned off.

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Speaker 1: Trump's wife Melania, was the only person willing to bid

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Speaker 1: very much for it. She won it for twenty dollars,

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Speaker 1: and then he paid with the Trump Foundation's money, and

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Speaker 1: that is really puts him on thin ice with the

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Speaker 1: I R S because that is not kosher. Well, basically,

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Speaker 1: the I R S says that if you're a charity

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Speaker 1: and you buy something, that has to be something that

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Speaker 1: you're using for a charitable purpose. Right, Trump can't use

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Speaker 1: his charity to buy something for himself or his business.

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Speaker 1: So the question what the heck charitable purpose would there

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Speaker 1: be in his six ft tall portrait of Donald Trump?

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Speaker 1: And we never got the answer. Wh wouldn't she just

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Speaker 1: pay for it from from their private We never got

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Speaker 1: the answer. I thought in a number of cases that

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Speaker 1: Trump's understanding of his charity was whenever he needed to

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Speaker 1: give money to another charity, he would use the Trump Foundation,

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Speaker 1: basically not understand ending that that's not how it works.

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Speaker 1: That even though in this case the money went to

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Speaker 1: a charity auction, and then we said we then we

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Speaker 1: found this later another portrait, a ten dollar portrait also

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Speaker 1: of Trump himself, that was also bought pay using charity money,

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Speaker 1: and that we actually were able to trace to his

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Speaker 1: golf resort outside Miami where it's hanging on the wall

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Speaker 1: of the sports bar. So that's clearly a legal so wait, wait,

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Speaker 1: so what so so there were two portraits, there were

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Speaker 1: two portraits one and so whatever happened to the twenty

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Speaker 1: dollar one? Is that in Trump Tower. All we know

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Speaker 1: is that afterward the artist at Milania Trump's direction, boxed

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Speaker 1: it up and shipped it to Trump's golf course in

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Speaker 1: Westchester County, New York, with the understanding they would hang

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Speaker 1: in the employee boardroom. I haven't been in the employee

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Speaker 1: boardroom there. They won't let us in, they won't talk

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Speaker 1: to us about it. So maybe it's there, and maybe

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Speaker 1: it's somewhere else, but it was clearly Children's hospital. But

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Speaker 1: the other one you tracked down though, and that was

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Speaker 1: we're again. Trump has a golf resort called Durrell outside Miami,

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Speaker 1: and so thanks to my Twitter followers, we found it

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Speaker 1: hanging on the wall of the sports bar there. So

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Speaker 1: that's you know, the charity paid for it, and it

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Speaker 1: was used to decorate the wall of Trump's for profit business,

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Speaker 1: which is like textbook self dealing text books. And you

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Speaker 1: became kind of famous over the course of this reporting

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Speaker 1: of sort of mixing Twitter with this old fashioned yellow

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Speaker 1: legal pad that you used to keep track of how

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Speaker 1: much money Trump was giving or not giving to charity. Um,

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Speaker 1: how did the yellow legal pad become such a thing?

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Speaker 1: You know, we had all these promises Trump had made

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Speaker 1: to give money to charity out of his own pocket

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Speaker 1: over the years, and I was trying to figure out

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Speaker 1: if he'd done any of that, And so the Trump

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Speaker 1: people wouldn't help me, and I try to think, Okay,

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Speaker 1: well I'll try to again, trying to prove, not prove

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Speaker 1: a negative, but proved Trump. Right, let me try. Let

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Speaker 1: me go calling the charities that I think seemed closest

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Speaker 1: to Trump, the ones most likely to have gotten his money.

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Speaker 1: I'll call him and see whether they ever gotten a

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Speaker 1: donation from him. And I knew it was going to

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Speaker 1: be futile a lot of the time, that I was

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Speaker 1: going to be calling and getting nothing, no answer. So

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Speaker 1: I needed a way to make futility look interesting. And

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Speaker 1: that's when I came up with that legal pad. Right,

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Speaker 1: could take a picture and at one glance sort of

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Speaker 1: show you how hard I was trying to find this

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Speaker 1: money and how I was failing. We're gonna move on

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Speaker 1: in a moment. But before we do, I have to

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Speaker 1: ask you why you're reporting, as excellent as it was,

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Speaker 1: didn't seem to resonate with with a many voters on

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Speaker 1: election day when it came to Donald Trump and his

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Speaker 1: use of charity dollars. I think people did care. I

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Speaker 1: have sort of a couple of theories about this. One

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Speaker 1: is that I don't think it was used very effectively

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Speaker 1: by the Democrats. I think you're you're sort of dependent

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Speaker 1: on you know, in some cases, you know, I can

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Speaker 1: only bring these things to light, somebody else has to

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Speaker 1: use them for political purposes and for them to quote

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Speaker 1: unquote matter and the election. So they used it a

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Speaker 1: couple of times, but not very much. That maybe one reason.

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Speaker 1: The other one is that there was just a lot

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Speaker 1: else going on in this election, and there are so

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Speaker 1: many other factors, you know, an FBI director chastising the

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Speaker 1: Democratic candidate writing a letter a week before the election,

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Speaker 1: you know, just to say that Trump one. I don't

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Speaker 1: think that means that it didn't matter. It just means

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Speaker 1: that a lot of other things matter too. Well. When

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Speaker 1: we come back, we're going to talk about your other

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Speaker 1: big scoop of this campaign cycle and has to do

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Speaker 1: with Kitty. We'll be back with David farenthought right after this.

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Speaker 1: During our last episode, we asked you what is the

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Speaker 1: best thing that happened to you in two thousand and

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Speaker 1: sixteen other than our podcast, of course, And here's what

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Speaker 1: you had to say. Hi, this is Lisa Craig from Cleveland, Ohio,

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Speaker 1: and the best thing about was the Cleveland Cavaliers winning

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Speaker 1: the World Championship and breaking our ruts here in Cleveland.

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Speaker 1: And we're also really proud of how we hosted the

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Speaker 1: r NC this year. Oh and the Indians made it

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Speaker 1: to the World Series. So I think Cleveland was the

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Speaker 1: best thing for me about So you asked for the

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Speaker 1: best thing that happened into thousands of sixteen. For me,

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Speaker 1: the best thing that happened was coming to this beautiful

429
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431
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Speaker 1: after being twenty five years in the dictator Ship, which

432
00:20:09,680 --> 00:20:13,919
Speaker 1: is a minial east In country. Thanks Katie, Bye. So, Brian,

433
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Speaker 1: you love to learn, right, or at least I like

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Speaker 1: Joel Sartor, who's a long time expert National Geographic Photographer.

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Speaker 1: And this has been very useful for me because with

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Speaker 1: lot of photos. As you can imagine, you should be

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Speaker 1: sending more of those photos to the team here at

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Speaker 1: the podcast. You sound like my mother. But anyway, The

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Speaker 1: special offer Gianna for Katie kurrg listeners. Right, we do.

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Speaker 1: Plus dot com slash Katie. That's the Great Courses Plus

455
00:21:29,240 --> 00:21:34,160
Speaker 1: dot com slash Katie. On our next episode, we will

456
00:21:34,200 --> 00:21:37,120
Speaker 1: be talking with Donald Trump. Well, actually we'll be talking

457
00:21:37,160 --> 00:21:40,440
Speaker 1: with Alec Baldwin, who plays Donald Trump. Does a fantastic

458
00:21:40,520 --> 00:21:42,440
Speaker 1: Tony Bennett. By the way, he does a lot of

459
00:21:42,480 --> 00:21:44,800
Speaker 1: fantastic impressions. Maybe he'll do something for us. He is

460
00:21:45,119 --> 00:21:48,280
Speaker 1: he is on Fuego, Yes, he is, as Donald Trump

461
00:21:48,359 --> 00:21:52,000
Speaker 1: would say, Uh, what questions do you have for Alec Baldwin?

462
00:21:52,080 --> 00:21:56,520
Speaker 1: Please leave us a message At nine to nine to four, four, six,

463
00:21:56,880 --> 00:22:05,720
Speaker 1: three seven, and we're back with David Farenthal from the

464
00:22:05,800 --> 00:22:09,080
Speaker 1: Washington Post. Let's talk about your other big scoop and

465
00:22:09,320 --> 00:22:14,160
Speaker 1: this was the notorious Access Hollywood Billy Bush tape. Um.

466
00:22:14,720 --> 00:22:16,240
Speaker 1: I'm not sure how much you're going to be able

467
00:22:16,280 --> 00:22:18,240
Speaker 1: to talk to me about this or how much you're

468
00:22:18,240 --> 00:22:21,480
Speaker 1: going to be able to reveal your sources, But come on, David,

469
00:22:21,800 --> 00:22:26,560
Speaker 1: give it up. How did you get that tape? I

470
00:22:26,680 --> 00:22:28,600
Speaker 1: can always say we had a source. It was not

471
00:22:28,640 --> 00:22:30,560
Speaker 1: something we were looking for. We had you know that

472
00:22:30,640 --> 00:22:33,639
Speaker 1: we knew existed beforehand, but you know, on our laps,

473
00:22:34,320 --> 00:22:36,280
Speaker 1: And maybe it was because I'm better known than I

474
00:22:36,440 --> 00:22:38,560
Speaker 1: was before because of the Trump Foundation stories. You know,

475
00:22:38,680 --> 00:22:40,000
Speaker 1: not look at a gift horse in the mouth and

476
00:22:40,080 --> 00:22:42,840
Speaker 1: we had Can you just describe sort of how it

477
00:22:43,000 --> 00:22:44,520
Speaker 1: came to you? In terms of did you get a

478
00:22:44,560 --> 00:22:47,399
Speaker 1: phone call? Uh, when you listen to it, I mean,

479
00:22:47,520 --> 00:22:51,800
Speaker 1: just give me some color commentary here, please, Well, uh,

480
00:22:52,680 --> 00:22:54,359
Speaker 1: we got it. I can't say much more about how

481
00:22:54,400 --> 00:22:56,440
Speaker 1: it came to us, but when we got it. You know,

482
00:22:56,600 --> 00:22:58,800
Speaker 1: I'm watching this videotape. In the beginning, it's just a

483
00:22:58,960 --> 00:23:01,360
Speaker 1: it's a it's a video of a bus, right, It's

484
00:23:01,359 --> 00:23:04,280
Speaker 1: a bus driving around this extremely boring back lot. At

485
00:23:04,320 --> 00:23:07,159
Speaker 1: the beginning, like this is so lame. Yeah, And you

486
00:23:07,200 --> 00:23:08,760
Speaker 1: can hear some mumbling, but you can't really hear what

487
00:23:08,800 --> 00:23:11,120
Speaker 1: the mumbling is about in the beginning. But then maybe

488
00:23:11,200 --> 00:23:13,879
Speaker 1: fifteen or sixteen seconds in, you hear Trump's voice and

489
00:23:13,920 --> 00:23:16,560
Speaker 1: you hear him telling this story about Billy Bush, about

490
00:23:16,560 --> 00:23:18,840
Speaker 1: trying to seduce this woman in Palm Beach, And you know,

491
00:23:19,320 --> 00:23:21,560
Speaker 1: immediately you knew the language. You know, you could tell

492
00:23:21,680 --> 00:23:23,919
Speaker 1: was Trump's voice, obviously, but you knew the language was different.

493
00:23:24,000 --> 00:23:25,879
Speaker 1: This was him talking about himself in a way that

494
00:23:26,200 --> 00:23:28,399
Speaker 1: even Donald Trump had not talked about himself in public.

495
00:23:28,880 --> 00:23:30,960
Speaker 1: It packs a lot in the two minutes, uh, And

496
00:23:31,320 --> 00:23:33,680
Speaker 1: I mean to the point that you know he's there's

497
00:23:33,720 --> 00:23:35,240
Speaker 1: that line in there that I always stuck with you

498
00:23:35,400 --> 00:23:37,239
Speaker 1: with me where he talks about groping women, and then

499
00:23:37,240 --> 00:23:39,159
Speaker 1: he says, when you're a star, they let you do it.

500
00:23:39,320 --> 00:23:42,040
Speaker 1: And then and there's like a wonderment in that in

501
00:23:42,160 --> 00:23:44,920
Speaker 1: his voice, there like a genuine wonder where he's like wow,

502
00:23:45,280 --> 00:23:47,600
Speaker 1: like I'm not I'm not bussing here, like I'm just

503
00:23:47,680 --> 00:23:49,600
Speaker 1: telling you, this is amazing that this happens to me.

504
00:23:49,840 --> 00:23:51,760
Speaker 1: I can't believe the world works this way. So we

505
00:23:51,840 --> 00:23:53,399
Speaker 1: knew it was a big deal. And we knew it

506
00:23:53,440 --> 00:23:55,200
Speaker 1: was you know, this was not him talking about women,

507
00:23:55,320 --> 00:23:57,280
Speaker 1: rating women's bodies or whatever. This was him saying like,

508
00:23:57,440 --> 00:23:59,600
Speaker 1: here's how I behave, Here's what I do to women.

509
00:23:59,600 --> 00:24:00,840
Speaker 1: Here's what I will do. Here's something I might do

510
00:24:00,920 --> 00:24:02,399
Speaker 1: to this woman we're about to meet right now, this

511
00:24:02,760 --> 00:24:05,840
Speaker 1: soap opera actress. And so the the sort of guy

512
00:24:05,920 --> 00:24:07,960
Speaker 1: got a lot of gears started in motion. Here, the

513
00:24:08,080 --> 00:24:11,040
Speaker 1: video team, the lawyers, you know, trying to figure out

514
00:24:11,160 --> 00:24:13,200
Speaker 1: getting everybody ready, like okay, listen, something really big is

515
00:24:13,240 --> 00:24:14,520
Speaker 1: going to happen, and we have don't have that much

516
00:24:14,560 --> 00:24:17,240
Speaker 1: time to do it. In did NBC sit on that tape?

517
00:24:17,680 --> 00:24:20,040
Speaker 1: I read later that they did that they had got

518
00:24:20,119 --> 00:24:22,880
Speaker 1: they had found it. Uh, the Monday of that week,

519
00:24:22,960 --> 00:24:25,040
Speaker 1: I guess in response to an AP story that was

520
00:24:25,080 --> 00:24:27,879
Speaker 1: about The Apprentice. Uh, I don't know. That was my

521
00:24:28,080 --> 00:24:29,840
Speaker 1: main worry all day long was that we were gonna

522
00:24:29,880 --> 00:24:32,680
Speaker 1: get beat by by NBC. UM. So we had a

523
00:24:32,760 --> 00:24:36,040
Speaker 1: call NBC obviously to say, hey, you know, you know,

524
00:24:36,400 --> 00:24:38,119
Speaker 1: are you gonna sue us? Because this is you think

525
00:24:38,160 --> 00:24:39,840
Speaker 1: this is your property? And we we've you know, we've

526
00:24:39,880 --> 00:24:42,560
Speaker 1: gotten it somehow. And b do you think it's a hoax?

527
00:24:42,640 --> 00:24:45,000
Speaker 1: Do you think this is? Um? You know that this

528
00:24:45,080 --> 00:24:47,520
Speaker 1: was because when all the offending words are said, you

529
00:24:47,560 --> 00:24:49,440
Speaker 1: can see the bus, but you can't see Trump or Bush,

530
00:24:49,480 --> 00:24:51,760
Speaker 1: can't see their mouth moving. So are you gonna NBC

531
00:24:52,080 --> 00:24:53,439
Speaker 1: gonna say it's a hoax? And we had a called

532
00:24:53,480 --> 00:24:55,080
Speaker 1: Trump for the same reason to say, to see if

533
00:24:55,080 --> 00:24:56,800
Speaker 1: he would say it was a hoax. So that was

534
00:24:57,080 --> 00:24:59,359
Speaker 1: so wait, yeah, yeah, So what did NBC say? What

535
00:24:59,440 --> 00:25:01,840
Speaker 1: did Trump's and miss basically did not get back to

536
00:25:01,920 --> 00:25:03,879
Speaker 1: us on the record. They didn't send the signal that hey,

537
00:25:03,880 --> 00:25:07,040
Speaker 1: We're gonna sue you, but they didn't comment either. Trump's

538
00:25:07,080 --> 00:25:09,200
Speaker 1: people At first we sent them a transcript of the

539
00:25:09,320 --> 00:25:11,600
Speaker 1: video of you know the parts that are the first

540
00:25:11,640 --> 00:25:13,840
Speaker 1: two minutes and they said, well, it doesn't sound like

541
00:25:13,920 --> 00:25:17,440
Speaker 1: Mr Trump. Can we see the whole video? And uh,

542
00:25:17,840 --> 00:25:20,000
Speaker 1: we were like, you know, there was some arguing back

543
00:25:20,000 --> 00:25:21,679
Speaker 1: and forth among the editors, and they decided yes, they

544
00:25:21,680 --> 00:25:23,000
Speaker 1: can see the video. So we sent them the full

545
00:25:23,080 --> 00:25:26,920
Speaker 1: video and it was, you know, probably it was about

546
00:25:26,960 --> 00:25:29,480
Speaker 1: three thirty. We told him, look, we're gonna publish it four. Uh,

547
00:25:29,640 --> 00:25:31,520
Speaker 1: and you haven't till four o'clock to comment or not.

548
00:25:31,560 --> 00:25:33,679
Speaker 1: We're gonna publish with you know, either way, and they

549
00:25:33,720 --> 00:25:35,960
Speaker 1: got back to us at four. It was almost like

550
00:25:36,040 --> 00:25:37,960
Speaker 1: a sort of digital stop the press this moment. I

551
00:25:38,000 --> 00:25:39,520
Speaker 1: had to yell at somebody to get her to stop,

552
00:25:39,800 --> 00:25:41,320
Speaker 1: you know, before she get the button to publish it.

553
00:25:41,600 --> 00:25:43,879
Speaker 1: And their statement that came in then was yes, Trump

554
00:25:44,000 --> 00:25:46,240
Speaker 1: did it. You know, this is him. He apologizes, locker

555
00:25:46,320 --> 00:25:48,560
Speaker 1: room talk that sort of thing. Wow. Wait, I just

556
00:25:48,640 --> 00:25:50,680
Speaker 1: have a question because I'm this is so fascinating to me.

557
00:25:50,800 --> 00:25:53,520
Speaker 1: Do you think NBC so they didn't get back to

558
00:25:53,600 --> 00:25:56,040
Speaker 1: you because you know, they took a lot of criticism

559
00:25:56,160 --> 00:25:59,240
Speaker 1: for sitting on the tape, holding onto it, maybe with

560
00:25:59,359 --> 00:26:01,840
Speaker 1: the suggestion and that they were protecting Billy Bush. You know,

561
00:26:02,000 --> 00:26:05,400
Speaker 1: this was their show Access. Hollywood is an NBC show,

562
00:26:05,720 --> 00:26:08,399
Speaker 1: so just like The Apprentice was an NBC show. But

563
00:26:08,520 --> 00:26:10,359
Speaker 1: he didn't say that they didn't get back to him.

564
00:26:10,400 --> 00:26:12,280
Speaker 1: He said that they didn't respond on the record. I know.

565
00:26:12,359 --> 00:26:15,119
Speaker 1: That's why I'm following up, baby, go ahead, go ahead,

566
00:26:15,160 --> 00:26:19,520
Speaker 1: and let the master work go ahead, David So Uh,

567
00:26:20,080 --> 00:26:22,160
Speaker 1: I don't know, really, I did not get that much

568
00:26:22,200 --> 00:26:24,280
Speaker 1: out of them that day. I should note that our

569
00:26:24,280 --> 00:26:26,920
Speaker 1: story published it like four oh two that afternoon, and

570
00:26:27,080 --> 00:26:29,280
Speaker 1: MSNBC and Katie Tour had a story about this up

571
00:26:29,320 --> 00:26:31,399
Speaker 1: at like four oh six, So we beat them, but

572
00:26:31,440 --> 00:26:33,240
Speaker 1: not by very much at all. I wonder, but I wonder.

573
00:26:33,440 --> 00:26:36,440
Speaker 1: I mean, it's very interesting whether I guess the the

574
00:26:36,760 --> 00:26:38,880
Speaker 1: official story is they were kind of running it through

575
00:26:38,920 --> 00:26:41,720
Speaker 1: their lawyers too, But it seems like they had a

576
00:26:41,800 --> 00:26:44,080
Speaker 1: lot of time and those were you know, a number

577
00:26:44,119 --> 00:26:46,640
Speaker 1: of days spent running it by the lawyers. It didn't

578
00:26:46,680 --> 00:26:48,440
Speaker 1: take you that long to do that, did it. I

579
00:26:48,520 --> 00:26:51,000
Speaker 1: wonder if they wanted the cover of not being the

580
00:26:51,080 --> 00:26:53,159
Speaker 1: ones to break the story and to let you all

581
00:26:53,240 --> 00:26:56,080
Speaker 1: have that honor. Who knows if if they did, you know,

582
00:26:56,200 --> 00:26:58,320
Speaker 1: and that's the origin of all this. I don't know,

583
00:26:58,440 --> 00:27:00,560
Speaker 1: but I'd be grateful for it, you know, however it happened,

584
00:27:01,440 --> 00:27:05,040
Speaker 1: I'm grateful for the h that it was me. Uh, no,

585
00:27:05,160 --> 00:27:08,280
Speaker 1: matter what, this changed the tenor of the campaign dramatically

586
00:27:08,480 --> 00:27:12,439
Speaker 1: for I would say maybe two weeks. Yeah, two weeks,

587
00:27:13,080 --> 00:27:16,639
Speaker 1: and uh, you know, Billie Bush lost his job at

588
00:27:16,760 --> 00:27:20,320
Speaker 1: NBC as a result of it. A number of senior Republicans,

589
00:27:20,400 --> 00:27:23,680
Speaker 1: including members the United States Senate, abandoned Trump. This is

590
00:27:23,760 --> 00:27:29,040
Speaker 1: when McCain finally abandoned Trump, and we now know that

591
00:27:29,240 --> 00:27:34,120
Speaker 1: behind the scenes, a lot of top Republicans were panicked,

592
00:27:34,240 --> 00:27:36,200
Speaker 1: convinced they were going to lose in the landslide. Some

593
00:27:36,440 --> 00:27:39,000
Speaker 1: even said that Trump he should drop out at this moment.

594
00:27:39,760 --> 00:27:43,119
Speaker 1: Do you know much from your reporting about what was

595
00:27:43,200 --> 00:27:47,639
Speaker 1: going on in Trump Tower as this was unfolding, Uh no,

596
00:27:48,000 --> 00:27:50,760
Speaker 1: not really. I mean I've read, you know, our version

597
00:27:50,840 --> 00:27:53,520
Speaker 1: of it, and it seemed like I read this interesting.

598
00:27:53,520 --> 00:27:56,320
Speaker 1: Glenn Thrush from Politico wrote something a few weeks ago

599
00:27:56,359 --> 00:27:59,040
Speaker 1: about sort of how Trump responded to it, and they

600
00:27:59,119 --> 00:28:01,119
Speaker 1: called that sort of one of the most consequential decisions

601
00:28:01,119 --> 00:28:04,000
Speaker 1: of the election. That Trump basically was unrepentant. You know,

602
00:28:04,040 --> 00:28:06,440
Speaker 1: he said, I'm sorry, but then you know, quick move

603
00:28:06,560 --> 00:28:08,440
Speaker 1: quickly to say, you know, this is the liberal media

604
00:28:08,680 --> 00:28:10,600
Speaker 1: to get me. This is a you know, conspiracy to

605
00:28:10,680 --> 00:28:14,000
Speaker 1: stop you the people from getting what you want, apparently

606
00:28:14,040 --> 00:28:15,880
Speaker 1: borrowing a playbook from a lot of things that Bill

607
00:28:15,920 --> 00:28:18,600
Speaker 1: clin did when he was under investigation for his affair.

608
00:28:19,960 --> 00:28:23,280
Speaker 1: So you know, Trump responded aggressively. I thought that the

609
00:28:23,800 --> 00:28:25,680
Speaker 1: that the true consequence of it for those couple of

610
00:28:25,720 --> 00:28:27,520
Speaker 1: weeks was going to be that it had set Trump

611
00:28:27,600 --> 00:28:30,200
Speaker 1: against the Republican establishment in a very open way. Remember,

612
00:28:30,200 --> 00:28:32,639
Speaker 1: he was attacking Paul Ryan and encouraging his people to

613
00:28:32,720 --> 00:28:34,760
Speaker 1: vote against Paul Ryan. It was this kind of like

614
00:28:34,800 --> 00:28:38,040
Speaker 1: Republican circular firing squad that had set off that lasted

615
00:28:38,080 --> 00:28:40,280
Speaker 1: a long time, lasted basically till the COMI letter. Yeah.

616
00:28:40,400 --> 00:28:43,560
Speaker 1: And and it was interesting in in those days following it,

617
00:28:43,920 --> 00:28:47,000
Speaker 1: Kelly and Conway wouldn't really come out to defend him

618
00:28:47,080 --> 00:28:49,480
Speaker 1: on this topic. The only person who really did was

619
00:28:49,800 --> 00:28:54,120
Speaker 1: Rudy Giuliani, right right, b Rudy Giuliani, who has been

620
00:28:54,160 --> 00:28:56,600
Speaker 1: rewarded for that so far, rewarded for that loyalty with

621
00:28:56,680 --> 00:29:01,200
Speaker 1: precisely nothing. Glenn brush Piece you referred to in Politico.

622
00:29:01,560 --> 00:29:04,360
Speaker 1: I highly recommend people who are political junkies to read it.

623
00:29:04,440 --> 00:29:08,080
Speaker 1: It goes into some depth on this topic, and and

624
00:29:08,160 --> 00:29:11,640
Speaker 1: it reports that Trump was actually pissed at Giuliani for

625
00:29:11,800 --> 00:29:14,400
Speaker 1: going out and defending it, for saying that he made

626
00:29:14,440 --> 00:29:17,000
Speaker 1: a mistake and he's apologized because they just wanted to

627
00:29:17,040 --> 00:29:19,720
Speaker 1: go on the attack. They wanted to throw out the

628
00:29:19,800 --> 00:29:23,560
Speaker 1: normal playbook and just divert the topic to the liberal

629
00:29:23,640 --> 00:29:26,400
Speaker 1: media trying to screw Donald Trump. What impact do you

630
00:29:26,480 --> 00:29:29,680
Speaker 1: think that huge, huge story had on the electorate when

631
00:29:29,720 --> 00:29:32,040
Speaker 1: all was said and done. Well, I think it did

632
00:29:32,160 --> 00:29:34,800
Speaker 1: have an impact on the electorate when it happened. I

633
00:29:34,880 --> 00:29:37,240
Speaker 1: think people it did changed people's minds about Trump. You

634
00:29:37,280 --> 00:29:39,920
Speaker 1: saw that in the polling averages. Uh. And I think

635
00:29:40,000 --> 00:29:42,600
Speaker 1: also it caused you know, there are a lot of

636
00:29:42,800 --> 00:29:45,120
Speaker 1: other Republicans who I think, we're willing to support Donald

637
00:29:45,160 --> 00:29:47,640
Speaker 1: Trump when they thought he could win, but you know,

638
00:29:47,760 --> 00:29:49,680
Speaker 1: disagree with him in a variety of things, and at

639
00:29:49,720 --> 00:29:51,320
Speaker 1: that point when they thought he was going to lose,

640
00:29:51,760 --> 00:29:54,080
Speaker 1: we're willing to back off. And think of Jason Chaffits,

641
00:29:54,160 --> 00:29:57,560
Speaker 1: the powerful House Oversight Committee chairman, saying I'm out. You know,

642
00:29:57,640 --> 00:29:59,400
Speaker 1: I can't look at my teenage daughter in the eye

643
00:29:59,480 --> 00:30:01,400
Speaker 1: when I hear this sort of stuff, you know, and

644
00:30:01,480 --> 00:30:04,920
Speaker 1: it's it's made people, I think, even more cynical about politicians,

645
00:30:05,040 --> 00:30:08,240
Speaker 1: if that's even possible, because there were so many people

646
00:30:08,320 --> 00:30:10,640
Speaker 1: at that moment who said, on principle, absolutely no, they

647
00:30:10,680 --> 00:30:13,000
Speaker 1: couldn't support Trump, they couldn't vote for him, who then

648
00:30:13,080 --> 00:30:16,360
Speaker 1: reversed themselves again before election day and said they were

649
00:30:16,400 --> 00:30:20,040
Speaker 1: for Trump, including chaff its Um And and now we're

650
00:30:20,080 --> 00:30:23,640
Speaker 1: having to back him because he's president elect. So what

651
00:30:23,800 --> 00:30:25,840
Speaker 1: have we learned from this? I mean, do you think

652
00:30:26,160 --> 00:30:29,480
Speaker 1: politicians are going to behave differently in response to scandals

653
00:30:29,520 --> 00:30:32,240
Speaker 1: as a result of this? I mean, I think that

654
00:30:32,560 --> 00:30:34,920
Speaker 1: this sort of picked up a lesson that George W.

655
00:30:35,160 --> 00:30:37,600
Speaker 1: Bush and before him, Bill Clinton had employed, which is

656
00:30:37,680 --> 00:30:40,480
Speaker 1: that if you don't seem ashamed by whatever you did,

657
00:30:40,560 --> 00:30:42,760
Speaker 1: people will start to think that it wasn't shameful after

658
00:30:42,840 --> 00:30:44,600
Speaker 1: all that. I think a lot of people look to

659
00:30:44,720 --> 00:30:48,880
Speaker 1: politicians to communicate the depth of their own mistakes. And

660
00:30:49,000 --> 00:30:50,880
Speaker 1: if you were able to convince people that you don't

661
00:30:50,880 --> 00:30:52,600
Speaker 1: think it's a big deal that you're moving on or

662
00:30:52,600 --> 00:30:55,160
Speaker 1: it's really somebody else's fault, you can move past it.

663
00:30:55,240 --> 00:30:57,400
Speaker 1: Not everybody can do that. I was going to say, though,

664
00:30:57,440 --> 00:30:59,480
Speaker 1: I'm going to have to take issue with that, David,

665
00:30:59,480 --> 00:31:03,320
Speaker 1: because I think this was a singularly Trumpian response, and

666
00:31:03,440 --> 00:31:07,080
Speaker 1: I'm just not sure anybody would have the bluster and

667
00:31:07,240 --> 00:31:10,720
Speaker 1: the hubris to kind of deflect a story like this

668
00:31:11,000 --> 00:31:13,320
Speaker 1: the way he did, don't. I mean, can you see

669
00:31:13,400 --> 00:31:15,840
Speaker 1: that as well? I think you're right. I think he

670
00:31:16,000 --> 00:31:18,320
Speaker 1: was an extreme case. I mean, he already at by

671
00:31:18,360 --> 00:31:19,600
Speaker 1: that point, had been the guy who said I could

672
00:31:19,600 --> 00:31:21,600
Speaker 1: shoot someone in Fifth Avenue and people wouldn't care. You know.

673
00:31:21,800 --> 00:31:24,400
Speaker 1: My colleague get Yahoo mapped By often said during the

674
00:31:24,480 --> 00:31:28,920
Speaker 1: course of this campaign, Trump cannot be shamed. He does.

675
00:31:29,120 --> 00:31:33,959
Speaker 1: He's genetically he's genetically incapable of feeling ashamed about anything.

676
00:31:34,520 --> 00:31:36,480
Speaker 1: But I think that's right. I think that's what we learned.

677
00:31:36,520 --> 00:31:39,280
Speaker 1: But it's interesting thing to me you mentioned other politicians.

678
00:31:39,320 --> 00:31:42,880
Speaker 1: I was really struck by those people. Sort of they

679
00:31:42,920 --> 00:31:45,400
Speaker 1: were reacting to what they thought voters would think, to

680
00:31:45,520 --> 00:31:47,160
Speaker 1: what they thought voters would care about. And I think

681
00:31:47,200 --> 00:31:49,640
Speaker 1: when voters, you know, I think after a while, when

682
00:31:49,640 --> 00:31:51,520
Speaker 1: they realized that voters were not turning on Trump in

683
00:31:51,600 --> 00:31:53,200
Speaker 1: the way they thought, they sort of slowly came back

684
00:31:53,200 --> 00:31:55,200
Speaker 1: because they wanted to be on the winning side. So, David,

685
00:31:55,200 --> 00:31:57,600
Speaker 1: I met you a few weeks ago at this conference

686
00:31:57,920 --> 00:32:01,120
Speaker 1: at Harvard that brought together all the time campaign operatives

687
00:32:01,160 --> 00:32:05,240
Speaker 1: on both sides, and Katie's to Harvard people, Harvard, Harvard, Harvard. Yeah,

688
00:32:05,280 --> 00:32:07,800
Speaker 1: we're gonna do the secret handshake after this, but you

689
00:32:07,920 --> 00:32:10,720
Speaker 1: can't watch it my safety school. By the way, David,

690
00:32:11,240 --> 00:32:13,920
Speaker 1: she says, not at all defensively. I was really struck

691
00:32:15,280 --> 00:32:18,720
Speaker 1: by saying. Robbie Mook, Hillary Clinton's campaign manager, said to me,

692
00:32:18,840 --> 00:32:22,800
Speaker 1: which is the negative frame on Trump was always take

693
00:32:22,880 --> 00:32:25,560
Speaker 1: something he said and kind of make fun of it,

694
00:32:25,680 --> 00:32:28,360
Speaker 1: attack it. But the media always showed the thing that

695
00:32:28,440 --> 00:32:32,120
Speaker 1: Trump said, and the negative frame on Hillary was something

696
00:32:32,240 --> 00:32:36,360
Speaker 1: totally outside of her campaign messaging the Clinton Foundation or

697
00:32:36,800 --> 00:32:40,560
Speaker 1: the emails or ben Ghazi, and so she didn't get

698
00:32:40,680 --> 00:32:43,080
Speaker 1: the opportunity to like say what she wanted to say.

699
00:32:43,520 --> 00:32:45,240
Speaker 1: And so for a lot of voters it kind of

700
00:32:45,320 --> 00:32:49,440
Speaker 1: boiled down to Trump the badass versus Hillary the crook.

701
00:32:49,840 --> 00:32:53,080
Speaker 1: And given that choice, particularly in the wake of the

702
00:32:53,600 --> 00:32:58,080
Speaker 1: FBI director's letter, Uh, they went with the badass um

703
00:32:58,520 --> 00:33:01,720
Speaker 1: and thinking at least they get some cha Angeine Washington, Well,

704
00:33:01,800 --> 00:33:04,240
Speaker 1: we talked about the you know, the shame thing. And

705
00:33:04,280 --> 00:33:06,840
Speaker 1: I think that's a really interesting They were two very

706
00:33:06,920 --> 00:33:09,160
Speaker 1: opposite cases of of how do you deal with a

707
00:33:09,240 --> 00:33:11,680
Speaker 1: shameful act? Right think of way Clinton reacted to the

708
00:33:11,800 --> 00:33:14,400
Speaker 1: email thing, which was for a long time giving shifting

709
00:33:14,440 --> 00:33:16,680
Speaker 1: explanations about how much she was allowed to do it

710
00:33:16,800 --> 00:33:19,320
Speaker 1: was totally fine, or maybe it was mostly fine, then

711
00:33:19,640 --> 00:33:23,200
Speaker 1: apologizing for it than apologizing more for it and dragging

712
00:33:23,240 --> 00:33:25,040
Speaker 1: it out and letting it be the main thing that

713
00:33:25,120 --> 00:33:26,920
Speaker 1: she talked about because she couldn't find the right thing

714
00:33:26,960 --> 00:33:29,280
Speaker 1: to say about it. Uh. And I think that's sort

715
00:33:29,320 --> 00:33:31,080
Speaker 1: of that she was an extreme case in the other way,

716
00:33:31,120 --> 00:33:33,440
Speaker 1: and that she showed so much sort of shame and

717
00:33:33,840 --> 00:33:36,600
Speaker 1: unhappiness about that that it allowed it to dominate the campaign.

718
00:33:36,720 --> 00:33:38,960
Speaker 1: So I think I think Robbie Mook was right. I

719
00:33:39,000 --> 00:33:41,000
Speaker 1: think that's I think he's right about uh, sort of

720
00:33:41,080 --> 00:33:43,400
Speaker 1: the two different frames. But Clinton someone did some of

721
00:33:43,440 --> 00:33:45,560
Speaker 1: that to herself, and I should say that she didn't

722
00:33:45,600 --> 00:33:49,240
Speaker 1: have some sort of other positive message about herself or

723
00:33:49,320 --> 00:33:51,840
Speaker 1: not anything that broke through. So she was relying on

724
00:33:52,040 --> 00:33:53,960
Speaker 1: Trump to sort of make her case for her to

725
00:33:54,040 --> 00:33:56,200
Speaker 1: be a sort of campaign against him rather than four

726
00:33:56,400 --> 00:33:59,840
Speaker 1: Can you imagine Hillary Clinton responding the email scandal by saying, hey,

727
00:34:00,160 --> 00:34:02,479
Speaker 1: I did it, deal with it. Well, it's interesting. One

728
00:34:02,520 --> 00:34:05,800
Speaker 1: of her biggest supporters made exactly that point that she

729
00:34:05,800 --> 00:34:08,239
Speaker 1: shouldn't have apologized, that she should have said, you know

730
00:34:08,320 --> 00:34:11,480
Speaker 1: what Cohen Palell suggested it. It was allowed under the rules.

731
00:34:11,640 --> 00:34:14,960
Speaker 1: I did it, and you know, there was nothing wrong

732
00:34:15,000 --> 00:34:16,640
Speaker 1: with it, and there was nothing you know, and it

733
00:34:16,719 --> 00:34:22,040
Speaker 1: didn't it didn't jeopardize national that that email was ever hacked, right.

734
00:34:22,560 --> 00:34:25,160
Speaker 1: I do think that. I mean, I'm playing armchair armchair

735
00:34:25,200 --> 00:34:27,279
Speaker 1: politician here, but I feel like if whatever she was

736
00:34:27,320 --> 00:34:29,480
Speaker 1: going to do, she should have started doing it in

737
00:34:29,800 --> 00:34:32,440
Speaker 1: May of two fifteen and stuck with it. And the

738
00:34:32,520 --> 00:34:35,280
Speaker 1: same with the Golden Sack speeches that you know released

739
00:34:35,320 --> 00:34:37,800
Speaker 1: that or don't. But you know, at this at the

740
00:34:37,880 --> 00:34:41,600
Speaker 1: Harvard thing, uh they have somebody asked one of Clinton's aids,

741
00:34:41,600 --> 00:34:43,160
Speaker 1: why didn't you just put out the transcripts of the

742
00:34:43,200 --> 00:34:46,560
Speaker 1: Goldmen Sack speeches before? And her response was, well, what

743
00:34:46,719 --> 00:34:48,520
Speaker 1: people would have picked through it. They would have asked

744
00:34:48,520 --> 00:34:50,120
Speaker 1: all these questions. It would have been a lot of hassle.

745
00:34:50,360 --> 00:34:52,720
Speaker 1: It was not a political calculation. It was them bearing

746
00:34:52,800 --> 00:34:55,640
Speaker 1: the weight of these years of Clinton scandals and not

747
00:34:55,760 --> 00:34:57,560
Speaker 1: wanting to get into it, and they created this something

748
00:34:57,600 --> 00:35:00,480
Speaker 1: that lasted, this mystery that lasted the It was just

749
00:35:00,760 --> 00:35:03,320
Speaker 1: drip drip, drip drip. And because she never gave a

750
00:35:03,560 --> 00:35:06,879
Speaker 1: satisfactory answer, every journalist had to ask her every time

751
00:35:06,960 --> 00:35:09,400
Speaker 1: she did an interview. And she could have said, hey,

752
00:35:09,520 --> 00:35:12,760
Speaker 1: refer to my comments from May. I've answered that question.

753
00:35:12,920 --> 00:35:15,240
Speaker 1: Let's talk about things in American people really care about.

754
00:35:15,440 --> 00:35:16,960
Speaker 1: You know, I think I'm going to run for office.

755
00:35:17,320 --> 00:35:21,799
Speaker 1: I'm kidding. Anyways, were talking about fake news significant ye

756
00:35:22,920 --> 00:35:26,279
Speaker 1: to to final sort of categories that I want to

757
00:35:26,360 --> 00:35:28,720
Speaker 1: talk to you about. And I know Brian's super interested

758
00:35:28,719 --> 00:35:31,400
Speaker 1: in this as well. You know, why wasn't fake news

759
00:35:31,960 --> 00:35:35,879
Speaker 1: discovered before election day? I mean, we saw all these

760
00:35:36,120 --> 00:35:39,600
Speaker 1: bogus stories circulating around Facebook. I had friends send me

761
00:35:39,680 --> 00:35:42,239
Speaker 1: things and say did you see this? And I'd be like,

762
00:35:42,480 --> 00:35:46,200
Speaker 1: it's insane. You know, Whoma and Hillary Clinton are lesbians,

763
00:35:46,320 --> 00:35:49,480
Speaker 1: or Whoma's associated with the Muslim Brotherhood or I mean,

764
00:35:49,520 --> 00:35:54,080
Speaker 1: there's so many crazy, crazy things out there. What happened?

765
00:35:54,200 --> 00:35:57,920
Speaker 1: What I mean? I think journalism I include myself in

766
00:35:58,000 --> 00:36:01,479
Speaker 1: this category. I think journalists really let the American people

767
00:36:01,520 --> 00:36:03,400
Speaker 1: down by not pointing this out earlier, and we know

768
00:36:03,520 --> 00:36:05,560
Speaker 1: the impact it had because there was one study that

769
00:36:05,640 --> 00:36:08,480
Speaker 1: showed that in the three months before the election, these

770
00:36:08,560 --> 00:36:11,680
Speaker 1: fake news stories were actually more widely shared than real

771
00:36:11,760 --> 00:36:15,520
Speaker 1: news stories. So what happened? Well, I do think that it's,

772
00:36:16,120 --> 00:36:18,359
Speaker 1: you know, part of our bubble is that we live

773
00:36:18,400 --> 00:36:21,080
Speaker 1: in a world where we read other legitimate news outlets,

774
00:36:21,200 --> 00:36:23,399
Speaker 1: you know, that's who we follow on Twitter's, we follow

775
00:36:23,440 --> 00:36:25,400
Speaker 1: on Facebook as journalists, and so I think in some

776
00:36:25,520 --> 00:36:27,960
Speaker 1: ways it was hard to see how much you know,

777
00:36:28,160 --> 00:36:31,759
Speaker 1: other people had self selected their their media feeds to

778
00:36:31,880 --> 00:36:33,719
Speaker 1: just tell them all fake news. I think we didn't

779
00:36:33,760 --> 00:36:36,319
Speaker 1: get it because we didn't see it. That's one thing. Uh.

780
00:36:37,200 --> 00:36:38,960
Speaker 1: Another thing is I think that it's you know, for

781
00:36:39,160 --> 00:36:41,600
Speaker 1: us to agree that we knew about it. You could

782
00:36:41,680 --> 00:36:43,520
Speaker 1: write about it, and there was some writing about it,

783
00:36:43,600 --> 00:36:46,399
Speaker 1: but you know, we are struggling to figure out, Okay, well,

784
00:36:46,480 --> 00:36:49,279
Speaker 1: how do we show people our credibility, you know, how

785
00:36:49,320 --> 00:36:50,959
Speaker 1: do we stand out how do we beat those people

786
00:36:50,960 --> 00:36:53,040
Speaker 1: in the marketplace if Facebook or Twitter is going to

787
00:36:53,080 --> 00:36:55,439
Speaker 1: treat them equally to us. And I think we're still

788
00:36:55,480 --> 00:36:57,000
Speaker 1: sort of trying to figure that out. And for me,

789
00:36:57,520 --> 00:36:59,279
Speaker 1: my own little piece of it adant, trying to be

790
00:36:59,440 --> 00:37:01,239
Speaker 1: very transp Aaron about what I was doing. You know,

791
00:37:01,320 --> 00:37:04,080
Speaker 1: here's how I'm gathering this information, Here's who I've asked,

792
00:37:04,120 --> 00:37:06,560
Speaker 1: Here's what I've asked, you know, and be open to

793
00:37:06,600 --> 00:37:09,040
Speaker 1: people's suggestions. So to degree that people saw me doing

794
00:37:09,120 --> 00:37:10,600
Speaker 1: that on Twitter, I was trying to show them, Look,

795
00:37:10,640 --> 00:37:12,880
Speaker 1: this is how you report a real news story. Uh,

796
00:37:13,040 --> 00:37:14,680
Speaker 1: you know, so you can have more trust than what

797
00:37:14,760 --> 00:37:16,320
Speaker 1: I'm produced. So what's going to be What can be

798
00:37:16,440 --> 00:37:19,520
Speaker 1: done about fake news without sort of censoring you know,

799
00:37:20,160 --> 00:37:23,880
Speaker 1: news that may not be desirable or whatever. I mean,

800
00:37:24,000 --> 00:37:25,360
Speaker 1: how do you how do you get your how do

801
00:37:25,400 --> 00:37:27,880
Speaker 1: you wrap your arms around this? Well, part of it

802
00:37:28,000 --> 00:37:30,200
Speaker 1: is a question for people like Twitter and Facebook. You know,

803
00:37:30,280 --> 00:37:31,960
Speaker 1: they have to decide do they want to you know,

804
00:37:32,080 --> 00:37:35,120
Speaker 1: is there a market reason forget about censorship or you know,

805
00:37:35,200 --> 00:37:37,640
Speaker 1: public good reason. Is there a market reason why they

806
00:37:38,000 --> 00:37:40,200
Speaker 1: might want to be able to give their customers a

807
00:37:40,280 --> 00:37:43,000
Speaker 1: way to distinguish between you real and fake things? You know,

808
00:37:43,040 --> 00:37:45,200
Speaker 1: what do they think there's a there's a you know,

809
00:37:45,280 --> 00:37:47,520
Speaker 1: a problem with their business model. You know, half of

810
00:37:47,520 --> 00:37:49,839
Speaker 1: the news they're showing is fake. Uh. The other thing

811
00:37:49,920 --> 00:37:52,279
Speaker 1: for us, because how do we think about ways for

812
00:37:52,400 --> 00:37:55,200
Speaker 1: the you know, the mainstream media, the true media. How

813
00:37:55,239 --> 00:37:58,680
Speaker 1: do we show people how hard we worked to produce

814
00:37:58,719 --> 00:38:00,760
Speaker 1: what we do. How do we show people the standards

815
00:38:00,840 --> 00:38:02,560
Speaker 1: we adhere to. I think we need to be a

816
00:38:02,600 --> 00:38:04,680
Speaker 1: lot more explicit about this. We think we live in

817
00:38:04,719 --> 00:38:07,080
Speaker 1: our world and think that, oh, everybody knows, you know,

818
00:38:07,360 --> 00:38:09,719
Speaker 1: you have to have two sources everything. Everybody knows things

819
00:38:09,760 --> 00:38:12,000
Speaker 1: are better on the record. Everybody knows the rules that

820
00:38:12,080 --> 00:38:15,520
Speaker 1: we follow, but not everybody. You're saying that journalists have

821
00:38:15,640 --> 00:38:18,440
Speaker 1: to have to sell the public on what good journalism

822
00:38:18,680 --> 00:38:20,840
Speaker 1: is and explain it to the public. I guess maybe

823
00:38:21,160 --> 00:38:23,080
Speaker 1: maybe so, huh yeah. I mean, if we're gonna grow

824
00:38:23,120 --> 00:38:24,680
Speaker 1: our audience. There's a lot of people who read us now,

825
00:38:24,719 --> 00:38:26,120
Speaker 1: but if we're going to reach those people who are

826
00:38:26,160 --> 00:38:27,920
Speaker 1: out there passing around fake news, like we need to

827
00:38:27,960 --> 00:38:29,880
Speaker 1: show them Okay, this is this is how we do it.

828
00:38:30,040 --> 00:38:32,439
Speaker 1: You know, we need to be sure we're not wrong.

829
00:38:32,440 --> 00:38:34,200
Speaker 1: Bec we can lose a lot of what having sort

830
00:38:34,200 --> 00:38:37,080
Speaker 1: of like a good housekeeping seal of approval for legitimate

831
00:38:37,160 --> 00:38:41,640
Speaker 1: news organizations. Craig Newmark, from the who started Craigslist, said,

832
00:38:41,920 --> 00:38:44,200
Speaker 1: is giving a million dollars to the pointer Institute, and

833
00:38:44,239 --> 00:38:46,880
Speaker 1: one of the things they're talking about is having some

834
00:38:47,120 --> 00:38:51,160
Speaker 1: kind of indication to viewers or readers that this is

835
00:38:51,200 --> 00:38:54,879
Speaker 1: from a legitimate news, particularly on social media. Yeah, yeah, yeah,

836
00:38:55,000 --> 00:38:57,520
Speaker 1: I mean I support that. I think there's you know,

837
00:38:57,960 --> 00:39:00,480
Speaker 1: there's a lot of difficulties inherent in that, and you know,

838
00:39:00,560 --> 00:39:03,200
Speaker 1: you worry about seeding the power to somebody to distinguish

839
00:39:03,360 --> 00:39:06,080
Speaker 1: my real or faith. Uh, you know, more want to

840
00:39:06,120 --> 00:39:07,719
Speaker 1: do that on your own with the way you you know,

841
00:39:07,800 --> 00:39:10,080
Speaker 1: with your own work. But I think it's important people

842
00:39:10,080 --> 00:39:12,600
Speaker 1: are paying attention to that. I mean, I was just

843
00:39:12,719 --> 00:39:15,120
Speaker 1: thinking about ways to make our you know, our stories

844
00:39:15,200 --> 00:39:18,080
Speaker 1: more trustworthy or more you know, more clear how we

845
00:39:18,200 --> 00:39:20,560
Speaker 1: know things. There's ways you could do stuff online to

846
00:39:20,640 --> 00:39:22,320
Speaker 1: show people, make it easier for people to see the

847
00:39:22,360 --> 00:39:25,320
Speaker 1: primary sources you relied on, to show people how you

848
00:39:25,480 --> 00:39:28,200
Speaker 1: know the things you know when when that's possible, I

849
00:39:28,200 --> 00:39:29,879
Speaker 1: think it would be really educational for a lot of people.

850
00:39:29,920 --> 00:39:32,160
Speaker 1: And covering President Donald Trump is going to be an

851
00:39:32,239 --> 00:39:36,000
Speaker 1: enormous challenge already. His team is floating stuff like eliminating

852
00:39:36,040 --> 00:39:38,200
Speaker 1: the daily briefings for the press. What do you think

853
00:39:38,239 --> 00:39:40,560
Speaker 1: are going to be the two or three biggest challenges

854
00:39:40,920 --> 00:39:45,200
Speaker 1: for reporters trying to hold this White House accountable. Well,

855
00:39:45,280 --> 00:39:48,480
Speaker 1: I think the biggest challenge we've identified so far is

856
00:39:48,480 --> 00:39:51,400
Speaker 1: Trump himself. That he often uses Twitter as a way

857
00:39:51,440 --> 00:39:55,040
Speaker 1: to reach a mass audience and with information that's wrong.

858
00:39:55,160 --> 00:39:57,000
Speaker 1: I mean, he'll he'll, he'll tell you'll tweet out something

859
00:39:57,000 --> 00:39:59,319
Speaker 1: that where the facts are wrong or the context is wrong.

860
00:39:59,680 --> 00:40:01,840
Speaker 1: And you know, the way that we had dealt with

861
00:40:01,960 --> 00:40:04,680
Speaker 1: things like that in the past was if the you know,

862
00:40:04,840 --> 00:40:07,200
Speaker 1: on the Sunday talk shows or a presidential speech or

863
00:40:07,239 --> 00:40:09,640
Speaker 1: something like that, if the president says something, the first

864
00:40:09,760 --> 00:40:11,839
Speaker 1: version of the story is written usually by somebody who's

865
00:40:11,840 --> 00:40:14,080
Speaker 1: not a subject matter expert in that thing, and the

866
00:40:14,200 --> 00:40:17,120
Speaker 1: story is the President said X, right, and then a

867
00:40:17,200 --> 00:40:18,920
Speaker 1: few hours later, somebody who's a sub a matter of

868
00:40:18,920 --> 00:40:21,160
Speaker 1: expert rights the second version that says the President says X,

869
00:40:21,440 --> 00:40:23,439
Speaker 1: Here's what it means, here's how you know, here's what's

870
00:40:23,480 --> 00:40:26,920
Speaker 1: going to happen next. All that context. But the way

871
00:40:26,960 --> 00:40:28,520
Speaker 1: that you have to do with Trump. Trump comes out

872
00:40:28,560 --> 00:40:31,399
Speaker 1: and says, you know, the Air Force one contract carts

873
00:40:31,440 --> 00:40:33,719
Speaker 1: four billion dollars. That's wrong. You know, millions of people

874
00:40:33,760 --> 00:40:36,479
Speaker 1: voted illegally, that's wrong. But you know, nobody knows about

875
00:40:36,480 --> 00:40:39,160
Speaker 1: climate change. That's wrong. So you can't have the first

876
00:40:39,280 --> 00:40:41,520
Speaker 1: version of the story responsible. You can to the first

877
00:40:41,640 --> 00:40:44,560
Speaker 1: version of the stories that be Trump says X and

878
00:40:44,600 --> 00:40:46,360
Speaker 1: then five hours later come back and say, hey, what

879
00:40:46,440 --> 00:40:48,080
Speaker 1: he said was totally wrong. I mean, the truth is

880
00:40:48,080 --> 00:40:49,600
Speaker 1: a you know, lie gets around the world before the

881
00:40:49,600 --> 00:40:52,600
Speaker 1: truth gets its pants on. So we wanna have something

882
00:40:52,640 --> 00:40:54,920
Speaker 1: where we can the first time we write about Trump's statement,

883
00:40:54,960 --> 00:40:56,879
Speaker 1: the first story we write about his tweet or whatever

884
00:40:57,440 --> 00:40:59,239
Speaker 1: is from the expert, and the expert says, hey, Trump

885
00:40:59,280 --> 00:41:00,719
Speaker 1: said this, but it's wrong, and I want you to

886
00:41:00,840 --> 00:41:02,719
Speaker 1: know that right away. Well, so it sounds like a

887
00:41:02,920 --> 00:41:05,160
Speaker 1: lot of the coverage is going to be intensely critical.

888
00:41:05,320 --> 00:41:08,600
Speaker 1: And do you worry about alienating the wide swath of

889
00:41:08,640 --> 00:41:13,279
Speaker 1: American readers, viewers, etcetera, who say, hey, give this guy

890
00:41:13,360 --> 00:41:16,480
Speaker 1: a chance. I mean, it's it's kind of a delicate balance,

891
00:41:16,560 --> 00:41:19,680
Speaker 1: isn't it. It is. I think the main question for

892
00:41:19,920 --> 00:41:25,120
Speaker 1: us is to avoid uh seeming sort of emotional or

893
00:41:25,239 --> 00:41:29,200
Speaker 1: invested in in a particular outcome, and meaning in Trump

894
00:41:29,560 --> 00:41:32,719
Speaker 1: not doing well. There's a big political ecosystem out there,

895
00:41:32,800 --> 00:41:34,680
Speaker 1: and our job is to tell you what happened, and

896
00:41:34,760 --> 00:41:36,960
Speaker 1: to tell you, you know, what's what's new about what

897
00:41:37,000 --> 00:41:38,759
Speaker 1: Trump is doing, but not to tell you, you know,

898
00:41:39,040 --> 00:41:41,040
Speaker 1: is it going to lead to the autocracy or is

899
00:41:41,080 --> 00:41:43,120
Speaker 1: it going to lead to America being great again? You know,

900
00:41:43,200 --> 00:41:44,839
Speaker 1: we have to just tell you what happened. Let other

901
00:41:44,880 --> 00:41:47,560
Speaker 1: people run with the with the meaning of it. I

902
00:41:47,640 --> 00:41:49,560
Speaker 1: think that's gonna be Uh. We have to sort of

903
00:41:49,600 --> 00:41:53,239
Speaker 1: restrain ourselves from commenting too much rather than just sort

904
00:41:53,239 --> 00:41:55,600
Speaker 1: of telling you what happened. David Farenthald, it's so fun

905
00:41:55,719 --> 00:41:57,560
Speaker 1: to talk to you. I could stay here all day,

906
00:41:57,960 --> 00:42:00,120
Speaker 1: and I have so much respect for your reporting. You

907
00:42:00,200 --> 00:42:02,440
Speaker 1: do our podcast. After you want to pull us or

908
00:42:02,520 --> 00:42:05,000
Speaker 1: did I just jinx it? Don't jinx it, but I

909
00:42:05,040 --> 00:42:06,879
Speaker 1: would love to come on again. Thank you. I really

910
00:42:06,880 --> 00:42:08,960
Speaker 1: appreciate it. You'll have a good home, Okay, Thank you, David.

911
00:42:11,480 --> 00:42:15,280
Speaker 1: A big fat thank you to Gianna Palmer for producing

912
00:42:15,400 --> 00:42:18,920
Speaker 1: the show and Jared O'Connell for engineering and mixing it.

913
00:42:19,239 --> 00:42:22,800
Speaker 1: Thanks to Mark Phillips for our fantastic theme music. And

914
00:42:23,160 --> 00:42:25,879
Speaker 1: thanks to you for listening throughout this year. We really

915
00:42:25,920 --> 00:42:29,160
Speaker 1: appreciate it. And remember you can email us at comments

916
00:42:29,320 --> 00:42:31,759
Speaker 1: at curric podcast dot com or you can find me

917
00:42:31,840 --> 00:42:34,440
Speaker 1: on social media. Brian is on social media too, he

918
00:42:34,480 --> 00:42:38,600
Speaker 1: has oh tens of follower Yeah, I'm at Katie Curic

919
00:42:38,680 --> 00:42:41,920
Speaker 1: on Twitter and Instagram and Katie dot curric on Snapchat.

920
00:42:42,040 --> 00:42:45,080
Speaker 1: What's your handle? Good buddy? You don't even know, do you? Yes?

921
00:42:45,160 --> 00:42:47,680
Speaker 1: I do? Isn't it? Is it at BM Goldsmith? No,

922
00:42:49,680 --> 00:42:53,440
Speaker 1: that's sad at goldsmith b on Twitter? Good because I

923
00:42:53,480 --> 00:42:56,200
Speaker 1: don't like BM Goldsmith. Okay. Best of all, you can

924
00:42:56,320 --> 00:42:59,239
Speaker 1: rate and review us on iTunes. Don't forget to subscribe

925
00:42:59,320 --> 00:43:02,440
Speaker 1: as well. Thank you again for listening. We've had so

926
00:43:02,640 --> 00:43:05,360
Speaker 1: much fun doing this. I hope you've had fun listening

927
00:43:05,480 --> 00:43:08,680
Speaker 1: to our antics and we'll see you in two thousand

928
00:43:08,719 --> 00:43:09,240
Speaker 1: and seventeen.