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

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

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Speaker 1: today's best minds. And a federal judge has blocked Florida's

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Speaker 1: ban on gender firm and care for trans youth. We

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Speaker 1: have such an interesting show today. Nprs Miles Parks stop

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Speaker 1: spy to talk to us about Republicans' latest fuckery with

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Speaker 1: the voter rolls. Then we'll talk to the New York

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Speaker 1: Times Ryan Mack about the bleak future in store for

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Speaker 1: Elon Musk's Twitter. But first we have the host of

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Speaker 1: the Think Like an Economist podcast, University of Michigan Professor

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Speaker 1: Justin Wolfers. Welcome to Fast Politics.

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Speaker 2: Welcome back Justin Fast Politics and Fast Economics.

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Speaker 3: Molly, Fast Economics. We need your help, man, I'm here

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Speaker 3: to help.

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Speaker 1: Let us talk first about the blockbuster jobs numbers.

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Speaker 2: The jobs we're blockbuster again, right, yeah, So let me

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Speaker 2: just give some context for your listeners. Yes, we economists

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Speaker 2: pay enormous attention to a monthly set of numbers called

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Speaker 2: the Jobs Report. Partly it's because we love it when

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Speaker 2: people find work. But it's not just that. This is

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Speaker 2: also the first big data release that tells us what

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Speaker 2: the economy was doing in May. So it's not just

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Speaker 2: that it's important, it's also that it's reliable and it's early,

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Speaker 2: and so it tells us when we get all the

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Speaker 2: rest of the indicators to May, it's also going to

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Speaker 2: say this economy just kept rocketing along.

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Speaker 1: There's a lot of anxiety here that we and this

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Speaker 1: has been going on for a while, that we were

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Speaker 1: going to go into a recession or something, you know,

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Speaker 1: that we were going to have a hard landing. These

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Speaker 1: jobs numbers explain why they're so relevant.

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Speaker 2: Right, So I will tell you, molly, Like every month

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Speaker 2: for the last maybe year and a half or two years,

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Speaker 2: literally every month a journalist has called me and said, boy,

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Speaker 2: do you think this means we're in a recession?

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Speaker 3: Right?

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Speaker 2: Every time, and in every single one of those months,

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Speaker 2: the economy has created more jobs than it normally does.

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Speaker 2: So it's not just that the economy is not a

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Speaker 2: lot of nuts. In that sentence, let me.

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Speaker 1: Go back, Yeah, tell us without the quadruple negatives.

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Speaker 4: Please, you're saying I should aure not the knots. Boom, yes, boom,

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Speaker 4: that's right. We got some real academic humor here. Continue. Yes, no,

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Speaker 4: I'm just kidding, hilarious.

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Speaker 2: Continue, So, if people thought we're in a recession, we

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Speaker 2: should be creating fewer jobs than we normally are, in fact,

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Speaker 2: we should be losing jobs. False and false. The opposite

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Speaker 2: of a recession is when we're creating jobs, and the

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Speaker 2: extreme opposite of a recession is when we're creating jobs

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Speaker 2: faster than usual.

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Speaker 3: Right, all of which is true right now.

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Speaker 2: All of which is true this month, last month, the

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Speaker 2: one before that, the one before that, and how long

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Speaker 2: do you want me to go on for molly okay?

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Speaker 1: On the internet, which we're trying to do less of,

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Speaker 1: but on Twitter, the tech bro crew, which includes Elon

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Speaker 1: Musk and his minions were mad about these job numbers

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Speaker 1: and felt that they weren't legit.

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Speaker 3: Can you explain a little bit about this incredibly dumb story.

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Speaker 2: Yeah, So, if you and you alone have a galaxy

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Speaker 2: brain that understands all of capitalism, which I do, Yes,

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Speaker 2: and some numbers come out that happen not to reflect

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Speaker 2: the reality that you see as you're walking down Sandhill

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Speaker 2: Road in Silicon Valley to buy an eighteen dollar latte.

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Speaker 3: We are really at peak naughtiness here. Justin continue, Yes, there's.

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Speaker 2: Only one conclusion you can draw, which is the numbers

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Speaker 2: must be fake. Reality could not possibly be anything else

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Speaker 2: you perceive it to be. I will tell you, Molly,

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Speaker 2: the fun thing about this is I teach freshman economics.

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Speaker 2: I have brilliant, wonderful eighteen year olds, and I teach

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Speaker 2: this stuff about how these numbers are calculated and so on,

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Speaker 2: and they fall a little bit of sleep because statistics

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Speaker 2: aren't for everyone. But any one of my freshmen could

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Speaker 2: fly out to Silicon Valley and I'm sure I could

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Speaker 2: get one of them out there for a mere two

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Speaker 2: thousand dollars an hour to give a quick little lecture

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Speaker 2: on how the numbers are collected. And let me tell you,

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Speaker 2: the moment someone tells you there's corruption involved, they're telling

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Speaker 2: you they don't know what they're saying. They could explain

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Speaker 2: the very simple mathematics of how it involves both addition

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Speaker 2: and subtraction and sometimes long division. And it turns out

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Speaker 2: the rest of the economy is just not necessarily what

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Speaker 2: your little bubble looks like.

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Speaker 3: Right.

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Speaker 1: Sometimes these numbers are adjusted after they're announced. Can you

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Speaker 1: explain a little bit about what where those factor into

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Speaker 1: I mean, have you seen big adjustments down and what

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Speaker 1: does that look like.

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Speaker 2: So here's how they collect the most important of those numbers,

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Speaker 2: which is called the Non farm Payrolls Report. Basically, every

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Speaker 2: big company in the country and a pretty good sampling

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Speaker 2: of smaller companies are asked every month to send in

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Speaker 2: a form or to electronically zap across a headcom to

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Speaker 2: the government, and then the government uses statistical voodoo otherwise

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Speaker 2: known as you know, just standard extrapolation to come up

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Speaker 2: with the numbers that you read about in the newspaper. Now,

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Speaker 2: sometimes it happens that the folks in the personnel department

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Speaker 2: are a little bit busy and they don't zap their

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Speaker 2: numbers across in time, and so every month they publish

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Speaker 2: the number before all the responses come back, and in

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Speaker 2: order to be responsible the next month, the statistical authorities so, well,

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Speaker 2: let's count all the forms that were returned, and so

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Speaker 2: they publish what's called the revised number. It is true

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Speaker 2: that the first number you read is an initial early

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Speaker 2: best guess. It is true that they're subsequently revised. One

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Speaker 2: of my favorite tech bro stories I've heard is that

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Speaker 2: they're always published is if the economy is strong, and

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Speaker 2: then later quietly Biden and his minions revise it down

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Speaker 2: when literally the exact opposite has been true over the

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Speaker 2: past few years. So it turns out these numbers ten

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Speaker 2: over a couple of months have been revised up more

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Speaker 2: often than down, But over a long run of history,

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Speaker 2: they're revised up and down roughly as often as each other,

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Speaker 2: and roughly as much under Republicans as Democrats. And this

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Speaker 2: is just an incredibly serious and boring statistical exercise, and

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Speaker 2: there's nothing more to it than meets the eye.

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Speaker 1: So I want to ask you, just I want to

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Speaker 1: get into this for another minute, even though it's so stupid.

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Speaker 1: Do you think it's a bad tension that we have

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Speaker 1: a certain And again, these are not all tech bros.

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Speaker 1: These are just very loudest ones, and they tend to

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Speaker 1: be the very libertarian ones. But do you think it's

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Speaker 1: bad that this crew is so convinced that the economy

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Speaker 1: is bad and wants to make it so or do

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Speaker 1: you think that's just a normal tension that we see.

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Speaker 2: Let me answer by broadening the lens and not just

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Speaker 2: picking on the tech bros. Right, So, the our word

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Speaker 2: recession is one we have just heard an absolutely relentless

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Speaker 2: drum beat of over the past two years, just month

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Speaker 2: after month, and each of your listeners has tuned into

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Speaker 2: a major TV station, or listen to a radio broadcast,

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Speaker 2: or read a headline from a major newspaper saying are

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Speaker 2: we in recession? Are we in recession? Are we in recession?

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Speaker 2: People are worried about a recession. And the amazing thing was,

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Speaker 2: if you're one of the statistical nerds that I am,

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Speaker 2: there never has been even a hint that we're in recession.

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Speaker 2: And so that raises this very deep question what the

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Speaker 2: hell's going on? And a bright young economists who came

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Speaker 2: up with this idea lovely expression. She called it a

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Speaker 2: vibe session. It's the vibe, man. The vibe is recessionary.

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Speaker 2: The reality, though, which is reflected in statistics, is not.

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Speaker 2: And I think it's kind of important because when I

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Speaker 2: say the reality is not, I am not saying your industry,

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Speaker 2: your job, your street, your part of the country is

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Speaker 2: doing okay. I don't know. What I do know is

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Speaker 2: when we add up the statistics across hundreds of millions

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Speaker 2: of people that on average, the economy today is doing

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Speaker 2: a lot better and has continued to improve for the

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Speaker 2: last couple of years. And so there's a deeper I

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Speaker 2: don't like saying this because normally I think the economics

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Speaker 2: media does a pretty good job, particularly from top outlets,

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Speaker 2: But this has been a case where the disjunction between

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Speaker 2: popular opinion and media reporting versus reality has been larger

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Speaker 2: than I've ever seen in my life. I don't have

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Speaker 2: a deep theory as to why, but I do know

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Speaker 2: that's true.

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Speaker 3: You don't have a deep theory into why. You want

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Speaker 3: to just make a gas.

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Speaker 2: I'll take a guess. So one, Molly, you might notice

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Speaker 2: that I'm on your show right now during the middle

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Speaker 2: of a booming economy. What are we talking about right now?

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Speaker 3: The economy?

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Speaker 2: Did you use the word recession when asking me some questions? Yes, okay,

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Speaker 2: so I'm talking about a recession because you asked me

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Speaker 2: about a recession, right, Molly, Why did you ask me

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Speaker 2: about a recession?

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Speaker 1: We're not in one, because there's been a lot of

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Speaker 1: anxiety about a recession.

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Speaker 2: So you have to talk about a recession because everyone

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Speaker 2: else is talking about a recession. Yes, and despite the

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Speaker 2: fact I know we're not in a recession, I'm talking

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Speaker 2: about a recession as well. It's a little bit of

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Speaker 2: a self reinforcing loop there. But let me give your

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Speaker 2: listeners to other explanations. One, there actually is something bad

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Speaker 2: going on at the moment. Inflation is high. Inflation sucks, right,

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Speaker 2: I hate it, it's awful.

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Speaker 3: I wanted to ask you about.

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Speaker 2: Yeah, so good. You've now got my bold opinion. I'm again.

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Speaker 1: Hey, so it's high, but it's actually better than we thought.

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Speaker 3: Right, talk to us about inflation. The numbers are going down.

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Speaker 2: Right, So, first of all, inflation is not the same

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Speaker 2: thing as recession. A recession means we're making less, we're

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Speaker 2: buying less, fewer of us have jobs. Yes, it's about

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Speaker 2: the stuff that's getting done. Inflation is about the price

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Speaker 2: tags associated with that stuff. And so those price tags

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Speaker 2: have been a source of angst. But the amount of

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Speaker 2: stuff we do has actually been going up. Now inflation

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Speaker 2: is fortunately on its way down. There are gradations. We

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Speaker 2: could talk about numbers, but let me just use normal

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Speaker 2: terms instead. There was a point about a year ago

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Speaker 2: where it was at the let's call it the holy

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Speaker 2: shit level where.

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Speaker 3: There was a lot of anxiety.

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Speaker 2: You couldn't help but notice it. You'd go to the

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Speaker 2: grocery store and stuff was more expensive, right, So that's

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Speaker 2: what inflation's like eight, nine, ten percent. Then it's now

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Speaker 2: fallen to the four or five percent, which is the

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Speaker 2: you know, it's kind of a minor annoyance, but you're

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Speaker 2: not seeing your paycheck getting robbed at at a really

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Speaker 2: crazy rate. And then once we get to two or

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Speaker 2: three two percent. No one will believe me when I

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Speaker 2: say this, but once inflation gets down to two percent,

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Speaker 2: you can barely notice it. And the reason you can

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Speaker 2: barely notice it is once it's two percent, some things

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Speaker 2: are getting a lot cheaper and some things are getting

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Speaker 2: a lot more expensive, and it actually requires a really

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Speaker 2: big spreadsheet to figure out which side of the ledger

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Speaker 2: is bigger. So we're down from holy shit levels towards

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Speaker 2: annoying but noticeable, and inflation is going to continue to

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Speaker 2: fade down. And the question is will it get all

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Speaker 2: the way to unnoticeable or will it stick it mildly annoying.

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Speaker 1: So I want to ask you, as we're talking about this,

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Speaker 1: the dead ceiling, big news drama, a lot of you know,

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Speaker 1: it could have really crashed the American economy and really

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Speaker 1: hurt the Biden presidency. I think a lot of us

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Speaker 1: had that anxiety that did not happen, and Biden.

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Speaker 3: World really did best.

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Speaker 1: The GOP time to me about a how they basted

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Speaker 1: them and be what it looks like now.

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Speaker 2: Great, So let me tell you a story in three acts.

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Speaker 2: First two acts are good news, and then we learned

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Speaker 2: the whole thing was a tragedy. The first act is

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Speaker 2: there were threats to not raise the debt ceiling, which

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Speaker 2: would have forced the US to default on its debt,

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Speaker 2: which could have caused a financial crisis, not unlike that

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Speaker 2: of two thousand and eight two thousand and nine, forcing

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Speaker 2: millions out of work. That could have happened. Good news

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Speaker 2: in this first act is that didn't happen. Eventually, Congress

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Speaker 2: decided to do what it ought to do, and what

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Speaker 2: it's done seventy times in the past is raise the

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Speaker 2: debt limit. Second act is who won and who lost?

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Speaker 2: And my reading is that Biden. And it's not everyone's reading,

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Speaker 2: by the way, but my reading is that Biden ate

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Speaker 2: McCarthy's lunch. Now you have to kind of go deep

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Speaker 2: in the weeds to see it and know a little

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Speaker 2: bit about how the government does accounting. So really, Biden

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Speaker 2: I wanted a clean debt ceiling bill, which was a

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Speaker 2: way of saying Biden wanted to give nothing up. Of

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Speaker 2: course that's my opening can. But in fact my opening

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Speaker 2: gambit is I give nothing up and you send over

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Speaker 2: a pint of ice cream. Love ice cream, but we

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Speaker 2: don't want to compare things to that. What he ended

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Speaker 2: up giving up was nominal spending caps, so caps on

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Speaker 2: government spending for the next two years. That sounds like

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Speaker 2: a lot. Now. Let's realize, though, that Republicans refused to

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Speaker 2: do anything on taxes, so it was all on the

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Speaker 2: revenue side, within sorry, all in spending side. Within government spending.

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Speaker 2: Two thirds of it's what's called mandatory spending, which is

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Speaker 2: like Social Security, and they said, well, we're not going

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Speaker 2: to touch any of that. The remaining one third is

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Speaker 2: what's called discretionary spending. This is the stuff where we

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Speaker 2: have to have annual bills appropriating money. And they said, yeah,

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Speaker 2: well look at that. Well half of that one third

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Speaker 2: is military spending, so we like the military, we don't

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Speaker 2: want to do anything there. So what you're left with

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Speaker 2: is caps on one half of one third, that is

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Speaker 2: one sixth of the federal budget. Those caps, there's actual

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Speaker 2: cap for the next two years. And then the bill

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Speaker 2: says and here's our goals for the future. Thing about

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Speaker 2: goals is they're just that, they're just words. So Biden

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Speaker 2: gave up two years of caps. Here's a funny thing. Now, Biden,

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Speaker 2: can't you know, he's kind of one at this point,

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Speaker 2: but he's got to give McCarthy something to go back

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Speaker 2: to his folks and claim a big win. Here's a

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Speaker 2: really weird thing. There's a part of the government called

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Speaker 2: the CBO Congressional Budget Office whose job is to say

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Speaker 2: how much money any new bill will cost or save.

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Speaker 2: And what they do is they say, well, it's obviously

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Speaker 2: these caps will cut government spending for the next two years,

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Speaker 2: and they say, we don't know what's going to happen

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Speaker 2: in the third fourth. And they say they accumulate the

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Speaker 2: total savings over ten years, but they don't know what's

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Speaker 2: going to happen in years three, four, five, six, seven, eight,

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Speaker 2: nine and ten. So they say, well, let's just assume

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Speaker 2: government spending sticks at its level at the end of

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Speaker 2: two years, except will allow for inflation adjustments. So it's

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Speaker 2: like they assume that the two year deal lasts for

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Speaker 2: ten years, and so carfee can go back, and so

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Speaker 2: imagine that we cut spending one hundred million dollars for

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Speaker 2: the next two years. You and I would say that's

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Speaker 2: two hundred million dollars worth of savings. The Congressional Budget

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Speaker 2: Office would say, yeah, but spending's going to remain lower

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Speaker 2: than that. Otherwise would be for one hundred million for

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Speaker 2: each of the next ten years. So therefore that's a

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Speaker 2: trillion dollars in savings. So McCarthy goes back to his

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Speaker 2: folks and shows these graphs, saying I saved us a

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Speaker 2: trillion dollars, and Biden quietly says, hey, you know what,

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Speaker 2: I didn't really give them very much at all, which

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Speaker 2: is the truth.

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Speaker 3: Right, So then what's the third act?

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Speaker 2: So can I give you one more thing on the

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Speaker 2: second act because it's kind of interesting? Yeah, yeah, of course,

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Speaker 2: the Republicans really wanted work requirements in order to get

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Speaker 2: government assistance. They're like, you know, too many people who

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Speaker 2: are poor are just sitting around enjoying being poor. What

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Speaker 2: we want them to do is have to work in

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Speaker 2: order to.

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Speaker 3: Be poor, right, right, They're so cruel.

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Speaker 1: I mean, I shouldn't be laughing, but the work, I mean,

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Speaker 1: the idea here is like you now, it's just let's

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Speaker 1: just pick on the people who need us anyway.

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Speaker 3: Gone.

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Speaker 2: Yeah, So they said, if your age fifty to fifty

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Speaker 2: four and want various forms of assistance, you have to

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Speaker 2: show that you're working or trying to work. And the

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Speaker 2: Democrats actually said, okay, but in return, let's agree that

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Speaker 2: work requirements, which actually already exist for a lot of

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Speaker 2: the population, right right, are inhumane for certain groups like veterans,

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Speaker 2: homeless people, and foster kids. And so they said, let's

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Speaker 2: just get rid of the work requirements for them, but

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Speaker 2: we'll boost them for these other guys you want to punch.

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Speaker 1: Amazing because the reality is homeless people, veterans, and faster kids.

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Speaker 2: Are on benefits at higher rates than the rest of

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Speaker 2: the population.

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Speaker 3: Right, I mean, it was laughed.

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Speaker 2: So guess what the number of people subject to work

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Speaker 2: requirements actually falls.

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Speaker 3: It's amazing, It's amazing.

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Speaker 2: Which is quite the negotiating outcome from McCarthy.

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Speaker 1: So is it fair to say that McCarthy is in

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Speaker 1: fact a golden retriever of a man?

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Speaker 2: McCarthy's new doesn't really know how it's done, And Biden's

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Speaker 2: seen this play before, and I don't know. I find

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Speaker 2: it hard to answer your question, Molly, because I really

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Speaker 2: like golden retrievers. But oh my goodness, if we see

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Speaker 2: Biden v. McCarthy, if we see that again, I'm going

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Speaker 2: to feel pretty good going into it. And so I

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Speaker 2: spent all my energy not gloating because I didn't want

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Speaker 2: to wake anyone up to how badly McCarthy got beat.

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Speaker 2: Let's get to the third act. So far, it's a

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Speaker 2: good news story. No, we didn't crash the economy and

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Speaker 2: Biden didn't give up most anything to get it. One

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Speaker 2: view is the system worked. Here's the problem. What we'd

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Speaker 2: spent the entire period of that debate doing is basically

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Speaker 2: standing up and saying, hey, I'm thinking about not repaying

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Speaker 2: my debts, and then the Republicans will say, I think

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Speaker 2: it even more right?

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Speaker 1: Right?

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Speaker 5: Right?

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Speaker 3: The brinkmanship is bad.

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Speaker 2: Yeah, have you ever met a bank manager, like, do

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Speaker 2: you have a mortgage or anything? Molly a car loan?

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Speaker 3: Sure?

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Speaker 2: Okay, So let me just give you some advice. Next

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Speaker 2: time you want to loan, don't walk into the bank

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Speaker 2: and say, you know, if things are going okay, I'll

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Speaker 2: make my payments. But if I feel kind of pissy

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Speaker 2: with this other guy, I'm not going to make a payment, right,

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Speaker 2: And that's effectively, what Congress did and what that's going

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Speaker 2: to do is cost us all because we know that

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Speaker 2: our commitment to repaying our debts is not that great.

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Speaker 2: And the world's bank managers, which is global financial markets,

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Speaker 2: are going to say, if I'm going to lend you,

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Speaker 2: I'm going to do it at a higher interest rate.

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Speaker 2: And then we also had all these people in Treasury,

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Speaker 2: their full time job normally is helping run the American economy.

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Speaker 2: They spent three months basically looking down the back of

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Speaker 2: our metaphorical couch for extra quarters in order to keep

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Speaker 2: the government open. And this is all just completely wasted work.

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Speaker 2: So my better half, Betsy Stevenson, had an op ed

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Speaker 2: in The New York Times yesterday in which he says,

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Speaker 2: this may be cost us twenty billion dollars. It's like

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Speaker 2: Congress just like just picture this, the number of school

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Speaker 2: buses full to the brim of dollar bills lined up

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Speaker 2: in front of Congress, and they've got McCarthy out there.

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Speaker 2: He probably sent Matt Gertz out to do it instead

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Speaker 2: with a cigarette lighter, just burning dollar bills hour by

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Speaker 2: hour by hour by hour. And so yes, the system worked,

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Speaker 2: but this was political point scoring. But it was political

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Speaker 2: point scoring with yours and my tax dollars, and you

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Speaker 2: should be pissed. Whoever won this shouldn't have burned a competition.

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Speaker 2: This is the most expensive campaign advertisement that either side

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Speaker 2: has ever written. They would never pay this much for

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Speaker 2: this sort of political advertising, and the reason they did

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Speaker 2: it is because it wasn't the RNC paying for the advertising,

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Speaker 2: it was your million instead.

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Speaker 3: Justin thank you so much a pleasure. Mine.

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Speaker 1: Miles Parks is a correspondent on NPR's Washington Desk. To

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Speaker 1: hear more of his reporting, head to NPR dot org.

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Speaker 3: Welcome Too Fast Politics, Miles Park.

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Speaker 5: Thank you so much for having me.

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Speaker 1: I'm so excited to have you, so gonna set the

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Speaker 1: stage here a little bit. You and the NPR team,

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Speaker 1: which is probably a lot of people on of you,

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Speaker 1: did an incredible investigation into one of the sort of

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Speaker 1: best ways to fight voter fraud and how that.

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Speaker 3: Mechanism is being disabled by the right. Can you explain

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Speaker 3: what Eric does?

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Speaker 5: So what Eric does is it takes all of this

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Speaker 5: government data. It takes data from state DMVs, it takes

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Speaker 5: data from the Social Security Administration, in the US Postal Service,

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Speaker 5: and then each state's voter records, It takes all of

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Speaker 5: that data, throws it in a bowl securely. All the

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Speaker 5: sensitive data is encrypted before Eric analyzes it. And then

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Speaker 5: a guy named Jeff Jonas, who is a kind of

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Speaker 5: world renowned cybersecurity guide, developed this technology that allowed for

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Speaker 5: this system to match up all of these different records

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Speaker 5: from different government systems and then spit out reports that

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Speaker 5: can be sent directly to local election officials so they

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Speaker 5: can update their roles. So it will say, hey, John Smith,

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Speaker 5: we are very confident that your voter, John Smith has

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Speaker 5: now moved to Rhode Island. You should reach out to

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Speaker 5: John Smith with a mailer to ask if they've moved

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Speaker 5: and see if it comes back to you. Then they

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Speaker 5: produce another report that says, hey, we have information that

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Speaker 5: John Smith seems to have voted twice in Rhode Island

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Speaker 5: and in South Carolina. And then they have another report

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Speaker 5: that says, you know, we have information that seems to

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Speaker 5: indicate John Smith has died in Georgia or something like that.

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Speaker 5: So it uses all this data to actually spit out

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Speaker 5: reports election officials can use. Because these are I think

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Speaker 5: we also forget that these offices are under resource. They

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Speaker 5: don't have some person in the back, who's able to

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Speaker 5: go through millions of pieces of data and try to

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Speaker 5: figure out whether their voters have moved. What we'll figure

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Speaker 5: out all of this stuff. So Eric did a lot

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Speaker 5: of that work with technology, right.

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Speaker 1: I mean, that is the thing that I'm struck by

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Speaker 1: is that these secretaries of state can't handle this. That's

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Speaker 1: why this database was created. And the original anxiety about

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Speaker 1: it was really that it was registering voters.

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Speaker 3: Right.

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Speaker 5: Well, the original original anxiety was that it was connected

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Speaker 5: to George Soros in some way, which was quickly proved false.

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Speaker 5: And I think once that got proved false, then it

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Speaker 5: started focusing on the One aspect of Eric is that

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Speaker 5: one of the reports it produces says, hey, John Smith moved,

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Speaker 5: got a driver's license but hasn't registered to vote. You

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Speaker 5: should send them a postcard. That's one of the things

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Speaker 5: that kind of gets muddled when the far right talks

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Speaker 5: about this. They imply that Eric is registering voters or

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Speaker 5: is actually doing anything. Eric is producing reports and then

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Speaker 5: the local election officials and all these states act on them.

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Speaker 5: So it's not like people are getting registered by Eric.

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Speaker 5: Eric is just telling the election official, hey, we think

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Speaker 5: you should reach out to this person and give them

437
00:23:02,880 --> 00:23:04,479
Speaker 5: some information on how to register.

438
00:23:04,880 --> 00:23:08,080
Speaker 3: Can you explain to us what is happening.

439
00:23:08,560 --> 00:23:11,920
Speaker 5: Yeah, absolutely, it's a very complicated story that's actually kind

440
00:23:11,920 --> 00:23:15,960
Speaker 5: of simple. Basically, states without this tool, which is called

441
00:23:16,000 --> 00:23:20,600
Speaker 5: the Electronic Registration Information Center, have no way to share

442
00:23:20,640 --> 00:23:24,480
Speaker 5: election data. So if you are let's say South Carolina,

443
00:23:24,640 --> 00:23:26,520
Speaker 5: and you want to know if one of your voters

444
00:23:26,840 --> 00:23:29,720
Speaker 5: moves to Connecticut gets a driver's license there, I think

445
00:23:29,760 --> 00:23:32,439
Speaker 5: people kind of assume that the government is talking to

446
00:23:32,480 --> 00:23:35,000
Speaker 5: itself in all these different ways. It is not. And

447
00:23:35,080 --> 00:23:38,920
Speaker 5: so this organization called the Electronic Registration Information Center, better

448
00:23:38,960 --> 00:23:41,520
Speaker 5: known as ERIC, was started about ten years ago to

449
00:23:41,560 --> 00:23:45,840
Speaker 5: help election officials know when their voters move, get notified

450
00:23:45,880 --> 00:23:49,360
Speaker 5: when their voters die, and then also critically for especially

451
00:23:49,400 --> 00:23:52,159
Speaker 5: for these Republican states, for the small amount of people

452
00:23:52,320 --> 00:23:54,800
Speaker 5: every federal election who do vote in more than one state,

453
00:23:54,840 --> 00:23:58,280
Speaker 5: which is illegal, and ERIC is the only way that

454
00:23:58,440 --> 00:24:01,040
Speaker 5: states have to catch that sort of voter fraud. And

455
00:24:01,119 --> 00:24:04,960
Speaker 5: so for the first nine years or so of its existence,

456
00:24:05,080 --> 00:24:07,359
Speaker 5: it was growing and beloved. I mean, I've been covering

457
00:24:07,440 --> 00:24:10,240
Speaker 5: voting for about half a decade now, and every time

458
00:24:10,240 --> 00:24:11,960
Speaker 5: I would talk to an election official, and I'm talking

459
00:24:11,960 --> 00:24:15,040
Speaker 5: about the most conservative election officials in the country from

460
00:24:15,119 --> 00:24:18,840
Speaker 5: West Virginia and Missouri and Texas. These people love this

461
00:24:18,920 --> 00:24:22,159
Speaker 5: thing as well as Democrats, who I think people some

462
00:24:22,320 --> 00:24:25,679
Speaker 5: Republicans think that Democrats don't want clean voter roles, but

463
00:24:26,080 --> 00:24:29,200
Speaker 5: the Democratic election officials say no, it's much more efficient

464
00:24:29,240 --> 00:24:31,679
Speaker 5: to have mail going to the right places, to have

465
00:24:31,720 --> 00:24:35,040
Speaker 5: our precincts set up where you know addresses are accurate.

466
00:24:35,119 --> 00:24:38,359
Speaker 5: So Eric was beloved for close to ten years until

467
00:24:38,680 --> 00:24:41,200
Speaker 5: last year when the far right started targeting it with

468
00:24:41,240 --> 00:24:45,399
Speaker 5: a smear campaign campaign that really effectively started kind of

469
00:24:45,400 --> 00:24:46,320
Speaker 5: blowing this thing up.

470
00:24:46,560 --> 00:24:48,800
Speaker 1: I want to talk about this because this all started

471
00:24:48,960 --> 00:24:50,919
Speaker 1: with an article in The Atlantic.

472
00:24:51,200 --> 00:24:53,720
Speaker 5: Well it started with an article. There's a couple different

473
00:24:53,760 --> 00:24:56,399
Speaker 5: starting points, so really it actually goes back to an

474
00:24:56,480 --> 00:24:57,760
Speaker 5: article in the Gateway Pundit.

475
00:24:57,960 --> 00:25:01,080
Speaker 1: I thought the Atlantic article came before for the Gateway Pundit,

476
00:25:01,080 --> 00:25:03,959
Speaker 1: and the Gateway Pundin article is based on the Atlantic article.

477
00:25:04,320 --> 00:25:06,600
Speaker 5: No, so the guy who wrote that article, he made

478
00:25:06,600 --> 00:25:09,280
Speaker 5: a joke in the piece that it wasn't an article

479
00:25:09,280 --> 00:25:11,360
Speaker 5: in the Atlantic, and that's why the far right loved it.

480
00:25:11,480 --> 00:25:13,639
Speaker 5: So I think it may have got misunderstood there basically

481
00:25:13,680 --> 00:25:16,439
Speaker 5: he wrote a blog post. This guy, Jay Christian Adams

482
00:25:16,440 --> 00:25:20,680
Speaker 5: wrote a blog post in twenty sixteen that loosely alleged

483
00:25:20,840 --> 00:25:24,679
Speaker 5: that George Soros was somehow behind Eric, and then the

484
00:25:24,720 --> 00:25:28,359
Speaker 5: Gayway Pundit found that article, found some interviews with the

485
00:25:28,359 --> 00:25:31,320
Speaker 5: same guy, turned that into what they called kind of

486
00:25:31,440 --> 00:25:34,480
Speaker 5: investigation into Eric, and then published their article in twenty

487
00:25:34,520 --> 00:25:35,000
Speaker 5: twenty two.

488
00:25:35,320 --> 00:25:38,000
Speaker 1: Okay, so let's talk about the Gateway Pundit. Gateway Pundit

489
00:25:38,119 --> 00:25:40,919
Speaker 1: for those of you who are not extremely online, and

490
00:25:40,960 --> 00:25:41,720
Speaker 1: I assume there.

491
00:25:41,600 --> 00:25:43,560
Speaker 3: Are a few people who listens podcast.

492
00:25:43,119 --> 00:25:46,440
Speaker 5: Who lucky them, lucky you guys right, who don't.

493
00:25:46,160 --> 00:25:49,040
Speaker 1: Know about the dumbest man on the Internet, Jim Hoff.

494
00:25:49,480 --> 00:25:53,400
Speaker 1: But the Gayway Pundit is this incredibly stupid site run

495
00:25:53,440 --> 00:25:56,800
Speaker 1: by a guy with two short bangs and his twin

496
00:25:56,880 --> 00:26:01,440
Speaker 1: brother that basically pumps out crazy cannspiracy theories but has

497
00:26:01,480 --> 00:26:05,960
Speaker 1: become really a sort of central educator on the right.

498
00:26:06,640 --> 00:26:12,400
Speaker 5: Yeah, and I will not be commenting on the has appearance, right, Well, yes,

499
00:26:12,920 --> 00:26:17,359
Speaker 5: but I I but our investigation did find that the

500
00:26:17,400 --> 00:26:20,879
Speaker 5: Gayway Pundit has become a real influential player, especially in

501
00:26:20,920 --> 00:26:23,840
Speaker 5: these states where these election officials are going to be

502
00:26:23,920 --> 00:26:26,080
Speaker 5: running in Republican primaries, and they're going to need to

503
00:26:26,080 --> 00:26:29,479
Speaker 5: be winning Republican primary voters in off election years. That

504
00:26:29,480 --> 00:26:32,359
Speaker 5: could be a very small percentage of diehard Republicans, and

505
00:26:32,400 --> 00:26:34,720
Speaker 5: a lot of those people are reading the Gayway pundit.

506
00:26:34,520 --> 00:26:38,919
Speaker 1: Because we're all going to die, so Gayway Pundit Cleta Mitchell.

507
00:26:39,320 --> 00:26:42,639
Speaker 1: So explain to us who Cleida Mitchell is and where

508
00:26:43,200 --> 00:26:45,800
Speaker 1: and sort of where she comes up with this idea.

509
00:26:46,000 --> 00:26:49,760
Speaker 5: Yeah, So, Clelida Mitchell is this very influential Republican election

510
00:26:49,880 --> 00:26:53,480
Speaker 5: attorney who kind of came to prominence after twenty twenty

511
00:26:53,520 --> 00:26:56,919
Speaker 5: for helping former President Trump try to overturn that election.

512
00:26:57,000 --> 00:26:59,800
Speaker 5: She was on the phone call with Georgia election officials

513
00:26:59,800 --> 00:27:03,000
Speaker 5: where Trump was asking them to find votes. And so

514
00:27:03,400 --> 00:27:05,560
Speaker 5: since twenty twenty, Kleida Mitchell has kind of found a

515
00:27:05,640 --> 00:27:09,399
Speaker 5: niche as a key election denier. She has started a

516
00:27:09,440 --> 00:27:13,040
Speaker 5: podcast called Who's Counting, which does a good job of

517
00:27:13,040 --> 00:27:16,399
Speaker 5: spreading a lot of election misinformation. She's also started this

518
00:27:16,440 --> 00:27:19,399
Speaker 5: thing called the Election Integrity Network, which is a coalition

519
00:27:19,720 --> 00:27:22,320
Speaker 5: of sort of grassroots groups all over the country that

520
00:27:22,400 --> 00:27:25,119
Speaker 5: are interconnected in this web that they're kind of aimed

521
00:27:25,119 --> 00:27:28,760
Speaker 5: at quote unquote election integrity, but in reality, our investigation

522
00:27:28,920 --> 00:27:31,960
Speaker 5: found that they're pushing to dismantle a lot of tools

523
00:27:32,240 --> 00:27:35,800
Speaker 5: and helpful things that actually help election integrity. So what

524
00:27:35,920 --> 00:27:39,560
Speaker 5: happens is the Gateway Pundit publishes this article in January

525
00:27:39,560 --> 00:27:42,280
Speaker 5: of twenty twenty two, and we found we did a

526
00:27:42,720 --> 00:27:45,200
Speaker 5: really cool social media analysis that looked at hundreds of

527
00:27:45,240 --> 00:27:47,879
Speaker 5: thousands of posts in far right channels and found that

528
00:27:47,920 --> 00:27:51,679
Speaker 5: they really latched on to this Gateway Pundit article. And

529
00:27:51,720 --> 00:27:54,600
Speaker 5: then Louisiana pulls out a week later, the Secretary of

530
00:27:54,640 --> 00:27:58,600
Speaker 5: State there announces it we found to a conservative activist group,

531
00:27:58,840 --> 00:28:01,600
Speaker 5: which is key because a lot of election officials I

532
00:28:01,600 --> 00:28:04,600
Speaker 5: talk to, Republican and Democrat, point to that actually even

533
00:28:04,640 --> 00:28:07,399
Speaker 5: more importantly than the Gateway punted article, because gatewayed publishes

534
00:28:07,400 --> 00:28:10,359
Speaker 5: stuff every day that the government doesn't respond to. But

535
00:28:10,440 --> 00:28:12,920
Speaker 5: the moment that an election official actually responded to it

536
00:28:13,040 --> 00:28:16,080
Speaker 5: and gave it credibility, that is what kind of started

537
00:28:16,080 --> 00:28:19,439
Speaker 5: this entire pressure campaign on the other Republican states. And

538
00:28:19,480 --> 00:28:22,960
Speaker 5: so what we found is that Cleta Mitchell saw Louisiana

539
00:28:23,040 --> 00:28:25,399
Speaker 5: pulled out and decided, Wow, this is a thing that

540
00:28:25,560 --> 00:28:28,800
Speaker 5: really seems to ignite our base. Started focusing her podcast

541
00:28:28,880 --> 00:28:31,719
Speaker 5: on it, these election integrity groups started focusing on it.

542
00:28:31,840 --> 00:28:35,520
Speaker 5: She started telling these groups, reach out to state legislatures

543
00:28:35,600 --> 00:28:38,880
Speaker 5: in your states, reach out to your election officials, and

544
00:28:39,080 --> 00:28:41,640
Speaker 5: that happened, which kind of, over the course of a year,

545
00:28:42,560 --> 00:28:45,320
Speaker 5: led to a number of these state election officials feeling

546
00:28:45,320 --> 00:28:47,120
Speaker 5: the pressure and actually pulling out.

547
00:28:47,560 --> 00:28:52,800
Speaker 1: So you talked through this on your NPR podcast and

548
00:28:52,880 --> 00:28:56,840
Speaker 1: also on the station I assume about exactly how this

549
00:28:56,960 --> 00:28:59,960
Speaker 1: went down, and you actually interviewed a secretary of state

550
00:29:00,800 --> 00:29:04,480
Speaker 1: talk to me about sort of how this went down.

551
00:29:05,240 --> 00:29:08,400
Speaker 5: So what it really boils down to is that for

552
00:29:08,720 --> 00:29:11,800
Speaker 5: a lot of last year, a lot of these Republican

553
00:29:11,840 --> 00:29:15,040
Speaker 5: election officials were on the record as saying they loved

554
00:29:15,120 --> 00:29:17,760
Speaker 5: this tool. Like this secretary of state who you mentioned

555
00:29:17,760 --> 00:29:19,720
Speaker 5: that I interviewed and I've talked at linked with the

556
00:29:19,800 --> 00:29:22,880
Speaker 5: number of times. His name's Frank Lrose in Ohio. He's

557
00:29:22,920 --> 00:29:26,560
Speaker 5: a Republican, but he's also pushed back at times against

558
00:29:26,720 --> 00:29:32,160
Speaker 5: election denihalism, and so he in February called this tool

559
00:29:32,640 --> 00:29:35,520
Speaker 5: one of the best tools we have for fighting election fraud.

560
00:29:35,520 --> 00:29:38,120
Speaker 5: And he talked me through basically said, you know, if

561
00:29:38,160 --> 00:29:41,120
Speaker 5: you want to win a Republican primary, you kind of

562
00:29:41,160 --> 00:29:44,320
Speaker 5: need these voters. So he almost had empathy for people

563
00:29:44,360 --> 00:29:47,320
Speaker 5: like Kyle Ardwin in Louisiana and West Allen in Alabama,

564
00:29:47,320 --> 00:29:49,360
Speaker 5: who were the first secretaries to pull out of this thing.

565
00:29:49,600 --> 00:29:52,000
Speaker 5: They were trying to win elections, and he was basically saying,

566
00:29:52,240 --> 00:29:55,479
Speaker 5: that's how politics works. They looked at this thing, said well,

567
00:29:55,800 --> 00:29:57,720
Speaker 5: my voters want this, I'm going to do it. But

568
00:29:57,800 --> 00:30:01,600
Speaker 5: hopefully over time it starts to kind of cool off,

569
00:30:01,880 --> 00:30:03,920
Speaker 5: states start to get into it, the pressure kind of

570
00:30:03,920 --> 00:30:08,200
Speaker 5: dies down. A month after that interview, LaRose pulled Ohio

571
00:30:08,360 --> 00:30:11,000
Speaker 5: out of eric and so we kind of saw in

572
00:30:11,040 --> 00:30:14,920
Speaker 5: real time as this investigation unfolded, the pressure building on

573
00:30:14,960 --> 00:30:18,600
Speaker 5: these election officials who just weeks before were defending it

574
00:30:18,640 --> 00:30:19,280
Speaker 5: on the record.

575
00:30:19,640 --> 00:30:22,520
Speaker 1: Oh wow, but they did it because they were scared

576
00:30:22,600 --> 00:30:25,840
Speaker 1: of pushback from the far right or I mean, they won't.

577
00:30:25,600 --> 00:30:29,320
Speaker 5: Say that, but well, the the well exactly exactly, they

578
00:30:29,360 --> 00:30:31,520
Speaker 5: will never say that. But what we found is this

579
00:30:31,600 --> 00:30:35,480
Speaker 5: common through line where especially the first few election officials

580
00:30:35,760 --> 00:30:38,560
Speaker 5: that pulled out, they're all going to be running in

581
00:30:38,600 --> 00:30:41,440
Speaker 5: Republican primaries in the next you know, year to year

582
00:30:41,480 --> 00:30:44,560
Speaker 5: and a half. Larrose, for instance, is expected to run

583
00:30:44,600 --> 00:30:48,280
Speaker 5: for Senate in Ohio. Florida pulled out. You know, obviously

584
00:30:48,280 --> 00:30:51,520
Speaker 5: DeSantis is in the presidential race Missouri and West Virginia.

585
00:30:51,560 --> 00:30:54,880
Speaker 5: Both those secretaries of state have now announced runs for governor,

586
00:30:55,200 --> 00:30:57,520
Speaker 5: and so there is kind of a through line of

587
00:30:57,680 --> 00:31:01,840
Speaker 5: politics actually kind of inner fearing an election administration here.

588
00:31:02,280 --> 00:31:05,240
Speaker 1: It's so interesting because it really is you're really seeing

589
00:31:05,840 --> 00:31:12,400
Speaker 1: how much the craziness of Republican primary politics has reverberations

590
00:31:12,400 --> 00:31:14,080
Speaker 1: that we can't even quantify.

591
00:31:14,480 --> 00:31:18,320
Speaker 5: Yeah, I mean that was to me why this story

592
00:31:18,480 --> 00:31:21,680
Speaker 5: was worth doing. Was it was not this feels like

593
00:31:21,680 --> 00:31:24,080
Speaker 5: a twenty twenty four story. Yeah, you know in terms

594
00:31:24,120 --> 00:31:27,600
Speaker 5: of this is not only because of how the election

595
00:31:27,680 --> 00:31:29,720
Speaker 5: is going to be run in twenty twenty four, but

596
00:31:29,800 --> 00:31:31,959
Speaker 5: I think there are a lot of Republicans who are

597
00:31:31,960 --> 00:31:34,479
Speaker 5: still members of ERIC who are pushing through this pressure.

598
00:31:34,520 --> 00:31:36,880
Speaker 5: This is not something where you can paint the Republican

599
00:31:36,920 --> 00:31:39,680
Speaker 5: Party with like one big broad brush. You know. What

600
00:31:39,720 --> 00:31:43,120
Speaker 5: we're seeing is people like Brad Raffisberger and Georgia who

601
00:31:43,280 --> 00:31:45,960
Speaker 5: has been one of the most vocal defenders of ERIC

602
00:31:46,240 --> 00:31:49,080
Speaker 5: and is still a member. It's an individual decision for

603
00:31:49,120 --> 00:31:51,640
Speaker 5: each one of these Republicans to say, will I stand

604
00:31:51,720 --> 00:31:54,680
Speaker 5: up to this pressure and defend this thing that I've

605
00:31:54,680 --> 00:31:55,920
Speaker 5: been on the record for defending.

606
00:31:56,040 --> 00:31:58,400
Speaker 1: The thing I would say about Brad Rathitsberger is that

607
00:31:58,520 --> 00:32:02,760
Speaker 1: he has shown that he has the monetary support to

608
00:32:02,800 --> 00:32:04,400
Speaker 1: be able to win a primary challenge.

609
00:32:04,440 --> 00:32:07,840
Speaker 3: We just saw that for him, So obviously, I mean.

610
00:32:08,040 --> 00:32:12,040
Speaker 5: It's easier for somebody who refeels confident in their political position, right,

611
00:32:12,120 --> 00:32:14,880
Speaker 5: But I mean, to be fair, Leroe's just won re

612
00:32:15,000 --> 00:32:17,959
Speaker 5: election last year in Ohio and defeated a primary challenger,

613
00:32:18,040 --> 00:32:21,440
Speaker 5: not a super serious primary challenger necessarily, but he had

614
00:32:21,560 --> 00:32:24,320
Speaker 5: just won a Republican primary last year, and so it

615
00:32:24,440 --> 00:32:27,240
Speaker 5: was interesting to see. But obviously he sees it seems

616
00:32:27,320 --> 00:32:30,480
Speaker 5: like I should say that he sees this Senate run

617
00:32:30,520 --> 00:32:32,800
Speaker 5: as this is something he needed to do.

618
00:32:33,240 --> 00:32:36,840
Speaker 1: Yeah, I mean, it's just such an interesting situation because

619
00:32:37,120 --> 00:32:40,120
Speaker 1: you really see the Gateway pundon has so much power.

620
00:32:40,600 --> 00:32:44,000
Speaker 5: Yes, absolutely, And what I think this story also showed

621
00:32:44,000 --> 00:32:46,360
Speaker 5: to me was that there's an infrastructure in a way

622
00:32:46,400 --> 00:32:48,480
Speaker 5: there wasn't four or five years ago to get this

623
00:32:48,560 --> 00:32:52,280
Speaker 5: election denial moving into policy. It's like it's not just

624
00:32:52,400 --> 00:32:55,360
Speaker 5: like throwing spaghetti at the wall in social media anymore.

625
00:32:55,400 --> 00:32:58,600
Speaker 5: Like these groups that I mentioned, these election integrity groups,

626
00:32:58,680 --> 00:33:01,120
Speaker 5: they didn't exist five years years ago. And so you

627
00:33:01,160 --> 00:33:03,120
Speaker 5: have these groups of people in all of these states

628
00:33:03,360 --> 00:33:05,200
Speaker 5: who have a way, like we found call outs in

629
00:33:05,240 --> 00:33:09,040
Speaker 5: all of these states where these groups were telling their members,

630
00:33:09,120 --> 00:33:12,000
Speaker 5: here's the emails for all of your all of the

631
00:33:12,000 --> 00:33:15,040
Speaker 5: state lawmakers and the election officials, here are the talking

632
00:33:15,080 --> 00:33:17,520
Speaker 5: points on ERIC. Reach out to them. Phone calls are

633
00:33:17,560 --> 00:33:20,280
Speaker 5: best in person, it's best. Email works too, And so

634
00:33:20,320 --> 00:33:22,840
Speaker 5: there is an infrastructure now that actually works to get

635
00:33:22,880 --> 00:33:25,640
Speaker 5: these ideas into the mainstream in a way that didn't

636
00:33:25,720 --> 00:33:27,440
Speaker 5: there what there wasn't five years ago.

637
00:33:27,600 --> 00:33:28,440
Speaker 3: Right exactly.

638
00:33:29,000 --> 00:33:34,120
Speaker 1: It's such a strange situation. So you can get a

639
00:33:34,160 --> 00:33:37,440
Speaker 1: state back into ERIC. It's not like there's some mandatory

640
00:33:37,760 --> 00:33:39,040
Speaker 1: leaving period.

641
00:33:38,800 --> 00:33:41,560
Speaker 5: Right exactly. So a lot of the election official I

642
00:33:41,640 --> 00:33:44,959
Speaker 5: talk to said, basically, we just got a weather, this storm,

643
00:33:45,000 --> 00:33:49,120
Speaker 5: and hopefully in maybe it's two years, maybe it's five years,

644
00:33:49,160 --> 00:33:51,360
Speaker 5: but some of these states that have left will look

645
00:33:51,440 --> 00:33:54,400
Speaker 5: to join again. Some of the Republican states have started,

646
00:33:54,600 --> 00:33:56,880
Speaker 5: including Lrose has kind of been leading this effort to

647
00:33:56,960 --> 00:34:00,360
Speaker 5: start almost like an ERIC competitor, where they will be

648
00:34:00,360 --> 00:34:04,200
Speaker 5: able to share data amongst at least probably Republican states,

649
00:34:04,200 --> 00:34:05,880
Speaker 5: but maybe they will be able to get a few

650
00:34:05,880 --> 00:34:09,560
Speaker 5: other states. It's unclear whether that will work as well

651
00:34:09,600 --> 00:34:12,040
Speaker 5: as Eric did and whether they will be to be

652
00:34:12,080 --> 00:34:14,560
Speaker 5: able to get as many states, but there is definitely

653
00:34:14,560 --> 00:34:17,880
Speaker 5: a world where Eric kind of pushes through this weird time.

654
00:34:18,360 --> 00:34:20,760
Speaker 5: I think it's fair to say that in twenty twenty

655
00:34:20,840 --> 00:34:24,240
Speaker 5: four these states will probably not have access to that data,

656
00:34:24,280 --> 00:34:26,920
Speaker 5: and it's really unclear how they'll be able to prosecute

657
00:34:27,000 --> 00:34:27,560
Speaker 5: voter fraud.

658
00:34:27,920 --> 00:34:35,160
Speaker 1: So interesting and so weird and also depressing. Thank you

659
00:34:35,239 --> 00:34:37,280
Speaker 1: so much for joining us. I hope you'll come back.

660
00:34:37,560 --> 00:34:39,200
Speaker 5: Yeah, thank you so much for having Mollie. It's been

661
00:34:39,239 --> 00:34:39,640
Speaker 5: a pleasure.

662
00:34:40,760 --> 00:34:43,600
Speaker 3: Hi. It's Mollie and I am.

663
00:34:43,480 --> 00:34:48,600
Speaker 1: Wildly excited that for the first time, Fast Politics, the

664
00:34:48,800 --> 00:34:52,000
Speaker 1: show you're listening to right now, is going to have

665
00:34:52,080 --> 00:34:58,880
Speaker 1: merch for sale over at shop dot fastpoliticspod dot com.

666
00:34:59,320 --> 00:35:02,280
Speaker 3: You can now oh buye shirts.

667
00:35:02,160 --> 00:35:06,839
Speaker 1: Hats, hoodies, and toe bags with our incredible designs. We've

668
00:35:06,880 --> 00:35:10,400
Speaker 1: heard your cries to spread the word about our podcast

669
00:35:10,560 --> 00:35:15,360
Speaker 1: and get a tow bag with my adorable Leo the

670
00:35:15,440 --> 00:35:19,439
Speaker 1: Rescue Puppy on it. And now you can grab this

671
00:35:19,920 --> 00:35:27,200
Speaker 1: merchandise only at shop dot fastpoliticspod dot com. Thanks for

672
00:35:27,360 --> 00:35:33,920
Speaker 1: your support. Ryan Mack is a technology reporter for the

673
00:35:33,920 --> 00:35:38,319
Speaker 1: New York Times. Welcome to Fast Politics, Ryan Mack.

674
00:35:38,560 --> 00:35:39,160
Speaker 6: How's it going?

675
00:35:39,480 --> 00:35:40,600
Speaker 3: You know, live in the.

676
00:35:40,600 --> 00:35:44,279
Speaker 1: Dream completely ironically, of course. So I want to talk

677
00:35:44,360 --> 00:35:48,759
Speaker 1: to you about your I mean, I guess, do you

678
00:35:48,840 --> 00:35:50,799
Speaker 1: get sick of the Elon Musk beat?

679
00:35:51,560 --> 00:35:51,759
Speaker 5: No?

680
00:35:51,960 --> 00:35:52,360
Speaker 6: I don't know.

681
00:35:53,000 --> 00:35:54,759
Speaker 7: I've covered him for a couple of years now, and

682
00:35:54,840 --> 00:35:57,000
Speaker 7: you know, I go in and out of covering certain

683
00:35:57,040 --> 00:36:00,520
Speaker 7: companies and focusing on different things, but it's always interesting

684
00:36:00,520 --> 00:36:01,320
Speaker 7: in Elon world.

685
00:36:01,400 --> 00:36:02,840
Speaker 6: So it's it's entertaining.

686
00:36:03,160 --> 00:36:04,279
Speaker 3: Did you at one time?

687
00:36:04,360 --> 00:36:07,520
Speaker 1: Because I went on a tour of the SpaceX factory

688
00:36:08,080 --> 00:36:12,319
Speaker 1: in twenty seventeen SpaceX No, of the Tesla factory in

689
00:36:12,360 --> 00:36:15,759
Speaker 1: twenty seventeen with a like a university board that I

690
00:36:15,880 --> 00:36:18,680
Speaker 1: was not on. I was just a guest, and and

691
00:36:18,760 --> 00:36:22,880
Speaker 1: I thought, this guy is incredible, this company is amazing.

692
00:36:23,600 --> 00:36:25,040
Speaker 3: Did you Did you.

693
00:36:25,040 --> 00:36:27,839
Speaker 1: Originally have that kind of feeling about Musk or did

694
00:36:27,880 --> 00:36:31,960
Speaker 1: you always just were you always just a serious, straight

695
00:36:32,000 --> 00:36:35,720
Speaker 1: reporter who didn't have expectations about your subject.

696
00:36:37,480 --> 00:36:39,760
Speaker 7: I'll give the reporter answer, which is as any straight

697
00:36:39,800 --> 00:36:40,440
Speaker 7: serious reporter.

698
00:36:40,480 --> 00:36:40,800
Speaker 6: I don't know.

699
00:36:40,840 --> 00:36:43,200
Speaker 7: I don't let my kind of personal feelings get in

700
00:36:43,200 --> 00:36:46,399
Speaker 7: the way of what's what I report on. I will say,

701
00:36:46,400 --> 00:36:49,200
Speaker 7: you know, I focus on accountability in my work and

702
00:36:50,040 --> 00:36:52,680
Speaker 7: that's been kind of the guiding light. And so you know,

703
00:36:52,960 --> 00:36:55,600
Speaker 7: I think you can you can analyze and view Elon

704
00:36:55,680 --> 00:36:58,440
Speaker 7: in a couple buckets. He is, of course very successful.

705
00:36:58,480 --> 00:36:59,759
Speaker 7: I don't need to tell people that. You look at

706
00:36:59,760 --> 00:37:02,000
Speaker 7: his at worth, you look at what he's done with

707
00:37:02,440 --> 00:37:06,400
Speaker 7: you know, normalizing electric vehicles and privatizing the space industry

708
00:37:06,480 --> 00:37:08,680
Speaker 7: like that is all no one's ever done that. You know,

709
00:37:08,719 --> 00:37:12,040
Speaker 7: he is very singular in having done all those achievements,

710
00:37:12,640 --> 00:37:15,279
Speaker 7: and you know you can read about that and you know,

711
00:37:15,360 --> 00:37:18,680
Speaker 7: read Ashley Vats's book and see that success. At the

712
00:37:18,719 --> 00:37:21,480
Speaker 7: same time, you know, I think I started really reporting

713
00:37:21,560 --> 00:37:25,000
Speaker 7: on him and his companies at my previous job at BuzzFeed,

714
00:37:25,239 --> 00:37:29,200
Speaker 7: and you know, we were doing you know, investigations into

715
00:37:29,239 --> 00:37:32,000
Speaker 7: what was happening at those Tesla factories, and my colleague

716
00:37:32,040 --> 00:37:35,040
Speaker 7: at the time, Carolyn and Donovan, was really looking at

717
00:37:35,120 --> 00:37:37,680
Speaker 7: kind of the human costs of these factory workers who

718
00:37:37,719 --> 00:37:39,600
Speaker 7: were building these electric cars at the kind of this

719
00:37:39,640 --> 00:37:42,000
Speaker 7: breakneck pace, you know, and they were getting hurt and

720
00:37:42,040 --> 00:37:44,800
Speaker 7: crushed and you know, not getting the support from the

721
00:37:44,840 --> 00:37:48,040
Speaker 7: company because they were fighting for the union. The company

722
00:37:48,080 --> 00:37:50,719
Speaker 7: was fighting against the union movement, and you know, you

723
00:37:50,760 --> 00:37:53,040
Speaker 7: can see these people getting hurt in this process. And

724
00:37:53,520 --> 00:37:57,120
Speaker 7: that's this lens that I've I've taken into, you know,

725
00:37:57,200 --> 00:37:58,840
Speaker 7: reporting on him and his companies.

726
00:37:58,880 --> 00:38:00,600
Speaker 6: Not to say that the other side of things, you.

727
00:38:00,600 --> 00:38:02,880
Speaker 7: Know, I do acknowledge, you know, he's very successful, but

728
00:38:03,600 --> 00:38:06,040
Speaker 7: in my work, that's that's how I've seen him.

729
00:38:06,120 --> 00:38:09,600
Speaker 1: One of the things that Elon wanted to try to

730
00:38:09,640 --> 00:38:12,399
Speaker 1: do was to get Twitter to make money. That has

731
00:38:12,440 --> 00:38:16,040
Speaker 1: sort of run counter. Can you explain to us what's

732
00:38:16,080 --> 00:38:20,640
Speaker 1: happening with Twitter's advertising and just sort of where we

733
00:38:20,680 --> 00:38:21,080
Speaker 1: are with that.

734
00:38:21,320 --> 00:38:23,600
Speaker 7: Well, I think the whole thing about making money, it

735
00:38:23,640 --> 00:38:26,280
Speaker 7: really depends on what week you get him answering that question,

736
00:38:26,480 --> 00:38:28,399
Speaker 7: because some weeks, you know, I don't care about money.

737
00:38:28,440 --> 00:38:30,760
Speaker 7: It's not about money, it's about free speech. I'm making

738
00:38:30,760 --> 00:38:34,480
Speaker 7: a principal decision, you know, supposedly as he says, defending

739
00:38:34,520 --> 00:38:37,120
Speaker 7: the digital town square or whatever. You know, in other weeks,

740
00:38:37,280 --> 00:38:39,279
Speaker 7: he is a businessman, you know, he has to run

741
00:38:39,320 --> 00:38:41,520
Speaker 7: this company he paid forty four billion dollars for it.

742
00:38:41,520 --> 00:38:44,759
Speaker 7: He loaded it with I think thirteen billion dollars worth

743
00:38:44,760 --> 00:38:47,399
Speaker 7: of debt to close this deal. You know, he has

744
00:38:47,440 --> 00:38:51,520
Speaker 7: to get the economics to work. And part of this economics,

745
00:38:51,600 --> 00:38:55,040
Speaker 7: you know, the main chunk of that is the advertising portion.

746
00:38:55,400 --> 00:39:00,359
Speaker 7: And historically, like any large social media company, advertising has

747
00:39:00,560 --> 00:39:05,839
Speaker 7: been the lifeblood of Twitter, and you know it, in

748
00:39:05,880 --> 00:39:08,640
Speaker 7: the past, advertising has accounted for about ninety percent of

749
00:39:08,719 --> 00:39:13,680
Speaker 7: revenue at the company. And in coming into the company

750
00:39:13,680 --> 00:39:16,880
Speaker 7: and buying it back in October, Elon Musk has essentially

751
00:39:16,920 --> 00:39:20,799
Speaker 7: cratered that business. He's talked about it somewhat openly, and

752
00:39:21,040 --> 00:39:23,720
Speaker 7: we you know, recently got some internal documents and spoke

753
00:39:23,760 --> 00:39:26,840
Speaker 7: to some employees current informer that kind of paid a

754
00:39:26,920 --> 00:39:29,279
Speaker 7: kind of bleaker picture of where the company is at

755
00:39:29,520 --> 00:39:30,320
Speaker 7: since the takeover.

756
00:39:30,600 --> 00:39:33,719
Speaker 1: It's interesting to me because one of the things that

757
00:39:33,880 --> 00:39:36,680
Speaker 1: I was sort of surprised by, and again this is

758
00:39:36,760 --> 00:39:40,440
Speaker 1: perhaps my own naivy day, but like he does just

759
00:39:40,640 --> 00:39:45,520
Speaker 1: blatantly lie about things, which you know, again I don't

760
00:39:45,520 --> 00:39:48,239
Speaker 1: know why that shocks me so much, but like there's

761
00:39:48,239 --> 00:39:52,200
Speaker 1: certain things, like you said, if there's an ad on

762
00:39:52,360 --> 00:39:53,680
Speaker 1: your tweet, you'll.

763
00:39:53,520 --> 00:39:56,440
Speaker 3: Get paid for it. Remember that that was just not

764
00:39:56,680 --> 00:39:57,440
Speaker 3: real at all.

765
00:39:57,520 --> 00:40:00,120
Speaker 7: Right, I think he's working towards that. I would say,

766
00:40:00,160 --> 00:40:02,879
Speaker 7: you know, there is you know, undoubtedly obfuscation.

767
00:40:03,600 --> 00:40:03,839
Speaker 6: You know.

768
00:40:03,960 --> 00:40:05,960
Speaker 7: I think the thing about lying is there has to

769
00:40:05,960 --> 00:40:07,280
Speaker 7: be intent to deceive.

770
00:40:07,640 --> 00:40:07,920
Speaker 3: Right.

771
00:40:08,160 --> 00:40:12,319
Speaker 7: I think he makes these very big predictions and large

772
00:40:12,320 --> 00:40:15,000
Speaker 7: projections that often don't hit the mark.

773
00:40:15,360 --> 00:40:15,600
Speaker 3: Right.

774
00:40:15,719 --> 00:40:18,400
Speaker 7: You know, we started our story this week about problems

775
00:40:18,440 --> 00:40:21,560
Speaker 7: with advertising by looking at some of the comments he

776
00:40:21,600 --> 00:40:24,440
Speaker 7: made back in April, and he gave this interview with

777
00:40:24,480 --> 00:40:26,960
Speaker 7: his BBC reporter This kind of it's been made fun

778
00:40:27,000 --> 00:40:28,919
Speaker 7: of quite a bit, but you know, he went on

779
00:40:29,000 --> 00:40:32,640
Speaker 7: this live interview with the BBC and talked about, you know,

780
00:40:32,719 --> 00:40:35,839
Speaker 7: advertisers are almost all back on the platform. You know,

781
00:40:36,600 --> 00:40:39,680
Speaker 7: we're gonna break even. He painted this picture of the

782
00:40:39,719 --> 00:40:42,640
Speaker 7: company really having turned a corner and doing well, and

783
00:40:42,840 --> 00:40:45,759
Speaker 7: actually these documents show otherwise.

784
00:40:46,280 --> 00:40:49,640
Speaker 1: And then you have the head of trust and safety

785
00:40:50,840 --> 00:40:54,680
Speaker 1: and the head of brand safety and ad quality, and

786
00:40:55,239 --> 00:40:58,880
Speaker 1: you have I mean, you have all of these people resigning.

787
00:40:59,200 --> 00:41:02,239
Speaker 7: Yeah, and actually actually probably contextualize what those documents showed

788
00:41:02,280 --> 00:41:04,759
Speaker 7: before going into that. But with the documents showed, you know,

789
00:41:04,920 --> 00:41:08,719
Speaker 7: US AD sales were down fifty nine percent for the

790
00:41:08,760 --> 00:41:11,400
Speaker 7: first weeks of quarter two. You know, the for that

791
00:41:11,520 --> 00:41:15,080
Speaker 7: would mean, you know, April into May, the company has

792
00:41:15,280 --> 00:41:18,160
Speaker 7: been down tens of millions of dollars compared to last year.

793
00:41:18,400 --> 00:41:20,719
Speaker 6: They're also undershooting.

794
00:41:20,719 --> 00:41:24,600
Speaker 7: They're already kind of adjusted projections for revenue this quarter,

795
00:41:25,040 --> 00:41:28,280
Speaker 7: and it kind of paints this bleak, bleak picture.

796
00:41:28,480 --> 00:41:29,760
Speaker 6: For the company moving forward.

797
00:41:29,840 --> 00:41:33,560
Speaker 7: You know, if they can't bring in this money, you know,

798
00:41:34,000 --> 00:41:35,960
Speaker 7: Twitter is going to have to keep operating at a loss,

799
00:41:36,000 --> 00:41:38,320
Speaker 7: and Elon Musk's going to have to keep sinking money

800
00:41:38,480 --> 00:41:40,480
Speaker 7: into this business. You know, That's what we kind of

801
00:41:40,560 --> 00:41:43,480
Speaker 7: showed in these documents. And with regards to the questions

802
00:41:43,480 --> 00:41:46,560
Speaker 7: you asked with the kind of folks that left recently,

803
00:41:46,600 --> 00:41:49,800
Speaker 7: this all happened last week when this kind of Daily

804
00:41:49,800 --> 00:41:51,040
Speaker 7: Wire incident popped up.

805
00:41:51,280 --> 00:41:55,040
Speaker 1: Will you tell our listeners what this did? It's so stupid,

806
00:41:55,120 --> 00:41:57,120
Speaker 1: but it worked, right. They were able to get you

807
00:41:57,160 --> 00:41:58,560
Speaker 1: along to do what they wanted.

808
00:41:58,840 --> 00:42:01,239
Speaker 7: Yeah, and I think of like still reporting it out.

809
00:42:01,239 --> 00:42:04,440
Speaker 7: But basically what happened is The Daily Wire, a right

810
00:42:04,440 --> 00:42:08,120
Speaker 7: wing podcast network and content network and by Ben Shapiro,

811
00:42:08,520 --> 00:42:11,480
Speaker 7: had a movie they wanted to promote called What Is

812
00:42:11,480 --> 00:42:16,080
Speaker 7: a Woman? Obviously playing into the culture war around trans issues,

813
00:42:16,719 --> 00:42:20,319
Speaker 7: and they wanted to promote this movie. If you have

814
00:42:21,040 --> 00:42:23,640
Speaker 7: been anywhere near Elon's Twitter feed, you obviously know this

815
00:42:23,760 --> 00:42:27,680
Speaker 7: is a culture war topic that is very near and

816
00:42:27,719 --> 00:42:32,520
Speaker 7: dear to his heart. He has himself, you know, exhibited.

817
00:42:33,920 --> 00:42:36,719
Speaker 3: Trans people, right, and I.

818
00:42:36,640 --> 00:42:38,759
Speaker 7: Don't think it's very secret what his politics are on

819
00:42:38,800 --> 00:42:42,640
Speaker 7: the topic. The Daily Wires says claims that they had,

820
00:42:42,760 --> 00:42:45,480
Speaker 7: you know, a deal to promote this movie essentially, I

821
00:42:45,480 --> 00:42:47,799
Speaker 7: think hour and a half long movie, by pushing it

822
00:42:47,800 --> 00:42:50,600
Speaker 7: into some kind of special area on Twitter that it

823
00:42:50,640 --> 00:42:53,600
Speaker 7: could be promoted. There was supposed to be some money exchange,

824
00:42:53,920 --> 00:42:57,319
Speaker 7: and they claim that it was then prevented because of

825
00:42:57,360 --> 00:43:01,080
Speaker 7: the content of the video itself. Twitter had these rules

826
00:43:01,120 --> 00:43:06,640
Speaker 7: around misgendering folks and policies intended to protect people who

827
00:43:06,800 --> 00:43:09,560
Speaker 7: were trance, and those have been kind of whittled away

828
00:43:09,600 --> 00:43:12,160
Speaker 7: under Elon Musk. Yeah, I guess some of the remaining

829
00:43:12,200 --> 00:43:16,360
Speaker 7: folks at the company decided to uphold those policies.

830
00:43:16,080 --> 00:43:19,759
Speaker 3: Or maybe not. I mean they could also be lying, right, It's.

831
00:43:19,520 --> 00:43:22,640
Speaker 7: A little unclear. Yeah, it's a little unclear. So this

832
00:43:22,719 --> 00:43:26,759
Speaker 7: kind of dust up happens. Elon comes in and says, well,

833
00:43:26,840 --> 00:43:29,680
Speaker 7: you know, actually before that, you know, the video was

834
00:43:29,719 --> 00:43:33,360
Speaker 7: actually labeled on Twitter as I guess, under the company's

835
00:43:33,360 --> 00:43:36,160
Speaker 7: popolicies around hate speech. And Elon comes in and says,

836
00:43:36,160 --> 00:43:38,560
Speaker 7: this is the wrong decision, you know, decisions made that

837
00:43:38,600 --> 00:43:42,759
Speaker 7: weren't and I wasn't involved with, and he blames his underlings,

838
00:43:42,840 --> 00:43:45,439
Speaker 7: and he then gives full promotion to this video, which

839
00:43:45,480 --> 00:43:48,160
Speaker 7: then gets pushed everywhere on Twitter, and then those people

840
00:43:48,160 --> 00:43:51,920
Speaker 7: get fired or resigned. Essentially, the head of trust and

841
00:43:51,960 --> 00:43:55,319
Speaker 7: safety and the head of brand safety all left last

842
00:43:55,360 --> 00:43:57,280
Speaker 7: week in this kind of kerfuffle.

843
00:43:57,440 --> 00:44:00,040
Speaker 3: And that was because of this, yeah part.

844
00:44:00,080 --> 00:44:02,239
Speaker 7: And you know other kind of I guess dust ups

845
00:44:02,239 --> 00:44:03,840
Speaker 7: in the past, but yeah, this was kind of I

846
00:44:03,840 --> 00:44:05,240
Speaker 7: guess the nail in the coffin.

847
00:44:05,520 --> 00:44:09,919
Speaker 1: I think about Linda Jakarino, who went over there. It's

848
00:44:09,920 --> 00:44:13,600
Speaker 1: hard to imagine taking a job there after the way

849
00:44:13,680 --> 00:44:17,760
Speaker 1: he's treated his other employees. It seems like a real

850
00:44:17,960 --> 00:44:19,600
Speaker 1: gamble in my mind.

851
00:44:19,960 --> 00:44:21,640
Speaker 6: Yeah, her first day was yesterday.

852
00:44:21,840 --> 00:44:23,960
Speaker 7: I think there is no shortage of people that want

853
00:44:24,000 --> 00:44:28,040
Speaker 7: to work with this very successful entrepreneur, you know, people

854
00:44:28,080 --> 00:44:30,759
Speaker 7: that maybe view him in that other bucket as this

855
00:44:31,200 --> 00:44:34,239
Speaker 7: successful man they can hitch their start of his you know,

856
00:44:34,680 --> 00:44:37,480
Speaker 7: they can build a name for themselves. You know, there's

857
00:44:37,520 --> 00:44:39,840
Speaker 7: all types of motivations for why someone would want to

858
00:44:39,880 --> 00:44:43,000
Speaker 7: work with Elon Musk. You know, everyone has confidence in themselves.

859
00:44:43,239 --> 00:44:45,160
Speaker 7: They're maybe like, you know, maybe they see what's happened

860
00:44:45,160 --> 00:44:46,879
Speaker 7: in the past and they think they can they can

861
00:44:46,920 --> 00:44:48,759
Speaker 7: buck that trend. And you know, and there are and

862
00:44:48,760 --> 00:44:51,080
Speaker 7: there are examples of people that I've worked successively with

863
00:44:51,120 --> 00:44:53,640
Speaker 7: him in the past when shot while at SpaceX for example,

864
00:44:53,880 --> 00:44:56,120
Speaker 7: being the you know, pre eminent example of that, the

865
00:44:56,440 --> 00:44:59,080
Speaker 7: kind of president at SpaceX who's been there for decades.

866
00:44:59,520 --> 00:45:03,000
Speaker 1: So one of the things I want to ask you is, Uh,

867
00:45:03,480 --> 00:45:11,880
Speaker 1: there was this RFK junior Twitter spaces yesterday. It didn't break,

868
00:45:12,480 --> 00:45:17,799
Speaker 1: but it did spread a lot of anti VAXX conspiracy theories.

869
00:45:17,920 --> 00:45:22,320
Speaker 1: I mean, we've gone from like, you know, actively trying

870
00:45:22,440 --> 00:45:26,000
Speaker 1: not to spread these things to actively trying to spread them.

871
00:45:26,040 --> 00:45:28,040
Speaker 1: I mean, what am I missing here?

872
00:45:28,360 --> 00:45:31,560
Speaker 7: And that happened on Lindy Acarina's first day, so I'm

873
00:45:31,560 --> 00:45:35,560
Speaker 7: sure that excited advertisers that she yeah.

874
00:45:35,520 --> 00:45:36,840
Speaker 6: Once to court.

875
00:45:37,000 --> 00:45:39,839
Speaker 7: I mean it just evidence is the shift in let's

876
00:45:39,840 --> 00:45:43,080
Speaker 7: call it Twitter one point zero. Everything before Elon and

877
00:45:43,160 --> 00:45:46,520
Speaker 7: Twitter two point zero everything that has come with Elon's ownership.

878
00:45:46,600 --> 00:45:51,040
Speaker 7: You know, Twitter used to have policies around COVID misinformation.

879
00:45:51,360 --> 00:45:54,480
Speaker 7: That was something they enforced through the pandemic. They developed

880
00:45:54,480 --> 00:45:59,040
Speaker 7: it because of concerns around misinformation on the virus and

881
00:45:59,120 --> 00:46:02,480
Speaker 7: the vaccines. Obviously, we can debate, you know, how effective

882
00:46:02,520 --> 00:46:08,160
Speaker 7: those were or whether those were completely correct to implement

883
00:46:08,200 --> 00:46:12,360
Speaker 7: at the time. Now, obviously Elon has given a platform

884
00:46:12,480 --> 00:46:17,280
Speaker 7: to the leading one of the leading anti VACS activists

885
00:46:17,560 --> 00:46:21,000
Speaker 7: in the US. You know, he's obviously now a presidential candidate.

886
00:46:21,160 --> 00:46:24,960
Speaker 7: So that's the guys by which Elon has welcomed him

887
00:46:25,000 --> 00:46:27,200
Speaker 7: into this platform to do a Twitter space like he

888
00:46:27,280 --> 00:46:29,880
Speaker 7: did with Ron DeSantis a couple of weeks ago, or

889
00:46:30,080 --> 00:46:32,799
Speaker 7: I don't know what last week or something. I don't

890
00:46:32,800 --> 00:46:36,360
Speaker 7: even know the weeks anymore, But that illustrates the difference

891
00:46:36,400 --> 00:46:40,440
Speaker 7: here in the approaches of Twitter past and present.

892
00:46:40,640 --> 00:46:45,080
Speaker 1: We're seeing less accountability for this kind of thing. I mean,

893
00:46:45,440 --> 00:46:48,959
Speaker 1: do you think that there's any chance that this wolf

894
00:46:49,040 --> 00:46:52,640
Speaker 1: spur government oversight. I mean, we've seen like the spread

895
00:46:52,640 --> 00:46:57,719
Speaker 1: of misinformation via Facebook, like causing things like genocide or

896
00:46:57,760 --> 00:47:01,360
Speaker 1: helping organize genocide. I mean, you know, we've really seen

897
00:47:01,640 --> 00:47:06,120
Speaker 1: some technology that is unregulated. I mean the road to

898
00:47:06,719 --> 00:47:10,200
Speaker 1: this is paved with the bodies. And I mean do

899
00:47:10,280 --> 00:47:15,319
Speaker 1: you see any impetus for anything happening or do you

900
00:47:15,360 --> 00:47:17,680
Speaker 1: think this will just continue as it does?

901
00:47:18,000 --> 00:47:20,120
Speaker 7: I don't think, you know, not in the US. I mean,

902
00:47:20,120 --> 00:47:23,399
Speaker 7: there's really no laws around, you know, how platforms should

903
00:47:23,400 --> 00:47:27,600
Speaker 7: handle misinformation in the US, and I frankly don't know

904
00:47:27,640 --> 00:47:29,239
Speaker 7: the answer to that or don't know if that would

905
00:47:29,280 --> 00:47:31,720
Speaker 7: be the best way to adjust it. I mean, Twitter,

906
00:47:32,000 --> 00:47:35,120
Speaker 7: at its core essentially is still a private company. It's

907
00:47:35,719 --> 00:47:39,160
Speaker 7: making decisions that its owner thinks his best with regards

908
00:47:39,200 --> 00:47:42,760
Speaker 7: to how it operates. You know, it's protected by Section

909
00:47:42,800 --> 00:47:45,839
Speaker 7: two thirty and which is much longer debate to be had.

910
00:47:46,160 --> 00:47:50,400
Speaker 7: But there isn't necessarily any law breaking in the US

911
00:47:50,560 --> 00:47:54,240
Speaker 7: with regards to hosting RFK Junior on a Twitter space.

912
00:47:54,440 --> 00:47:56,600
Speaker 7: There could be a counter argument to be made that

913
00:47:56,640 --> 00:48:00,920
Speaker 7: people hearing him and his belief systems and his viewpoints

914
00:48:01,040 --> 00:48:04,400
Speaker 7: will actually go against it. You know they won't, right, right,

915
00:48:04,640 --> 00:48:06,960
Speaker 7: I might can be the business in fact in et cetera,

916
00:48:07,000 --> 00:48:07,440
Speaker 7: et cetera.

917
00:48:08,239 --> 00:48:09,560
Speaker 3: Right, you could go either way.

918
00:48:09,520 --> 00:48:13,000
Speaker 7: But there are you know, rules around social platforms and

919
00:48:13,239 --> 00:48:17,000
Speaker 7: misinformation and disinformation in the EU, and they have to

920
00:48:17,120 --> 00:48:20,840
Speaker 7: show compliance with these laws, and that could be a

921
00:48:20,840 --> 00:48:25,279
Speaker 7: potential avenue for regulation with regards to how you know,

922
00:48:25,400 --> 00:48:28,560
Speaker 7: speech is I guess regulated in these places.

923
00:48:28,600 --> 00:48:31,120
Speaker 6: So, you know, I don't think it's going to happen

924
00:48:31,120 --> 00:48:31,680
Speaker 6: in the US.

925
00:48:31,760 --> 00:48:34,200
Speaker 7: You know, the EU has these laws around compliance for

926
00:48:34,239 --> 00:48:37,279
Speaker 7: social media companies with miss info and disinfo.

927
00:48:37,480 --> 00:48:39,480
Speaker 6: But we'll see. I guess what happens with Twitter.

928
00:48:39,480 --> 00:48:43,759
Speaker 1: Is Elon's goal to host all the presidential candidates in spaces.

929
00:48:44,239 --> 00:48:45,520
Speaker 7: I think you would tell you that, And yeah, he

930
00:48:45,520 --> 00:48:48,640
Speaker 7: said he's he'd welcome other candidates as well.

931
00:48:48,560 --> 00:48:52,040
Speaker 1: But he has sort of made overtures that he's going

932
00:48:52,200 --> 00:48:53,919
Speaker 1: to support to Santa's right.

933
00:48:54,239 --> 00:48:56,520
Speaker 7: Yeah, I mean it's he's made no secret that he's

934
00:48:56,520 --> 00:48:59,200
Speaker 7: going to support Disantas. I mean, he's tweeted it multiple

935
00:48:59,280 --> 00:49:02,160
Speaker 7: times at this point. Maybe he sees himself as like

936
00:49:02,200 --> 00:49:04,719
Speaker 7: the you know, Twitter is Howard Stern in a way

937
00:49:05,160 --> 00:49:07,520
Speaker 7: where he hosts these people and he talks about talks

938
00:49:07,520 --> 00:49:09,880
Speaker 7: with them, and he just wants to be a podcast

939
00:49:09,880 --> 00:49:13,160
Speaker 7: host and like, you know, content creator. No, I don't know,

940
00:49:13,520 --> 00:49:16,680
Speaker 7: it could be an ego thing here, but yeah, he

941
00:49:16,719 --> 00:49:20,120
Speaker 7: said as much. He's wanted to bring any candidate on

942
00:49:20,239 --> 00:49:23,439
Speaker 7: to talk in his Twitter space. Whether that's the most

943
00:49:23,480 --> 00:49:25,120
Speaker 7: effective use of his time, I don't know. I'm not

944
00:49:25,160 --> 00:49:28,400
Speaker 7: a Tessa shareholder or a SpaceX shareholder, but you know

945
00:49:28,480 --> 00:49:30,800
Speaker 7: he was in a Twitter space yesterday for two hours

946
00:49:30,840 --> 00:49:35,439
Speaker 7: with RFK Junior talking about everything from vaccines to bio

947
00:49:35,560 --> 00:49:36,839
Speaker 7: weapons to nuclear power.

948
00:49:37,040 --> 00:49:40,799
Speaker 3: So better him than me. So I think the top

949
00:49:40,880 --> 00:49:42,200
Speaker 3: line here is that there.

950
00:49:42,120 --> 00:49:46,080
Speaker 1: Is a chance that everyone, no matter even if you're

951
00:49:46,080 --> 00:49:48,360
Speaker 1: the richest man in the world, all he wants to

952
00:49:48,400 --> 00:49:49,440
Speaker 1: be is a podcaster.

953
00:49:49,960 --> 00:49:51,320
Speaker 6: I mean, I feel like there's.

954
00:49:52,680 --> 00:49:55,279
Speaker 7: Yeah, I think for him, and maybe he wakes up

955
00:49:55,320 --> 00:49:56,960
Speaker 7: next week and he doesn't want to do this anymore.

956
00:49:56,960 --> 00:49:58,400
Speaker 6: Who knows, I'll just take.

957
00:49:58,160 --> 00:50:01,480
Speaker 3: His money and he can have Mike podcast. Thank you

958
00:50:01,600 --> 00:50:04,160
Speaker 3: so much, Ryan, this is great, of course, thanks for

959
00:50:04,200 --> 00:50:10,960
Speaker 3: having me. No moment, Jesse Cannon.

960
00:50:11,040 --> 00:50:14,839
Speaker 2: My Jung Fast. This RFK junior guy, he really likes

961
00:50:14,880 --> 00:50:16,760
Speaker 2: to spread the disinformation.

962
00:50:17,160 --> 00:50:22,040
Speaker 1: Elon Musk held a Twitter spaces with RFK where he

963
00:50:22,200 --> 00:50:25,760
Speaker 1: said a lot of really stupid crap. I'm talking about RFK,

964
00:50:26,160 --> 00:50:28,640
Speaker 1: though I'm sure that Elon said.

965
00:50:28,840 --> 00:50:31,239
Speaker 3: It was a little evenly distributed.

966
00:50:31,800 --> 00:50:35,000
Speaker 1: It was two hours of stupid crap from both of

967
00:50:35,040 --> 00:50:39,279
Speaker 1: those guys. Again, I don't know where they caught the

968
00:50:39,320 --> 00:50:43,680
Speaker 1: brain worms that they have, but it's pretty depressing to

969
00:50:43,840 --> 00:50:51,200
Speaker 1: watch them spreading disinformation and misinformation, especially when we know

970
00:50:51,320 --> 00:50:55,120
Speaker 1: that RFK Junior is really just acting as a spoiler

971
00:50:55,640 --> 00:50:58,719
Speaker 1: to elect Donald Trump, and for that he is our

972
00:50:58,800 --> 00:51:03,560
Speaker 1: moment Offucker. That's it for this episode of Fast Politics.

973
00:51:03,920 --> 00:51:06,839
Speaker 1: Tune in every Monday, Wednesday, and Friday to hear the

974
00:51:06,880 --> 00:51:10,320
Speaker 1: best minds in politics makes sense of all this chaos.

975
00:51:10,600 --> 00:51:13,160
Speaker 1: If you enjoyed what you've heard, please send it to

976
00:51:13,200 --> 00:51:16,600
Speaker 1: a friend and keep the conversation going. And again, thanks

977
00:51:16,600 --> 00:51:17,240
Speaker 1: for listening.