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Speaker 1: We welcome Welcome back, Bob left the podcast. My guest

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Speaker 1: today is Ryan Mack, co author of the book Character Limit,

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Speaker 1: How Elon Musk Destroyed Twitter? Ryan, what does the average

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Speaker 1: person not know about Elon Musk?

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Speaker 2: Oh Man? Where to start? Gosh, so much, especially in

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Speaker 2: the last couple weeks days that he's been in the news,

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Speaker 2: But I don't know. One of the things that I've

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Speaker 2: found very interesting is explaining to people how much power

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Speaker 2: he has now, especially with this position he has close

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Speaker 2: to the White House. But even before then, you know,

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Speaker 2: he's you know, one of the most successful menstionneurs of

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Speaker 2: his time. He controls these major companies in Tesla and SpaceX,

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Speaker 2: and in some ways he is the second most important

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Speaker 2: person in the White House next to Donald Trump. Now,

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Speaker 2: so yeah, we can get into all that, but there's

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Speaker 2: a lot to unpack.

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Speaker 1: Well, why don't we start there, because that's what's happening

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Speaker 1: right this second. He is working with the Department of

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Speaker 1: Government Efficiency. You know a lot of people say, oh,

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Speaker 1: the guy cut all these people at Twitter, It's gonna

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Speaker 1: be great. What might we anticipate in his work there?

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Speaker 2: It's actually kind of crazy reporting out what happened at

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Speaker 2: Twitter and seeing almost the exact same playbook being talked

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Speaker 2: about with this Department of Government Efficiency. I think first

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Speaker 2: of all, what should be made clear is that we

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Speaker 2: really don't know what this DOGE is. Obviously it's a

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Speaker 2: reference to a meme cryptocurrency, which is the abbreviation of

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Speaker 2: Department of Government Efficiency. But we don't know what this

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Speaker 2: is gonna do. They've talked about it, you know, cutting

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Speaker 2: two trillion dollars, you know, Elon and Vivek Ramaswami his

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Speaker 2: co head of this department, making decisions over hiring and firing.

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Speaker 2: But what they've talked about, we've compared to what we

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Speaker 2: reported on it in Twitter. You know, for example, at Twitter,

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Speaker 2: he cut more than at this point eighty percent of

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Speaker 2: the staff then, you know, compared to when he what

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Speaker 2: he when he took over, that's thousands of jobs. You know,

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Speaker 2: if he applies that to the federal government, that's tens

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Speaker 2: of thousands of jobs that could be that could be removed.

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Speaker 2: You know. He slash costs kind of ruthlessly at Twitter

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Speaker 2: as well, cutting things from rent payments to janitorial services,

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Speaker 2: to food services, to systems that help make the website run.

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Speaker 2: And we could see that kind of kind of slash

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Speaker 2: and burn technique play out in the government to an

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Speaker 2: extent we've ever seen before. So, you know, we have

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Speaker 2: to talk very generally about what he's saying he's doing.

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Speaker 2: And of course Elon is someone that makes a lot

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Speaker 2: of promises, but the precedent has been kind of set

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Speaker 2: with what he did at Twitter.

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Speaker 1: Let's start from the beginning. What is your gig? What

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Speaker 1: is your purview with The New York Times?

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Speaker 2: Sure, so, I'm a I'm a reporter on our tech desk.

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Speaker 2: We have people around the world who cover the technology industry.

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Speaker 2: On that desk, I do what we call accountability accountability reporting,

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Speaker 2: you know, a lot of investigative work, a lot of

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Speaker 2: deeper dives into the movers and shakers of the industry.

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Speaker 2: I've covered Facebook and Mark Zuckerberg in the past. I've

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Speaker 2: covered Jack Dorsey, and I've covered Elon not only in

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Speaker 2: this job but in previous gigs. I've worked at BuzzFeed

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Speaker 2: News before this, at Forbes magazine as well, and my

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Speaker 2: rem at The Times is to, you know, find these stories,

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Speaker 2: dig deeper into these guys, and you know, report out

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Speaker 2: these monumental business decisions or happenings in the industry. So,

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Speaker 2: you know, two and a half years ago, when Elon

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Speaker 2: decided to buy Twitter, I was one of a couple

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Speaker 2: of reporters that was thrown head on into covering this

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Speaker 2: kind of unprecedented transaction, this transaction of one man, you know,

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Speaker 2: the world's richest man worth two hundred billion dollars at

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Speaker 2: the time, buying a whole company for forty four billion dollars,

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Speaker 2: and one thing led to another. I was teamed up

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Speaker 2: with Kate Conger, my co author now in the book,

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Speaker 2: and we were reporting two or three stories a day

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Speaker 2: to the point where we couldn't fit everything into the

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Speaker 2: pages of The New York Times. And you know, that's

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Speaker 2: where we got this book and kind of continued it

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Speaker 2: from there. But at the Times these days, I effectively

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Speaker 2: cover Elon full time. You know, there he is someone

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Speaker 2: that is incredibly powerful, incredibly interesting, and I'm one of

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Speaker 2: a couple of reporters where are dedicated to, you know,

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Speaker 2: following him and understanding what he's up to, whether that's

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Speaker 2: a Tesla or SpaceX or now in the White House.

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Speaker 1: Okay, let's go back a step. How did you get

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Speaker 1: the gig at the New York Times?

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Speaker 2: Gosh, I guess I have to start from the very beginning.

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Speaker 2: Go for it, Let's do it. Coming out of college, I,

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Speaker 2: you know, in college, didn't really know what I wanted

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Speaker 2: to do.

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Speaker 1: And where'd you go to college?

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Speaker 2: I went to Stanford, to Stanford, and like a good

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Speaker 2: immigrant son, you know, son of immigrants, I was on

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Speaker 2: the med school track and looking to become a doctor

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Speaker 2: and realizing that the life sciences weren't necessarily for me.

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Speaker 2: But I was always doing the school paper in school

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Speaker 2: and having a lot of fun with it and realizing,

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Speaker 2: you know, maybe I could pursue this as a profession.

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Speaker 2: You know, I started doing internships while in school. I

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Speaker 2: worked for the half Moon Bay Review in half Moon Bay, California.

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Speaker 2: I worked at the Orange County Register, my hometown paper

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Speaker 2: where I grew up in Orange County. I ended up

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Speaker 2: working for The Times as an intern and in Bloomberg,

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Speaker 2: and eventually, you know, you know, what the heck, I'll

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Speaker 2: give this a shot. And after school I went to

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Speaker 2: Forbes worked there for five and a half years as

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Speaker 2: a staff writer on the magazine covering Silicon Valley. I

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Speaker 2: was based in San Francisco, so it made it a

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Speaker 2: lot easier and actually at Forbes, I started on this

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Speaker 2: thing called the Wealth Team, which is the team that

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Speaker 2: puts together these kind of Forbes ritualists, you know, the

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Speaker 2: Forbes billionaires, the Forbes four hundred riches, Americans, And actually

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Speaker 2: it's a great place to start. As a cub reporter,

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Speaker 2: you are essentially doing these calculations that ever one sees

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Speaker 2: of you know, so and so is worth billions of dollars.

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Speaker 2: But behind the scenes, as reporters, you are doing the

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Speaker 2: nitty gritty of those calculations. You are tallying up SEC

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Speaker 2: documents and looking at the financial filings. You are valuing

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Speaker 2: things like megayachts and wine collection and art. You're talking

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Speaker 2: to you know, some of these people talk to us

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Speaker 2: and like, you know, say, you know, I'm actually up

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Speaker 2: this year. You're missing my picasto that I just bought,

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Speaker 2: or you know that that wine collection is too low

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Speaker 2: for me. Actually it should be higher because the price

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Speaker 2: of burgundy went up. So as a twenty two year

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Speaker 2: old reporter who was making peanuts thirty five thousand dollars

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Speaker 2: a year talking to the richest people in the world,

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Speaker 2: it was kind of surreal for me. And actually that's

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Speaker 2: where I developed kind of my early network, my rolodex.

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Speaker 2: As a reporter, you know, you're as good as your

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Speaker 2: sources and the people you talk to, and it was

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Speaker 2: kind of a start for me, kind of a because

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Speaker 2: you know, you're that young, you don't usually get introduced

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Speaker 2: into those circles. And from there I kind of built

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Speaker 2: a platform, you know, I moved off that team. I

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Speaker 2: was able to generate my own story ideas, did a

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Speaker 2: lot of profiles of CEOs and those types. I actually

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Speaker 2: covered music briefly with zach O Maalley Greenberg at Forbes,

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Speaker 2: who was a business music business guy, and kind of

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Speaker 2: learned from him as well. And uh yeah, moved to

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Speaker 2: BuzzFeed a couple of years later, kind of pivoted a

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Speaker 2: little bit to do more investigative type reporting, where I

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Speaker 2: reported quite a bit on Facebook and Mark Zuckerberg and

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Speaker 2: won some awards there and ended up at the Times

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Speaker 2: three and a half years ago.

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Speaker 1: Now, so, okay, are you just working at BuzzFeed and

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Speaker 1: The Times calls you also BuzzFeed took an ax to

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Speaker 1: their news department, So how did the transition happen?

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Speaker 2: Very very sad. Yeah, because BuzzFeed, if you remember that

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Speaker 2: kind of optimist era of digital media. Right, you have BuzzFeed,

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Speaker 2: you have Vox, you have all these startups popping up

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Speaker 2: with the promise that they could figure out, you know,

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Speaker 2: how to put things online better than the legacy publications.

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Speaker 2: And you know, ultimately what we learned is that they

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Speaker 2: were at the whims of these tech platforms. These giants,

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Speaker 2: right and Facebook, you know, love that content initially and

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Speaker 2: then didn't like it so much and turn off the spigot,

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Speaker 2: and that's what caused the collapse. But in that kind

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Speaker 2: of heyday, which I kind of joined towards the end of,

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Speaker 2: you know, the venture capital fueled digital media revolution, it

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Speaker 2: was great. You had these amazing resources to pursue these

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Speaker 2: long term stories. You know, you didn't have to deal

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Speaker 2: with the nitty gritty day to day coverage your news cycle.

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Speaker 2: I could kind of pull out and do a three

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Speaker 2: month investigation into Facebook or Google or I covered a

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Speaker 2: lot of Peter Tiel, for example. It was a billionaire

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Speaker 2: who was on the Facebook board. And I kind of

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Speaker 2: built my name on those stories. And BuzzFeed had the

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Speaker 2: resources to do that and gave the opportunity to kind

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Speaker 2: of mid career or early to mid career reporters like myself.

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Speaker 2: So it was a great place to work, super collegial.

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Speaker 2: Everyone is like friends and sharing each other's work, you know,

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Speaker 2: and just an amazing place to be a great energy. Unfortunately,

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Speaker 2: what we learned is that that model wasn't a great business,

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Speaker 2: you know, And when this bigot was turned off by

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Speaker 2: the digital platforms, there were layoffs and quite a few

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Speaker 2: until the point where the news division is effectively dead.

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Speaker 2: I left before the very very end of BuzzFeed News Buzzy.

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Speaker 2: The New York Times saw my work and liked it

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Speaker 2: and recruited me. But yeah, that's that's kind of how

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Speaker 2: that played out.

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Speaker 1: Okay. At this late date, in the wake of the election,

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Speaker 1: the New York Times the paper of record irrelevant of the truth.

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Speaker 1: It is seen as more of a left wing publication

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Speaker 1: in light of the election and the election of Trump.

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Speaker 1: Has there been any wee valuation or adjustment at the

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Speaker 1: Times in general?

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Speaker 2: You know, I don't want to speak for the paper.

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Speaker 2: I've only been there for three and a half years.

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Speaker 2: I know that the paper has focused so much on independence,

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Speaker 2: you know. Obviously there's a perception of bias one or

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Speaker 2: the other. I mean, you could talk to progressives, for example,

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Speaker 2: that I think the paper is actually conservative, but I

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Speaker 2: think above all it's this idea of editorial independence that

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Speaker 2: the paper values more than most. And honestly, you know,

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Speaker 2: that could sound like me blowing smoke up people's asses,

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Speaker 2: but in reality, you know, there is no political bent

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Speaker 2: to the paper that I've experience, and you know, on

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Speaker 2: my desk, you know, I'm I cover technology. There's no

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Speaker 2: remit to go after whatever, report on certain targets, whatever.

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Speaker 2: But you know, we for me, it's always been about

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Speaker 2: interrogating power. That that for me has kind of been

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Speaker 2: my north star, regardless of political affiliation. And you know,

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Speaker 2: I think my career has kind of proven that that

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Speaker 2: I have focused on the powers, the titans of industry,

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Speaker 2: you know, the powers that be, you know, the richest

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Speaker 2: people in the world, the people that control these platforms,

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Speaker 2: that control these companies that shape our daily lives. And

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Speaker 2: that's how I hope I continue to report, you know,

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Speaker 2: and and you know, I think that's that's kind of okay.

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Speaker 1: Point in time. The mainstream media is a pejorative. Yeah,

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Speaker 1: what do people not understand about The New York Times? Hm?

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Speaker 2: Hm, you know, that's a that's a good question. I mean,

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Speaker 2: the mainstream media has it's it's so easy to criticize, right,

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Speaker 2: And my world of looking at social media and seeing

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Speaker 2: people that criticize the New York Times, particularly like when

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Speaker 2: when I'll use Elon Musk as an example, right, who says,

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Speaker 2: you know, you shouldn't get your your news from the

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Speaker 2: New York Times or the wash Journal or the Washington Post.

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Speaker 2: Everything is actually going to be on X. You know

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Speaker 2: you could X is the news source. You are the

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Speaker 2: news source because it's it's people feeding X. Well, a

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Speaker 2: lot of that news comes from mainstream media. You know

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Speaker 2: that that we're the ones reporting it that is then

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Speaker 2: aggregated into those tweets or or or shared in those videos.

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Speaker 2: And I think that's the kind of uh, shortsightedness. I

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Speaker 2: think in all this that like when when I when

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Speaker 2: I cover social media, for example, when people talk about

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Speaker 2: social media replacing newspapers or CNN or whatever, a lot

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Speaker 2: of what they draw from is is what we work

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Speaker 2: on every day. You know that information couldn't come out

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Speaker 2: without a reporter at the New York Times calling someone

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Speaker 2: up and writing up a story which then gets aggregated

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Speaker 2: or pulled out into a tweet or put on TikTok,

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Speaker 2: for example. And it's that ecosystem that I think people

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Speaker 2: don't understand. And yeah, it's it's it's kind of sad

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Speaker 2: because Elon Musk and Lynda Akarino, the CEO of X

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Speaker 2: now it's called X because that's what they've changed the

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Speaker 2: name to have made this kind of antagonistic point that

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Speaker 2: like X is actually the replacement for the New York

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Speaker 2: Times or for whatever fill in news source. You know.

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Speaker 2: But the last two decades of Twitter I have shown that,

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Speaker 2: you know, the platform has been super reliant on news

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Speaker 2: generators and reporters, and to alienate them seems pretty shortsighted.

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Speaker 2: And not to understand that that ecosystem seems kind of

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Speaker 2: absurd to be.

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Speaker 1: Okay, so you're working at the New York Times, can

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Speaker 1: you literally follow anything you want? Or do they say

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Speaker 1: stay on this.

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Speaker 2: It's a bit of give and take. You know, I'm

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Speaker 2: hopefully you know, if I wake up one morning, I'm

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Speaker 2: editor is like, you know, we should pay more attention

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Speaker 2: to this. I'll definitely listen. You know, It's it's not

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Speaker 2: completely me generating everything, but there's also a give and take.

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Speaker 2: And if I say, like, look, we should be paying

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Speaker 2: attention to this more. I think this is undercovered. I

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Speaker 2: have that freedom, and it's a it's a nice place

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Speaker 2: to be in your career. I don't necessarily have a

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Speaker 2: beat at the company. You know, we have we have

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Speaker 2: reporters that cover Tesla. We have reporters that cover X. Kate,

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Speaker 2: my co author, covers X on a full time basis.

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Speaker 2: We have reporters that cover Google and you know, name

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Speaker 2: whatever company. I help those reporters, I'll supplement them. But

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Speaker 2: if there's a deep dive that we need to go

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Speaker 2: into or you know understand you know, musk relationship with

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Speaker 2: the White House, for example, which is something we've been

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Speaker 2: trying to do over the last couple weeks to months,

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Speaker 2: you know, the deploy me on that and I'll kind

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Speaker 2: of shift my resources and my efforts to that kind

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Speaker 2: of story.

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Speaker 1: So you've been covering starting and forwards, you and covering

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Speaker 1: the wealthy, forgetting people who inherit their wealth, people who

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Speaker 1: make their wealth. What similarities to all these people have.

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Speaker 2: That's something I've always thought about, and I don't think

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Speaker 2: there is one unique characteristic. You know, it's actually really funny.

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Speaker 2: I've I've I have been around them. I guess I've

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Speaker 2: been around them more relative, more around more rich people

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Speaker 2: relative to the average person. But there's all types, you know,

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Speaker 2: there's all types of people I can like there. For example,

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Speaker 2: some folks do not want to talk to us about

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Speaker 2: their wealth at all. You know, they would they would

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Speaker 2: hang up the phone as soon as I called, never

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Speaker 2: return my emails, that kind of thing. Some people want

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Speaker 2: to get down to the every last detail. You know,

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Speaker 2: you miss this. I think my holding over here in

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Speaker 2: these stocks and you know, I'm actually up by one

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Speaker 2: hundred million over here and down, and it's it's like

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Speaker 2: it was like such an exercise in egos in a way,

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Speaker 2: and you start to realize that, like it's gonna sound

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Speaker 2: like weird, but like, you know, they're normal people like us,

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Speaker 2: you know it. They have the same hang ups or

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Speaker 2: you know, same motivations in some ways, same fears. And

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Speaker 2: that's actually really like it took me a while to

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Speaker 2: understand that, Like there wasn't like one gene, for example,

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Speaker 2: that causes you to become super wealthy. I think there's

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Speaker 2: a lot of luck at play, a lot of where

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Speaker 2: you start in life. Sure there's things like hard work

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Speaker 2: and that kind of thing and effort, but Yeah, there's

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Speaker 2: there's no one characteristic that I could ascribe to, or

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Speaker 2: else I would have followed that and made myself a

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Speaker 2: billionaire myself.

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Speaker 1: Okay, we live in an era where money is everything,

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Speaker 1: and people who have money are seen in comp usually

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Speaker 1: they made the money in one specific vertical, and people

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Speaker 1: who have made money there's a conception by the hoyploy

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Speaker 1: that these people are experts in all areas. Having covered

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Speaker 1: these people, are they experts in all areas or are

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Speaker 1: they just super smart in the one area they're in

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Speaker 1: and they're as blind as the next person in another area.

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Speaker 2: Well, I've read your review of our books. I think

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Speaker 2: we're aligned on this. But one of the findings of

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Speaker 2: our book. Let's let's talk about Elon specifically here, right,

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Speaker 2: I mean a generational entrepreneur like that that I'm not

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Speaker 2: going to deny that by any means. He brought in

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Speaker 2: the electric car revolution. He foresaw this industry to privatize

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Speaker 2: space and get things into into orbit, which now he

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Speaker 2: has a virtual monopoly with the US government and getting

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Speaker 2: things into space, you know, to see that and be

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Speaker 2: successful in that not once but twice, because there's a

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Speaker 2: lot of credit at the same time, it doesn't necessarily

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Speaker 2: make him, you know, smart in another area. He is

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Speaker 2: not a epidemiologist, you know, when he was making comments

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Speaker 2: about COVID, for example. He is not by any means

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Speaker 2: an expert in social media and how to run a

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Speaker 2: social media platform, which I think is in some ways

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Speaker 2: the original sin when he bought Twitter, right, it's this

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Speaker 2: idea that he had been so successful in these other

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Speaker 2: areas that, you know, how hard could it be. This

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Speaker 2: is a social media company. It's you know, in his view,

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Speaker 2: servers and a website. You know, you're just plugging it

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Speaker 2: in and letting it go and keeping it online. How

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Speaker 2: hard could that be compared to you know, developing autopilot

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Speaker 2: or making a rocket land on a ship in the

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Speaker 2: middle of the ocean. And I think that is the

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Speaker 2: original sin because what he didn't realize is social media

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Speaker 2: isn't a tech problem. It's actually a human problem. You are,

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Speaker 2: it's a societal problem. It's a social problem. You're connecting

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Speaker 2: people online and figuring out ways for them to communicate

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Speaker 2: without killing each other or you know, causing huge disturbances

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Speaker 2: and making everyone feel at least most people feeling like

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Speaker 2: they're welcome to exhibit and express themselves, and yeah, so

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Speaker 2: I mean that that kind of central understanding which powered

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Speaker 2: our book is one that I come back to a

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Speaker 2: lot that oftentimes there are people that are very successful

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Speaker 2: that think because they've reached such success in one area,

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Speaker 2: they can be successful in another, and oftentimes that's not

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Speaker 2: the case.

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Speaker 1: Okay, when Elong makes the offer to buy Twitter to

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Speaker 1: what did we were you on the Elong beat?

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Speaker 2: Hmmm? I started covering him, like really covering him in

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Speaker 2: twenty eighteen at BuzzFeed. There were some issues at his factories.

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Speaker 2: This is a period of turmoil for him, and there

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Speaker 2: were some kind of investigative stories, and he was a

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Speaker 2: subject I would return to every now and then, but

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Speaker 2: it wasn't by any means like my main focus. And

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Speaker 2: by the time he made the offer for Twitter in

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Speaker 2: April of twenty ti twenty two, I don't think I

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Speaker 2: had written a single story on him for The Times

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Speaker 2: I had joined gosh, I think the year prior, year

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Speaker 2: and a half prior, I hadn't written about him for

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Speaker 2: The Times, but they had known my pastwork and covering him.

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Speaker 2: There's actually a trial we can talk about that I

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Speaker 2: was kind of in some ways involved with with him,

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Speaker 2: but at the times. You know, they saw my past

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Speaker 2: work and they're like, Okay, this is a pretty monumental event.

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Speaker 2: That's actually take some time and understand how serious this is,

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Speaker 2: because what you have to remember is when he actually

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Speaker 2: made the bid, people didn't know if he was serious.

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Speaker 2: You know, it was he made a reference to a

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Speaker 2: weed joke, you know, for fifty four twenty fifty four

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Speaker 2: to twenty per share was his offer amounted to forty

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Speaker 2: four billion dollars. His offer letter was pretty thin when

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Speaker 2: he when he made the offer, it wasn't clear if

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Speaker 2: he was going to go through. And then obviously he

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Speaker 2: tried to pull out of it a couple weeks later later.

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Speaker 2: But they had me spend a lot of time trying

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Speaker 2: to interrogate whether or not this thing was going to happen,

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Speaker 2: and you know, what was the mindset and what was

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Speaker 2: the thinking. So I started to dig in and work

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Speaker 2: closely with Kate. And when he finally did take over

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Speaker 2: the company after all the kind of legal wrangling in

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Speaker 2: October twenty twenty two, it was kind of all hands

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Speaker 2: on deck. You know, it was two or three stories

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Speaker 2: a day. You know, it was, oh my gosh, he's

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Speaker 2: laying off people over in this part of the company,

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Speaker 2: or he stopped paying rent over here. At one point

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Speaker 2: we heard the janitorial services were cut, so like I'm

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Speaker 2: in a toilet paper in the offices. But everything was

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Speaker 2: like a fire. Every day. We'd wake up when we

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Speaker 2: have two or three things to report on about his takeover.

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Speaker 2: And since then, it's kind of hasn't stopped. You know,

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Speaker 2: it's led from one thing and the other into the book,

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Speaker 2: into our coverage of him now as a political figure,

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Speaker 2: and that kind of that roller coaster hasn't stopped.

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Speaker 1: Okay, there's a legendary story before you were at the

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Speaker 1: New York Times where a reporter Drove Tesla wrote a

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Speaker 1: review Elon mark Back said, hey, there's data. I checked

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Speaker 1: the data in the car and he contradicted you're reporting.

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Speaker 1: Are you hearing from Elon? You or Kate or somebody

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Speaker 1: else saying you got it wrong.

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Speaker 2: Actually, I've had a couple interactions with Elon, so we

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Speaker 2: can start from beginning, and actually I know that anecdote,

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Speaker 2: and what I would say about that is like fighting

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Speaker 2: against the media has been core to Elon's beliefs since

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Speaker 2: he started his companies. You know, you guys, remember there

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Speaker 2: was a lot of doubt about his companies, particularly Tesla,

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Speaker 2: where he in some ways relied on the media to

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Speaker 2: get at his message. He was very big on getting

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Speaker 2: on magazine covers and in stories, to the point where

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Speaker 2: we were talking to folks used to work with him,

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Speaker 2: and he'd be up at three am reading like some

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Speaker 2: obscure Belgian blog, you know, trying to understand like every

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Speaker 2: piece of coverage was near and dear to him. He

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Speaker 2: had a Google, earned his own name. He cared so much,

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Speaker 2: you know. And on the other hand, he battled with

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Speaker 2: the media anytime they got something wrong. And so that

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Speaker 2: story you reference actually is very telling, and it was

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Speaker 2: it kind of in some ways proved his belief that

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Speaker 2: there was this kind of media force against him and

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Speaker 2: against his success, and he was out to kind of

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Speaker 2: prove them wrong, which in some ways you can see

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Speaker 2: the line that's drawn to the Twitter acquisition. In terms

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Speaker 2: of my interactions with him throughout my career, I don't

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Speaker 2: think he's ever denied a story I've reported. My famous

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Speaker 2: run in with him was that when I was at

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Speaker 2: BuzzFeed in twenty eighteen, and do you remember when those

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Speaker 2: kids got caught in the cave and time of course, man,

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Speaker 2: the whole bit exactly. So for the listeners, like, you know,

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Speaker 2: cave rescuer gets involved, a British guy living in Thailand.

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Speaker 2: Elon then gets involved. It's like this crazy thing. He

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Speaker 2: builds a submarine to get these kids out of this

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Speaker 2: winding underwater cave. They don't end up using the submarine,

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Speaker 2: but Elon gets a lot of press out of it,

430
00:26:26,880 --> 00:26:30,119
Speaker 2: and one of the rescuers goes on CNN and says, like,

431
00:26:30,200 --> 00:26:33,240
Speaker 2: you know, that didn't work, like he can stick that

432
00:26:33,320 --> 00:26:35,520
Speaker 2: sub up his butt, you know, and you know kind

433
00:26:35,520 --> 00:26:39,119
Speaker 2: of was like it was a complete distraction and he

434
00:26:39,160 --> 00:26:42,000
Speaker 2: didn't help at all. And in return, Elon calls this

435
00:26:42,040 --> 00:26:45,280
Speaker 2: guy a pedo guy you know, on Twitter, and it

436
00:26:45,320 --> 00:26:47,359
Speaker 2: was like this kind of I mean, those reactions now

437
00:26:47,359 --> 00:26:50,719
Speaker 2: were kind of part and parcel of who he is

438
00:26:50,720 --> 00:26:52,720
Speaker 2: on Twitter, but at that point in time, it was

439
00:26:52,800 --> 00:26:56,880
Speaker 2: kind of a insane accusation to call someone a pedophile

440
00:26:56,920 --> 00:27:04,359
Speaker 2: on Twitter with no basis. So in that process I

441
00:27:04,440 --> 00:27:08,879
Speaker 2: ended up emailing Elon asking him why he thought this

442
00:27:08,920 --> 00:27:12,560
Speaker 2: guy was a pedophile. Elon emails me back with this

443
00:27:12,680 --> 00:27:16,440
Speaker 2: kind of screed. Bear in mind I'd never really talked

444
00:27:16,440 --> 00:27:19,720
Speaker 2: to him. The screed of why this guy was a

445
00:27:19,760 --> 00:27:22,159
Speaker 2: quote child rapist who had a twelve year old bride.

446
00:27:22,600 --> 00:27:24,439
Speaker 2: Since she's trying to get me as a reporter at

447
00:27:24,440 --> 00:27:28,840
Speaker 2: BuzzFeed to write a story about this guy being a pedophile,

448
00:27:30,960 --> 00:27:33,520
Speaker 2: we actually didn't did not end up doing that. We

449
00:27:34,160 --> 00:27:36,040
Speaker 2: reported it out a little more, and it turns out

450
00:27:36,040 --> 00:27:39,199
Speaker 2: that Elon was kind of making all this up and

451
00:27:39,240 --> 00:27:41,840
Speaker 2: trying to get a reporter to dump this out into

452
00:27:41,840 --> 00:27:45,160
Speaker 2: the world, and so we ended up publishing Elon's email

453
00:27:45,200 --> 00:27:47,760
Speaker 2: in full, to kind of show his state of mind,

454
00:27:47,960 --> 00:27:50,679
Speaker 2: you know, to show that he was willing to destroy

455
00:27:50,720 --> 00:27:57,240
Speaker 2: someone's reputation and to show that this, you know, was

456
00:27:57,320 --> 00:28:00,359
Speaker 2: kind of a typical behavior for anyone, much less us

457
00:28:00,520 --> 00:28:02,879
Speaker 2: someone who runs, you know, a major company in Tesla.

458
00:28:04,840 --> 00:28:09,119
Speaker 2: Fast forward that that that cave Rescuer ends up suing Elon.

459
00:28:09,920 --> 00:28:15,480
Speaker 2: Elon tries to depose me in court to for the case.

460
00:28:16,440 --> 00:28:19,399
Speaker 2: But you know, that was a that was a crazy

461
00:28:19,400 --> 00:28:21,080
Speaker 2: interaction in my career and one of the times that

462
00:28:21,119 --> 00:28:22,840
Speaker 2: I've ever gotten to interact with them.

463
00:28:22,880 --> 00:28:27,520
Speaker 1: So, Okay, a lot of these people can push back

464
00:28:27,840 --> 00:28:34,080
Speaker 1: very hard, accurately or inaccurately. I've dealt with some reporters

465
00:28:34,119 --> 00:28:38,800
Speaker 1: who laugh I know in my own particular cases, sometimes

466
00:28:39,200 --> 00:28:41,840
Speaker 1: you know, you hold your ground, but you shake when

467
00:28:41,880 --> 00:28:44,480
Speaker 1: these people push back. Do you ever feel any personal

468
00:28:44,520 --> 00:28:47,760
Speaker 1: anxiety where you say, no, I'm a reporter, I'm getting

469
00:28:47,760 --> 00:28:49,360
Speaker 1: it right. I work for the New York Times, Or

470
00:28:49,400 --> 00:28:52,640
Speaker 1: sometimes you say, well, shit, do I really have it right?

471
00:28:55,160 --> 00:28:58,480
Speaker 2: That? That's I think about that every day. And you know,

472
00:28:58,480 --> 00:29:01,360
Speaker 2: as a reporter, the worst thing that can happen is

473
00:29:01,400 --> 00:29:03,360
Speaker 2: for you to get something wrong, especially in such a

474
00:29:03,400 --> 00:29:08,320
Speaker 2: key story where the people that you're reporting on have

475
00:29:08,440 --> 00:29:13,280
Speaker 2: unlimited resources to challenge you or to possibly sue you,

476
00:29:13,720 --> 00:29:16,560
Speaker 2: or you know, one of the biggest scoops in my

477
00:29:16,640 --> 00:29:19,760
Speaker 2: career was finding out that Peter Teele was funding the

478
00:29:19,760 --> 00:29:24,280
Speaker 2: Gawker case, the whole cloaging Gawker case that took out Gawker.

479
00:29:24,360 --> 00:29:27,760
Speaker 2: You know, I broke that story, and you know, I

480
00:29:27,800 --> 00:29:30,440
Speaker 2: was a cub reporter at Forbes, a younger reporter at Forbes,

481
00:29:30,480 --> 00:29:33,400
Speaker 2: and before we publish, you know, there was a doubt

482
00:29:33,600 --> 00:29:35,680
Speaker 2: in my mind that flash. You know, I was so

483
00:29:35,680 --> 00:29:38,800
Speaker 2: solid on the reporting, it's spent months doing it, but

484
00:29:39,280 --> 00:29:41,600
Speaker 2: was like, you know what if that point zero zero

485
00:29:41,600 --> 00:29:44,000
Speaker 2: one percent chance is right? You know what if I've

486
00:29:44,000 --> 00:29:48,280
Speaker 2: been duped the whole time into reporting this out what happens,

487
00:29:48,320 --> 00:29:52,040
Speaker 2: you know, and like that that feeling never goes away.

488
00:29:52,080 --> 00:29:54,360
Speaker 2: And I think being paranoid in some ways as a

489
00:29:54,360 --> 00:29:57,480
Speaker 2: reporter helps you because that will lead you to make

490
00:29:57,520 --> 00:29:59,640
Speaker 2: the next call, right, You'll be like, actually, you know

491
00:29:59,680 --> 00:30:01,920
Speaker 2: I have sources. Maybe I'll go for six or seven,

492
00:30:02,360 --> 00:30:05,120
Speaker 2: or maybe I'll make that extra send that extra email.

493
00:30:07,560 --> 00:30:09,320
Speaker 2: You know, It's helped in my career that I've been

494
00:30:09,360 --> 00:30:14,120
Speaker 2: supported by major publications that have great resources, in Forbes,

495
00:30:14,120 --> 00:30:19,040
Speaker 2: in the New York Times. But you know that that

496
00:30:19,160 --> 00:30:21,640
Speaker 2: fear stays with you. And I don't know, I could

497
00:30:21,680 --> 00:30:24,960
Speaker 2: be eighty years old and you know, have worked the

498
00:30:24,960 --> 00:30:29,200
Speaker 2: New York Times for fifty years. I'd be worried if

499
00:30:29,200 --> 00:30:31,440
Speaker 2: that fear wasn't part of me still, you know, because

500
00:30:31,480 --> 00:30:34,600
Speaker 2: you always want to have a little bit of paranoia,

501
00:30:34,640 --> 00:30:39,320
Speaker 2: I guess. And yeah, when when when it's someone like Elon,

502
00:30:40,520 --> 00:30:43,360
Speaker 2: you gotta you gotta do everything by the book, You gotta,

503
00:30:43,520 --> 00:30:46,720
Speaker 2: you gotta, you gotta be so solid on your reporting.

504
00:30:47,560 --> 00:30:54,680
Speaker 2: And I've always led with that, you know, my I yeah,

505
00:30:54,680 --> 00:30:56,960
Speaker 2: I've always relied on the reporting and the like you know,

506
00:30:57,000 --> 00:31:02,280
Speaker 2: my source saying and everything like that, and yeah, but

507
00:31:02,360 --> 00:31:04,240
Speaker 2: you know, if someone tries to intimidate us at the

508
00:31:04,280 --> 00:31:07,240
Speaker 2: same time, like, I'm not going to back down. You know,

509
00:31:07,280 --> 00:31:09,240
Speaker 2: if we know something is right, we're gon we're gonna

510
00:31:09,280 --> 00:31:13,320
Speaker 2: go with it. And it's kind of that weird balance.

511
00:31:21,120 --> 00:31:25,760
Speaker 1: Okay, it's a summer of twenty twenty two. Yeah, elon

512
00:31:26,240 --> 00:31:29,800
Speaker 1: trying to wiggle out. We know there's gonna be a

513
00:31:29,960 --> 00:31:34,320
Speaker 1: case in the Delaware Court. You're in the New York Times.

514
00:31:35,480 --> 00:31:38,160
Speaker 1: I agree with you one hundred percent. New York Times

515
00:31:38,240 --> 00:31:41,080
Speaker 1: has boots on the ground everywhere, whereas all these other

516
00:31:41,120 --> 00:31:46,160
Speaker 1: people's just opinion at the source. Do you think, hey,

517
00:31:46,160 --> 00:31:48,320
Speaker 1: this is going to close based on the history of

518
00:31:48,320 --> 00:31:51,560
Speaker 1: the Delaware Court? And to what degree are you penetrating

519
00:31:51,600 --> 00:31:55,200
Speaker 1: the company in terms of sources such that when the

520
00:31:55,240 --> 00:31:57,600
Speaker 1: case goes down, you're prepared.

521
00:31:59,160 --> 00:32:01,760
Speaker 2: You're doing everything. You You're calling the people on the deal.

522
00:32:01,800 --> 00:32:07,320
Speaker 2: You're calling the lawyers, the bankers, the pr people, you know,

523
00:32:07,440 --> 00:32:09,160
Speaker 2: people who you might think might be in the room.

524
00:32:09,200 --> 00:32:12,200
Speaker 2: You know, these are very high level conversations too, So

525
00:32:12,400 --> 00:32:16,720
Speaker 2: when you're talking about sourcing, you're talking about C suite level.

526
00:32:16,880 --> 00:32:18,800
Speaker 2: You know, those are the people that are having conversations,

527
00:32:19,720 --> 00:32:22,280
Speaker 2: and what we realize is that on the Twitter side

528
00:32:22,280 --> 00:32:25,160
Speaker 2: of things, they had no idea what to expect from Elon.

529
00:32:25,440 --> 00:32:28,920
Speaker 2: You know, they had some meetings about what it would.

530
00:32:29,480 --> 00:32:31,400
Speaker 2: Bear in mind as he was backing out, there were

531
00:32:31,480 --> 00:32:34,160
Speaker 2: still some meetings that were going on between him and

532
00:32:34,200 --> 00:32:38,560
Speaker 2: the company over there was a calculat there was that

533
00:32:38,600 --> 00:32:40,840
Speaker 2: bot argument, for example, and there were they were trying

534
00:32:40,840 --> 00:32:47,000
Speaker 2: to assuage him concerns about bots because the company was

535
00:32:47,000 --> 00:32:48,680
Speaker 2: bound by it's pretty sure your duty to make this

536
00:32:48,720 --> 00:32:50,760
Speaker 2: deal happen, right, which is why they sue the company.

537
00:32:52,320 --> 00:32:56,640
Speaker 2: But you know, gosh, that was it was a crazy period.

538
00:32:57,920 --> 00:33:00,640
Speaker 2: Kate was ready to go to Delaware and and spend

539
00:33:00,680 --> 00:33:03,360
Speaker 2: like weeks there. I was having to read up on

540
00:33:03,440 --> 00:33:06,160
Speaker 2: chancery court, which I knew very little about until that,

541
00:33:06,280 --> 00:33:10,640
Speaker 2: until that kind of moment. But that's kind of the

542
00:33:10,680 --> 00:33:14,200
Speaker 2: beauty of reporting, right, Like you're thrown into the deep

543
00:33:14,320 --> 00:33:17,160
Speaker 2: end of these weird situations you might have never been

544
00:33:17,160 --> 00:33:19,600
Speaker 2: in before, you might have never thought about the court

545
00:33:19,600 --> 00:33:24,080
Speaker 2: of chancery, and suddenly you're making calls to trial lawyers

546
00:33:24,080 --> 00:33:29,000
Speaker 2: who have you know, practiced there or represented other companies

547
00:33:29,000 --> 00:33:33,240
Speaker 2: who're trying to find comparisons to maybe legal precedents that

548
00:33:33,280 --> 00:33:36,280
Speaker 2: I played out similar to this, where you know the

549
00:33:36,320 --> 00:33:38,720
Speaker 2: court has compelled someone to buy a company like that.

550
00:33:39,840 --> 00:33:41,160
Speaker 2: What did you think did you think it? Did you

551
00:33:41,160 --> 00:33:42,360
Speaker 2: think it was going to happen at that time?

552
00:33:42,400 --> 00:33:45,280
Speaker 1: Absolutely? Because of the Delaware Court. It blew my mind.

553
00:33:45,320 --> 00:33:47,800
Speaker 1: All these people. You know, I got a book up

554
00:33:47,840 --> 00:33:50,640
Speaker 1: my ass about Kara Swisher anyway, Oh no, no, no,

555
00:33:50,640 --> 00:33:53,040
Speaker 1: no, no no, there's law involvement that happened to be a

556
00:33:53,120 --> 00:33:55,640
Speaker 1: lawyer or whatever. And if you know anything about why

557
00:33:55,720 --> 00:33:58,800
Speaker 1: all this stuff is in Delaware. But let's go back

558
00:33:58,800 --> 00:34:02,960
Speaker 1: to your story. You're saying, you're yeah, will everybody talk

559
00:34:03,000 --> 00:34:05,680
Speaker 1: to you? And to what degree is the fact that

560
00:34:05,720 --> 00:34:08,040
Speaker 1: you're writing for the New York Times to your advantage

561
00:34:08,080 --> 00:34:09,040
Speaker 1: or disadvantage?

562
00:34:12,080 --> 00:34:14,359
Speaker 2: It goes both ways, actually, and I've been on both

563
00:34:14,400 --> 00:34:16,319
Speaker 2: sides of it. When I was at BuzzFeed, I used

564
00:34:16,320 --> 00:34:18,120
Speaker 2: to think, you know, those reporters of the New York Times,

565
00:34:18,160 --> 00:34:20,319
Speaker 2: they must get everything handed to them on a silver

566
00:34:20,400 --> 00:34:23,319
Speaker 2: platter because they're the New York Times. Who wouldn't want

567
00:34:23,360 --> 00:34:25,960
Speaker 2: to talk to them? And then now being on this

568
00:34:26,000 --> 00:34:29,640
Speaker 2: side of things, there are instances where people are like, sorry,

569
00:34:29,640 --> 00:34:31,760
Speaker 2: you guys are too big, Like I'm worried about my reputation.

570
00:34:31,800 --> 00:34:33,239
Speaker 2: I want this information to be out there, but like

571
00:34:33,280 --> 00:34:36,040
Speaker 2: in a smaller setting. And so it's kind of funny

572
00:34:36,360 --> 00:34:42,080
Speaker 2: seeing that play out in a way. That being said,

573
00:34:42,080 --> 00:34:45,880
Speaker 2: when you're at the New York Times, you know, it

574
00:34:46,000 --> 00:34:48,960
Speaker 2: carries the gravitas that has the name, the rename recognition.

575
00:34:49,239 --> 00:34:52,400
Speaker 2: When you call someone, they're not asking you to spell

576
00:34:52,440 --> 00:34:55,280
Speaker 2: out you know what do you who does that mean? BuzzFeed?

577
00:34:55,360 --> 00:34:57,200
Speaker 2: Can you spell that for me? You know, you're at

578
00:34:57,239 --> 00:34:59,080
Speaker 2: the New York Times, You're like, oh, okay, I get it.

579
00:34:59,160 --> 00:35:03,799
Speaker 2: You know, you know where you're coming from. But in

580
00:35:03,840 --> 00:35:07,360
Speaker 2: those moments, it's it's it's kind of you kind of

581
00:35:07,400 --> 00:35:09,600
Speaker 2: have to be lucky, and sometimes people don't want to

582
00:35:09,600 --> 00:35:11,520
Speaker 2: talk to you at all. You bear in mind, you know,

583
00:35:11,600 --> 00:35:15,319
Speaker 2: the people around the Twitter deal are especially on the

584
00:35:15,320 --> 00:35:19,279
Speaker 2: Twitter side, are scared. Are scared shit lists. You know,

585
00:35:19,320 --> 00:35:20,600
Speaker 2: they don't want to ruin the deal. They don't want

586
00:35:20,600 --> 00:35:24,239
Speaker 2: to say anything to report that could scupper this, this

587
00:35:24,320 --> 00:35:29,560
Speaker 2: monumental deal, this payout for shareholders and whatever. But in

588
00:35:29,600 --> 00:35:32,840
Speaker 2: writing a book, what I found is that oftentimes after

589
00:35:32,920 --> 00:35:36,480
Speaker 2: the fact, if you say, you know, actually my book

590
00:35:36,520 --> 00:35:37,799
Speaker 2: is going to come out in a year or two

591
00:35:37,840 --> 00:35:41,439
Speaker 2: years time. You know, people might be open to talking

592
00:35:41,440 --> 00:35:43,120
Speaker 2: to you for that because it's going to be past

593
00:35:43,280 --> 00:35:47,160
Speaker 2: the period where it's sensitive for them. You know, this

594
00:35:47,200 --> 00:35:48,960
Speaker 2: is something that is for history in a way. It's

595
00:35:49,000 --> 00:35:52,120
Speaker 2: not just a daily story, and so we use that

596
00:35:52,120 --> 00:35:54,880
Speaker 2: to our advantage for the book. You know, some people

597
00:35:54,920 --> 00:35:57,320
Speaker 2: that that didn't talk to us for these stories in

598
00:35:57,360 --> 00:36:01,080
Speaker 2: the New York Times came back to us or towards

599
00:36:01,080 --> 00:36:02,640
Speaker 2: the very end of the process they were like, actually,

600
00:36:02,680 --> 00:36:04,440
Speaker 2: I want to make this one thing very clear and

601
00:36:04,520 --> 00:36:07,640
Speaker 2: just the specific thing. But it ended up being very helpful.

602
00:36:09,760 --> 00:36:13,600
Speaker 2: So you get all types. I'll say it. In the moment,

603
00:36:13,640 --> 00:36:17,040
Speaker 2: it's quite hard to get people to speak immediately, but

604
00:36:17,080 --> 00:36:18,960
Speaker 2: if you give them a little time, if you work

605
00:36:18,960 --> 00:36:21,560
Speaker 2: on them a little bit, you keep reminding them there's

606
00:36:21,560 --> 00:36:25,440
Speaker 2: always a chance. And I've learned that kind of perseverance

607
00:36:25,560 --> 00:36:28,319
Speaker 2: is kind of the you know, one of my most

608
00:36:28,320 --> 00:36:29,520
Speaker 2: important traits as a reporter.

609
00:36:30,600 --> 00:36:32,800
Speaker 1: Okay, I dealt with the press a lot, calling me

610
00:36:32,960 --> 00:36:36,480
Speaker 1: for stuff. Yeah, like being on television news like the

611
00:36:36,520 --> 00:36:38,640
Speaker 1: classic story of the Today Show. They provide you a

612
00:36:38,680 --> 00:36:43,560
Speaker 1: limo there, but not back. You're just fodder. You're just

613
00:36:43,680 --> 00:36:46,280
Speaker 1: hit and run. I've had a lot of people forget

614
00:36:46,280 --> 00:36:48,839
Speaker 1: a lot of times. You know, music is a you know,

615
00:36:49,520 --> 00:36:51,520
Speaker 1: a lot of these papers have already fired all the

616
00:36:51,600 --> 00:36:53,839
Speaker 1: music people. You know. This is a lot of times

617
00:36:53,880 --> 00:36:56,960
Speaker 1: I'll put some junior person who's ignorant on the issue.

618
00:36:57,280 --> 00:37:01,040
Speaker 1: But if you get a reporter from an publication, a

619
00:37:01,080 --> 00:37:04,839
Speaker 1: lot of times they're aggressive. And most people who deal

620
00:37:04,840 --> 00:37:07,160
Speaker 1: with the press deal with the press and frequently what

621
00:37:07,320 --> 00:37:08,440
Speaker 1: is your style?

622
00:37:11,920 --> 00:37:14,640
Speaker 2: I tend not to be aggressive, largely because I am

623
00:37:14,719 --> 00:37:20,160
Speaker 2: not a beat reporter, like I don't have to file

624
00:37:20,280 --> 00:37:23,880
Speaker 2: tomorrow about you know, the news of the day that

625
00:37:24,000 --> 00:37:26,799
Speaker 2: happened at Facebook or Twitter or you know X you

626
00:37:26,800 --> 00:37:30,960
Speaker 2: know or Google. I have time to like build relationships,

627
00:37:30,960 --> 00:37:34,560
Speaker 2: and any good reporter is doing that. And everyone has

628
00:37:34,560 --> 00:37:36,840
Speaker 2: their own style. But I've never found the kind of

629
00:37:37,880 --> 00:37:40,399
Speaker 2: you know, give me the information before everyone else, or

630
00:37:41,360 --> 00:37:44,719
Speaker 2: some people use kind of like a bullying you know,

631
00:37:44,800 --> 00:37:46,839
Speaker 2: why why did you talk to the Washington Posts over there,

632
00:37:46,920 --> 00:37:49,839
Speaker 2: Wall Journal and not to me? Next time? I want

633
00:37:49,880 --> 00:37:52,200
Speaker 2: you to come to me first. I've never found that

634
00:37:52,239 --> 00:37:55,960
Speaker 2: works for me. Some people it works great, and it

635
00:37:56,000 --> 00:38:01,080
Speaker 2: makes them really dogged reporters. My style is to talk

636
00:38:01,080 --> 00:38:05,240
Speaker 2: to people NonStop. You know, if I think the worst

637
00:38:05,280 --> 00:38:08,680
Speaker 2: thing for someone to feel like is for someone to

638
00:38:08,719 --> 00:38:13,120
Speaker 2: feel used in a way. And I don't know, maybe

639
00:38:13,160 --> 00:38:15,960
Speaker 2: the social engineering is like the wrong word here, but

640
00:38:16,040 --> 00:38:20,040
Speaker 2: something lighter than that. But I want to make people

641
00:38:20,040 --> 00:38:22,399
Speaker 2: feel like they can talk to me about anything, and

642
00:38:22,520 --> 00:38:26,120
Speaker 2: so you know, it's not just me checking in for

643
00:38:26,200 --> 00:38:28,000
Speaker 2: information when I need it. You know, I'm talking to

644
00:38:28,040 --> 00:38:31,040
Speaker 2: them about their holidays or you know, what do you

645
00:38:31,040 --> 00:38:34,280
Speaker 2: have for Thanksgiving? And you know, how is your Christmas break?

646
00:38:34,840 --> 00:38:37,480
Speaker 2: You know, how is your soccer team doing. I'm building

647
00:38:37,480 --> 00:38:41,560
Speaker 2: those relationships. I'm meeting people for drinks to the point

648
00:38:41,560 --> 00:38:46,239
Speaker 2: where when it comes to talking about something that is

649
00:38:46,280 --> 00:38:48,560
Speaker 2: relevant to my reporting, it feels natural for them to

650
00:38:48,880 --> 00:38:52,480
Speaker 2: talk to me about it. And that isn't like a

651
00:38:52,640 --> 00:38:56,120
Speaker 2: unique finding like that could a banker could do that

652
00:38:56,239 --> 00:38:58,520
Speaker 2: or a lawyer. You know, it's just a relationship building

653
00:38:58,520 --> 00:39:01,879
Speaker 2: and people building. And it took me a long time

654
00:39:01,920 --> 00:39:04,440
Speaker 2: to realize that. You know, I wasn't the biggest chatterbox

655
00:39:04,480 --> 00:39:07,480
Speaker 2: in college and I still am not. But like, you

656
00:39:07,560 --> 00:39:10,520
Speaker 2: don't ever want to make people feel like you're just

657
00:39:10,560 --> 00:39:13,560
Speaker 2: pumping them for information, and then you'll never sty'll never

658
00:39:13,560 --> 00:39:16,719
Speaker 2: see you again, and you know I and that just

659
00:39:16,719 --> 00:39:19,440
Speaker 2: feels weird and not human to me anyway, So I

660
00:39:19,440 --> 00:39:22,359
Speaker 2: guess yeah, playing the human card is actually is actually

661
00:39:22,400 --> 00:39:23,200
Speaker 2: pretty important to me.

662
00:39:24,480 --> 00:39:27,160
Speaker 1: Okay, let's talk about the deal now. The first thing

663
00:39:27,200 --> 00:39:30,880
Speaker 1: that is striking in the book that comes back to

664
00:39:30,960 --> 00:39:35,960
Speaker 1: haunt Elon is he makes a deal with no due diligence.

665
00:39:36,360 --> 00:39:37,359
Speaker 1: Tell us about that.

666
00:39:39,120 --> 00:39:41,160
Speaker 2: It's one of the it's one of the crazier things

667
00:39:41,160 --> 00:39:45,759
Speaker 2: about this whole thing. You know, these mergers or takeovers

668
00:39:45,800 --> 00:39:52,320
Speaker 2: take months, sometimes years to close, and that's because bankers

669
00:39:52,360 --> 00:39:56,600
Speaker 2: and lawyers are haggling over every small detail. They're running

670
00:39:56,600 --> 00:40:00,239
Speaker 2: their fine tooth combs through every legal filing and findancial

671
00:40:00,320 --> 00:40:05,680
Speaker 2: filing and making sure everything is up to standard. And

672
00:40:05,719 --> 00:40:09,800
Speaker 2: in this case, Elon wanted to bouldoze a deal through

673
00:40:10,000 --> 00:40:13,240
Speaker 2: in days as fast as he could. After the after

674
00:40:13,360 --> 00:40:17,200
Speaker 2: the after his offer letter. Remember he was on the board,

675
00:40:18,080 --> 00:40:20,319
Speaker 2: he had bought this this chunk of the company. He

676
00:40:20,360 --> 00:40:22,279
Speaker 2: was on the board because Twitter brought him in to

677
00:40:22,360 --> 00:40:26,120
Speaker 2: be closer to them. He ended up kind of getting

678
00:40:26,120 --> 00:40:28,239
Speaker 2: into a fight with the CEO and offering to buy

679
00:40:28,239 --> 00:40:31,719
Speaker 2: the company kind of in this huff, right, and right

680
00:40:31,760 --> 00:40:35,040
Speaker 2: after that, he wants the deal to close immediately, so

681
00:40:35,200 --> 00:40:38,280
Speaker 2: much so that he tells his his bankers and his lawyers,

682
00:40:38,760 --> 00:40:41,040
Speaker 2: you know, we don't need to I don't need to

683
00:40:41,080 --> 00:40:43,680
Speaker 2: look at the books here, I don't need any non

684
00:40:43,680 --> 00:40:46,680
Speaker 2: public information. I don't need to sign an NDA to

685
00:40:46,719 --> 00:40:49,720
Speaker 2: get that non public information. I just want the company,

686
00:40:49,960 --> 00:40:51,919
Speaker 2: and you're gonna You're going to force that deal through

687
00:40:52,000 --> 00:40:54,719
Speaker 2: for me, so I have that company by the end

688
00:40:54,760 --> 00:40:57,400
Speaker 2: of the month. And that's what he did. There was

689
00:40:57,520 --> 00:41:02,120
Speaker 2: no diligence. You remember when he steps on stage at

690
00:41:02,120 --> 00:41:04,680
Speaker 2: the TED conference in I think it was a Vancouver

691
00:41:05,360 --> 00:41:08,759
Speaker 2: in the day after his offer letter comes out, he

692
00:41:08,840 --> 00:41:11,279
Speaker 2: makes all these grand pock proclamations. You know, he says

693
00:41:11,280 --> 00:41:13,040
Speaker 2: he's going to get rid of the bots, He's going

694
00:41:13,080 --> 00:41:19,120
Speaker 2: to say free speech, and that was said without any

695
00:41:19,400 --> 00:41:24,480
Speaker 2: understanding of the business. So yeah, there was no there

696
00:41:24,520 --> 00:41:27,480
Speaker 2: was no looking under the hood effectively, and that was

697
00:41:27,520 --> 00:41:30,360
Speaker 2: one of the major quirks of this deal that caused

698
00:41:30,440 --> 00:41:33,920
Speaker 2: him a lot of problems down the line. Basically immediately

699
00:41:33,920 --> 00:41:37,360
Speaker 2: as soon as soon as the merger agreement assigned.

700
00:41:39,719 --> 00:41:43,920
Speaker 1: Is that evidence of how Elon works, and he's impulsive.

701
00:41:44,520 --> 00:41:47,880
Speaker 1: Let me move forward. Worry about the repercussions after.

702
00:41:49,880 --> 00:41:53,799
Speaker 2: Totally. I mean, I'll reference something he said about the

703
00:41:53,840 --> 00:41:57,400
Speaker 2: Department of Government efficiency. He said something to the effect of,

704
00:41:57,480 --> 00:41:59,839
Speaker 2: you know, if something we cut and it goes wrong,

705
00:42:00,120 --> 00:42:02,080
Speaker 2: just put it back. You know, it's not like I'm

706
00:42:02,080 --> 00:42:04,560
Speaker 2: gonna learn about this and what this thing does. I'm

707
00:42:04,560 --> 00:42:08,600
Speaker 2: gonna cut it. See if something breaks, something some people suffer,

708
00:42:09,239 --> 00:42:11,160
Speaker 2: and if it's enough pain, then I'll just put it back.

709
00:42:11,880 --> 00:42:15,200
Speaker 2: And this kind of impulsiveness is how he's operated through

710
00:42:15,200 --> 00:42:21,200
Speaker 2: his whole career. It's very gut instinct, it's very primal

711
00:42:21,239 --> 00:42:24,360
Speaker 2: in some ways, and it served him well. You know,

712
00:42:24,520 --> 00:42:27,440
Speaker 2: he's going to look back on his successes. Is net worth,

713
00:42:27,560 --> 00:42:30,440
Speaker 2: you know, he's worth more than three hundred billion dollars.

714
00:42:30,480 --> 00:42:33,480
Speaker 2: He has two major companies. He's gonna be like, what

715
00:42:33,760 --> 00:42:36,759
Speaker 2: this has served me so well throughout my life. You know,

716
00:42:36,800 --> 00:42:40,560
Speaker 2: why why shouldn't I do things this way? And in

717
00:42:40,600 --> 00:42:42,759
Speaker 2: this case, in the Twitter case obviously came back to

718
00:42:42,760 --> 00:42:47,120
Speaker 2: bite him because he doesn't do the due diligence. He

719
00:42:47,160 --> 00:42:49,919
Speaker 2: signs the merger agreement and then he starts to get

720
00:42:49,920 --> 00:42:52,759
Speaker 2: really cold feet and tries to find ways to back out.

721
00:42:52,840 --> 00:42:55,880
Speaker 2: You know, he starts to name issues around bots, for example,

722
00:42:58,600 --> 00:43:03,680
Speaker 2: and you know that's kind of the source of his pain.

723
00:43:03,760 --> 00:43:08,280
Speaker 1: Now with this deal, Okay, since you cover Elon in general,

724
00:43:08,360 --> 00:43:11,120
Speaker 1: let's go to Tesla for a second. So we have

725
00:43:11,200 --> 00:43:14,560
Speaker 1: the Tesla self driving. They're the first movers in that area.

726
00:43:15,040 --> 00:43:18,680
Speaker 1: He gets rid of light ar relying solely on cameras.

727
00:43:19,400 --> 00:43:25,279
Speaker 1: The competitors go to lightar. Okay, Meanwhile, the government is

728
00:43:25,320 --> 00:43:30,080
Speaker 1: investing for crashes. Is he doubling down based on his

729
00:43:30,239 --> 00:43:34,080
Speaker 1: personality saying fuck you? Well, do you think he really

730
00:43:34,160 --> 00:43:38,040
Speaker 1: evaluates a cameras are enough or is he convinced himself

731
00:43:38,120 --> 00:43:38,959
Speaker 1: cameras are enough?

732
00:43:39,800 --> 00:43:42,080
Speaker 2: I think in these beliefs, he's convinced himself. I think

733
00:43:42,120 --> 00:43:44,759
Speaker 2: it's very key to understanding he's not just saying things right,

734
00:43:44,800 --> 00:43:48,799
Speaker 2: he fundamentally believes them. And the thing with lightar is

735
00:43:48,840 --> 00:43:52,160
Speaker 2: that he believes that cars should see like humans do,

736
00:43:52,520 --> 00:43:55,359
Speaker 2: and and and cameras will be enough, and cameras are

737
00:43:55,400 --> 00:43:58,600
Speaker 2: much cheaper than a light r you know that being said,

738
00:43:58,760 --> 00:44:04,040
Speaker 2: look at the progress of where Tesla is relative to Weimo.

739
00:44:04,200 --> 00:44:06,560
Speaker 2: For example, Wemo actually has cars on the streets that

740
00:44:06,600 --> 00:44:10,160
Speaker 2: are taxing people around with no drivers, and Tesla is

741
00:44:10,200 --> 00:44:14,640
Speaker 2: still kind of making these half promises about full self driving.

742
00:44:14,719 --> 00:44:16,799
Speaker 2: Oh actually it's not full self driving. You have to

743
00:44:16,880 --> 00:44:21,480
Speaker 2: be engaged with the driver. And he's kind of jerked

744
00:44:21,480 --> 00:44:26,840
Speaker 2: people around on the meeting of Autopilot. You know, he

745
00:44:26,920 --> 00:44:30,560
Speaker 2: had that Robotaxi event a couple months ago. I'm sure

746
00:44:30,600 --> 00:44:33,520
Speaker 2: you saw it right where he had. You know, it's

747
00:44:33,560 --> 00:44:35,920
Speaker 2: a staged event. You're on a literally on a movie

748
00:44:35,960 --> 00:44:39,840
Speaker 2: set in Burbank, driving people around these pretend cities on

749
00:44:39,880 --> 00:44:44,840
Speaker 2: these supposedly autonomous vehicles. And he's selling that to people

750
00:44:44,880 --> 00:44:48,160
Speaker 2: as if it's like the next revolution, when if you

751
00:44:48,280 --> 00:44:52,160
Speaker 2: walk over to Hollywood you can find a Waimo, you know,

752
00:44:52,239 --> 00:44:56,840
Speaker 2: going up and down the street with no driver. So yeah,

753
00:44:56,920 --> 00:45:00,600
Speaker 2: I mean, I think the one thing understand a boy

754
00:45:00,640 --> 00:45:03,279
Speaker 2: Elon is that one of his superpowers, if not his

755
00:45:03,320 --> 00:45:07,560
Speaker 2: most super important superpower, is his ability as a salesman,

756
00:45:07,680 --> 00:45:10,000
Speaker 2: his ability to sell you on a mission and a vision.

757
00:45:10,760 --> 00:45:14,360
Speaker 2: And people love that, you know, because you know, we

758
00:45:14,440 --> 00:45:18,480
Speaker 2: can we can debate whether or not he hits his

759
00:45:18,480 --> 00:45:23,120
Speaker 2: his his goals or his his projections. You know, he said,

760
00:45:23,200 --> 00:45:25,600
Speaker 2: for example, we get to Mars by twenty twenty five.

761
00:45:26,400 --> 00:45:30,680
Speaker 2: I don't think that's going to happen. But you know,

762
00:45:31,800 --> 00:45:34,160
Speaker 2: you look at this robotax event and me as a report,

763
00:45:34,200 --> 00:45:36,560
Speaker 2: I'm like, I'm very skeptical, like this is. But you

764
00:45:36,640 --> 00:45:39,880
Speaker 2: have people that are screaming and cheering and posting online.

765
00:45:40,239 --> 00:45:42,319
Speaker 2: You know, they're loving the robots that are serving the

766
00:45:42,360 --> 00:45:45,120
Speaker 2: beers and everything, and you're like, wow, he really has

767
00:45:45,120 --> 00:45:48,960
Speaker 2: sold this to people. People really believe him on this stuff,

768
00:45:49,120 --> 00:45:54,680
Speaker 2: and I don't. Uh, yeah, I don't know. It's it's

769
00:45:54,719 --> 00:45:56,320
Speaker 2: it's an interesting quality that he has.

770
00:45:56,440 --> 00:46:09,080
Speaker 1: So okay. You also say that he has these advisors

771
00:46:09,200 --> 00:46:12,319
Speaker 1: that he trusts in the deal. They really have no

772
00:46:12,480 --> 00:46:17,439
Speaker 1: portfolio that have no expertise in this area whatsoever.

773
00:46:18,960 --> 00:46:22,680
Speaker 2: Aside from having large Twitter followings. You know, he's yeah,

774
00:46:22,719 --> 00:46:24,879
Speaker 2: he's in the room with some of these venture capitalists,

775
00:46:25,239 --> 00:46:27,920
Speaker 2: people that have had good careers in technology, have built

776
00:46:28,320 --> 00:46:31,680
Speaker 2: massive fortunes investing, but in terms of running a social

777
00:46:31,680 --> 00:46:34,759
Speaker 2: media company something to the scale of Twitter, which few

778
00:46:34,760 --> 00:46:40,440
Speaker 2: companies are. You know, there's Facebook, there's Instagram, Reddit, But

779
00:46:41,160 --> 00:46:44,040
Speaker 2: these guys don't have an understanding of this, and they

780
00:46:44,520 --> 00:46:48,080
Speaker 2: again view this as a technical problem as opposed to

781
00:46:48,080 --> 00:46:52,840
Speaker 2: a people problem. And so he's taking advice from people

782
00:46:53,680 --> 00:46:55,600
Speaker 2: of his same background of the same ilk you know,

783
00:46:55,680 --> 00:47:01,000
Speaker 2: like David Sachs, Jason kalakhanis of these investors that have

784
00:47:01,040 --> 00:47:03,040
Speaker 2: put money into this deal. He at one point takes

785
00:47:03,080 --> 00:47:08,000
Speaker 2: advice from his own biographer, Walter Isaacson, and he and

786
00:47:08,160 --> 00:47:11,600
Speaker 2: also in that process decides to fire people that have

787
00:47:11,920 --> 00:47:14,560
Speaker 2: worked at the company for years studying this stuff and

788
00:47:14,600 --> 00:47:19,400
Speaker 2: working on it, largely because he believes they're inept or

789
00:47:19,440 --> 00:47:25,080
Speaker 2: they're too woke or whatever. And that's I think that Hubris.

790
00:47:25,600 --> 00:47:28,040
Speaker 2: Again we talk about these kind of gut decisions that

791
00:47:28,080 --> 00:47:32,560
Speaker 2: he makes, but that is what has hampered him throughout

792
00:47:32,560 --> 00:47:36,080
Speaker 2: the deal and throughout the takeover to the point where

793
00:47:36,080 --> 00:47:39,600
Speaker 2: the company is, you know, valuation wise is crashed. It's

794
00:47:39,640 --> 00:47:42,120
Speaker 2: advertising as crashed, the user numbers have crashed.

795
00:47:43,840 --> 00:47:47,800
Speaker 1: Yeah, but even his lawyer, you know, even the people,

796
00:47:47,880 --> 00:47:50,840
Speaker 1: you know, the the he's not using blue chip people

797
00:47:51,080 --> 00:47:52,160
Speaker 1: even to make the deal.

798
00:47:54,000 --> 00:47:58,000
Speaker 2: Yeah. Alex Alex Spiro, who is his I'm sure you've

799
00:47:58,040 --> 00:48:00,879
Speaker 2: come across me as a is a big celebrity lawyer.

800
00:48:00,880 --> 00:48:05,640
Speaker 2: He represented Megan the Stallion and Robert Kraft. He's a

801
00:48:05,719 --> 00:48:09,200
Speaker 2: Jay Z's lawyer and actually becomes Elon's lawyer in the

802
00:48:09,239 --> 00:48:12,080
Speaker 2: Pedal Guy case where he defends them successfully against deformation,

803
00:48:13,800 --> 00:48:16,240
Speaker 2: ends up being one of the kind of lead lawyers

804
00:48:16,280 --> 00:48:20,319
Speaker 2: on this deal, and also someone who ends up like

805
00:48:21,560 --> 00:48:24,160
Speaker 2: somewhat leading the legal department in the early days of Twitter.

806
00:48:24,960 --> 00:48:27,239
Speaker 2: You know, this is a trial lawyer. This isn't you know.

807
00:48:27,680 --> 00:48:31,319
Speaker 2: He may be wildly successful, but does he have the

808
00:48:31,360 --> 00:48:35,040
Speaker 2: expertise to understand what it means when an FTC consent

809
00:48:35,120 --> 00:48:38,680
Speaker 2: decree comes in from the FTC? Does he understand what

810
00:48:39,160 --> 00:48:43,279
Speaker 2: happens when the EU demands information? And does he have

811
00:48:43,320 --> 00:48:48,080
Speaker 2: international law expertise to under like to grasp that. I

812
00:48:48,080 --> 00:48:50,920
Speaker 2: don't think so, you know. And Elon fired a lot

813
00:48:50,960 --> 00:48:52,959
Speaker 2: of the people or got or encouraged them to leave,

814
00:48:53,719 --> 00:48:55,880
Speaker 2: folks that have kind of specialized in these areas and

815
00:48:55,920 --> 00:49:00,440
Speaker 2: working in it for their whole careers. And again, that's

816
00:49:00,480 --> 00:49:02,160
Speaker 2: just kind of one of the wilder things of this,

817
00:49:03,280 --> 00:49:06,799
Speaker 2: of this whole thing. There was really no transition in

818
00:49:06,880 --> 00:49:10,160
Speaker 2: place to understand how Twitter operated when he took over

819
00:49:10,200 --> 00:49:12,799
Speaker 2: the company. There were a handful of meetings, but there

820
00:49:12,880 --> 00:49:16,799
Speaker 2: wasn't like a transition plan. There wasn't like, Okay, these

821
00:49:16,880 --> 00:49:19,319
Speaker 2: these people work over here. I understand that this is

822
00:49:19,320 --> 00:49:22,160
Speaker 2: how the advertising business works. Let me understand that that

823
00:49:22,200 --> 00:49:24,759
Speaker 2: came after he took over the company and after he

824
00:49:24,840 --> 00:49:31,520
Speaker 2: laid off hundreds and thousands of people, and that has

825
00:49:31,560 --> 00:49:32,920
Speaker 2: fundamentally damaged to the company.

826
00:49:34,000 --> 00:49:37,160
Speaker 1: Okay, let's since you mentioned let's stay with the FTC

827
00:49:37,280 --> 00:49:41,479
Speaker 1: for one second. There was the lawsuit where Elon said

828
00:49:41,520 --> 00:49:46,640
Speaker 1: he was going to take the company private. This is Tesla. Okay,

829
00:49:46,800 --> 00:49:49,160
Speaker 1: the government sued him. He made an agreement that he's

830
00:49:49,200 --> 00:49:53,319
Speaker 1: been bitching about ever since, trying to overturn. You make

831
00:49:53,360 --> 00:49:55,520
Speaker 1: a point that there are certain deals with the FTC.

832
00:49:55,840 --> 00:50:00,480
Speaker 1: The FTC keeps asking for compliance and they don't imply.

833
00:50:01,560 --> 00:50:04,360
Speaker 1: Does this basically say, if you're one of these guys,

834
00:50:04,400 --> 00:50:07,200
Speaker 1: you have these balls, the real rules really don't apply.

835
00:50:09,040 --> 00:50:14,160
Speaker 2: So the yeah, so the the the UH funding secured

836
00:50:14,200 --> 00:50:16,400
Speaker 2: stuff that that Tesla take private, that was the that

837
00:50:16,440 --> 00:50:19,640
Speaker 2: was the securities in the Change Commission sec But that

838
00:50:19,640 --> 00:50:24,960
Speaker 2: that case, I feel like he is where he draws

839
00:50:25,040 --> 00:50:29,000
Speaker 2: his understanding of how unaccountable he is. There is nothing

840
00:50:29,000 --> 00:50:32,359
Speaker 2: that can hold him accountable. And I'll explain why, which

841
00:50:32,400 --> 00:50:34,440
Speaker 2: is in twenty eighteen, he tweets that he has the

842
00:50:34,480 --> 00:50:38,799
Speaker 2: money to take Tesla private funding secured. It has at

843
00:50:38,840 --> 00:50:40,759
Speaker 2: four hundred and twenty dollars a share. He's going to

844
00:50:40,800 --> 00:50:43,560
Speaker 2: take Tesla private, take it off the public market where

845
00:50:43,560 --> 00:50:45,800
Speaker 2: it's not doing well because he's not hitting his numbers

846
00:50:45,800 --> 00:50:48,640
Speaker 2: with the Model three rollout, and he says, screw it.

847
00:50:48,800 --> 00:50:50,279
Speaker 2: You know, I don't need the public market. I have

848
00:50:50,320 --> 00:50:52,600
Speaker 2: my private investors. You know, he didn't have that money,

849
00:50:52,840 --> 00:50:54,960
Speaker 2: so I was going to come from Saudi Arabia wasn't coming.

850
00:50:55,920 --> 00:50:59,600
Speaker 2: And the SEC sues him because he puts out a

851
00:50:59,600 --> 00:51:05,240
Speaker 2: statement that manipulates the markets and offers a false promise

852
00:51:05,320 --> 00:51:10,400
Speaker 2: to investors. YadA YadA. They sue him. He fights it,

853
00:51:10,440 --> 00:51:15,319
Speaker 2: briefly settles what is what is he? What does he

854
00:51:15,360 --> 00:51:17,120
Speaker 2: have to pay for that? You know, he personally pays

855
00:51:17,120 --> 00:51:19,319
Speaker 2: a twenty million dollar fine. Testa pays a twenty million

856
00:51:19,360 --> 00:51:23,280
Speaker 2: dollar fine. He's no longer able to be chairman, whatever

857
00:51:23,320 --> 00:51:26,279
Speaker 2: that means. But what is a for What does forty

858
00:51:26,320 --> 00:51:30,720
Speaker 2: million dollars mean to someone who's worth one hundred billion dollars?

859
00:51:30,800 --> 00:51:33,000
Speaker 2: You know it it's a parking ticket for you and me.

860
00:51:33,960 --> 00:51:39,520
Speaker 2: And his fundamental understanding of that that he could either

861
00:51:39,560 --> 00:51:43,239
Speaker 2: fight something or simply pay the fine is crucial to

862
00:51:43,280 --> 00:51:46,280
Speaker 2: how he operates now because when you get to something

863
00:51:46,360 --> 00:51:49,800
Speaker 2: like operating Twitter and complying to an FDC consent decree,

864
00:51:50,760 --> 00:51:52,439
Speaker 2: He'll be like, you know, I don't care about that,

865
00:51:52,600 --> 00:51:54,080
Speaker 2: Like what are they going to do to me? Like

866
00:51:54,680 --> 00:51:55,920
Speaker 2: are they going to They're not going to take the

867
00:51:55,960 --> 00:51:59,000
Speaker 2: company away from me. They'll levy a fine. What is

868
00:51:59,000 --> 00:52:01,240
Speaker 2: the fine going to be? Tens of millions dollars? Hundreds

869
00:52:01,239 --> 00:52:03,239
Speaker 2: of millions of dollars? You know, I'm now worth two

870
00:52:03,280 --> 00:52:06,239
Speaker 2: hundred billion dollars. And not only that, I'm going to

871
00:52:06,320 --> 00:52:11,000
Speaker 2: fight on every level using the legal system to really

872
00:52:11,000 --> 00:52:15,640
Speaker 2: clog this process up. Right now, he is being asked

873
00:52:15,760 --> 00:52:20,360
Speaker 2: to sit for a deposition by the SEC in a

874
00:52:20,440 --> 00:52:23,279
Speaker 2: in a related securities cases. He's simply not showing up

875
00:52:23,280 --> 00:52:27,440
Speaker 2: to his depositions, you know, and he's flaunting the SEC.

876
00:52:27,640 --> 00:52:29,239
Speaker 2: He had a tweet the other day which is a

877
00:52:29,239 --> 00:52:33,000
Speaker 2: play on something. He's kind of childish thing he said,

878
00:52:33,000 --> 00:52:37,759
Speaker 2: but he says, the SEC stands for you know, the

879
00:52:38,200 --> 00:52:41,239
Speaker 2: E stands for Elons, So you know S, you know,

880
00:52:41,680 --> 00:52:44,279
Speaker 2: suck Elon's blank. You know that's what the s. You

881
00:52:44,280 --> 00:52:46,839
Speaker 2: know that that was his tweet the other day, And

882
00:52:47,080 --> 00:52:49,880
Speaker 2: you start to realize that in some ways he's he's

883
00:52:51,520 --> 00:52:54,799
Speaker 2: blown past the gravitational pool of accountability. You know, he

884
00:52:54,880 --> 00:52:58,840
Speaker 2: is so rich, he has so many resources. The government

885
00:52:58,920 --> 00:53:02,440
Speaker 2: is so reliant on him with that, you know, he

886
00:53:02,480 --> 00:53:06,080
Speaker 2: doesn't care, and that I think is a crucial understanding

887
00:53:06,200 --> 00:53:08,360
Speaker 2: understanding how he operates.

888
00:53:09,280 --> 00:53:12,840
Speaker 1: Okay, to make the deal. It's not all his own money.

889
00:53:12,880 --> 00:53:16,400
Speaker 1: He raises some Silicon Valley money, raises some Wall Street money.

890
00:53:17,080 --> 00:53:22,080
Speaker 1: Anybody who followed this closely knew that was a bad deal.

891
00:53:23,520 --> 00:53:26,040
Speaker 1: Why did they put the money in and it's been

892
00:53:26,080 --> 00:53:29,200
Speaker 1: proven to be a bad deal. They can't lay the

893
00:53:29,239 --> 00:53:29,759
Speaker 1: money off.

894
00:53:30,680 --> 00:53:34,400
Speaker 2: Yeah, he raised what was publicly just goes about seven

895
00:53:34,480 --> 00:53:38,600
Speaker 2: billion dollars of external capital, not including the debt, which

896
00:53:38,600 --> 00:53:40,080
Speaker 2: we can talk about later, which is kind of a

897
00:53:40,960 --> 00:53:46,400
Speaker 2: millstone around the company's neck. But the external this external

898
00:53:46,480 --> 00:53:49,640
Speaker 2: venture capital. Some of it came from Qatar, for example.

899
00:53:49,680 --> 00:53:53,000
Speaker 2: Some of it came from Kingdom holding the Saudi Prince,

900
00:53:53,040 --> 00:53:58,399
Speaker 2: who is effectively overseen by MBS Mohammed bin Salman, who

901
00:53:58,480 --> 00:54:03,080
Speaker 2: rolled over his steak from he already owned past Twitter share,

902
00:54:03,080 --> 00:54:05,160
Speaker 2: so he just rolled over his investment about two billion

903
00:54:05,200 --> 00:54:08,719
Speaker 2: dollars into into Elon's deal. But a lot of these

904
00:54:08,719 --> 00:54:11,879
Speaker 2: were blue like leading venture Capital from the Silicon Valley.

905
00:54:11,880 --> 00:54:17,000
Speaker 2: We're talking about Siquoy Capital, Andreas and Horowitz, you know,

906
00:54:17,080 --> 00:54:20,960
Speaker 2: and these and these firms believe in him as an entrepreneur,

907
00:54:21,239 --> 00:54:23,920
Speaker 2: you know, they they see him as a generational talent.

908
00:54:24,600 --> 00:54:26,840
Speaker 2: He has done so much with SpaceX and with Tesla

909
00:54:27,440 --> 00:54:29,200
Speaker 2: that they're just going to take a punt on this

910
00:54:29,280 --> 00:54:33,919
Speaker 2: Twitter deal. And even if it's a money loser, they're

911
00:54:33,960 --> 00:54:37,240
Speaker 2: still close to him. They've they've it's it's especially buying

912
00:54:37,280 --> 00:54:40,080
Speaker 2: loyalty in a way where on his next deal they

913
00:54:40,160 --> 00:54:42,440
Speaker 2: might come to him. And so I think about this

914
00:54:42,560 --> 00:54:45,160
Speaker 2: message that we reported in the book from Mark Andreesen,

915
00:54:45,200 --> 00:54:49,720
Speaker 2: the head of Andresen Horowitz, who was offered to invest

916
00:54:49,800 --> 00:54:51,919
Speaker 2: I think to initially two hundred million dollars. The number

917
00:54:52,000 --> 00:54:55,360
Speaker 2: changed after but the emails, the messages came out. He's like,

918
00:54:55,360 --> 00:54:57,080
Speaker 2: you know, two hundred million. Sure, we don't even need

919
00:54:57,160 --> 00:55:00,200
Speaker 2: to do dilig just whatever he wants, he gets the check.

920
00:55:01,239 --> 00:55:04,560
Speaker 2: And that's the level of influence he has over these people.

921
00:55:05,480 --> 00:55:07,880
Speaker 2: And if you actually think about this deal, yes, it

922
00:55:08,080 --> 00:55:11,200
Speaker 2: individually it's a money loser for the Sequoia Capitals of

923
00:55:11,200 --> 00:55:13,560
Speaker 2: the world, for the and recent Horowitz of the world,

924
00:55:14,560 --> 00:55:18,279
Speaker 2: but these same firms invested in this ai company that

925
00:55:18,320 --> 00:55:21,880
Speaker 2: Elon spun up off of the data from Twitter Xai.

926
00:55:23,280 --> 00:55:26,880
Speaker 2: And yes, it's funny money, but that company was recently

927
00:55:27,000 --> 00:55:32,040
Speaker 2: valued at fifty billion dollars in a private venture round.

928
00:55:32,800 --> 00:55:35,440
Speaker 2: So you know, if you're looking at the plus minuses

929
00:55:35,520 --> 00:55:38,719
Speaker 2: here Twitter goes down, it's now about eight to nine

930
00:55:38,719 --> 00:55:41,360
Speaker 2: billion dollars if you believe Fidelity's valuation of the company.

931
00:55:41,880 --> 00:55:44,000
Speaker 2: But he's been able to create xai out of zero

932
00:55:44,320 --> 00:55:47,600
Speaker 2: zero to fifty and so you're like, you know, maybe

933
00:55:47,600 --> 00:55:51,760
Speaker 2: they have a point. You know, maybe they've they've gained

934
00:55:51,760 --> 00:55:54,680
Speaker 2: the system enough where they understand that just being close

935
00:55:54,719 --> 00:55:56,800
Speaker 2: to Elon begets more wealth.

936
00:55:57,719 --> 00:56:00,080
Speaker 1: And what about the banks that loan them the money?

937
00:56:00,800 --> 00:56:04,200
Speaker 2: Do they think that? Yeah? Those that's where it gets.

938
00:56:05,640 --> 00:56:05,920
Speaker 1: Rough.

939
00:56:06,239 --> 00:56:08,480
Speaker 2: You know. I have to look back on the actual

940
00:56:08,680 --> 00:56:13,279
Speaker 2: debt that was raised. I think it was gosh, I'm

941
00:56:13,480 --> 00:56:16,359
Speaker 2: off top of mad, like twelve thirteen billion dollars. It

942
00:56:16,400 --> 00:56:20,879
Speaker 2: amounts to a billion dollars in interest payments alone a year,

943
00:56:22,120 --> 00:56:27,080
Speaker 2: and those I mean, it's it's it's it's the worst

944
00:56:27,120 --> 00:56:30,799
Speaker 2: deal since the Financial crisis for these banks. These banks

945
00:56:30,800 --> 00:56:33,160
Speaker 2: are whole, They weren't able to sell on this debt,

946
00:56:33,760 --> 00:56:40,120
Speaker 2: and they're holding these this horrible you know, this horrible debt.

947
00:56:40,120 --> 00:56:42,600
Speaker 2: You know, it's just like they're getting crushed by it.

948
00:56:43,960 --> 00:56:45,960
Speaker 2: These are the Morgan Stanley's, the Moral Think Bank of

949
00:56:46,000 --> 00:56:51,320
Speaker 2: America had some of the debt Mizuno as well, and

950
00:56:51,719 --> 00:56:55,840
Speaker 2: for them, I think the calculus is is somewhat even similar.

951
00:56:56,000 --> 00:56:59,399
Speaker 2: You know, maybe we take this hit, really ugly hit

952
00:56:59,760 --> 00:57:03,000
Speaker 2: on our books from this this Twitter deal. But let's

953
00:57:03,000 --> 00:57:06,440
Speaker 2: say SpaceX goes public in the next couple of years,

954
00:57:06,680 --> 00:57:08,759
Speaker 2: we want to be on that i PO. You know,

955
00:57:08,840 --> 00:57:11,360
Speaker 2: that's a that's a billion dollar i PO for us

956
00:57:12,320 --> 00:57:15,160
Speaker 2: and so and you know what happens with Neuralink and

957
00:57:15,320 --> 00:57:20,640
Speaker 2: Boring Company. You know, they're probably doing the plus minus

958
00:57:20,680 --> 00:57:23,080
Speaker 2: calculation and thinking, actually, they will still come out on top,

959
00:57:23,120 --> 00:57:26,680
Speaker 2: even if even if Twitter is a historically bad deal.

960
00:57:27,800 --> 00:57:31,200
Speaker 1: Okay, one of the reasons he wanted to buy what

961
00:57:31,400 --> 00:57:37,040
Speaker 1: was then called Twitter was because of content moderation. Okay,

962
00:57:37,160 --> 00:57:40,760
Speaker 1: he felt it was too tight. He comes in and

963
00:57:40,800 --> 00:57:45,160
Speaker 1: he immediately blows out everybody involved in content moderation.

964
00:57:47,240 --> 00:57:47,280
Speaker 3: It.

965
00:57:47,600 --> 00:57:51,400
Speaker 2: Uh yeah, he's he's he doesn't hide what he wants

966
00:57:51,400 --> 00:57:54,400
Speaker 2: to do, you know. Uh, he was mad about the

967
00:57:54,400 --> 00:57:57,720
Speaker 2: Babylon Bee being suspended, if that that that kind of

968
00:57:57,760 --> 00:58:01,880
Speaker 2: right wing satirial satirical account. He was upset with Trump

969
00:58:02,000 --> 00:58:05,000
Speaker 2: not being on the platform, even though he technically didn't

970
00:58:05,120 --> 00:58:08,960
Speaker 2: like Trump. He thought it was a bad implication for

971
00:58:09,000 --> 00:58:11,880
Speaker 2: free speech whatever, you know, and we'll talk about what

972
00:58:11,920 --> 00:58:17,440
Speaker 2: he means by free speech. But yeah, well, actually, actually

973
00:58:17,480 --> 00:58:20,120
Speaker 2: what happened was he tried to play nice with some

974
00:58:20,160 --> 00:58:22,640
Speaker 2: of the content moderation folks. You remember this guy Yoel Roth.

975
00:58:23,640 --> 00:58:26,960
Speaker 2: We ended up like severely harassing on the platform, to

976
00:58:27,000 --> 00:58:31,120
Speaker 2: the point where Roth had to sell his home because

977
00:58:31,120 --> 00:58:35,880
Speaker 2: he was being chased in real life by this online harassment.

978
00:58:37,160 --> 00:58:41,080
Speaker 2: They were trying to get along. Elon was taking advice

979
00:58:41,120 --> 00:58:43,240
Speaker 2: from this guy Yoel Roth ahead of Trust and Safety.

980
00:58:44,240 --> 00:58:48,640
Speaker 2: Yoel eventually resigns, I think in the two or three

981
00:58:48,680 --> 00:58:52,080
Speaker 2: weeks after the deal closes, and then kind of all

982
00:58:52,080 --> 00:58:54,520
Speaker 2: hell breaks loose. He goes after you Wel he continues

983
00:58:54,520 --> 00:58:59,720
Speaker 2: to fire Trust and Safety people. But yeah, content moderation

984
00:59:00,120 --> 00:59:04,400
Speaker 2: for Elon was one of his big bugaboobs begaboos, and

985
00:59:04,480 --> 00:59:07,240
Speaker 2: like it still is to this day. You know, he's

986
00:59:08,120 --> 00:59:11,880
Speaker 2: he still thinks that Twitter was or the previous administration

987
00:59:12,120 --> 00:59:18,600
Speaker 2: was influenced by progressive woke wokeism or whatever, and that

988
00:59:18,720 --> 00:59:22,919
Speaker 2: everything flowed from that, and uh yeah, now you kind

989
00:59:22,920 --> 00:59:26,000
Speaker 2: of that's that's kind of how you can understand or

990
00:59:26,080 --> 00:59:28,080
Speaker 2: the content that's on Twitter now these days.

991
00:59:28,440 --> 00:59:31,720
Speaker 1: Okay, it's not only the United States of America. There's

992
00:59:31,760 --> 00:59:33,480
Speaker 1: been the recent thing with Brazil.

993
00:59:34,600 --> 00:59:35,960
Speaker 2: Yeah, is he just.

994
00:59:37,760 --> 00:59:41,600
Speaker 1: Flying naked or is this is it at some point

995
00:59:41,760 --> 00:59:46,720
Speaker 1: with no content moderation team in place? Is it gonna

996
00:59:46,840 --> 00:59:52,800
Speaker 1: ultimately impact Twitter just as a business negatively in a

997
00:59:52,960 --> 00:59:55,880
Speaker 1: huge way. I Mean, we had the situation in Brazil

998
00:59:56,480 --> 00:59:59,560
Speaker 1: and the person said, I'm standing up for everybody else

999
00:59:59,560 --> 01:00:00,640
Speaker 1: who will stand up?

1000
01:00:01,440 --> 01:00:04,760
Speaker 2: Yeah, So a couple of things there. There is still

1001
01:00:04,760 --> 01:00:06,920
Speaker 2: a little bit of content moderation. There's automated processes in

1002
01:00:06,960 --> 01:00:11,080
Speaker 2: place that at at X there's still people. There's there's

1003
01:00:11,120 --> 01:00:14,360
Speaker 2: still some humans in the building. He's gotten rid of

1004
01:00:14,360 --> 01:00:18,120
Speaker 2: a lot of them, but there's still some around with

1005
01:00:18,240 --> 01:00:21,040
Speaker 2: content moderation. A couple there's there's a couple things you

1006
01:00:21,080 --> 01:00:23,680
Speaker 2: have to understand, which is that advertisers have been driven

1007
01:00:23,720 --> 01:00:26,439
Speaker 2: away by the lack of content moderation, right like it's

1008
01:00:26,480 --> 01:00:32,600
Speaker 2: a It's caused major brands to leave or spend less money,

1009
01:00:32,760 --> 01:00:35,320
Speaker 2: because why would they want their content to be next

1010
01:00:35,360 --> 01:00:39,080
Speaker 2: to a white nationalist who's been let back on the platform.

1011
01:00:39,120 --> 01:00:42,640
Speaker 2: Why would they want it next to tweets about Nazism

1012
01:00:43,080 --> 01:00:48,200
Speaker 2: or you know, videos of violence, which are somewhat commonplace

1013
01:00:48,240 --> 01:00:53,680
Speaker 2: on the platform. Now, the Brazil situation is very interesting

1014
01:00:53,720 --> 01:00:58,200
Speaker 2: to me, and it goes back to Elon's political alliances

1015
01:00:58,240 --> 01:01:03,000
Speaker 2: around the world. Elon was friends with Jayra Bolsonaro, the

1016
01:01:03,040 --> 01:01:08,520
Speaker 2: previous president in Brazil who was voted out essentially and

1017
01:01:08,560 --> 01:01:16,400
Speaker 2: replaced by the leftist president. But in doing you know,

1018
01:01:16,440 --> 01:01:17,760
Speaker 2: and then there were they had their own kind of

1019
01:01:17,840 --> 01:01:22,320
Speaker 2: January sixth moment where supporters of Bolsonaro storm government buildings

1020
01:01:22,360 --> 01:01:26,240
Speaker 2: in Brazil, which led to these decisions by the courts

1021
01:01:26,240 --> 01:01:32,240
Speaker 2: down in Brazil to essentially demand that Twitter ex remove

1022
01:01:33,400 --> 01:01:37,120
Speaker 2: these voices that caused for people to question the election

1023
01:01:37,600 --> 01:01:39,480
Speaker 2: and cause this kind of unrest. You know, we can

1024
01:01:39,520 --> 01:01:42,720
Speaker 2: debate whether or not it's good for any government body

1025
01:01:42,720 --> 01:01:46,280
Speaker 2: to get involved in online speech, but that was the

1026
01:01:46,360 --> 01:01:50,360
Speaker 2: law of the land in Brazil, and Elon decided to

1027
01:01:50,400 --> 01:01:55,880
Speaker 2: fight that in part because he was aligned with, you know,

1028
01:01:55,920 --> 01:01:59,160
Speaker 2: the old president, and he made a huge think about

1029
01:01:59,160 --> 01:02:02,040
Speaker 2: this to the point where X was pulled out of

1030
01:02:02,080 --> 01:02:05,880
Speaker 2: the country for a couple of weeks and then ultimately folded.

1031
01:02:05,920 --> 01:02:07,800
Speaker 2: You know, he complied with the orders and now the

1032
01:02:09,880 --> 01:02:13,120
Speaker 2: platform is back in the country. He'd loved to make

1033
01:02:13,640 --> 01:02:16,920
Speaker 2: if you analyze this case, he loved to make himself.

1034
01:02:17,080 --> 01:02:19,080
Speaker 2: He loves to make himself kind of the free speech

1035
01:02:19,160 --> 01:02:22,280
Speaker 2: advocate or seen as the free speech advocate. But let's

1036
01:02:22,280 --> 01:02:24,640
Speaker 2: compare this to what's happening in India, for example, where

1037
01:02:24,640 --> 01:02:27,479
Speaker 2: he's very close with the Prime Minister there in Arender Modi.

1038
01:02:28,000 --> 01:02:32,400
Speaker 2: The Modi government has demanded that Twitter sensor content regularly.

1039
01:02:32,640 --> 01:02:34,880
Speaker 2: I think of something like a documentary that was put

1040
01:02:34,960 --> 01:02:38,880
Speaker 2: up by the BBC about Modi himself. It was put

1041
01:02:38,920 --> 01:02:42,320
Speaker 2: on YouTube and shared across platforms. You know, Twitter complied

1042
01:02:42,360 --> 01:02:45,960
Speaker 2: with those takedown orders. There was no standing up for

1043
01:02:46,000 --> 01:02:50,760
Speaker 2: free speech in that case by Elon and opposing that

1044
01:02:50,760 --> 01:02:54,960
Speaker 2: that government takedown requests. He complied very easily. He's complied

1045
01:02:55,000 --> 01:02:57,760
Speaker 2: with government takedown requests in Turkey, for example, where he's

1046
01:02:58,080 --> 01:03:03,320
Speaker 2: aligned with Erdawan. So this kind of free speech mantra

1047
01:03:04,360 --> 01:03:09,320
Speaker 2: that he purports is very selectively applied and really depends

1048
01:03:09,360 --> 01:03:13,320
Speaker 2: on the market who he's aligned with. As well as

1049
01:03:13,320 --> 01:03:14,920
Speaker 2: his own kind of personal interests.

1050
01:03:22,520 --> 01:03:26,600
Speaker 1: Okay, since we're doing a three D view on Elon. Yeah,

1051
01:03:26,760 --> 01:03:30,439
Speaker 1: just one thing, a little lose gravel here. He makes

1052
01:03:30,480 --> 01:03:33,280
Speaker 1: all these deals with these other car companies that they

1053
01:03:33,280 --> 01:03:37,680
Speaker 1: can use his superchargers. Then he wipes out his complete

1054
01:03:37,720 --> 01:03:41,120
Speaker 1: supercharger team. What was going on there?

1055
01:03:43,240 --> 01:03:46,160
Speaker 2: Your guess is as good as mine. He was He

1056
01:03:46,240 --> 01:03:50,280
Speaker 2: often he sometimes does this where he decides like there

1057
01:03:50,320 --> 01:03:53,520
Speaker 2: needs to be a kicking the ass for a part

1058
01:03:53,520 --> 01:03:56,600
Speaker 2: of his company, and he'll like severely jolt it, you know.

1059
01:03:56,640 --> 01:03:58,200
Speaker 2: And again we go back to that idea of if

1060
01:03:58,280 --> 01:04:00,919
Speaker 2: I if I break it, I can always bring him back.

1061
01:04:00,960 --> 01:04:03,360
Speaker 2: I can always reintroduce it. And actually he did that.

1062
01:04:03,440 --> 01:04:06,920
Speaker 2: He fired his whole supercharging team and they actually ended

1063
01:04:07,000 --> 01:04:11,480
Speaker 2: up bringing a lot of them back, which he's done

1064
01:04:11,480 --> 01:04:15,400
Speaker 2: this throughout his career. But if you're if you're GM,

1065
01:04:15,760 --> 01:04:18,560
Speaker 2: if you're Chevy, if you're even Rivian, you know you're

1066
01:04:18,720 --> 01:04:22,080
Speaker 2: you're probably raising your eyebrows and being like, damn, we really,

1067
01:04:23,360 --> 01:04:25,640
Speaker 2: you know, do we want to reevaluate this relationship? We

1068
01:04:25,680 --> 01:04:28,560
Speaker 2: got in bed with our main competitor and he is

1069
01:04:28,680 --> 01:04:32,520
Speaker 2: completely erratic and we don't know why he did it.

1070
01:04:32,640 --> 01:04:36,040
Speaker 2: Still like was it because he one day he woke

1071
01:04:36,120 --> 01:04:37,800
Speaker 2: up and was like, actually, you know what, I don't

1072
01:04:37,840 --> 01:04:40,960
Speaker 2: think I want my competitors to use my Like why

1073
01:04:40,960 --> 01:04:43,360
Speaker 2: would I give away my secret sauce? You know, let

1074
01:04:43,360 --> 01:04:46,520
Speaker 2: them build their own they you know, this is that's capitalism.

1075
01:04:46,520 --> 01:04:51,720
Speaker 2: I should be able to control my own chargers. But

1076
01:04:51,800 --> 01:04:56,760
Speaker 2: this kind of erratic nature and you know, unexplained decisions

1077
01:04:56,840 --> 01:05:00,880
Speaker 2: are part and parcel of him. Uh and it is

1078
01:05:01,360 --> 01:05:03,400
Speaker 2: it is fascinating to watch it play out over and

1079
01:05:03,440 --> 01:05:07,880
Speaker 2: over again as a reporter. But yeah, if i'm if

1080
01:05:07,920 --> 01:05:12,240
Speaker 2: I'm Mary Bara a GM, I'm I'm watching this with

1081
01:05:12,240 --> 01:05:16,480
Speaker 2: with Rai's eyebrows and wondering what's going on and just

1082
01:05:17,120 --> 01:05:19,480
Speaker 2: always seems like a master of five alarm fire with him.

1083
01:05:20,200 --> 01:05:25,200
Speaker 1: Okay, so he'sh Twitter, he starts bringing people in. If

1084
01:05:25,200 --> 01:05:28,040
Speaker 1: you don't tell him what he wants to hear, he

1085
01:05:28,080 --> 01:05:32,000
Speaker 1: immediately gets rid of you. So is this also a

1086
01:05:32,080 --> 01:05:35,120
Speaker 1: characteristic of his that he surrounds himself with the y

1087
01:05:35,200 --> 01:05:37,040
Speaker 1: s people can hear a contrary opinion.

1088
01:05:40,320 --> 01:05:43,760
Speaker 2: So it's a bit more nuanced. Actually in the book

1089
01:05:43,800 --> 01:05:46,240
Speaker 2: we go into this a little bit, but one on

1090
01:05:46,240 --> 01:05:49,640
Speaker 2: one he can actually be receptive to criticism and as

1091
01:05:49,640 --> 01:05:52,120
Speaker 2: long as you have the domain expertise, you have the

1092
01:05:52,200 --> 01:05:55,919
Speaker 2: numbers to back it up, the details. You know, if

1093
01:05:55,920 --> 01:05:59,240
Speaker 2: he asks you a question, you're able to answer it.

1094
01:05:59,400 --> 01:06:04,520
Speaker 2: And I the what we talked about. He likes to

1095
01:06:04,520 --> 01:06:07,040
Speaker 2: break things down to what he called first principles, like

1096
01:06:07,160 --> 01:06:09,840
Speaker 2: how do things operate? And if you're able to explain

1097
01:06:09,920 --> 01:06:12,120
Speaker 2: things to him in those first principles, he'll appreciate that.

1098
01:06:12,920 --> 01:06:15,920
Speaker 2: But what he doesn't tolerate is being criticized in a

1099
01:06:15,920 --> 01:06:19,560
Speaker 2: group setting or being made to look like he is

1100
01:06:22,880 --> 01:06:26,680
Speaker 2: dumb in a group setting. Essentially, we opened the book

1101
01:06:26,760 --> 01:06:30,880
Speaker 2: with this with this instance. Actually was well, this is

1102
01:06:30,880 --> 01:06:33,280
Speaker 2: a one on one thing, so it's kind of contradictory

1103
01:06:33,280 --> 01:06:35,320
Speaker 2: to what I said, But of this instance of a

1104
01:06:35,400 --> 01:06:41,200
Speaker 2: data scientist challenging him on why he tweeted out that

1105
01:06:41,240 --> 01:06:45,360
Speaker 2: conspiracy theory about Paul Pelosi. Do you remember that, yes? Yeah,

1106
01:06:45,400 --> 01:06:49,320
Speaker 2: So Paul Pelosi was attacked Nancy Pelosi's husband on far

1107
01:06:49,400 --> 01:06:52,200
Speaker 2: right media. It became this kind of rumor that he

1108
01:06:52,280 --> 01:06:57,960
Speaker 2: was attacked by a jilted lover, and Elon actually shared

1109
01:06:57,960 --> 01:07:02,440
Speaker 2: that came within days after the his acquisition. He shared

1110
01:07:02,480 --> 01:07:09,920
Speaker 2: that conspiracy theory essentially and this data scientist who's already

1111
01:07:09,920 --> 01:07:12,480
Speaker 2: going to quit, manages to get a one on one

1112
01:07:12,480 --> 01:07:15,240
Speaker 2: meeting with Elon and tell him. He goes in the

1113
01:07:15,280 --> 01:07:19,840
Speaker 2: meeting with the hope that he could maybe change him

1114
01:07:19,920 --> 01:07:22,640
Speaker 2: or change his perspective. And this is a data scientist

1115
01:07:22,920 --> 01:07:27,240
Speaker 2: who had studied misinformation and how it spreads on social platforms.

1116
01:07:27,960 --> 01:07:30,240
Speaker 2: And he says, like, look, I can't believe you would

1117
01:07:30,240 --> 01:07:34,400
Speaker 2: believe you know, you're in like the top tenth percentile

1118
01:07:34,720 --> 01:07:37,840
Speaker 2: of folks who would fall for something this ridiculous, Like

1119
01:07:37,880 --> 01:07:40,200
Speaker 2: how could you believe that you really need to like

1120
01:07:40,880 --> 01:07:45,320
Speaker 2: understand online sources better and like how information flows and

1121
01:07:45,520 --> 01:07:48,680
Speaker 2: really be more skeptical of what you read online. And

1122
01:07:48,760 --> 01:07:51,520
Speaker 2: like they get into a shouting match. Elon says, fuck you,

1123
01:07:51,520 --> 01:07:55,240
Speaker 2: you know, and the guy was going to quit anyway

1124
01:07:55,280 --> 01:07:59,360
Speaker 2: with like you know, fires him, and you start to

1125
01:07:59,400 --> 01:08:02,640
Speaker 2: realize that, yes, he doesn't he doesn't like to be

1126
01:08:02,720 --> 01:08:05,560
Speaker 2: challenged very often. And there are other scenes of the

1127
01:08:05,600 --> 01:08:07,560
Speaker 2: book where he fires people on the spot as well.

1128
01:08:10,000 --> 01:08:12,600
Speaker 2: And we go back to, you know, operating on gout instinct.

1129
01:08:15,800 --> 01:08:17,640
Speaker 2: It's worked for him in the past, It's worked for

1130
01:08:17,720 --> 01:08:20,479
Speaker 2: him at Tesla. You know, he has worked people to

1131
01:08:20,520 --> 01:08:22,519
Speaker 2: the bone, and if they don't get the things he

1132
01:08:22,560 --> 01:08:24,880
Speaker 2: wants done for him, he'll just find the next person

1133
01:08:24,920 --> 01:08:30,800
Speaker 2: who will. And he's built these major companies off those techniques,

1134
01:08:31,000 --> 01:08:33,840
Speaker 2: and he has simply applied those to Twitter. And you

1135
01:08:33,880 --> 01:08:35,360
Speaker 2: see that over and over again in the book.

1136
01:08:36,400 --> 01:08:41,360
Speaker 1: Okay, he starts taking cost cutting to an extreme level.

1137
01:08:41,840 --> 01:08:44,800
Speaker 1: You talk about getting rid of janitorial services. You talk

1138
01:08:44,840 --> 01:08:49,200
Speaker 1: about him actually ripping out a server. Is this like

1139
01:08:49,320 --> 01:08:52,400
Speaker 1: an emotional thing that he is setting. Let me put

1140
01:08:52,400 --> 01:08:58,160
Speaker 1: it differently, is he setting an example or he's for

1141
01:08:58,200 --> 01:09:00,400
Speaker 1: a guy who's worth two hundred north of two hundred billion.

1142
01:09:00,840 --> 01:09:03,160
Speaker 1: Is he really trying to save every bedding?

1143
01:09:04,920 --> 01:09:07,760
Speaker 2: We had this bit of reporting even before Twitter at

1144
01:09:07,800 --> 01:09:13,000
Speaker 2: Tesla and one it was during the the I think

1145
01:09:13,040 --> 01:09:16,880
Speaker 2: the model why ramp up, you know, one of the

1146
01:09:16,960 --> 01:09:22,639
Speaker 2: crossover SUVs whatever, And he decides to motivate people by

1147
01:09:22,720 --> 01:09:25,479
Speaker 2: removing the cereal from the offices and the factories. This

1148
01:09:26,040 --> 01:09:29,000
Speaker 2: free cereal that people get, it's the only free food

1149
01:09:29,000 --> 01:09:33,760
Speaker 2: that they get. And everyone's just lays like up in arms.

1150
01:09:33,760 --> 01:09:35,920
Speaker 2: They're like, you're gonna starve the people in the factory

1151
01:09:35,920 --> 01:09:38,240
Speaker 2: who are working for you, who like literally rely on

1152
01:09:38,240 --> 01:09:41,920
Speaker 2: this for breakfast. And sustenance. And what are you saving here?

1153
01:09:42,040 --> 01:09:46,000
Speaker 2: You're saving how much are Lucky charms? You know, mass

1154
01:09:46,040 --> 01:09:48,759
Speaker 2: produce Lucky charms, you know, a thousand couple thousand bucks.

1155
01:09:49,600 --> 01:09:51,839
Speaker 2: But it's for him, it's proving a point, right, it's

1156
01:09:52,400 --> 01:09:55,639
Speaker 2: if I can endure this pain, you can endure this pain,

1157
01:09:56,200 --> 01:09:58,519
Speaker 2: and I am willing to cut whatever we need to

1158
01:09:58,560 --> 01:10:03,200
Speaker 2: cut to make this company successful, whether that's lucky charms,

1159
01:10:03,400 --> 01:10:09,639
Speaker 2: whether that's servers, whether that's toilet paper, which I keep

1160
01:10:09,680 --> 01:10:12,600
Speaker 2: referencing but actually happened. They didn't have toilet paper the

1161
01:10:12,640 --> 01:10:17,240
Speaker 2: offices for a bit. But it's like a it's a

1162
01:10:17,240 --> 01:10:22,840
Speaker 2: psychological thing, almost right. And what he believes is he's

1163
01:10:22,880 --> 01:10:24,599
Speaker 2: going to be the one enduring this pain as well.

1164
01:10:24,640 --> 01:10:27,160
Speaker 2: He'll be the one sleeping at the factory if he

1165
01:10:27,240 --> 01:10:30,080
Speaker 2: can not eat and be on the factory line or

1166
01:10:30,080 --> 01:10:31,679
Speaker 2: be on the factory floor. And if he can sleep

1167
01:10:31,680 --> 01:10:34,720
Speaker 2: in the office, which he did set up be set

1168
01:10:34,760 --> 01:10:38,720
Speaker 2: up a bed in one of the conference rooms, then

1169
01:10:38,760 --> 01:10:41,600
Speaker 2: you like individual employees should be able to tolerate that

1170
01:10:41,640 --> 01:10:44,760
Speaker 2: pain too. Now on the flip side of that, you know,

1171
01:10:44,800 --> 01:10:46,720
Speaker 2: I talk to a lot of people about this, and

1172
01:10:46,840 --> 01:10:49,880
Speaker 2: it does motivate a certain amount of people. But then

1173
01:10:50,000 --> 01:10:52,120
Speaker 2: these people start to realize, like the upside for them,

1174
01:10:52,600 --> 01:10:54,320
Speaker 2: like what is it in Like what do they get?

1175
01:10:54,360 --> 01:10:59,000
Speaker 2: They get a couple shares, they get a good salary,

1176
01:10:59,600 --> 01:11:03,960
Speaker 2: but his upside is, you know, millions of shares that

1177
01:11:04,040 --> 01:11:05,920
Speaker 2: could increase in value. So he's the one seeing the

1178
01:11:05,960 --> 01:11:10,760
Speaker 2: upside from the enduring of pain by these folks. And

1179
01:11:10,800 --> 01:11:13,679
Speaker 2: whereas you know, the individual contributor to the individual employee

1180
01:11:13,720 --> 01:11:17,880
Speaker 2: isn't seeing the same kind of payoff. I guess he

1181
01:11:17,880 --> 01:11:21,920
Speaker 2: would argue, you know, he's made many people at Tesla millionaires,

1182
01:11:21,960 --> 01:11:26,720
Speaker 2: but you know, the cutting has always been a part

1183
01:11:26,760 --> 01:11:30,639
Speaker 2: of him and his identity, and it's something he's now

1184
01:11:30,680 --> 01:11:32,479
Speaker 2: taking into to the White House.

1185
01:11:33,720 --> 01:11:39,519
Speaker 1: Okay, he doesn't pay all these severance fees, etc. What

1186
01:11:39,720 --> 01:11:42,960
Speaker 1: is the status of the people were owed money, both

1187
01:11:43,000 --> 01:11:46,880
Speaker 1: in terms of severance, any stocked payouts, even people are

1188
01:11:46,960 --> 01:11:50,519
Speaker 1: owed money for office space. Are those people continuing to

1189
01:11:50,560 --> 01:11:53,200
Speaker 1: be stiffed or did anybody ever get paid?

1190
01:11:54,479 --> 01:11:56,960
Speaker 2: So there've been a series of lawsuits that have been filed.

1191
01:11:57,160 --> 01:11:59,880
Speaker 2: When we talk about the employees, there's thousands of employees

1192
01:12:00,080 --> 01:12:03,960
Speaker 2: that are currently awaiting arbitration. A lot of them had

1193
01:12:04,040 --> 01:12:10,160
Speaker 2: arbitration clauses in their contracts. So they're fighting essentially to

1194
01:12:10,200 --> 01:12:15,880
Speaker 2: get that severance paid to them, which is being overseen

1195
01:12:15,920 --> 01:12:21,040
Speaker 2: by a couple of different legal firms. But Elon has

1196
01:12:21,080 --> 01:12:23,800
Speaker 2: not relented. He has not signaled that he's just going

1197
01:12:23,840 --> 01:12:25,800
Speaker 2: to simply settle and pay these things. He's going to fight,

1198
01:12:26,000 --> 01:12:30,960
Speaker 2: which ultimately may cost him more, or you know, maybe

1199
01:12:31,040 --> 01:12:33,200
Speaker 2: his calculation is that some people would simply give up

1200
01:12:33,560 --> 01:12:37,599
Speaker 2: and forget about it. He's taking the same tact with

1201
01:12:38,840 --> 01:12:41,479
Speaker 2: the four executives that he fired in like literally the

1202
01:12:41,479 --> 01:12:46,040
Speaker 2: minutes after taking over, he fired the CEO, chief legal officer,

1203
01:12:46,800 --> 01:12:51,000
Speaker 2: General counsel, and chief financial officer. That was literally his

1204
01:12:51,000 --> 01:12:53,160
Speaker 2: first action upon taking over the company in October twenty

1205
01:12:53,160 --> 01:12:57,040
Speaker 2: twenty two. And all these these four executives were do

1206
01:12:57,200 --> 01:13:02,040
Speaker 2: golden parachutes on a change of on a transfer power, which, uh,

1207
01:13:02,360 --> 01:13:04,679
Speaker 2: you know, any kind of merger or anything, they would

1208
01:13:04,680 --> 01:13:08,639
Speaker 2: do these golden parachutes, which amounted to about I think

1209
01:13:08,640 --> 01:13:10,519
Speaker 2: it's around one hundred and twenty million dollars total for

1210
01:13:10,560 --> 01:13:14,720
Speaker 2: all four of them combined. So he's he's fired them

1211
01:13:14,840 --> 01:13:18,960
Speaker 2: for cause, which allowed him to say, actually, you know,

1212
01:13:19,000 --> 01:13:20,880
Speaker 2: I fired you for cause, Therefore you don't deserve your

1213
01:13:20,880 --> 01:13:23,400
Speaker 2: golden parachutes or you're you're not, I don't have to

1214
01:13:23,400 --> 01:13:26,600
Speaker 2: pay you those And of course those executives sued.

1215
01:13:27,200 --> 01:13:27,679
Speaker 1: And.

1216
01:13:29,240 --> 01:13:31,879
Speaker 2: You know, now they're paying millions of dollars to lawyers

1217
01:13:31,920 --> 01:13:35,719
Speaker 2: to litigate that, and eventually they'll probably be a settlement

1218
01:13:37,200 --> 01:13:40,599
Speaker 2: where those executives get tired and they maybe don't get

1219
01:13:40,600 --> 01:13:44,400
Speaker 2: their full package, but they you know, they get some

1220
01:13:44,520 --> 01:13:50,559
Speaker 2: part of it. And that's kind of his strategy. You know,

1221
01:13:50,600 --> 01:13:54,720
Speaker 2: with rent as well, he stopped paying rent just you know,

1222
01:13:55,120 --> 01:13:58,439
Speaker 2: just didn't write the checks and continued to occupy the

1223
01:13:58,439 --> 01:14:02,200
Speaker 2: buildings until the real estate companies were forced to come

1224
01:14:02,240 --> 01:14:06,880
Speaker 2: to table and negotiate with him. We often think about

1225
01:14:06,920 --> 01:14:09,479
Speaker 2: like too big to fail, and we use that with banks.

1226
01:14:10,200 --> 01:14:17,599
Speaker 2: Obviously the financial crisis. There's a quality of Elon. There's

1227
01:14:17,640 --> 01:14:19,479
Speaker 2: a quality of that to Elon as well. You know,

1228
01:14:19,600 --> 01:14:23,240
Speaker 2: this idea that he is too big that people have

1229
01:14:23,280 --> 01:14:26,559
Speaker 2: to play on his terms. He's not going to simply

1230
01:14:26,600 --> 01:14:30,559
Speaker 2: do things because he signed a contract or because he's

1231
01:14:30,640 --> 01:14:34,200
Speaker 2: legally obligated to. But he can bend the rules and

1232
01:14:34,240 --> 01:14:36,760
Speaker 2: get people to play on his playing field if he

1233
01:14:36,760 --> 01:14:39,480
Speaker 2: wants to, and he's done that constantly with Twitter.

1234
01:14:39,600 --> 01:14:42,439
Speaker 1: Okay, changes the name of the company, puts up a

1235
01:14:42,560 --> 01:14:45,280
Speaker 1: huge sign which breaks the law, and has to take

1236
01:14:45,320 --> 01:14:50,600
Speaker 1: it down. Is he aware that he's breaking laws and

1237
01:14:50,680 --> 01:14:54,360
Speaker 1: regulations or is he just doing whatever he's doing because

1238
01:14:54,400 --> 01:14:58,480
Speaker 1: he's blind to the rest of the world.

1239
01:15:00,000 --> 01:15:02,080
Speaker 2: I think he just simply doesn't care. I mean his lawyer,

1240
01:15:02,080 --> 01:15:04,400
Speaker 2: we had we quote his lawyer in the book. You know,

1241
01:15:05,080 --> 01:15:09,960
Speaker 2: Elon does not care about FTC consent decrees. Now that

1242
01:15:10,040 --> 01:15:17,200
Speaker 2: applies to building permits, it applies to whatever rules governed signage.

1243
01:15:18,240 --> 01:15:25,200
Speaker 2: In San Francisco, the company, the city sent inspectors to

1244
01:15:25,400 --> 01:15:29,800
Speaker 2: inspect the so called Twitter hotel in the building where

1245
01:15:29,800 --> 01:15:32,200
Speaker 2: he built these hotel rooms for employees to stand because

1246
01:15:32,240 --> 01:15:34,799
Speaker 2: he didn't want to pay for out of time employees

1247
01:15:34,800 --> 01:15:37,559
Speaker 2: to stay in hotels, so they would sleep in conference

1248
01:15:37,640 --> 01:15:41,840
Speaker 2: rooms on mixshift beds and shower in the office and

1249
01:15:41,880 --> 01:15:45,080
Speaker 2: that kind of thing. So the city set investigators and

1250
01:15:45,200 --> 01:15:47,800
Speaker 2: like you know, they continued to operate and have people

1251
01:15:47,840 --> 01:15:53,320
Speaker 2: stay there. He just doesn't care, you know. And that

1252
01:15:53,479 --> 01:15:58,479
Speaker 2: is a again going back to accountability. That's just a

1253
01:15:58,560 --> 01:16:01,800
Speaker 2: key understanding that he has. He is bigger than any

1254
01:16:01,880 --> 01:16:04,000
Speaker 2: law or any regulator or any agency.

1255
01:16:04,800 --> 01:16:08,320
Speaker 1: If we pull the lens all the way back. Those

1256
01:16:08,360 --> 01:16:12,160
Speaker 1: of us who were active on Twitter and we're aware

1257
01:16:12,200 --> 01:16:16,720
Speaker 1: of the changes he made, expected a the service to

1258
01:16:16,800 --> 01:16:19,000
Speaker 1: go down. There were a couple of hiccups, but nothing

1259
01:16:19,080 --> 01:16:23,880
Speaker 1: significant and for the whole thing to collapse on some level.

1260
01:16:24,240 --> 01:16:29,160
Speaker 1: It has not collapsed. So on some level do we say, hey,

1261
01:16:29,320 --> 01:16:32,800
Speaker 1: by cutting the vast majority of employeing and costs, he.

1262
01:16:32,960 --> 01:16:37,640
Speaker 2: Was right, You're right in that it hasn't collapsed, but

1263
01:16:37,680 --> 01:16:41,960
Speaker 2: there's been some pretty severe outages. Australia in December twenty

1264
01:16:41,960 --> 01:16:44,120
Speaker 2: twenty two was without it for a couple of days.

1265
01:16:46,240 --> 01:16:48,160
Speaker 2: You know, he's taking down a lot of the tools

1266
01:16:48,160 --> 01:16:53,160
Speaker 2: that oversee content moderation that have allowed you know, there

1267
01:16:54,560 --> 01:16:56,599
Speaker 2: the bought problem is still a major issue, you know,

1268
01:16:56,680 --> 01:16:57,960
Speaker 2: and there were there were a lot of tools that

1269
01:16:57,960 --> 01:17:02,080
Speaker 2: he took out that were overseeing that. When we talk

1270
01:17:02,120 --> 01:17:04,400
Speaker 2: about cuts, it's not just the platform itself, it's the

1271
01:17:04,400 --> 01:17:08,640
Speaker 2: people that also oversee advertising and what happened with advertising

1272
01:17:08,760 --> 01:17:14,799
Speaker 2: at the company. It's completely cratered. So yes, I think

1273
01:17:15,360 --> 01:17:20,880
Speaker 2: at the time, all these speculator all the speculation about

1274
01:17:21,040 --> 01:17:25,080
Speaker 2: Twitter collapsing after he did all the layoffs, I think

1275
01:17:25,160 --> 01:17:30,880
Speaker 2: was a bit premature, and Twitter over the years had

1276
01:17:30,880 --> 01:17:33,759
Speaker 2: built up a pretty robust infrastructure to keep itself online.

1277
01:17:34,120 --> 01:17:36,920
Speaker 2: You know, obviously we all remember the days of the

1278
01:17:36,960 --> 01:17:39,960
Speaker 2: fail well and you know those error messages that we

1279
01:17:40,040 --> 01:17:43,280
Speaker 2: we get or during big events like the Super Bowl,

1280
01:17:43,680 --> 01:17:46,160
Speaker 2: the site crashing or not the super Bowl, but the

1281
01:17:46,240 --> 01:17:50,679
Speaker 2: World Cup and stuff like that. But by and large,

1282
01:17:50,720 --> 01:17:53,400
Speaker 2: it's it's a bit a pretty robust system that Eleono

1283
01:17:53,479 --> 01:17:55,519
Speaker 2: has been able to take over, and so it stayed online.

1284
01:17:56,560 --> 01:17:59,320
Speaker 2: That being said, you know, it doesn't guarantee that it's

1285
01:17:59,320 --> 01:18:04,839
Speaker 2: going to stay online forever. And who knows. I reserve judgment,

1286
01:18:04,840 --> 01:18:05,880
Speaker 2: I guess, is what I'll say to that.

1287
01:18:06,479 --> 01:18:09,360
Speaker 1: Okay, he has this vision of turning what is now

1288
01:18:09,439 --> 01:18:14,439
Speaker 1: called next into a universal payment system, you know, akin

1289
01:18:14,479 --> 01:18:17,680
Speaker 1: to what they have in China, in light of what

1290
01:18:17,720 --> 01:18:21,040
Speaker 1: you were talking about self driving cars, et cetera. Is

1291
01:18:21,080 --> 01:18:24,160
Speaker 1: this complete fantasy? He says? You know, that's what he

1292
01:18:24,200 --> 01:18:27,360
Speaker 1: wanted to do with PayPal. Legend is that's why he

1293
01:18:27,479 --> 01:18:30,880
Speaker 1: was squeezed out. Should we pay any heed to that?

1294
01:18:33,600 --> 01:18:36,599
Speaker 2: Elan is a man that makes big, big promises. That's

1295
01:18:36,600 --> 01:18:40,439
Speaker 2: been his whole career. He's going to get humans to Mars.

1296
01:18:40,720 --> 01:18:46,080
Speaker 2: He's going to defeat climate change. He is going to

1297
01:18:46,120 --> 01:18:48,880
Speaker 2: build the everything up, and that's what he has pitched

1298
01:18:48,880 --> 01:18:51,559
Speaker 2: to investors. That's why he was in our book. We

1299
01:18:51,600 --> 01:18:54,000
Speaker 2: go over as some of the projections that he had

1300
01:18:54,080 --> 01:18:56,840
Speaker 2: for three years from now, five years from now, ten

1301
01:18:56,920 --> 01:19:03,120
Speaker 2: years from now, and some of those numbers were, you know, fantastical,

1302
01:19:03,240 --> 01:19:06,920
Speaker 2: you know, they were just fantasy looking at it now.

1303
01:19:07,880 --> 01:19:10,320
Speaker 2: And he sold investors on this idea that he could

1304
01:19:11,000 --> 01:19:14,040
Speaker 2: build the weed Chat of the US, you know, a

1305
01:19:14,080 --> 01:19:18,040
Speaker 2: place where you could watch videos, you would hail your cab,

1306
01:19:18,160 --> 01:19:21,320
Speaker 2: you would order your food, you would have your bank.

1307
01:19:22,400 --> 01:19:25,160
Speaker 2: And by and large, that vision has not been achieved.

1308
01:19:25,280 --> 01:19:27,879
Speaker 2: You know, they've been still working on those money transmitter

1309
01:19:27,960 --> 01:19:34,240
Speaker 2: licenses in all those states. You know, there's really you

1310
01:19:34,280 --> 01:19:36,880
Speaker 2: can't pay for anything with X at this point in time.

1311
01:19:37,880 --> 01:19:42,799
Speaker 2: But yeah, it's it's again these promises that he continues

1312
01:19:42,800 --> 01:19:46,160
Speaker 2: to make, and that's how he's operated as companies. It's

1313
01:19:46,200 --> 01:19:48,439
Speaker 2: always on this idea of a vision, this mission that

1314
01:19:48,479 --> 01:19:53,040
Speaker 2: we need it to and for for X, that mission

1315
01:19:53,080 --> 01:19:55,880
Speaker 2: has been the everything app mean, by the way, he

1316
01:19:56,280 --> 01:19:59,720
Speaker 2: hasn't mentioned that very recently. If you notice how we

1317
01:19:59,760 --> 01:20:02,240
Speaker 2: talked about it, he doesn't really talk about payments anymore.

1318
01:20:02,240 --> 01:20:04,960
Speaker 2: He doesn't talk about the everything app so maybe that

1319
01:20:05,000 --> 01:20:07,920
Speaker 2: was a convenient message for him to raise capital at

1320
01:20:07,960 --> 01:20:12,600
Speaker 2: the time. But yeah, I guess that's kind of a

1321
01:20:12,640 --> 01:20:14,639
Speaker 2: wait and see moment still as well.

1322
01:20:22,200 --> 01:20:25,400
Speaker 1: Okay, let's talk about Linda Yakarno. So we read all

1323
01:20:25,439 --> 01:20:28,920
Speaker 1: about this stuff. I'm into a small conference where she

1324
01:20:29,040 --> 01:20:37,000
Speaker 1: was spoke very unimpressive, a sales lady at best. You know,

1325
01:20:37,200 --> 01:20:40,559
Speaker 1: the news was all about how much power she would

1326
01:20:40,600 --> 01:20:42,479
Speaker 1: be given. I mean, I got to meet the woman

1327
01:20:42,520 --> 01:20:44,360
Speaker 1: for three minutes to know you're not going to put

1328
01:20:44,360 --> 01:20:47,479
Speaker 1: her in control? What was going on in with you?

1329
01:20:47,640 --> 01:20:48,559
Speaker 1: Linda yak Arino.

1330
01:20:50,960 --> 01:20:53,240
Speaker 2: Lynda Yakarino has passed over for the head job at

1331
01:20:53,400 --> 01:20:58,800
Speaker 2: Mbcuniversal and was very successful what she did there. You know,

1332
01:20:58,880 --> 01:21:04,720
Speaker 2: she's a big advertising person, pretty highp executive. It worked

1333
01:21:04,720 --> 01:21:07,120
Speaker 2: a way up, but kind of saw the writing on

1334
01:21:07,160 --> 01:21:10,400
Speaker 2: the wall when she didn't get the gig and had

1335
01:21:10,439 --> 01:21:16,599
Speaker 2: always been angling for a top job. Her politics aligned

1336
01:21:16,760 --> 01:21:21,040
Speaker 2: with Elon. You know, she was involved with some Trump

1337
01:21:21,080 --> 01:21:26,040
Speaker 2: councils on the first Trump administration, and her Twitter feed

1338
01:21:26,200 --> 01:21:28,479
Speaker 2: was pretty obviously right leading. There were a lot of

1339
01:21:28,520 --> 01:21:30,920
Speaker 2: engagements with right wing accounts and that kind of thing,

1340
01:21:31,880 --> 01:21:36,400
Speaker 2: and she saw an opportunity, so they began a conversation.

1341
01:21:37,840 --> 01:21:41,960
Speaker 2: Remember Elon had that very bizarre period in December twenty

1342
01:21:42,000 --> 01:21:43,920
Speaker 2: twenty two where he had a lot of self doubts

1343
01:21:43,960 --> 01:21:46,360
Speaker 2: of whether he wanted to run the company, and he

1344
01:21:46,400 --> 01:21:48,320
Speaker 2: tweeted out that thing about, you know, should I still

1345
01:21:48,360 --> 01:21:53,120
Speaker 2: be CEO of Twitter. It's this dark moment in the book,

1346
01:21:53,160 --> 01:21:56,559
Speaker 2: and it's I think one thing you realize about Elon

1347
01:21:56,640 --> 01:21:59,080
Speaker 2: is this very human He goes through these very up

1348
01:21:59,120 --> 01:22:03,240
Speaker 2: and down and in that point in time, our reporting

1349
01:22:04,640 --> 01:22:06,559
Speaker 2: put him in like a very dark place. You know,

1350
01:22:06,600 --> 01:22:09,400
Speaker 2: he had a lot of self doubts, and so there

1351
01:22:09,520 --> 01:22:11,920
Speaker 2: was kind of some public chatter about, you know, maybe

1352
01:22:11,960 --> 01:22:14,000
Speaker 2: he's looking for someone to like take the burden off.

1353
01:22:14,840 --> 01:22:16,800
Speaker 2: And so Linda Yakarino comes in and they start to

1354
01:22:16,840 --> 01:22:19,800
Speaker 2: have those conversations. She interviews him at a conference and

1355
01:22:19,880 --> 01:22:23,559
Speaker 2: ads conference where they have decent rapport on stage, and

1356
01:22:23,600 --> 01:22:27,080
Speaker 2: a couple months later she's named as the CEO. You know,

1357
01:22:27,160 --> 01:22:29,679
Speaker 2: I think anyone looking at that company knows who calls

1358
01:22:29,720 --> 01:22:34,320
Speaker 2: the shots. But also Linda oversees a lot of the

1359
01:22:34,360 --> 01:22:37,720
Speaker 2: aspects of the business that Elon just has no care for,

1360
01:22:37,880 --> 01:22:44,200
Speaker 2: and that particular part is advertising because Twitter, despite what

1361
01:22:44,240 --> 01:22:46,559
Speaker 2: Elan has said about subscriptions and despite what he has

1362
01:22:46,600 --> 01:22:51,640
Speaker 2: said about wanting to develop new revenue streams, is supremely

1363
01:22:52,000 --> 01:22:55,439
Speaker 2: dependent on advertising, and so Linda is there to do

1364
01:22:55,479 --> 01:23:01,559
Speaker 2: that but also carry his message. So you often get

1365
01:23:01,560 --> 01:23:05,800
Speaker 2: this very weird dynamic where she has to speak out

1366
01:23:05,840 --> 01:23:08,240
Speaker 2: of both sides of her mouth, or she has to

1367
01:23:08,280 --> 01:23:13,679
Speaker 2: pairt what Elon says, and uh, yeah, it's it's it's

1368
01:23:13,680 --> 01:23:18,040
Speaker 2: fascinating watching her and what she says online.

1369
01:23:18,280 --> 01:23:22,320
Speaker 1: Okay, then you have the famous meeting with the advertisers

1370
01:23:22,720 --> 01:23:25,840
Speaker 1: where Elon basically says, fuck you, we don't need you.

1371
01:23:26,520 --> 01:23:30,920
Speaker 1: What's the status of advertising today on X?

1372
01:23:32,560 --> 01:23:34,680
Speaker 2: So? I think you're referencing the deal Book conference where

1373
01:23:34,680 --> 01:23:37,360
Speaker 2: he goes yes as go fuck yourself. Yes, he says,

1374
01:23:37,360 --> 01:23:39,960
Speaker 2: Bob Igers in the audience, that kind of thing, you know,

1375
01:23:40,160 --> 01:23:45,040
Speaker 2: just that that from I often forget about that moment,

1376
01:23:45,400 --> 01:23:48,120
Speaker 2: but like I also now remember where I was for it,

1377
01:23:48,160 --> 01:23:52,160
Speaker 2: and to think that felt like a decade ago now.

1378
01:23:52,200 --> 01:23:57,880
Speaker 2: But the status of advertising on X is it's still declining.

1379
01:23:58,560 --> 01:24:00,680
Speaker 2: We get kind of piecemeal numbers. Obvious they don't they

1380
01:24:00,680 --> 01:24:04,120
Speaker 2: don't have to report those anymore. But I guess some

1381
01:24:04,200 --> 01:24:07,920
Speaker 2: numbers internally in the US, for example, that it's it's

1382
01:24:07,920 --> 01:24:11,559
Speaker 2: still declining after year over year, after a pretty bad

1383
01:24:11,640 --> 01:24:15,280
Speaker 2: year in the in the previous twelve months, it's still declining.

1384
01:24:16,080 --> 01:24:20,320
Speaker 2: And so Elon has wanted to put out this message

1385
01:24:20,360 --> 01:24:25,360
Speaker 2: that actually advertisers are returning, but you should, we should

1386
01:24:25,360 --> 01:24:29,160
Speaker 2: all be kind of skeptical about what returning means. Does

1387
01:24:29,160 --> 01:24:31,880
Speaker 2: that mean they're returning and spending five dollars? You know,

1388
01:24:31,880 --> 01:24:34,720
Speaker 2: what's the actual spend is actually the main question. And

1389
01:24:35,600 --> 01:24:39,120
Speaker 2: like you can often I pay attention a lot to

1390
01:24:39,160 --> 01:24:42,719
Speaker 2: what's on the Explore page that that on the training topics.

1391
01:24:43,080 --> 01:24:46,240
Speaker 2: You see that not right? So that big like banner

1392
01:24:46,320 --> 01:24:49,040
Speaker 2: ad is about a five hundred, it's anywhere from tw

1393
01:24:49,080 --> 01:24:51,200
Speaker 2: hundred fifty to fi hundred thousand dollars a day to

1394
01:24:51,280 --> 01:24:53,640
Speaker 2: take over that page and run that banner ad, and

1395
01:24:53,680 --> 01:24:56,080
Speaker 2: it's often very empty. You know, that used to be

1396
01:24:56,120 --> 01:25:00,360
Speaker 2: a place where artists promoted their new albums, or there

1397
01:25:00,360 --> 01:25:03,719
Speaker 2: were movie promotions, or if there was a new Apple

1398
01:25:03,960 --> 01:25:07,680
Speaker 2: you know, whatever, iPhone launch or whatever, they would take

1399
01:25:07,720 --> 01:25:10,400
Speaker 2: over that space. And that goes empty a lot of

1400
01:25:10,439 --> 01:25:13,479
Speaker 2: the time. And that just speaks to where the platform

1401
01:25:13,560 --> 01:25:17,240
Speaker 2: is at with advertisers being very queasy about the content

1402
01:25:17,600 --> 01:25:18,880
Speaker 2: and the people that remain on.

1403
01:25:20,800 --> 01:25:26,160
Speaker 1: X okay. A month or two ago, Tim Cooked very publicly,

1404
01:25:26,280 --> 01:25:31,120
Speaker 1: Tim Apple very publicly said, oh, yeah, we're advertising on

1405
01:25:31,439 --> 01:25:34,840
Speaker 1: x So you'd talk about Andreese and Horowitch, talk about

1406
01:25:34,840 --> 01:25:38,080
Speaker 1: Morgan Stanley, these people looking to the future. To what

1407
01:25:38,240 --> 01:25:42,400
Speaker 1: degree are other companies afraid of Elong?

1408
01:25:43,680 --> 01:25:49,640
Speaker 2: Oh completely? You know Cook got in a fight with

1409
01:25:49,680 --> 01:25:52,960
Speaker 2: Elon very famously and then had to like invite him

1410
01:25:53,000 --> 01:25:55,600
Speaker 2: to headquarters to like smooth it over. And that was

1411
01:25:55,640 --> 01:25:59,920
Speaker 2: over the thirty percent fee in the app store, right,

1412
01:26:00,320 --> 01:26:07,120
Speaker 2: And you know, I think these these even the top

1413
01:26:07,160 --> 01:26:10,360
Speaker 2: of the top, the Top Sea, Sundar Pichai, Sasha Nadella,

1414
01:26:11,040 --> 01:26:14,360
Speaker 2: Reid Hoffmann, they don't want to be seen on the

1415
01:26:14,400 --> 01:26:17,559
Speaker 2: other end of a shotgun blast from Elon Musk. You know,

1416
01:26:17,600 --> 01:26:23,040
Speaker 2: it creates a huge headache for you. And on some level,

1417
01:26:23,360 --> 01:26:25,880
Speaker 2: you know, like if he tweets value, you're just gonna

1418
01:26:25,920 --> 01:26:30,360
Speaker 2: have millions of people coming after you and denigrating you,

1419
01:26:30,479 --> 01:26:37,080
Speaker 2: and you know, a just criticizing you. And I think

1420
01:26:37,280 --> 01:26:39,639
Speaker 2: all of them believe that it's just easier to play

1421
01:26:39,720 --> 01:26:42,120
Speaker 2: nice with him. And so when Tim Cook says we're

1422
01:26:42,160 --> 01:26:45,920
Speaker 2: still advertising on X. It's interesting, right, like they still

1423
01:26:45,920 --> 01:26:48,200
Speaker 2: could be advertising an X, but what is the amount?

1424
01:26:48,439 --> 01:26:52,080
Speaker 2: You know, how does that compare in the past. And

1425
01:26:52,120 --> 01:26:55,880
Speaker 2: bear in mind that these companies are also very they

1426
01:26:55,880 --> 01:26:57,519
Speaker 2: have to do it's best for their business as well,

1427
01:26:57,560 --> 01:27:00,479
Speaker 2: and so if they are trying to break out of

1428
01:27:00,520 --> 01:27:06,000
Speaker 2: the duopoly of Google and Facebook and online advertising, you know,

1429
01:27:06,439 --> 01:27:11,160
Speaker 2: X offers a pretty reliable place, I guess, or at

1430
01:27:11,240 --> 01:27:14,479
Speaker 2: least an alternative. And that's not new to Elon. That's

1431
01:27:14,479 --> 01:27:16,880
Speaker 2: how it's always been. That's why Twitter was able to

1432
01:27:16,880 --> 01:27:19,840
Speaker 2: survive as a kind of alternative to the kind of

1433
01:27:19,840 --> 01:27:26,160
Speaker 2: two giants. But when when Tim Cook says something like that,

1434
01:27:26,240 --> 01:27:28,920
Speaker 2: it definitely feels like a very placating move, right, it's

1435
01:27:28,960 --> 01:27:33,240
Speaker 2: a it's a it's an olive branch to Elon.

1436
01:27:34,080 --> 01:27:38,040
Speaker 1: Okay, you follow this very closely. What's the long term

1437
01:27:38,560 --> 01:27:42,040
Speaker 1: maybe not that long term, not immediate, but short term

1438
01:27:42,320 --> 01:27:48,720
Speaker 1: prognosis with X? Do they miss the debt payments? Is

1439
01:27:48,760 --> 01:27:51,639
Speaker 1: it essentially like truth social it is gonna live forever?

1440
01:27:51,880 --> 01:27:54,760
Speaker 1: Relevant to the economics, what's gonna happen here?

1441
01:27:56,000 --> 01:27:58,759
Speaker 2: I don't think they miss the debt payments. I wouldn't

1442
01:27:58,760 --> 01:28:01,640
Speaker 2: be surprised if elon Treasury Finance members some negotiation that

1443
01:28:01,680 --> 01:28:04,760
Speaker 2: probably that's actually already happened that there's been reporting on

1444
01:28:04,800 --> 01:28:09,599
Speaker 2: that X will operate as long as elon Ce's fit.

1445
01:28:09,720 --> 01:28:12,639
Speaker 2: You know, it's his favorite toy. He's created the thing

1446
01:28:15,360 --> 01:28:18,000
Speaker 2: that he wants the most. It's it's essentially his echo chamber, right.

1447
01:28:18,240 --> 01:28:20,759
Speaker 2: It's the place where he's now become the most followed user,

1448
01:28:21,240 --> 01:28:24,519
Speaker 2: he's the most engaged with account. This wasn't his intent

1449
01:28:24,560 --> 01:28:26,200
Speaker 2: when he bought the company, but he's been able to

1450
01:28:26,320 --> 01:28:31,599
Speaker 2: use it to leverage UH and get the presidential candidate

1451
01:28:31,600 --> 01:28:35,760
Speaker 2: he's wanted into the White House. So for him, it's

1452
01:28:35,760 --> 01:28:40,200
Speaker 2: been a you know, absent all the discussion about finances

1453
01:28:40,200 --> 01:28:46,519
Speaker 2: and everything. It's been a great success, and he's shaped

1454
01:28:46,560 --> 01:28:50,160
Speaker 2: it in a way. He's driven away the reporters that

1455
01:28:50,280 --> 01:28:52,960
Speaker 2: used to thrive on there, the news outlets that used

1456
01:28:52,960 --> 01:28:55,320
Speaker 2: to thrive on there, the people used to criticize them,

1457
01:28:56,280 --> 01:28:59,960
Speaker 2: and recreated it to become this place where his favorit.

1458
01:29:00,200 --> 01:29:02,679
Speaker 2: People have the blue check marks, they've bought them. They're

1459
01:29:02,680 --> 01:29:05,880
Speaker 2: elevated in his replies. They're the ones being shared on

1460
01:29:05,920 --> 01:29:12,559
Speaker 2: the for you page, the algorithmically curated page, and yeah,

1461
01:29:12,680 --> 01:29:15,920
Speaker 2: I think you could be happier in some ways. You know,

1462
01:29:16,360 --> 01:29:19,519
Speaker 2: his net worth has never been higher. You know, he's

1463
01:29:19,560 --> 01:29:21,719
Speaker 2: now worth three hundred and twenty three twenty five billion

1464
01:29:21,760 --> 01:29:25,080
Speaker 2: dollars give and take on any given day. So we

1465
01:29:25,120 --> 01:29:29,559
Speaker 2: talk about the financial problems that Twitter has had, but

1466
01:29:29,600 --> 01:29:32,320
Speaker 2: again in the plus minus column, like he's he's on,

1467
01:29:32,479 --> 01:29:38,040
Speaker 2: he's up, you know, and uh so, Yeah, I think

1468
01:29:38,160 --> 01:29:41,000
Speaker 2: I think X continues to exist forever, for however long

1469
01:29:41,040 --> 01:29:41,600
Speaker 2: he wants it to.

1470
01:29:42,280 --> 01:29:48,880
Speaker 1: If Elon doesn't buy Twitter, Elon doesn't put money into

1471
01:29:48,920 --> 01:29:51,360
Speaker 1: the election process, does Trump win?

1472
01:29:54,600 --> 01:30:04,280
Speaker 2: Oh man, I gotta get my DeLorean out. I don't know.

1473
01:30:05,000 --> 01:30:06,560
Speaker 2: It's a bad place to be in a reporter to

1474
01:30:06,600 --> 01:30:13,240
Speaker 2: make these kinds of predictions. Yeah. Maybe, maybe maybe. If

1475
01:30:13,240 --> 01:30:18,400
Speaker 2: he doesn't buy Twitter, he doesn't become so steeped in

1476
01:30:18,600 --> 01:30:23,600
Speaker 2: right wing culture. You know, after he buys Twitter, he

1477
01:30:24,120 --> 01:30:28,760
Speaker 2: becomes really invested in immigration and starts pushing effectively the

1478
01:30:28,800 --> 01:30:33,920
Speaker 2: great replacement theory. He starts caring about voter fraud and

1479
01:30:34,080 --> 01:30:36,599
Speaker 2: you know, the thing that it has been talked about

1480
01:30:36,600 --> 01:30:41,080
Speaker 2: ad nauseum on Fox News, And he starts engaging with

1481
01:30:41,080 --> 01:30:43,479
Speaker 2: folks like Tucker Carlson and that kind of stuff. So

1482
01:30:45,120 --> 01:30:50,439
Speaker 2: I view that process as radicalizing him towards Trump, because

1483
01:30:51,160 --> 01:30:54,160
Speaker 2: you have to remember he was never a Trumper. He

1484
01:30:54,240 --> 01:30:58,599
Speaker 2: supported Ron de Santis in as late as May twenty

1485
01:30:58,680 --> 01:31:01,760
Speaker 2: twenty three. You remember that Twitter space he had where

1486
01:31:01,800 --> 01:31:04,640
Speaker 2: he DeSantis launched his you know, his disastrous but he

1487
01:31:04,720 --> 01:31:11,120
Speaker 2: launched his his presidential campaign on x with Elon, but

1488
01:31:12,360 --> 01:31:17,599
Speaker 2: owning Twitter, engaging in that sphere really pushed him further

1489
01:31:17,680 --> 01:31:20,920
Speaker 2: and further to the right, to the point where he

1490
01:31:21,040 --> 01:31:25,960
Speaker 2: arrived on Trump's doorstep and they began a fast and

1491
01:31:26,479 --> 01:31:29,120
Speaker 2: aggressive relationship that kind of continues to this day.

1492
01:31:29,920 --> 01:31:34,479
Speaker 1: Okay, is this bro man that's going to continue? Really? Elon?

1493
01:31:34,560 --> 01:31:38,799
Speaker 1: Although South African is a classic American one man job,

1494
01:31:39,439 --> 01:31:43,600
Speaker 1: what do we know? Trump is uneducated, stupid, and impulsive.

1495
01:31:44,320 --> 01:31:48,040
Speaker 1: So therefore, seeing over the overall landscape and being manipulative,

1496
01:31:48,479 --> 01:31:51,160
Speaker 1: what you see is what you get. What we learned

1497
01:31:51,280 --> 01:31:55,920
Speaker 1: with Trump is hey, barring you know, are worst fears.

1498
01:31:56,640 --> 01:32:01,320
Speaker 1: He's constrained by the government. Also, he may not agree

1499
01:32:01,600 --> 01:32:06,799
Speaker 1: with Elon on certain things. Also, Elon likes to move fast,

1500
01:32:06,880 --> 01:32:11,559
Speaker 1: the government moves at a glacial piece. To continue would

1501
01:32:11,600 --> 01:32:17,240
Speaker 1: absolutely require compromise on Elon's part. Is that going to happen,

1502
01:32:17,800 --> 01:32:19,640
Speaker 1: or is Elon going to jump the rails at some

1503
01:32:19,720 --> 01:32:22,400
Speaker 1: point say you know this is just fucked up. You

1504
01:32:22,439 --> 01:32:24,160
Speaker 1: don't know what you're doing. I gotta I gotta lead.

1505
01:32:25,320 --> 01:32:28,360
Speaker 2: I thinks a big question. I think Trump does want

1506
01:32:28,400 --> 01:32:32,080
Speaker 2: Elon to break some of that those processes, right, That's

1507
01:32:32,080 --> 01:32:35,080
Speaker 2: why he's brought him in for the Department of Government

1508
01:32:35,120 --> 01:32:42,200
Speaker 2: Government Government Efficiency, Right. I think it's more analyzing it

1509
01:32:42,240 --> 01:32:45,280
Speaker 2: on a kind of one to one relationship standpoint as

1510
01:32:45,280 --> 01:32:51,280
Speaker 2: opposed to anything policy related, like can their egos exist

1511
01:32:51,400 --> 01:32:54,599
Speaker 2: in the same space for however long? We've already started

1512
01:32:54,600 --> 01:32:58,160
Speaker 2: to see reports about folks around Trump tiring of the

1513
01:32:58,240 --> 01:33:03,280
Speaker 2: amount that that that Elon has spent at the president's

1514
01:33:03,280 --> 01:33:06,960
Speaker 2: side in mar A Lago. That's the big question, you know.

1515
01:33:07,360 --> 01:33:09,360
Speaker 2: I wish I was in the game of predictions, but

1516
01:33:10,280 --> 01:33:14,400
Speaker 2: historically they have tended not to get along well with

1517
01:33:14,479 --> 01:33:21,639
Speaker 2: other people that have similar egos, similar desires to take

1518
01:33:21,640 --> 01:33:27,600
Speaker 2: control and be the leader. In my opinion, though, and

1519
01:33:27,720 --> 01:33:29,960
Speaker 2: this is complete speculation, I think both of them are

1520
01:33:30,000 --> 01:33:34,760
Speaker 2: aware of that, and they understand that like they're being

1521
01:33:34,800 --> 01:33:37,800
Speaker 2: watched in this way, and so to do things as

1522
01:33:37,800 --> 01:33:41,559
Speaker 2: they've always done them might not be the way to go.

1523
01:33:42,560 --> 01:33:47,280
Speaker 2: But who knows. I mean, these guys make split decisions,

1524
01:33:47,439 --> 01:33:51,880
Speaker 2: especially Elon, and one day you could wake up and

1525
01:33:51,880 --> 01:33:55,599
Speaker 2: find himself on the other side of Trump's good list

1526
01:33:55,680 --> 01:33:59,600
Speaker 2: or bad list, and it may lead to fireworks, but

1527
01:33:59,640 --> 01:34:01,479
Speaker 2: hopefully were there to report it out.

1528
01:34:02,439 --> 01:34:07,680
Speaker 1: Okay, So for two years Elon has been heavily invested

1529
01:34:07,720 --> 01:34:12,639
Speaker 1: in x miyor to him becoming very right wing when

1530
01:34:12,680 --> 01:34:17,080
Speaker 1: the lefties were still gun home on buying Tesla's question was,

1531
01:34:18,240 --> 01:34:21,360
Speaker 1: and this was an issue for the stock who's running

1532
01:34:21,400 --> 01:34:24,679
Speaker 1: the ship? He's so busy at X. Is the real

1533
01:34:24,720 --> 01:34:28,559
Speaker 1: story that he's just a figurehead and these companies run

1534
01:34:28,600 --> 01:34:29,480
Speaker 1: by themselves.

1535
01:34:30,680 --> 01:34:34,120
Speaker 2: It's actually really interesting that he's developed these kind of

1536
01:34:34,160 --> 01:34:38,040
Speaker 2: deep benches at these companies, and that's largely because he's

1537
01:34:38,200 --> 01:34:40,720
Speaker 2: at Tesla and SpaceX has been able to shape them

1538
01:34:40,720 --> 01:34:43,160
Speaker 2: from the ground up. You know, Tesla, he was an

1539
01:34:43,160 --> 01:34:47,240
Speaker 2: early investor that took over at SpaceX he founded, but

1540
01:34:47,320 --> 01:34:49,640
Speaker 2: he was able to mold these companies and put in

1541
01:34:49,840 --> 01:34:53,960
Speaker 2: place the people he wants to oversee, you know, at

1542
01:34:54,000 --> 01:34:59,360
Speaker 2: SpaceX rocket manufacturing or design. So all these people are

1543
01:34:59,840 --> 01:35:02,400
Speaker 2: very very familiar with how he works and what we've

1544
01:35:02,439 --> 01:35:05,640
Speaker 2: heard from people at these companies, is he's kind of

1545
01:35:05,640 --> 01:35:09,479
Speaker 2: the troubleshooter. If there's a major problem that's happening at

1546
01:35:09,520 --> 01:35:12,080
Speaker 2: Tesla or SpaceX, let's say in twenty eighteen, for example,

1547
01:35:12,080 --> 01:35:16,000
Speaker 2: on the on the factory line manufacturing Model threes, you

1548
01:35:16,040 --> 01:35:22,160
Speaker 2: know he'll get in and try and problem solve very

1549
01:35:23,479 --> 01:35:27,160
Speaker 2: aggressively and spend all his time on that problem until

1550
01:35:27,200 --> 01:35:31,360
Speaker 2: it's fixed. And so actually, if you think about it,

1551
01:35:31,400 --> 01:35:35,439
Speaker 2: like people call elon the sun in these companies, and

1552
01:35:35,520 --> 01:35:37,360
Speaker 2: you don't want to get too close to sunny get burned.

1553
01:35:37,800 --> 01:35:40,120
Speaker 2: So like if he is, if he's working with you

1554
01:35:41,439 --> 01:35:44,559
Speaker 2: on your part of the company or whatever project you're

1555
01:35:46,000 --> 01:35:48,040
Speaker 2: you're probably fucked. Like you're probably in there, you're probably

1556
01:35:48,080 --> 01:35:49,680
Speaker 2: in deep shit, right. You don't want to be in

1557
01:35:49,720 --> 01:35:51,000
Speaker 2: that position. You don't want to be in the same

1558
01:35:51,080 --> 01:35:55,120
Speaker 2: room with him. And so, by and large, these companies,

1559
01:35:55,240 --> 01:35:58,680
Speaker 2: when there is no crisis, he's run themselves. SpaceX has

1560
01:35:58,720 --> 01:36:02,000
Speaker 2: a very strong number too, and when shot well he

1561
01:36:02,080 --> 01:36:04,559
Speaker 2: had he has other vps around him as well that

1562
01:36:04,600 --> 01:36:08,880
Speaker 2: run that company and know about way more about rocket

1563
01:36:08,920 --> 01:36:11,759
Speaker 2: science or manufacturing than he does, and he defers to them.

1564
01:36:12,120 --> 01:36:16,679
Speaker 2: Same thing at Tesla with X, he didn't have that right.

1565
01:36:17,200 --> 01:36:20,760
Speaker 2: This wasn't a company he built up from the ground up,

1566
01:36:20,880 --> 01:36:23,439
Speaker 2: and so when he came in and fired everyone, there

1567
01:36:23,479 --> 01:36:26,080
Speaker 2: was really no one he could trust. He couldn't he

1568
01:36:26,080 --> 01:36:29,840
Speaker 2: couldn't leave and like let this thing operate week to week.

1569
01:36:30,600 --> 01:36:33,439
Speaker 2: So that's kind of what explains why he spent so

1570
01:36:33,520 --> 01:36:36,799
Speaker 2: much time there when after he took over, and explains

1571
01:36:36,800 --> 01:36:41,519
Speaker 2: why he eventually hired Linda Ekerino. But that's that's just

1572
01:36:41,520 --> 01:36:44,040
Speaker 2: an understanding of how it operates. So x is I

1573
01:36:44,040 --> 01:36:46,080
Speaker 2: guess getting there to the point where it can maybe

1574
01:36:46,080 --> 01:36:48,080
Speaker 2: be a little more self sustainable and he's not spending

1575
01:36:48,120 --> 01:36:52,040
Speaker 2: all his time there. But yeah, that's just that's how

1576
01:36:52,080 --> 01:36:52,479
Speaker 2: he works.

1577
01:36:52,880 --> 01:36:59,479
Speaker 1: Okay. Steve Jobs the legendary entrepreneur before Elon Musk. It's

1578
01:36:59,520 --> 01:37:04,640
Speaker 1: been well documented that the other Steve Wozniak was the

1579
01:37:04,680 --> 01:37:09,960
Speaker 1: brilliant engineer, and Steve Jobs was an incredible marketer and

1580
01:37:10,000 --> 01:37:15,840
Speaker 1: an incredible trends spotter. What is Elon's expertise? Is he

1581
01:37:15,920 --> 01:37:17,480
Speaker 1: really that great an engineer?

1582
01:37:20,160 --> 01:37:22,639
Speaker 2: As I said earlier, he's he's a great salesman. He's

1583
01:37:22,680 --> 01:37:27,120
Speaker 2: a great person in pushing people towards a vision and

1584
01:37:27,200 --> 01:37:31,280
Speaker 2: motivating them, collecting them in a room and getting him

1585
01:37:31,439 --> 01:37:34,800
Speaker 2: marching in the same direction. And that's for the causes

1586
01:37:34,880 --> 01:37:40,480
Speaker 2: of making humanity multiplanetary. It's for the causes of electrifying

1587
01:37:41,280 --> 01:37:43,959
Speaker 2: cars and bringing up and bringing about the ev revolution.

1588
01:37:46,000 --> 01:37:50,719
Speaker 2: And it's funny because Jobs and Musk are often talked

1589
01:37:50,960 --> 01:37:54,960
Speaker 2: talked about as having this quote reality distortion field, right,

1590
01:37:55,040 --> 01:38:00,040
Speaker 2: this ability to you know, if it's not real in

1591
01:38:00,040 --> 01:38:02,880
Speaker 2: real life, they able to make it happen. You know,

1592
01:38:03,600 --> 01:38:06,920
Speaker 2: no one saw the iPhone before Steve Jobs, no one

1593
01:38:06,960 --> 01:38:11,160
Speaker 2: saw self landing rockets before Elon, And they're able to

1594
01:38:11,200 --> 01:38:14,599
Speaker 2: push people and the boundaries to get them working towards

1595
01:38:14,640 --> 01:38:21,880
Speaker 2: these once impossible goals. And you know, I think I

1596
01:38:21,880 --> 01:38:24,880
Speaker 2: think that's it's borrowed from Star Trek. I'm not a Treky,

1597
01:38:24,960 --> 01:38:27,280
Speaker 2: but I think the reality distortion field is a Star

1598
01:38:27,360 --> 01:38:29,720
Speaker 2: Trek thing. But we've talked to so many people that

1599
01:38:30,240 --> 01:38:34,120
Speaker 2: say that about someone like Elon right, like that he

1600
01:38:34,280 --> 01:38:39,040
Speaker 2: has that ability to make people believe. And you see

1601
01:38:39,040 --> 01:38:41,439
Speaker 2: that quality not just that his companies, but outside his companies.

1602
01:38:41,479 --> 01:38:44,599
Speaker 2: You talk about the Robotaxi event. Again, he got people

1603
01:38:44,640 --> 01:38:48,000
Speaker 2: believing in this thing that's not even real. You know,

1604
01:38:48,040 --> 01:38:50,519
Speaker 2: that is on a movie set you know, farrying people

1605
01:38:50,600 --> 01:38:53,639
Speaker 2: around from fake city to fake city on a movie set.

1606
01:38:54,720 --> 01:38:58,439
Speaker 2: And when we talk about like his greatest quality as

1607
01:38:58,439 --> 01:39:00,200
Speaker 2: an entrepreneur, I think that that's it.

1608
01:39:02,080 --> 01:39:03,880
Speaker 1: Okay, But is he a good engineer?

1609
01:39:08,680 --> 01:39:13,880
Speaker 2: Uh? I guess it depends. Yeah, he's given himself the

1610
01:39:13,960 --> 01:39:16,840
Speaker 2: chief engineer title. I think at some of his companies,

1611
01:39:21,720 --> 01:39:23,080
Speaker 2: I don't know enough about his day to day at

1612
01:39:23,120 --> 01:39:25,960
Speaker 2: Tesla and SpaceX. At Twitter, for example, he is not

1613
01:39:26,080 --> 01:39:32,200
Speaker 2: in the code, he is not tweaking the algorithm. In fact,

1614
01:39:32,320 --> 01:39:35,120
Speaker 2: what we reported is he barely even uses as a

1615
01:39:35,160 --> 01:39:38,040
Speaker 2: computer that you know, he had. He had to have

1616
01:39:38,080 --> 01:39:40,320
Speaker 2: an assistant that set up his computer for him whenever

1617
01:39:40,360 --> 01:39:43,920
Speaker 2: he needed to really use a computer. But he views

1618
01:39:43,960 --> 01:39:47,600
Speaker 2: everything on his iPhone. Presentations have to be delivered to

1619
01:39:47,680 --> 01:39:50,240
Speaker 2: him so it's readable on a you know, whatever the

1620
01:39:50,280 --> 01:39:53,599
Speaker 2: size screen of an iPhone is, and from there he's

1621
01:39:53,600 --> 01:39:56,400
Speaker 2: able to dictate his vision and you know, tell people

1622
01:39:56,520 --> 01:40:00,000
Speaker 2: and direct them. But I don't think he's doing much

1623
01:40:00,080 --> 01:40:04,000
Speaker 2: engineering these days, particularly at Twitter or X.

1624
01:40:04,760 --> 01:40:08,839
Speaker 1: Okay, my dealings with Walter isaacs in have been relatively minimal,

1625
01:40:09,560 --> 01:40:12,080
Speaker 1: but I got to bug up my ass based on

1626
01:40:12,200 --> 01:40:17,120
Speaker 1: that dealing and the reverence this guy gets so he

1627
01:40:17,160 --> 01:40:21,360
Speaker 1: writes a book on Elon. People who are not don't

1628
01:40:21,400 --> 01:40:25,120
Speaker 1: follow this space, and we rave and then everybody follows

1629
01:40:25,120 --> 01:40:28,640
Speaker 1: this space. Said this guy drank the kool aid. And

1630
01:40:28,680 --> 01:40:33,559
Speaker 1: then reading your book where Elon literally asked Walter Isaacs

1631
01:40:33,640 --> 01:40:36,880
Speaker 1: in advice, whether consciously what this will do for the

1632
01:40:36,880 --> 01:40:42,040
Speaker 1: book or not, that compromises the entire reporting. But as

1633
01:40:42,080 --> 01:40:44,760
Speaker 1: a reporter, I'm sure you've read the book. What do

1634
01:40:44,840 --> 01:40:46,479
Speaker 1: you say about Isaacs in book?

1635
01:40:47,400 --> 01:40:52,200
Speaker 2: I've read the book, and I'll say this, I really

1636
01:40:52,240 --> 01:40:54,800
Speaker 2: loved the I read the Steve Jobs book cover to cover.

1637
01:40:55,880 --> 01:40:58,680
Speaker 2: I loved it. You know, I learned so much about him.

1638
01:40:59,000 --> 01:41:01,240
Speaker 2: But then I started to realize is that there are

1639
01:41:02,080 --> 01:41:06,439
Speaker 2: issues with these kind of great man biographies. Right, You're

1640
01:41:06,479 --> 01:41:11,200
Speaker 2: starting with this premise that these are great men and

1641
01:41:14,840 --> 01:41:17,400
Speaker 2: that they can be forgiven in some instances because they

1642
01:41:17,400 --> 01:41:19,519
Speaker 2: are such great men because have done such great things.

1643
01:41:20,560 --> 01:41:23,800
Speaker 2: And I think as a reporter, like leading with that

1644
01:41:25,240 --> 01:41:30,160
Speaker 2: is uncomfortable for me, particularly because I view my job

1645
01:41:30,200 --> 01:41:33,479
Speaker 2: as having to interrogate power. Like Yes, on one hand,

1646
01:41:33,520 --> 01:41:36,640
Speaker 2: I can understand that he's built great companies, but on

1647
01:41:36,680 --> 01:41:39,840
Speaker 2: the other hand, if he is harming, if people are

1648
01:41:39,840 --> 01:41:43,479
Speaker 2: getting hurt in his factories if he is breaking the law, like,

1649
01:41:44,040 --> 01:41:46,439
Speaker 2: it's my job to report that and expose that and

1650
01:41:46,479 --> 01:41:51,040
Speaker 2: show that and understand that there are costs to being

1651
01:41:52,200 --> 01:41:56,519
Speaker 2: this supposed great man. And I think, you know, with

1652
01:41:56,560 --> 01:41:59,160
Speaker 2: the Isaacson case, we'll just let a reporting do the talking.

1653
01:41:59,200 --> 01:42:02,799
Speaker 2: You know, our reporting showed that he was providing advice

1654
01:42:03,040 --> 01:42:07,280
Speaker 2: to Elon on Twitter Blue, the subscription service. He was

1655
01:42:07,600 --> 01:42:09,880
Speaker 2: telling him that a lot of people will use a service,

1656
01:42:09,920 --> 01:42:12,559
Speaker 2: how we should you know, he should price it low

1657
01:42:12,640 --> 01:42:16,200
Speaker 2: because it's going to be such a game changer. He

1658
01:42:16,400 --> 01:42:20,160
Speaker 2: was giving Elon advice on labeling. You know, there was

1659
01:42:20,160 --> 01:42:23,720
Speaker 2: that kind of tiff with NPR and whether or not

1660
01:42:23,880 --> 01:42:27,120
Speaker 2: it should be labeled as state sponsored media, and apparently

1661
01:42:27,120 --> 01:42:31,120
Speaker 2: Isaacson got involved with that as well. And as a reporter,

1662
01:42:31,280 --> 01:42:33,120
Speaker 2: that's just not a place I want to be in,

1663
01:42:33,240 --> 01:42:36,080
Speaker 2: I don't, you know. I want my I want my subject. Yes,

1664
01:42:36,120 --> 01:42:37,559
Speaker 2: I want to be in the room with my subject

1665
01:42:37,600 --> 01:42:39,760
Speaker 2: if they allow me to. But I'm not going to

1666
01:42:39,760 --> 01:42:43,960
Speaker 2: compromise my integrity as reporter. I don't want to be

1667
01:42:44,000 --> 01:42:47,880
Speaker 2: an advisor to him by any means. And I think

1668
01:42:47,920 --> 01:42:53,439
Speaker 2: that's the trap that he fell into, you know, Elon

1669
01:42:53,560 --> 01:42:57,760
Speaker 2: is extremely influential. He he he has a gravitational pull.

1670
01:42:58,000 --> 01:43:01,360
Speaker 2: We see this with all the people that come to

1671
01:43:01,439 --> 01:43:04,320
Speaker 2: kind of kiss his ring, and you know a lot

1672
01:43:04,320 --> 01:43:09,280
Speaker 2: of people described it as a king's court, and you

1673
01:43:09,439 --> 01:43:12,160
Speaker 2: kind of if you guess, if you're around it long enough,

1674
01:43:12,160 --> 01:43:15,800
Speaker 2: you fall into that. And we see that time and

1675
01:43:15,840 --> 01:43:19,559
Speaker 2: time again in our book with Walter, and I think

1676
01:43:19,600 --> 01:43:23,080
Speaker 2: that caloused your judgment when it comes to reporting. I

1677
01:43:23,120 --> 01:43:25,479
Speaker 2: think one of my main issues with his book is

1678
01:43:25,479 --> 01:43:29,040
Speaker 2: that he took Elon for face value on a lot

1679
01:43:29,080 --> 01:43:32,560
Speaker 2: of reporting. You know, as a reporter, you want to

1680
01:43:32,560 --> 01:43:39,280
Speaker 2: get multiple perspectives. And so for example, Elon's daughter has

1681
01:43:39,320 --> 01:43:41,760
Speaker 2: come out as trans daughter and said, you know, you've

1682
01:43:41,760 --> 01:43:43,720
Speaker 2: written about me a lot in your book, but you

1683
01:43:43,840 --> 01:43:46,080
Speaker 2: never reached out to me to get my perspective, and

1684
01:43:46,120 --> 01:43:48,320
Speaker 2: you just took my dad's and they don't have a

1685
01:43:48,400 --> 01:43:50,920
Speaker 2: very good relationship effect. Elon has said that his daughter

1686
01:43:51,000 --> 01:43:54,240
Speaker 2: is dead to him. So he simply took Elon's perspective

1687
01:43:54,360 --> 01:44:00,600
Speaker 2: about the trans daughter and ran with it. And I

1688
01:44:00,640 --> 01:44:04,120
Speaker 2: don't think that's good reporting. You know, that would that

1689
01:44:04,160 --> 01:44:06,759
Speaker 2: would never fly in a basic reporting one in one class,

1690
01:44:08,400 --> 01:44:10,320
Speaker 2: and yeah, that's that's kind of my view on it.

1691
01:44:12,800 --> 01:44:16,960
Speaker 1: Okay, starlink, we have the issue in the Ukraine War,

1692
01:44:17,760 --> 01:44:21,280
Speaker 1: we read about the plethora of satellites. No one has

1693
01:44:21,280 --> 01:44:26,160
Speaker 1: ever made satellite communications profitable. What's the future of starlink

1694
01:44:26,600 --> 01:44:30,400
Speaker 1: and to what degree do we have to fear Elon

1695
01:44:30,600 --> 01:44:33,599
Speaker 1: Musk's actions on the world stage.

1696
01:44:37,040 --> 01:44:41,360
Speaker 2: Starlink is another source of his power and one that

1697
01:44:43,120 --> 01:44:49,960
Speaker 2: ah gosh, I mean, it makes him so influential. You

1698
01:44:49,960 --> 01:44:52,960
Speaker 2: think about the Ukraine War and him essentially controlling the

1699
01:44:52,960 --> 01:44:58,360
Speaker 2: communications and pipes for the Ukraine defense effort as he

1700
01:44:58,760 --> 01:45:01,960
Speaker 2: simultaneously is calling for a cease fire and end of

1701
01:45:02,000 --> 01:45:07,160
Speaker 2: the war and an appeasing of putin right, it's kind

1702
01:45:07,200 --> 01:45:09,400
Speaker 2: of just nuts, Like we haven't really never seen this,

1703
01:45:09,479 --> 01:45:11,680
Speaker 2: and I think starlink only will get more powerful, like

1704
01:45:12,160 --> 01:45:16,599
Speaker 2: because Elon has a virtual monopoly on getting things into space,

1705
01:45:16,600 --> 01:45:20,639
Speaker 2: at least in the private industry, Like he controls how

1706
01:45:20,680 --> 01:45:24,800
Speaker 2: those satellites get up and he knows that and it

1707
01:45:25,360 --> 01:45:27,200
Speaker 2: gives him a lot of power. I think of the

1708
01:45:27,280 --> 01:45:32,840
Speaker 2: hurricanes that just hit, you know, the Southeast, and how

1709
01:45:32,880 --> 01:45:37,200
Speaker 2: FEMA was reliant on starlink and getting communications to that region,

1710
01:45:38,960 --> 01:45:45,000
Speaker 2: and Elon is is literally criticizing FEMA on on X

1711
01:45:45,120 --> 01:45:48,960
Speaker 2: he is shitting on the federal government, He's shitting on Biden,

1712
01:45:49,720 --> 01:45:51,599
Speaker 2: and yet FEMA has to play ball with him because

1713
01:45:51,640 --> 01:45:56,840
Speaker 2: he controls Starlink like it's an incredible position to be in,

1714
01:45:58,240 --> 01:46:02,120
Speaker 2: and I think Starlink will become more and more a

1715
01:46:02,120 --> 01:46:03,800
Speaker 2: source of his power. I think I saw today in

1716
01:46:03,840 --> 01:46:06,200
Speaker 2: the news that they had signed a contract with T

1717
01:46:06,360 --> 01:46:13,400
Speaker 2: Mobile to provide I guess calling service, So you know,

1718
01:46:13,400 --> 01:46:15,800
Speaker 2: we start to see more of those deals overseas. It's

1719
01:46:15,880 --> 01:46:23,480
Speaker 2: quite influential as well. And yeah, yeah, it's it's definitely

1720
01:46:23,600 --> 01:46:25,320
Speaker 2: an underrated part of his empire.

1721
01:46:28,320 --> 01:46:32,240
Speaker 1: Okay, and the Boring Company, which he never really took

1722
01:46:32,280 --> 01:46:37,800
Speaker 1: that seriously, but we almost never hear about it. Neuralink,

1723
01:46:37,880 --> 01:46:42,240
Speaker 1: he had his press conference. Are those businesses or those hobbies?

1724
01:46:44,840 --> 01:46:46,559
Speaker 2: I mean their businesses in the sense that they've raised

1725
01:46:46,600 --> 01:46:51,879
Speaker 2: venture capital and they employ people, and you know, Neuralink

1726
01:46:51,920 --> 01:46:56,759
Speaker 2: had its first patient that got something implanted. It's very

1727
01:46:57,000 --> 01:47:02,240
Speaker 2: very developmental early stages. The Boring Company, I guess it

1728
01:47:02,240 --> 01:47:05,360
Speaker 2: built that tunnel in Vegas that you know, shuttles people

1729
01:47:05,439 --> 01:47:10,680
Speaker 2: mile cars can drive through it, but there hasn't been

1730
01:47:10,760 --> 01:47:15,280
Speaker 2: very much from that from that company either, I would

1731
01:47:15,320 --> 01:47:17,880
Speaker 2: say those are not his main focuses. You know, obviously

1732
01:47:17,880 --> 01:47:20,080
Speaker 2: he'll dip into uralink stuff when they have an announcement

1733
01:47:20,320 --> 01:47:24,000
Speaker 2: or things like that. But when we talk about those

1734
01:47:24,080 --> 01:47:26,479
Speaker 2: kinds of teams that he's built up from the ground up,

1735
01:47:27,040 --> 01:47:29,160
Speaker 2: those companies are good examples of that where they kind

1736
01:47:29,200 --> 01:47:32,040
Speaker 2: of self operate without him. He'll dip in when he

1737
01:47:32,080 --> 01:47:36,800
Speaker 2: needs to, and when there's a big announcement he can

1738
01:47:36,880 --> 01:47:40,160
Speaker 2: draw attention to it. But it certainly adds to his lore,

1739
01:47:40,320 --> 01:47:45,800
Speaker 2: right Like he he's the man that runs six companies ostensibly,

1740
01:47:45,880 --> 01:47:49,680
Speaker 2: and it gives him a great legend. And oftentimes I

1741
01:47:49,720 --> 01:47:51,559
Speaker 2: get asked, you know, does how is he able to

1742
01:47:51,600 --> 01:47:54,559
Speaker 2: do this? Like I can't even do my own job,

1743
01:47:54,600 --> 01:47:56,960
Speaker 2: how can you do six and like run all these companies? Well,

1744
01:47:56,960 --> 01:48:00,760
Speaker 2: the truth is, like there's more to it, and he

1745
01:48:00,800 --> 01:48:03,960
Speaker 2: certainly loves to profit off this this image. But it's

1746
01:48:03,960 --> 01:48:06,240
Speaker 2: not like he is. He is going into New orlank

1747
01:48:06,280 --> 01:48:08,200
Speaker 2: every day and working on that from a nine to five.

1748
01:48:12,080 --> 01:48:16,920
Speaker 1: So what have you learned now that the book is out?

1749
01:48:20,000 --> 01:48:26,960
Speaker 2: Hmmm. So we end our book in March when he

1750
01:48:27,000 --> 01:48:28,920
Speaker 2: meets Donald Trump for the first time in a long

1751
01:48:28,960 --> 01:48:31,760
Speaker 2: time and they go in there at Florida, at the

1752
01:48:31,800 --> 01:48:35,120
Speaker 2: home of Nelson Pelts, the activist investory tried to take

1753
01:48:35,160 --> 01:48:39,559
Speaker 2: over Disney recently, and it was purposeful one. We couldn't

1754
01:48:39,560 --> 01:48:43,719
Speaker 2: get We couldn't continue the book forever, and we wanted

1755
01:48:43,920 --> 01:48:47,280
Speaker 2: a clean kind of cut to understand. You know, I

1756
01:48:47,280 --> 01:48:50,040
Speaker 2: think people understand like from that moment on is when

1757
01:48:50,040 --> 01:48:53,760
Speaker 2: he when he you know, really got in better of

1758
01:48:53,800 --> 01:48:58,479
Speaker 2: the Trump. But when we ended the book, I do

1759
01:48:58,560 --> 01:49:02,639
Speaker 2: not think we could have predict did the extent which

1760
01:49:02,680 --> 01:49:05,880
Speaker 2: with which with with which he would support Donald Trump.

1761
01:49:06,640 --> 01:49:09,160
Speaker 2: You know, there is a different world in which I

1762
01:49:09,160 --> 01:49:11,000
Speaker 2: thought it would have been a much more quieter support.

1763
01:49:11,080 --> 01:49:13,960
Speaker 2: He would have maybe announced his endorsement a couple of

1764
01:49:14,040 --> 01:49:17,840
Speaker 2: days before the election, and he would not have done

1765
01:49:18,760 --> 01:49:20,880
Speaker 2: by any means what we saw happen, you know, which

1766
01:49:20,920 --> 01:49:27,000
Speaker 2: is to pour two hundred million dollars into his campaign

1767
01:49:27,200 --> 01:49:30,880
Speaker 2: and to literally go to rallies in Pennsylvania and stuff

1768
01:49:30,920 --> 01:49:36,920
Speaker 2: for him and have this charity whatever lottery giveaway to

1769
01:49:37,040 --> 01:49:41,480
Speaker 2: people who register to vote for him. That was unfathomable

1770
01:49:41,520 --> 01:49:43,120
Speaker 2: to me, and I'd like, that wasn't even on the

1771
01:49:43,160 --> 01:49:48,800
Speaker 2: cards for me. And what I've learned from that is

1772
01:49:49,960 --> 01:49:53,160
Speaker 2: just his convictions, and what when you have that much money,

1773
01:49:53,280 --> 01:49:56,800
Speaker 2: what you're able to do about those convictions. You know,

1774
01:49:56,840 --> 01:49:59,479
Speaker 2: he spent two hundred million dollars on Donald Trump's campaign,

1775
01:50:00,640 --> 01:50:03,720
Speaker 2: and that seems like a really smart bet considering what

1776
01:50:03,800 --> 01:50:05,760
Speaker 2: he'll get out of that. You know, he'll be able

1777
01:50:05,800 --> 01:50:09,479
Speaker 2: to appoint people that he wants the government. He might

1778
01:50:09,600 --> 01:50:15,120
Speaker 2: he has the influence an ear of the president, and

1779
01:50:15,200 --> 01:50:18,800
Speaker 2: so that two hundred million dollars looks incredibly cheap, where

1780
01:50:18,960 --> 01:50:22,240
Speaker 2: whereas that's unfathomable. You know, who else could do that

1781
01:50:22,320 --> 01:50:27,080
Speaker 2: in the world. And so yeah, I've learned, I've learned

1782
01:50:27,080 --> 01:50:31,440
Speaker 2: how that works, I guess, or how that looks, and yeah, yeah.

1783
01:50:31,120 --> 01:50:35,400
Speaker 1: So well, I'm actually also interested in what you learn personally.

1784
01:50:35,960 --> 01:50:40,040
Speaker 1: You write a book, you're with your co author we

1785
01:50:40,160 --> 01:50:42,240
Speaker 1: both work at the New York Times, comes out on

1786
01:50:42,320 --> 01:50:47,479
Speaker 1: a major publisher, It comes out relative to your expectations

1787
01:50:47,520 --> 01:50:49,360
Speaker 1: and feedback. What did you learn.

1788
01:50:51,200 --> 01:50:54,080
Speaker 2: About writing a book or moving through the.

1789
01:50:54,000 --> 01:50:58,639
Speaker 1: World there or writing this book? Yeah, writing this book,

1790
01:50:58,680 --> 01:51:07,439
Speaker 1: in this book coming out, what do we learn? Let

1791
01:51:07,479 --> 01:51:10,040
Speaker 1: me throw on a couple of things. Yeah, sure, sure, sure,

1792
01:51:10,400 --> 01:51:14,880
Speaker 1: everybody's focused on their life. You're leading up to publication, date.

1793
01:51:15,000 --> 01:51:19,080
Speaker 1: In your world, this is the most important thing since

1794
01:51:19,120 --> 01:51:22,439
Speaker 1: your status, of the writer's subject matter of the book,

1795
01:51:22,479 --> 01:51:27,360
Speaker 1: the publisher. There's a lot of ink media coverage around

1796
01:51:27,360 --> 01:51:34,040
Speaker 1: its publication, then the media moves on. Okay, there's been

1797
01:51:34,040 --> 01:51:37,519
Speaker 1: a lot of people have written books recently. I had

1798
01:51:37,520 --> 01:51:39,760
Speaker 1: to listen to Kara Swisher write about her and talk

1799
01:51:39,800 --> 01:51:43,120
Speaker 1: about her fucking book for over a year, came out,

1800
01:51:43,479 --> 01:51:46,439
Speaker 1: was gone within like a week, and really no one

1801
01:51:46,520 --> 01:51:48,679
Speaker 1: bought it. And there are a lot of other people

1802
01:51:48,840 --> 01:51:53,200
Speaker 1: like that. Okay, your book certainly has a higher pedigree,

1803
01:51:53,320 --> 01:51:57,599
Speaker 1: more interest. Do you find you're getting continued feedback? Do

1804
01:51:57,640 --> 01:52:00,320
Speaker 1: you find that you're hearing from people that you didn't

1805
01:52:00,360 --> 01:52:04,000
Speaker 1: used to hear from. Do you find that you're being criticized?

1806
01:52:04,360 --> 01:52:07,200
Speaker 1: Are you stunned that the world has moved on what

1807
01:52:07,320 --> 01:52:11,040
Speaker 1: has been your emotional experience in addition to what has

1808
01:52:11,040 --> 01:52:11,719
Speaker 1: been incoming.

1809
01:52:14,360 --> 01:52:17,879
Speaker 2: So I've been surprised because we've had a renewed interest

1810
01:52:17,960 --> 01:52:21,000
Speaker 2: in our book after the election, because more people want

1811
01:52:21,040 --> 01:52:23,120
Speaker 2: to understand what he's going to do with government, how

1812
01:52:23,160 --> 01:52:27,960
Speaker 2: we operate, and people can read about that about in

1813
01:52:28,040 --> 01:52:29,800
Speaker 2: like what he did with Twitter. So we just came

1814
01:52:29,880 --> 01:52:32,000
Speaker 2: back from a tour of Europe actually to where we

1815
01:52:32,000 --> 01:52:34,960
Speaker 2: talked about the book. We spoke at web Summit and Lisbon,

1816
01:52:35,439 --> 01:52:39,840
Speaker 2: we did some events in London. So on that level,

1817
01:52:40,080 --> 01:52:43,080
Speaker 2: I honestly thought I talked to a lot of authors

1818
01:52:43,120 --> 01:52:44,680
Speaker 2: who are like, you know, enjoy it while you can,

1819
01:52:44,840 --> 01:52:48,800
Speaker 2: because a week later, like, people aren't gonna talking about

1820
01:52:48,800 --> 01:52:51,840
Speaker 2: your book. That's just the natural progression of things. And

1821
01:52:51,880 --> 01:52:55,160
Speaker 2: for us to be continuing to talk to people two

1822
01:52:55,160 --> 01:52:57,679
Speaker 2: and a half months out of publication, to people like yourself,

1823
01:52:57,800 --> 01:53:00,960
Speaker 2: has been an incredible privilege. And so I'm just trying

1824
01:53:00,960 --> 01:53:04,880
Speaker 2: to kind of ride that wave and enjoy it and

1825
01:53:05,920 --> 01:53:11,160
Speaker 2: kind of impart the knowledge that we've learned about him too.

1826
01:53:11,840 --> 01:53:14,640
Speaker 2: You know, people that are interested.

1827
01:53:15,680 --> 01:53:19,559
Speaker 3: Well, Ryan, this has been incredibly edifying. You're a very

1828
01:53:19,640 --> 01:53:24,439
Speaker 3: sharp guy. You're on the case. There's certainly a movie

1829
01:53:24,760 --> 01:53:27,000
Speaker 3: unspooling in front of us. I know you will be

1830
01:53:27,160 --> 01:53:29,720
Speaker 3: reporting on it. I want to thank you so much

1831
01:53:29,760 --> 01:53:32,040
Speaker 3: for taking this time to speak with my audience.

1832
01:53:33,200 --> 01:53:34,479
Speaker 2: Thanks so much, Bob. I want to say this has

1833
01:53:34,479 --> 01:53:36,639
Speaker 2: been an honor. I know I've read you for so long.

1834
01:53:36,800 --> 01:53:39,519
Speaker 2: You're such a time in the industry, So thank you

1835
01:53:39,560 --> 01:53:39,840
Speaker 2: so much.

1836
01:53:39,880 --> 01:53:44,080
Speaker 3: Having man till next time, This is Bob left st